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The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England
Functional Ecology
Abstract
Carbon dioxide flux measurements using the eddy covariance (EC) methodology have helped researchers to develop models of ecosystem carbon balance. However, making reliable predictions of carbon fluxes is not straightforward due to phenological changes and possible abiotic/biotic stresses that profoundly influence tree functioning.
To assess the influence of canopy phenological state on CO 2 flux, we installed two different digital camera systems at different viewing angles (an outdoor webcam with a near‐horizontal view and a commercial ‘fisheye’ digital camera with a downward view) on a flux measurement tower in southern England and tracked the visual change of the canopy in this oak‐dominated ( Quercus robur L.) forest over two growing seasons.
Changes in the setting of the camera's white balance substantially affected the quality of the webcam images. However, the timing of the onset of greening and senescence was, nevertheless, detectable for the individual trees as well as the overall canopy for both years. The greening‐up date assessed from the downward images from a hemispherical lens was ∼5 days earlier than from the horizontal‐view images, because of ground vegetation development (not visible in the horizontal view).
The effects of a late air frost in 2010 were evident in the canopy greenness, and these led to reductions in daily gross primary productivity ( GPP ). The cameras recorded differences between individual tree crowns, showing their different responses to the late frost.
A major new finding from this work is the strong relationship between GPP and H ue, which was stronger than the relationship between GPP and NDVI .
... Vegetation phenology is the study of plant natural life cycle stages (Schwartz 2013) or the annual timing of development events (Polgar and Primack, 2011), such as the date and duration of budburst, flowering, leaf-out and autumn leaf-fall. Such phenological events are related to carbon, energy and water cycles within terrestrial ecosystems (Garrity et al., 2011;Mizunuma et al., 2013), operating from local to global scales, as around 55% of the Earth`s land surface is covered by grasslands, shrub lands and forests (Bartholomé and Belward, 2005). As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change (Thackeray et al. 2010;Menzel et al. 2006), mainly in temperate environments with deciduous species (Fisher and Mustard 2007). ...
... Despite these advantages, there are still issues related to viewing angle and areal representativeness, which can confound the comparisons Hmimina et al., 2013). While satellite sensors have synoptic views , nearsurface sensors are often close to the horizontal , which may result in nearsurface sensors receiving a smaller contribution from the background cover Mizunuma et al., 2013;Liu et al., 2017). Secondly, because ground/near-surface data are usually not georeferenced, it is necessary to assume that such data are representative of the satellite pixel(s) area ). ...
... Vegetation phenology is the study of plant natural life cycle stages (Schwartz 2013) or the annual timing of development events (Polgar and Primack, 2011), such as the date and duration of budburst, flowering, leaf-out and autumn leaf-fall. Such phenological events are related to carbon, energy and water cycles within terrestrial ecosystems (Garrity et al., 2011;Mizunuma et al., 2013), operating from local to global scales, as around 55% of the Earth`s land surface is covered by grasslands, shrub lands and forests (Bartholomé and Belward, 2005). These phenological events are also related to many other processes occurring iteratively in the biosphere-atmosphere layer (Richardson et al., 2013a), as schematized in For example, phenology controls the development and senescence of foliage, which then controls the physiological activity of the canopy (Figure 2-1). ...
Vegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.
(Permalink to thesis@ http://hdl.handle.net/10443/4131)
... budburst, flowering) (Lieth, 1974) and is an important proxy of climate and environmental change (Polgar and Primack, 2011;Chen et al., 2019). Variations in vegetation phenology are related to carbon, energy, nutrient and water cycles within terrestrial ecosystems (Garrity et al., 2011;Mizunuma et al., 2013;Estiarte and Peñuelas, 2015), operating from local to global scales, as around 55% of the Earth s land surface is covered by grasslands, shrub lands and forests (Bartholomé and Belward, 2005). Therefore, many important feedbacks to the climate system are affected by the seasonality of vegetation (Richardson et al., 2013a). ...
... Results from studies comparing time series data, but not phenodates (e.g. Mizunuma et al. (2013) compared time series of carbon flux vs. satellite VI), are discussed throughout the text but not included in Table 2. Additionally, for studies presenting comparisons across diverse land cover types (e.g. forest, agriculture and grasslands (Moon et al., 2019)), only the forest-related results are presented in Table 2; however, this was not always possible in some studies due to a lack of explicit statistical measures for each land cover type (e.g. ...
... Carbon fluxes of vegetated surfaces can be estimated by eddy covariance instrumentation (Wu et al., 2017;Donnelly et al., 2019). Once the carbon balance of an ecosystem is calculated, it is possible to characterize whether this system is acting as a sink or source of carbon over time (Wilkinson et al., 2012;Mizunuma et al., 2013). Because phenocam are usually co-located at these instrumentation sites, the seasonal cycles of canopy photosynthesis (carbon fluxes) can be investigated in terms of camera-derived canopy developments (Toomey et al., 2015). ...
Vegetation phenology is the study of recurring plant life cycle stages, seasonality which is linked to many ecosystem processes and is an important proxy of climate and environmental change. Remote sensing has been playing an important and increasing role in the monitoring and assessment of vegetation phenology. The aim of this review is to critically examine key studies related to remote sensing of vegetation phenology, with a special focus on temperate and boreal forests. Specifically, we focus on how the latest ground, near-surface and aerial data have been used to assess the satellite-derived Land Surface Phenology (LSP) metrics and the agreements that has been achieved in the last 15 years. Results demonstrated that the timing of satellite-derived LSP events can be detected, in the best-case scenarios, with a certainty of around half-week for spring metrics (e.g. Day of Year -DOY- of start of growing season) and around one week for autumn metrics (e.g. DOY of end of growing season). With expected shifts in plant phenology averaging <1 day per decade, such LSP uncertainties (in terms of absolute phenological dates) could greatly over- or under-estimate these species-level shifts; but the spatial variation in phenology can be consistently monitored. An increasing number of studies have investigated autumn phenology in the last decade, but autumn phenological dates continue to be more challenging to retrieve and interpret than spring dates. Emerging opportunities to further advance remote sensing of forest phenology is presented that includes synergetic use of multiple orbital sensors and its LSP evaluation with data from new sensors at a ground, near-surface and airborne level; yet traditional ground-based observations will continue to be highly useful to accurately record the timing of species-specific phenological events. This review might provide a guide for planning and managing remote sensing of forest phenology.
... But such a near-horizontal view may affect the VI C and the phenophases via the bidirectional reflectance [25,26], and the oblique angle produces a greater effective leaf area and might lead to an earlier peak of VI C [27]. Conversely, a vertical view can capture more understory phenology [20,28], but it reduces the FOV [29] and is susceptible to the tower-structure [30][31][32]. However, the effect of camera inclination-angle on phenophases and its uncertainty are seldom assessed [30]. ...
... Conversely, a vertical view can capture more understory phenology [20,28], but it reduces the FOV [29] and is susceptible to the tower-structure [30][31][32]. However, the effect of camera inclination-angle on phenophases and its uncertainty are seldom assessed [30]. Similarly, few studies investigated the effects of inclination-angles of spectroradiometer and routine radiometers on VI N and VI B [33,34]. ...
... The canopy images from four azimuth-angles (east, south, west, and north) with an inclination-angle of 30 • and four inclination-angles (15,30,45, and 60 • ) in the north direction were manually collected using a digital camera (Coolpix L120, Nikon Corporation, Japan) on the top of the eddy flux tower (48 m above the ground). Seven tripod heads (each in the east, south, and west directions, and four in the north direction) were installed to ensure the position of camera stable for each measurement. ...
Near-surface remote sensing is an effective tool for in situ monitoring of canopy phenology, but the uncertainties involved in sensor-types and their deployments are rarely explored. We comprehensively compared three types of sensor (i.e., digital camera, spectroradiometer, and routine radiometer) at different inclination-and azimuth-angles in monitoring canopy phenology of a temperate deciduous forest in Northeast China for three years. The results showed that the greater contribution of understory advanced the middle of spring (MOS) for large inclination-angle of camera and spectroradiometer. The length of growing season estimated by camera from the east direction extended 11 d than that from the north direction in 2015 due to the spatial heterogeneity, but there was no significant difference in 2016 and 2018.The difference infield of view of sensors caused the MOS and the middle of fall, estimated by camera, to lag a week behind those by spectroradiometer and routine radiometer. Overall, the effect of azimuth-angle was greater than that of inclination-angle or sensor-type. Our assessments of the sensor types and their deployments are critical for the long-term accurate monitoring of phenology at the site scale and the regional/global-integration of canopy phenology data.
... Understanding the relationship between canopy phenological factors, such as defoliation or leaf expansion, and gross primary production (GPP) in different forest vegetation types is important (Muraoka et al., 2010;Polgar and Primack, 2011;Richardson et al., 2010;Wang et al., 2018), as colour indices from digital camera imagery can been used to detect phenological phases (Lang et al., 2017) and model GPP in forest vegetation (Mizunuma et al., 2013). The popularization of mounting digital cameras on the same structures used for eddy covariance (EC) instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. ...
... Digital camera networks have been expanding in North America , Japan (Nagai et al., 2018), Europe, Australia and Brazil (Wingate et al., 2015) in different natural forest ecosystems. Mizunuma et al., 2013;Peichl et al., 2015;Saitoh et al., 2012). Based on these relationships, the temporal and spatial distributions of GPP can be estimated from colour indices calculated from digital RGB data (Baldocchi et al., 2005;Mizunuma et al., 2013). ...
... Mizunuma et al., 2013;Peichl et al., 2015;Saitoh et al., 2012). Based on these relationships, the temporal and spatial distributions of GPP can be estimated from colour indices calculated from digital RGB data (Baldocchi et al., 2005;Mizunuma et al., 2013). However, previous studies have generally focused on natural forest vegetation or special plant species that exhibit significant seasonal variations and represent natural forest types rather than introduced plants. ...
The widespread increase in the number of digital cameras mounted on flux towers provides an opportunity to better understand the relationship between the seasonality of canopy photosynthesis and canopy phenology. The challenge is due to fewer variations in rubber defoliation of rubber canopy. We examined the relationship between colour indices calculated from digital camera images and gross primary production (GPP) obtained from daily flux tower observations of carbon dioxide over two years in a rubber plantation and used these colour indices to model GPP. According to the results, (1) the strength of green (S green), Hue and GPP exhibited clear seasonal patterns in the rubber plantation, and the relationship between the camera-based indices and GPP appeared to have distinct characteristics at different phenological stages; (2) the peak GPP from the flux tower measurements lagged behind the peak in the colour indices calculated from digital camera imagery; (3) GPP was strongly correlated with S green derived from camera imagery in the rubber plantation, especially in the leaf expansion period; and (4) the GPP simulated by colour indices (S green and Hue) was underestimated, and the fraction of absorbed photosynthetically active radiation (FPAR) was the best parameter for modelling GPP in normal years. Our results indicate that colour indices calculated from digital camera images can be used to model GPP in rubber plantations and to monitor biotic and abiotic stress events. Future research should measure the pigment contents of canopy leaves to precisely quantify the relationship between colour indices and GPP.
... frost and drought damage) controls. Repeat images from unattended in situ digital cameras or webcams (Richardson et al. 2007;Ahrends et al. 2008;Ide and Oguma 2010;Migliavacca et al. 2011;Sonnentag et al. 2012;Mizunuma et al. 2013;Inoue et al. 2015;Toomey et al. 2015;Wingate et al. 2015) have been used to track vegetation phenology in a range of ecosystems at a fine temporal resolution (daily to sub-daily) averaged over a greater spatial extent (10-100's of metres) than traditional, manual, methods. Vegetation products, based on the relative reflectance of green to red and/or blue in these digital images, are at a temporal and spatial scale where they can be related directly to measured CO 2 fluxes (Botta et al. 2000;Knorr et al. 2010;Migliavacca et al. 2011;Saitoh et al. 2012;Mizunuma et al. 2013;Westergaard-Nielsen et al. 2013;Zhou et al. 2013;Toomey et al. 2015). ...
... Repeat images from unattended in situ digital cameras or webcams (Richardson et al. 2007;Ahrends et al. 2008;Ide and Oguma 2010;Migliavacca et al. 2011;Sonnentag et al. 2012;Mizunuma et al. 2013;Inoue et al. 2015;Toomey et al. 2015;Wingate et al. 2015) have been used to track vegetation phenology in a range of ecosystems at a fine temporal resolution (daily to sub-daily) averaged over a greater spatial extent (10-100's of metres) than traditional, manual, methods. Vegetation products, based on the relative reflectance of green to red and/or blue in these digital images, are at a temporal and spatial scale where they can be related directly to measured CO 2 fluxes (Botta et al. 2000;Knorr et al. 2010;Migliavacca et al. 2011;Saitoh et al. 2012;Mizunuma et al. 2013;Westergaard-Nielsen et al. 2013;Zhou et al. 2013;Toomey et al. 2015). However, this method is site specific without standard protocols (Ide and Oguma 2010;Migliavacca et al. 2011) and is not straightforward in isolated upland areas such as peatlands. ...
... Studies using digital images to track phenology have typically used relative brightness of green (Richardson et al. 2007;Ahrends et al. 2008;Parihar et al. 2013;Alberton et al. 2014), greenness excess index (Ide and Oguma 2010;Coops et al. 2011;Hufkens et al. 2012;Saitoh et al. 2012) or both (Migliavacca et al. 2011;Mizunuma et al. 2013). However, these products are strongly affected by illumination conditions , with the blue band showing most variability. ...
In peatlands plant growth and senescence affect annual ecosystem carbon dioxide (CO2) exchange, and CO2 fluxes show considerable inter-annual variability. Phenology is a fundamental indicator of ecosystem carbon dynamics and can be measured from remote sensing systems, but the extent to which satellite products provide useful proxies of peatland vegetation phenology is not well known. Using MODIS vegetation products coupled with field observations of phenology from a basic camera system and measurements of Gross Primary Productivity (GPP) measured using a closed chamber method, we sought to establish the extent to which satellite observations capture phenological processes at a UK peatland site. Daily, true-colour digital images were captured with a time-lapse camera (Brinno TLC100) between 23-Apr-2013 and 29-Oct-2013 and converted into a Green-Red Vegetation Index (GRVI). These were compared with a range of MODIS vegetation products at various spatial resolutions. We found that vegetation products with finer spatial resolution (<500 m) more accurately captured spring green-up (e.g. Normalized Difference Vegetation Index 16-day product), whereas those with 8-day temporal resolution better captured whole-season dynamics. The 8-day Gross Primary Productivity (GPP8) and the fraction of absorbed photosynthetically active radiation (fPAR8) products had the strongest daily Pearson's correlations with camera-derived GRVI (r > 0.90). The camera-GRVI (P = 0.005, r = 0.98) and MODIS-GRVI (P = 0.041, r = 0.89) products showed the strongest significant correlations with gross primary productivity measured using static chambers in the field. This work demonstrates that freely available MODIS data captured up to 92% of the daily variation in phenology over an upland peatland. This approach shows great potential for capturing ecosystem carbon dynamics which underpin carbon trading schemes, a budding funding source for peatland restoration projects.
... The Red-Green-Blue (RGB) colour channels of each orthophoto were used to calculate the Green Chromatic Coordinate (GCC) colour index (G/R+G+B) [4], which is a measure of canopy greenness widely used to monitor phenological development of trees [4,11]. UAV-derived GCCs are identified as GCCUAV in this paper. ...
... Time series of GCCUAV values from the four sessile oak trees exhibited clear seasonality (Figure 1), in a trend similar to studies using very high temporal resolution images from fixed digital cameras over deciduous woodland [11]. A period of stable and minimal GCCUAV values over winter can be seen, followed by a relatively quick and sustained increase over spring and a relatively slow decrease over summer. ...
... From DOY 56 (25/02/2015) to around DOY 92-98 (02/04/2015 -08/04/2015) the four deciduous trees maintained similar minimal GCCUAV values (Figure 1). This can be explained as deciduous trees are leafless with very low physiological activity over winter time [11]. After this point, GCCUAV values were detected to increase at different rates for each tree. ...
... The strength of the green channel relative to the total of digital numbers or chromatic co-ordinates of green , has been used to detect the period between budbreak and abscission (e.g. ), while the Green Excess Index or 2G_RB (Woebbecke et al. 1995), which emphasises the difference between green and red/blue, has also been used widely (e.g. . The strength of green shows sensitivity to the effects of environmental conditions on forest canopies, e.g., showing a decline caused by frost damage and a subsequent post-frost recovery Mizunuma et al. 2013). Although an increase of green-based indices was evident with the onset of leaves, the sharp increase did not agree with the gradual increases of chlorophyll development in a deciduous forest ) and peaks of these indices were observed earlier than the peaks of daily gross primary productivity (GPP) from the carbon flux measurements Mizunuma et al. 2013). ...
... The strength of green shows sensitivity to the effects of environmental conditions on forest canopies, e.g., showing a decline caused by frost damage and a subsequent post-frost recovery Mizunuma et al. 2013). Although an increase of green-based indices was evident with the onset of leaves, the sharp increase did not agree with the gradual increases of chlorophyll development in a deciduous forest ) and peaks of these indices were observed earlier than the peaks of daily gross primary productivity (GPP) from the carbon flux measurements Mizunuma et al. 2013). In autumn, the increase in the strength of red coincided with the decline of chlorophyll content, whereas no significant correlations were seen for green-based indices during the gradual decrease after the onset peak ). ...
... Although R green decreases with the increase of chlorophyll concentration, S green does not show such a negative dependency. This may explain the observation that S green does not decrease rapidly in the autumn in previous in situ studies for 'turn-to-yellow' species Mizunuma et al. 2013). In agreement with a previous study , Hue from images showed a good correlation with Hue from reflectance across camera models and balance settings. ...
In the terrestrial biosphere forests have a significant role as a carbon sink. Under
recent climate change, it is increasingly important to detect seasonal change or
‘phenology’ that can influence the global carbon cycle. Monitoring canopies using
camera systems has offered an inexpensive means to quantify the phenological
changes. However, the reliability is not well known. In order to examine the
usefulness of cameras to observe forest phenology, we analysed canopy images taken
in two deciduous forests in Japan and England and investigate which colour index is
best for tracking forest phenology and predict carbon uptake by trees. A camera test
using model leaves under controlled conditions has also carried out to examine
sensitivity of colour indices for discriminating leaf colours. The main findings of the
present study are: 1) Time courses of colour indices derived from images taken in
deciduous forests showed typical patterns throughout the growing season. Although
cameras are not calibrated instrument, analysis of images allowed detecting the
timings of phenological events such as leaf onset and leaf fall; 2) The strength of the
green channel (or chromatic coordinate of green) was useful to observe leaf
expansion as well as damage by spring late frost. However, the results of the camera
test using model leaves suggested that this index was not sufficiently sensitive to
detect leaf senescence. Amongst colour indices, Hue was the most robust metric for
different cameras, different atmospheric conditions and different distances. The test
also revealed Hue was useful to track nitrogen status of leaves; 3) Modelling results
using a light use efficiency model for GPP showed a strong relationship between
GPP and Hue, which was stronger than the relationships using alternative traditional
indices.
... The archived images used in the present analysis are all taken between 11:00 and 13:00 local time (LT). The camera setup is specific to each camera type but a common requirement to observe the seasonal colour fraction time series is to set the colour balance to "fixed" mode (on the Stardot cameras) or the white balance to "manual" mode (for the Nikon Coolpix cameras) (Mizunuma et al., 2013(Mizunuma et al., , 2014. ...
... Using the PROSAIL model and a few assumptions on how model parameters varied over the season (Table 2, Fig. 12) we investigated the ability of the PROSAIL model to simulate seasonal variations of the RGB signals measured by two different cameras installed at the Alice Holt site. For this, a proxy for seasonal variations in LAI was obtained by fitting a relationship to values of LAI estimated from measurements of transmittance as described in Mizunuma et al. (2013), while the expected range and seasonal variations of [Chl], [Car], C brown and N were estimated from several published studies on oak leaves (Demarez et al., 1999;Gond et al., 1999;Percival et al., 2008;Sanger, 1971;Yang et al., 2014), as summarised in Table 2. We also manually adjusted the parameter B RGB in Eq. (4) to match the RGB values measured by the camera during the winter period prior to budburst (highlighted by the grey bar in Fig. 12). ...
... Lawton, personal communication, 2012). However, at Alice Holt, both a Stardot camera and a Nikon camera have been operating since 2009 (Mizunuma et al., 2013). Using a similar approach but with the Nikon camera, we then tested the idea that colour fractions seen by this other camera would also suggest similar variations in leaf pigment and structural parameters over the season. ...
Plant phenological development is orchestrated through subtle changes in
photoperiod, temperature, soil moisture and nutrient availability.
Presently, the exact timing of plant development stages and their response
to climate and management practices are crudely represented in land surface
models. As visual observations of phenology are laborious, there is a need
to supplement long-term observations with automated techniques such as those
provided by digital repeat photography at high temporal and spatial
resolution. We present the first synthesis from a growing observational
network of digital cameras installed on towers across Europe above deciduous
and evergreen forests, grasslands and croplands, where vegetation and
atmosphere CO2 fluxes are measured continuously. Using colour indices
from digital images and using piecewise regression analysis of time series,
we explored whether key changes in canopy phenology could be detected
automatically across different land use types in the network. The piecewise
regression approach could capture the start and end of the growing season,
in addition to identifying striking changes in colour signals caused by
flowering and management practices such as mowing. Exploring the dates of
green-up and senescence of deciduous forests extracted by the piecewise
regression approach against dates estimated from visual observations, we
found that these phenological events could be detected adequately (RMSE < 8 and 11 days for leaf out and leaf fall, respectively). We also
investigated whether the seasonal patterns of red, green and blue colour
fractions derived from digital images could be modelled mechanistically
using the PROSAIL model parameterised with information of seasonal changes
in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From
a model sensitivity analysis we found that variations in colour fractions,
and in particular the late spring `green hump' observed repeatedly in
deciduous broadleaf canopies across the network, are essentially dominated
by changes in the respective pigment concentrations. Using the model we were
able to explain why this spring maximum in green signal is often observed
out of phase with the maximum period of canopy photosynthesis in ecosystems
across Europe. Coupling such quasi-continuous digital records of canopy
colours with co-located CO2 flux measurements will improve our
understanding of how changes in growing season length are likely to shape
the capacity of European ecosystems to sequester CO2 in the future.
... Richardson et al. 2009;Ide & Oguma 2010). The strength of green shows sensitivity to the effects of environmental conditions on forest canopies, for example, showing a decline caused by frost damage and a subsequent post-frost recovery Mizunuma et al. 2013). ...
... Although an increase of green-based indices was evident with the onset of leaves, the sharp increase did not agree with the gradual increases of chlorophyll development in a deciduous forest (Nagai et al. 2011) and peaks of these indices were observed earlier than the peaks of daily gross primary productivity (GPP) from the carbon flux measurements (Ahrends et al. 2009;Mizunuma et al. 2013). In autumn, the increase in the strength of red coincided with ...
... Meanwhile, other studies transformed the RGB into the HSL (Hue, Saturation, Lightness) colour system (Smith 1978) and demonstrated that Hue showed significant potential for estimating leaf area from side-view images ) and that a seasonal transition in Hue showed a trend similar to that of chlorophyll development of deciduous leaves (Nagai et al. 2011). In a recent study, we used the HSL colour scheme to track the changes in canopy development in a deciduous forest and to relate this to the rate of canopy photosynthesis; we discovered that Hue was a more useful expression of leaf colour to estimate carbon uptake than the indices previously used by others (Mizunuma et al. 2013). ...
Digital images of tree canopies have been analysed to understand how forest phenology responds to climate change. Researchers have used different colour indices to carry out quantitative analyses, but uncertainties over the performance of the various indices are hampering progress in their use.
To compare the various indices under controlled conditions, we carried out experiments using a low‐cost off‐the‐shelf digital camera with a set of standard colour charts as model leaves for different stages: emerging leaves, yellowish green; newly expanded leaves, green; fully mature leaves, dark green; senescent leaves, yellow. Two models of cameras, a compact digital camera and a surveillance ‘live image’ camera were used, and photographs were taken by two cameras for each model under clear or overcast sky conditions with two colour balance settings. The indices were also compared with those derived from spectral reflectance.
Colour indices based on hue distinguished leaf colour samples with only a small influence of camera models, balance setting and sky conditions, while indices based on green were strongly influenced by camera models and were relatively insensitive to leaf colours. The strength of the green channel relative to the total of digital numbers took similar values for the mature and senescent replica leaves, highlighting its poor ability to identify the change of colour in autumn. Spectral‐based hue was also sensitive to the gradation of leaf colours and showed a good correlation with the digital representation of hue regardless of camera models and balance setting. Remarkably, the primitive digital number of red, N red , also discriminated leaf colours well, with a small influence of the factors investigated here, showing a good correlation with the reflectance of the red band, except from images taken by the surveillance cameras with auto balance.
Hue was a robust index across the image set, while the green‐based indices often used to quantify canopy phenology in previous studies performed poorly. Hue was well correlated with spectral reflectance indices and worked better than all other indices to discriminate leaf colours. We recommend using hue as a colour index for tracking different stages of leaf development.
... Several studies have demonstrated the capability of red-green-blue (RGB) digital imagery to provide accurate phenological information (Ahrends et al., 2008;Ahrends et al., 2009;Alberton et al., 2014;Bater et al., 2011;Migliavacca et al., 2011, Mizunuma et al., 2013, Richardson et al., 2007and Richardson et al., 2009). Using blue, green and red brightness levels (values) is affected by illumination angle, which can be eliminated by nonlinear transformation of RGB digital number to RGB chromatic coordinates (Sonnentag et al., 2012). ...
... Knowledge of reaching the desired moisture content before harvest would be very beneficial for commercial crop production. Many color indices have been proposed and used to distinguish soil from plants, describe canopy greenness, observe ground cover, estimate the temporal/spatial distributions of photosynthetic productivity, monitor flowering and carbon dioxide uptake, nitrogen status, seasonal growth and disease, indicate soil moisture content, and manage irrigation (Thorp and Dierig, 2011, Meyer and Neto, 2008, Sakamoto et al., 2011, Saitoh et al., 2012, Sonnentag et al., 2012, Zerger et al., 2012, Lee and Lee, 2013, Mizunuma et al., 2013, Garcia-Mateos et al., 2014, Escarabajal-Henarejos et al., 2014, Zhou, et al., 2015. ...
... Previous research has demonstrated that the seasonal patterns of red, green, and blue (RGB) color channel information from digital images has the ability to track seasonal changes in plant development stages (Morisette et al., 2009;Nagai et al., 2011;Richardson et al., 2007;Woebbecke et al., 1995). For example, the green chromatic coordinate (GCC) has been extensively used in deciduous broadleaf forests (DBF) to extract phenological information (Ahrends et al., 2009;Gillespie et al., 1987;Klosterman et al., 2014;Linkosalmi et al., 2016;Melaas et al., 2016;Mizunuma et al., 2013). In contrast, the red chromatic coordinate (RCC) has received less attention for phenological research. ...
... Canopy development is the result of photosynthesis as well as the necessary condition for photosynthesis (Ahrends et al., 2009;Linkosalmi et al., 2016;Mizunuma et al., 2013). The variation in the total amount of pigments in a canopy, as a reflection of canopy development, causes the dynamic change of camera-based indices (e.g. ...
... Plant phenological events influence carbon, energy and water cycles within terrestrial ecosystems (Garrity et al., 2011;Mizunuma et al., 2013), operating from local to global scales, as around 55% of the Earth's land surface is covered by grasslands, shrub lands and forests (Bartholomé and Belward, 2005). As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change (Menzel et al., 2006;Thackeray et al., 2010), mainly in temperate environments (Fisher and Mustard, 2007). ...
... Despite these advantages, such networks still present issues related to viewing angle, domination of the field of view by trees closest to the sensor and areal representativeness, which can confound the comparisons (Hmimina et al., 2013;Hufkens et al., 2012). While satellite sensors have synoptic views , near-surface sensors are often oriented with a view angle that is close to be horizontal , which may result in near-surface sensors receiving a smaller contribution from the background cover, such as snow and understorey vegetation, Liu et al., 2017;Mizunuma et al., 2013) and higher contribution from the leaf layers under the canopy top (Keenan et al., 2014). Secondly, because ground/near-surface data are usually not georeferenced, an assumption must be made that such data are representative of the satellite pixel(s) area . ...
The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. This article assesses the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data (5 cm spatial resolution, ~7 day temporal resolution) were acquired in tandem with an intensive ground campaign during the spring season of 2015 across a 15 ha mixed woodland. Phenophase transition dates were estimated at an individual tree-level using UAV time series of Normalized Difference Vegetation Index (NDVI) and Green Chromatic Coordinate (GCC) and validated against visual observations of tree phenology. UAV-derived start of season dates could be predicted with an accuracy of <1 week. The analysis was scaled to a plot level, where ground (visual assessment and understorey development), UAV and Landsat metrics were compared, indicating UAV data is effective for tracking canopy phenology, as opposed to ecosystem dynamics detected by satellites. The UAV data were used to automatically map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This, and a large temporal gap in the Landsat series, accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R² < 0.50) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, providing information which could improve characterization of vegetation phenology at multiple scales.
... Cameras have been deployed on a range of ecosystems, including deciduous and mixed species ecosystems (Richardson et al., 2007;Ahrends et al., 2009;Sonnentag et al., 2012;Mizunuma et al., 2013), grasslands (e.g. Migliavacca et al., 2011), peatlands (Westergaard-Nielsen et al., 2013;Peichl et al., 2015;Linkosalmi et al., 2016) and coniferous forests (Nagai et al., 2012;Linkosalmi et al., 2016). ...
... Migliavacca et al., 2011), peatlands (Westergaard-Nielsen et al., 2013;Peichl et al., 2015;Linkosalmi et al., 2016) and coniferous forests (Nagai et al., 2012;Linkosalmi et al., 2016). Budburst and leaf senescence of deciduous species and their relationship with CO 2 exchange have been a focus in a number of studies (Richardson et al., 2007;Ahrends et al., 2009;Mizunuma et al., 2013;Wingate et al., 2015). Reasons for plant colour changes have been related to the variation of green biomass, and also to changes in the biochemical properties of leaves occurring over the season (Keenan et al., 2014;Yang et al., 2014). ...
In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.
... Color indices derived from digital repeat photography have also been correlated with canopy photosynthesis in deciduous broadleaf forest (Richardson et al. 2007, 2009b, Ahrends et al. 2009, Mizunuma et al. 2012, grasslands (Migliavacca et al. 2011), and desert shrublands (Kurc and Benton 2010). However, each of these studies was limited to one or two sites and it is unclear how well results from these efforts generalize within and across PFTs at regional to continental scales. ...
... Our analysis also revealed several limitations of canopy greenness as a predictor of GPP. For example, there was a pronounced peak in GCC at the end of spring in DBF sites (also noted by Mizunuma et al. 2012) that preceded the peak in GPP by several weeks. Peak GCC is caused by seasonal variation in foliage pigments (e.g., Sims and Gamon 2002) and is accentuated by the oblique viewing angle used by the cameras in this study (Keenan et al. 2014). ...
The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.
... Mizunuma et al. (2011) tested several RGB indices to detect phenology of a beech tree at the MTK site, where fogs often result in poor image quality, and found that HUE was the most robust index. Mizunuma et al. (2013) captured damage caused by late frost in spring in ADFC images of a deciduous broad leaved forest (AHS). Choi et al. (2011) used time-lapse ADFC images and RGB indices to detect the phenology of a Korean deciduous forest (GDK). ...
... Saitoh et al. (2012c) compared RGB indices obtained from AFDC images collected at TKC and TKY with GPP data derived from tower observations. Mizunuma et al. (2013) showed that HUE corresponded well with the GPP data collected at the AHS site. Ide et al. (2011) showed that the GR and GRVI, both of which were derived from ADFC images, corresponded well with GPP at the TMK site. ...
The Phenological Eyes Network (PEN), which was established in 2003, is a network of long-term ground observation sites. The aim of the PEN is to validate terrestrial ecological remote sensing, with a particular focus on seasonal changes (phenology) in vegetation. There are three types of core sensors at PEN sites: an Automatic Digital Fish-eye Camera, a HemiSpherical SpectroRadiometer, and a Sun Photometer. As of 2014, there are approximately 30 PEN sites, among which many are also FluxNet and/or International Long Term Ecological Research sites. The PEN is now part of a biodiversity observation framework. Collaborations between remote sensing scientists and ecologists working on PEN data have produced various outcomes about remote sensing and long-term in situ monitoring of ecosystem features, such as phenology, gross primary production, and leaf area index. This article reviews the design concept and the outcomes of the PEN, and discusses its future strategy.
... A late-summer decline in GEP was evident in most years, which did not closely match either G cc or LAI. In contrast to previous suggestions that hue is more correlated to GEP and LAI than G cc (Mizunuma et al. 2013), we found no positive correlation between hue and GEP (R ¼À0.2, P ¼ 0.03) or LAI (R ¼À0.3, P , 0.01) at our site. Indeed the seasonal cycle of hue is critically dependent on the color balance of the camera (Appendix: Fig. A1) and is thus unlikely to be suitable for multisite applications. ...
... Our results show that G cc was insensitive to substantial interannual changes in maximum leaf area index, which were primarily caused by damage from a winter ice storm. Other studies have reported similar difficulty in detecting events that induce defoliation (Mizunuma et al. 2013). Our analysis resolves this apparent contradiction in the literature. ...
Digital repeat photography is becoming widely used for near-surface remote sensing of vegetation. Canopy greenness, which has been used extensively for phenological applications, can be readily quantified from camera images. Important questions remain, however, as to whether the observed changes in canopy greenness are directly related to changes in leaf-level traits, changes in canopy structure, or some combination thereof.
We investigated relationships between canopy greenness and various metrics of canopy structure and function, using five years (2008–2012) of automated digital imagery, ground observations of phenological transitions, leaf area index (LAI) measurements, and eddy covariance estimates of gross ecosystem photosynthesis from the Harvard Forest, a temperate deciduous forest in the northeastern United States. Additionally, we sampled canopy sunlit leaves on a weekly basis throughout the growing season of 2011. We measured physiological and morphological traits including leaf size, mass (wet/dry), nitrogen content, chlorophyll fluorescence, and spectral reflectance and characterized individual leaf color with flatbed scanner imagery.
Our results show that observed spring and autumn phenological transition dates are well captured by information extracted from digital repeat photography. However, spring development of both LAI and the measured physiological and morphological traits are shown to lag behind spring increases in canopy greenness, which rises very quickly to its maximum value before leaves are even half their final size. Based on the hypothesis that changes in canopy greenness represent the aggregate effect of changes in both leaf-level properties (specifically, leaf color) and changes in canopy structure (specifically, LAI), we developed a two end-member mixing model. With just a single free parameter, the model was able to reproduce the observed seasonal trajectory of canopy greenness. This analysis shows that canopy greenness is relatively insensitive to changes in LAI at high LAI levels, which we further demonstrate by assessing the impact of an ice storm on both LAI and canopy greenness. Our study provides new insights into the mechanisms driving seasonal changes in canopy greenness retrieved from digital camera imagery. The nonlinear relationship between canopy greenness and canopy LAI has important implications both for phenological research applications and for assessing responses of vegetation to disturbances.
... Several studies have demonstrated the capability of red-green-blue (RGB) digital imagery to provide accurate phenological information (Ahrends et al., 2008(Ahrends et al., , 2009Alberton et al., 2014;Bater et al., 2011;Migliavacca et al., 2011;Mizunuma et al., 2013;Richardson et al., 2007Richardson et al., , 2009. The digital cameras are usually fixed above a vegetated target (e.g. ...
... Traditionally, digital images collected to track vegetation phenology are processed by analysing a single region of interest (ROI) within the image, considered as a reference of the mean behaviour of the entire ecosystem (Migliavacca et al., 2011;Richardson et al., 2006;Sonnentag et al., 2012). Few published studies use more than one ROI to evaluate phenological differences between plants included in the same image (Ahrends et al., 2008;Alberton et al., 2014;Henneken et al., 2013;Mizunuma et al., 2013) and only the study by Ide and Oguma (2013) performs a pixel per pixel analysis. An interesting and poorly investigated improvement of this technique is to analyse the spatial information in the considered frame to identify spatial differences in phenology within the image (Ide and Oguma, 2013). ...
Plant phenology is a commonly used and suitable indicator of the impact of climate change on vegetation. In mountainous areas, phenology is governed by environmental drivers such as air temperature, photoperiod and the presence of snow. In this study, digital images collected over 3 years (2009, 2010 and 2011) in a subalpine grassland site were used to investigate the relationship between the timing of snowmelt and the beginning of the growing season in both the spatial and the temporal dimension.
... Canopy development can be reflected by the variability in the aggregate pigment content present in the canopy, leading to dynamic changes in color indices throughout the growth period (Liu et al., 2020;Yang et al., 2014). Colour indices can provide reliable observations about canopy development at a daily temporal resolution (Zhou et al., 2019), and the changes of colour indices reflect the seasonal variation of GPP (Mizunuma et al., 2013;Saitoh et al., 2012). Automated and continuous imagery with minimal cloud-related disruptions provided by phenocam, making it a cost-effective method for monitoring vegetation dynamics (Richardson et al., 2009). ...
Vegetation phenology serves as an important indicator for climate change and plays a crucial role in affecting the terrestrial water, energy, and carbon cycles. The green chromatic coordinate (GCC) obtained from digital repeat photographs has been widely applied in estimating phenology from the perspective of greenness, while the performance of satellite derived GCC is not well understood. We used flux tower GPP from seven deciduous broadleaf forest (DBF) and three grassland (GRA) sites over the Northern Hemisphere. The aim was to compare phenological events with GCC (obtained from digital repeat photographs and satellite remote sensing (GCC MO-DIS)) and the enhanced vegetation index (EVI). Meanwhile, we also explored the performance of these three indices in simulating GPP utilizing the light use efficiency (LUE) model at the DBF and GRA sites. Phenology retrieved by GCC, GCC MODIS , and EVI was all significantly correlated with GPP-estimated values at all sites (P < 0.001). It indicates the comparable performance of GCC, GCC MODIS , and EVI in estimating phenological events. The RMSE values between the GPP and three indices-estimated phenological events revealed that the three indices excelled in estimating the start of growing season (SOS) compared to the end of growing season (EOS) and the length of growing season (GSL). In terms of GPP estimation performance, the R 2 values of GCC MODIS and EVI-estimated GPP increased by 2 % and 1 %, respectively, compared to GCC-simulated GPP. Meanwhile, the RMSE values for GCC MODIS and EVI reduced by 0.08, and the bias values were reduced by 0.06 and 0.12, respectively. This study showed that GCC obtained from satellite remote sensing data could be utilized as an effective tool in extracting phenology and has a great potential to estimate GPP, at least across the DBF and GRA regions.
... Later in the growing season, the severity of stress or disturbance can translate into commensurately advanced senescence Xie et al., 2015), which would reduce the leaf maturity period or the entire period between leaf emergence and senescence. Studies have shown with a variety of cameras that following disturbance, which led to leaf damage, there may be a pronounced decline in canopy seasonal maximum greenness, which would impact various leaf stages extracted from the greenness curve (Hufkens, Friedl, Keenan, et al., 2012;Ide et al., 2011;Keenan et al., 2014;Menzel et al., 2015;Mizunuma et al., 2013;Richardson, Hufkens, Milliman, Aubrecht, Furze, et al., 2018). These studies also show such a decline is distinct from the greenness patterns at nearby sites or growing seasons with no recorded disturbance at the same site, suggesting this feature is a promising signal of disruption to ecosystem processes due to disturbance or stress. ...
Climate change leads to an increased frequency of severe weather events as well as stressful growing conditions. Together these changes may impact the resilience of ecosystems. To keep track of such effects, conservation managers monitor the “ecological integrity” or coherence of ecosystem processes, such as the cycling of carbon and water. Networked phenocams can produce near-continuous observations of leaf function in the context of climate change, capturing declines due to disturbance or stress. Here we explore the application of phenocams to detect responses to disturbance and stress using 14 examples from the PhenoCam Network. We selected these previously published and new examples to include a variety of disturbances in the form of hurricanes, a windstorm, frost, insect defoliation, and stress due to drought. Frost and herbivory disturbances led to both reductions and extensions in the duration of the rising section of the greenness curve, while hurricanes generally led to reductions in the duration of the plateau section and entire leaf-on period. We found that changes of at least ±20% in the duration of the rising section in the seasonal greenness curve, ±20% in the duration of the plateau section following the seasonal greenness peak, and ±10% in the duration of the entire leaf-on period were a reliable signal of leaf functional declines due to disturbance or stress. If such declines become increasingly frequent and severe as a consequence of climate change, this could impact ecological integrity through interruptions to ecosystem processes. Comparing the duration of these periods in a given year to the average for other years with these thresholds resulted in average true detection rates of 86% and false-positive detection rates of 11% when sampling from probability density functions of 344 broadleaf and needleleaf PhenoCam site-years. Here we show that phenocams are powerful ecological integrity monitoring tools, which can be efficiently applied to quantify dynamic responses to disturbance or stress.
... The warming climate and the varying precipitation are closely correlated with the phenological events (leaf unfolding, leaf senescence) of vegetation, determining the photosynthate allocation processes and shifting the agricultural production (Ge et al., 2015;Guo et al., 2021;Liu et al., 2016a;Piao et al., 2019). The change in phenological events of vegetation (forest, arable cropland) can profoundly influence the carbon and water cycles (Garrity et al., 2011;Mizunuma et al., 2013). Similarly, the phenology of agricultural events are important indicators that control the growth stages and influence the carbon allocations between plant organs (Guo et al., 2020b). ...
The extraction of phenological events in forest and agriculture commonly relies on Vegetation Indices (VI) composed by visible and near infrared bands from satellite images. However, the textural information playing an important role in image fusion, image classification and change detection is commonly ignored. In this study, high-throughput images collected from an Unmanned Aerial Vehicle (UAV) platform during the growth stages of summer maize were used to identify the Tasseling Date (TD) based on both spectral and textural information. The spectral and textural information were extracted using various VI and the Gray Level Co-occurrence Matrix (GLCM), respectively. The results showed that the Normalized Green Blue Difference Index (NGBDI), and the Green Blue Difference Index (GBDI) of VI and the Contrast Information (Contrast) of GLCM performed better than other variables. A new index was generated by integrating spectral and textural information using the Improved Adaptive Feature Weighting Method (IAFWM), and then the TDs were identified for each plot. The Root Mean Square Error (RMSE) of new index was 5.77 days and it was the lowest among all variables. The potential ability of more advanced machine learning and deep learning in integrating the spectral and textural information should be investigated.
... Satellite remote sensing, as a feasible means for monitoring large-scale vegetation dynamics, has been widely used in GPP modeling (Prince and Goward, 1995;Running et al., 2004;Xiao et al., 2004a, b;Sims et al., 2008;Damm et al., 2010;Gitelson et al., 2012). Due to the limitation of cloud contamination, however, the temporal resolutions of optical remote sensing products are typically coarser than daily, ranging from 8-day to half-month (Mizunuma et al., 2013). It is therefore challenging to depend on satellite remote sensing to track the effects of ...
The accurate estimation of temporally-continuous gross primary production (GPP) is important for a mechanistic understanding of the global carbon budget, as well as the carbon exchange between land and atmosphere. Ground-based PhenoCams can provide near-surface observations of plant phenology with high temporal resolution and possess great potential for use in modeling the seasonal dynamics of GPP. However, due to the site-level empirical approaches for estimating the fraction of absorbed photosynthetically active radiation (fAPAR), a broad application of PhenoCams in GPP modeling has been restricted. In this study, the stage of vegetation phenology (Pscalar) is proposed, which is calculated from the excess green index (ExGI) derived from PhenoCam data. We integrate Pscalar with the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in order to generate a daily time-series of the fAPAR (fAPARCAM), and then to estimate daily GPP (GPPCAM) with a light use efficiency model in a semi-arid grassland area from 2012 to 2014. Over the three continuous years, the daily fAPARCAM exhibited similar temporal behavior to the eddy covariance–measured GPP (GPPEC), and the overall determination coefficients (R²) were all > 0.81. GPPCAM agreed well with GPPEC, and these agreements were highly statistically significant (p < 0.01); R² varied from 0.80 to 0.87, the relative error (RE) varied from -2.9% to 2.81%, and the root mean square error (RMSE) ranged from 0.83 to 0.98 gC/m²/d. GPPCAM was then resampled to 8-day temporal resolution (GPPCAM8d), and further evaluated by comparisons with MODIS GPP products (GPPMOD17) and vegetation photosynthesis model (VPM)–derived GPP (GPPVPM). Validation revealed that the variance explained by GPPCAM8d was still the greatest among these three GPP products. The RMSE and RE of GPPCAM8d were also lower than those of the other two GPP products. The explanatory power of predictors in GPP modeling was also explored; the fAPAR was found to be the most influential predictor, followed by photosynthetically active radiation (PAR). The contributions of the environmental stress indices of temperature and water (Tscalar and Wscalar, respectively) were less than that of PAR. These results highlight the potential for PhenoCam images in high temporal resolution GPP modeling. Our GPP modeling method will help reduce uncertainties by using PhenoCam images for monitoring the seasonal development of vegetation production.
... This index is easy to estimate and is correlated with important phenological events, such as canopy greening [36] and seasonal canopy-level photosynthesis [37]. However, the green chromatic coordinate is simultaneously affected by leaf color and canopy structure [35], is insensitive to significant levels of defoliation [38], and in coniferous-dominated forests is mostly associated with changes in pigmentation of existing leaves instead of leaf emergence and senescence [37]. By detecting the structures that these indexes represent, convolutional neural networks are capable of extracting new, and more complex data from existing datasets (e.g. ...
Phenology has become a field of growing importance due to the increasingly apparent impacts of climate change. However, the time-consuming, subjective and tedious nature of traditional human field observations have hindered the development of large-scale phenology networks. Such networks are rare and rely on time-lapse cameras and simplistic color indexes to monitor phenology. To automatize rapid, detailed and repeatable analyzes, we propose an Artificial Intelligence (AI) framework based on machine learning and computer vision techniques. Our approach extracts multiple ecologically-relevant indicators from time-lapse digital photography datasets. The proposed framework consists of three main components: (i) a random forest model to automatically select relevant images based on color information; (ii) a convolutional neural network (CNN) to identify and localize open tree buds; and (iii) a density-based spatial clustering algorithm to cluster open bud detections across the time-series. We tested this framework on a dataset including thousands of black spruce and balsam fir tree images captured using our phenological camera network. The performed experiments showed the efficiency of the proposed approach under challenging perturbation factors, such as significant image noise. Our framework is exceedingly faster and more accurate than human analysts, reducing the time-series processing time from multiple days to under an hour. The proposed methodology is particularly appropriate for large-scale and long-term analyzes of ecological imagery datasets. Our work demonstrates that the use of computer vision and machine learning methods represents a promising direction for the implementation of national, continental, or even global plant phenology networks.
... One such index is the green chromatic coordinate (G cc ), calculated as the mean green channel intensity, normalized against the sum of the RGB channel intensities (Wingate et al., 2015;Richardson et al., 2018a). Other indices have been proposed and shown to be superior in some applications (Mizunuma et al., 2013;Nasahara and Nagai, 2015), and alternative methods leveraging the near-infrared sensitivity of the imaging sensors commonly used in today's digital cameras have also been developed (Petach et al., 2014;Filippa et al., 2018). These indices are analogous to the indices routinely calculated from airborne and satellite remote sensing images, such as enhanced vegetation index (EVI), NDVI, and chlorophyll carotenoid index (CCI). ...
... While greenness indices are imperfect for capturing leaf-level physiology [54], research supports greenness as a tool in understanding carbon uptake dynamics across many plant functional types and ecosystems [47][48][49]55,56]. Lower spring greenness in PI trees may indicate diminished productivity relative to AI trees during this time. ...
The infiltration of stormwater runoff for use by urban trees is a major co-benefit of green infrastructure for desert cities with limited water resources. However, the effects of this passive irrigation versus regular, controlled moisture inputs, or active irrigation, is largely unquantified. We monitored the ecohydrology of urban mesquite trees (Prosopis spp.) under these contrasting irrigation regimes in semiarid Tucson, AZ. Measurements included soil moisture, sap velocity, canopy greenness, and leaf-area index. We expected both irrigation types to provide additional deep (>20 cm) soil moisture compared to natural conditions, and that trees would depend on this deep moisture for transpiration and phenological activity. Results show that active irrigation supported higher soil moisture throughout the study than passive irrigation. Passive irrigation only provided additional deep moisture when green infrastructure features received impervious runoff from a city street. Sap velocity and greenness were similar under both irrigation types, outside of isolated periods of time. These differences occurred during the extremely wet summer 2017 when passively irrigated trees exhibited a greenness peak, and the dry conditions of spring when actively irrigated trees had higher sap flow and relative greenness. Finally, it was not determined that deep soil moisture had a stronger relationship with mesquite productivity than shallow moisture, but both relationships were stronger in the spring, before summer rains. This study aims to contribute empirical observations of green infrastructure performance for urban watershed management.
... PhenoCam is a near-surface remote sensing network device that provides data with high temporal and spatial resolution (Richardson et al., 2018). PhenoCam-derived vegetation indices have been correlated with photosynthesis in forests (Hufkens et al., 2012;Mizunuma et al., 2013;Toomey et al., 2015), grasslands (Migliavacca et al., 2011;Wingate et al., 2015;Luo et al., 2018), and shrublands (Kurc and Benton, 2010) in North America, Europe, and Australia. However, few PhenoCam observations have been made in the East Asia semiarid region. ...
... Previous studies showed a clear phenomenon relating the change of G cc prior to minimal levels during the foliar senescence of deciduous canopies (Richardson et al. 2009, Toomey et al. 2015, Brown et al. 2016. Our results suggested that 2nd change point may occur while plant canopy already exhibit low levels of photosynthesis rate (Fig. 3c) and chlorophyll concentration (Fig. 3a), which is in agreement with other studies (Ahrends et al. 2009, Mizunuma et al. 2013, Keenan et al. 2014). In addition, it had been shown that both the canopy level photosynthesis rate and foliar chlorophyll concentration (Fig. 3a, c) already finish the rapid decrease and stay in the minimum level in the date of 3rd change point of G cc 's curve, which suggested that G cc could detect the end of growing season by analyzing the downtrend rate during the senescence. ...
Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.
... The weatherproof box was fixed to a pole extending 2.5 m away from the south-west side of the tower; the box was inverted to give an uninterrupted view to the horizon over the top of the forest canopy. Details of this camera system are described in Mizunuma et al. (2013). Some of the same oak and ash trees were visible in images from both camera systems. ...
Digital hemispherical photography is a valuable method for monitoring changes in the biosphere's response to climate change. In forests, cameras have often been fitted to existing towers and masts. However, such towers are logistically difficult and expensive to install. Ground-based automatic camera systems offer an alternative that removes the barriers associated with above-canopy photography, but to date there have been few comparisons between upward- and downward-facing images at the same site. This study addresses this issue, by comparing a pair of cameras, one ground-based, one tower-based, viewing the same trees in a deciduous oak (Quercus robur L.) plantation forest in south-eastern England. Over 6 years, the upward-facing ground-based camera system was able to detect key spring phenological events to the same extent as the more usual downward-facing camera (mean difference of 2 days for green-up date). However, the upward- and downward-facing systems were less well-matched in detecting specific events at the end of the growing season, although both systems displayed similar temporal trends. Upward-facing cameras can therefore act as a reliable and comparable alternative to tower-based phenocam systems, as well as beingmore suitable for wider spatial coverage without the need for expensive installation infrastructure. In addition to the increased ease of access with upward-facing camera systems, the images from them also allow canopy structural dynamics such as canopy closure to be estimated.
... Their low cost allowed for installing multiple cameras in publicly-accessible sites without the need for additional security measures. Various studies have shown that cameras with different spectral responses and white balance settings resulted in similar phenological metrics (e.g., Mizunuma et al., 2013;Sonnentag et al., 2012;Zhao et al., 2012). Our study did highlight that viewing angle has an effect on the retrieval of phenological metrics. ...
Remote sensing studies of vegetation phenology increasingly benefit from freely available satellite imagery acquired with high temporal frequency at fine spatial resolution. Particularly for heterogeneous landscapes this is good news, given the drawback of medium-resolution sensors commonly used for phenology retrieval (e.g., MODIS) to properly represent the fine-scale spatial variability of vegetation types. The Sentinel-2 mission acquires spectral data globally at 10 to 60 m resolution every five days. To illustrate the mission's potential for studying vegetation phenology, we retrieved phenological parameters for the Dutch barrier island Schiermonnikoog for a full season of Sentinel-2A observations in 2016. Overlapping orbits resulted in two acquisitions per 10 days, similar to what is achieved globally since the launch of Sentinel-2B. For eight locations on the island's salt marsh we compared greenness chromatic coordinate (GCC) series derived from digital repeat RGB-cameras with vegetation index series derived from Sentinel-2 (NDVI and GCC). For each series, a double hyperbolic tangent model was fitted and thresholds were applied to the modelled data to estimate start-, peak-, and end-of-season (SOS/PS/EOS). Variability in Sentinel-2 derived SOS, when taken as the midpoint between minimum and peak NDVI, was well-explained by camera GCC-based SOS (R² = 0.74, MSD = 8.0 days, RMSD = 13.0 days). However, EOS estimates from camera GCC series were on average almost two months before NDVI-based estimates. This could partially be explained by the observed exponential relationship between GCC and NDVI, as well as by the combined effect of viewing angle differences and the presence of non-photosynthetic elements in the vegetation canopy. A two-layer canopy radiative transfer model incorporating reduced chlorophyll levels in the upper layer provided a physically-based explanation of the viewing angle effect. Finally, we applied the phenology retrieval approach to NDVI series for all pixels of the island in order to map spatial patterns of phenology at fine resolution. Our results demonstrate the potential of the Sentinel-2 mission for providing spatially-detailed retrievals of phenology.
... At the ecosystem scale, and across a wide range of ecosystem types, measures of canopy greenness derived from digital imagery have also been shown to correlate well with the seasonal dynamics of gross photosynthesis, as derived from eddy covariance measurements of surface-atmosphere CO 2 flux 18,26,[50][51][52][53] . Thus, information about the state of the canopy, and its level of physiological activity, can be inferred from the PhenoCam data presented here. ...
Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.
... Colour changes in plant tissue are unlikely to occur without a biochemical or biophysical mechanism, and digital photography has provided insight into these mechanisms (Keenan et al., 2014;Yang et al., 2014). For deciduous species, budburst and leaf senescence events and also their relationship with CO 2 exchange have been in a focus in a number of studies, and these phenomena have been analysed with various colour indices (Richardson et al., 2007;Ahrends et al., 2009;Sonnentag et al., 2012;Mizunuma et al., 2013;Wingate et al., 2015). ...
Ecosystems' potential to provide services, e.g. to sequester carbon, is largely driven by the phenological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phenological information from on the ground measurements would provide valuable reference data for earth observed phenology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches (Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region. Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phenological dates for the season start and end of deciduous species studied here, thus providing important species-specific data for model validation and for explaining the temporal variation in EO phenology products.
... Recently, a network of fine-resolution digital cameras installed in the field known as "phenocams", emerged as a new method to monitor vegetation phenology (Richardson et al., 2007). While this method reduces the subjectivity of human observations, it is limited by its relatively small spatial extent across the globe (Mizunuma et al., 2013;Klosterman et al., 2014). Alternatively, as the reflectance properties of vegetated land varies seasonally in relation to vegetation phenology, the systematic, multi-temporal data collected by optical satellite sensors offer a unique mechanism to monitor vegetation dynamics as this approach allows monitoring of an entire ecosystem rather than individual trees (Reed et al., 2009;White et al., 1997White et al., , 1999. ...
Mangrove forest phenology at the regional scale have been poorly investigated and its driving factors remain unclear. Multi-temporal remote sensing represents a key tool to investigate vegetation phenology, particularly in environments with limited accessibility and lack of in situ measurements. This paper presents the first characterisation of mangrove forest phenology from the Yucatan Peninsula, south east Mexico. We used 15-year time-series of four vegetation indices (EVI, NDVI, gNDVI and NDWI) derived from MODIS surface reflectance to estimate phenological parameters which were then compared with in situ climatic variables, salinity and litterfall. The Discrete Fourier Transform (DFT) was used to smooth the raw data and four phenological parameters were estimated: start of season (SOS), time of maximum greenness (Max Green), end of season (EOS) and length of season (LOS). Litterfall showed a distinct seasonal pattern with higher rates during the end of the dry season and during the wet season. Litterfall was positively correlated with temperature (r=0.88, p<0.01) and salinity (r=0.70, p<0.01). The results revealed that although mangroves are evergreen species the mangrove forest has clear greenness seasonality which is negatively correlated with litterfall and generally lagged behind maximum rainfall. The dates of phenological metrics varied depending on the choice of vegetation indices reflecting the sensitivity of each index to a particular aspect of vegetation growth. NDWI, an index associated to canopy water content and soil moisture had advanced dates of SOS, Max Green and EOS while gNDVI, an index primarily related to canopy chlorophyll content had delayed dates. SOS ranged between day of the year (DOY) 144 (late dry season) and DOY 220 (rainy season) while the EOS occurred between DOY 104 (mid-dry season) to DOY 160 (early rainy season). The length of the growing season ranged between 228 and 264 days. Sites receiving a greater amount of rainfall between January and March showed an advanced SOS and Max Green. This phenological characterisation is useful to understand the mangrove forest dynamics at the landscape scale and to monitor the status of mangrove. In addition the results will serve as a baseline against which to compare future changes in mangrove phenology due to natural or anthropogenic causes.
... Cameras have been deployed on a range of ecosystems, including deciduous and mixed species ecosystems (Richardson et al., 2007, Ahrends et al., 2009, Mizunuma et al., 2013, grasslands (e.g. Migliavacca et al., 2011), peatlands (Westergaard-Nielsen et al., 2013, Peichl et al., 2015, Linkosalmi et al., 2016 and coniferous forests (Nagai et al., 2012, Linkosalmi et al., 2016. ...
In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. Networked cameras can provide information about snow cover and vegetation status with a broad spatial coverage and high temporal resolution, and serve as ground truths to earth observations, and be useful for gap-filling of cloudy areas in earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen-science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1–3 cameras. Here, we document the network, basic camera information and access to images (see, https://doi.org/10.5281/zenodo.777952) in the permanent data repository (https://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series from cameras are consisted of half-hourly images collected between 2014 and 2016. Additionally, we present example colour index time series derived from image time series from two contrasting sites.
... However, since this work only examined the hue color value of targets, uncertainty remains about the true spectral or radiometric quality of SFM 3D-RGB point clouds. While measures like hue can in part remove some variations due to differences in illumination[55], multiple factors leading to variation in lighting were present in the study: varying lighting conditions (weather, cloud cover, angle between sun, camera and tree), differences in sensors, and sensor calibrations. Even so, it may be expected that given calibrated or normalized photos, similar results would be obtained in so far that SFM 3D color points would be placed in the correct location in 3D space. ...
Remote sensing of the structural and spectral traits of vegetation is being transformed by structure from motion (SFM) algorithms that combine overlapping images to produce three-dimensional (3D) red-green-blue (RGB) point clouds. However, much remains unknown about how these point clouds are used to observe vegetation, limiting the understanding of the results and future applications. Here, we examine the content and quality of SFM point cloud 3D-RGB fusion observations. An SFM algorithm using the Scale Invariant Feature Transform (SIFT) feature detector was applied to create the 3D-RGB point clouds of a single tree and forest patches. The fusion quality was evaluated using targets placed within the tree and was compared to fusion measurements from terrestrial LIDAR (TLS). K-means clustering and manual classification were used to evaluate the semantic content of SIFT features. When targets were fully visible in the images, SFM assigned color in the correct place with a high accuracy (93%). The accuracy was lower when targets were shadowed or obscured (29%). Clustering and classification revealed that the SIFT features highlighted areas that were brighter or darker than their surroundings, showing little correspondence with canopy objects like leaves or branches, though the features showed some relationship to landscape context (e.g., canopy, pavement). Therefore, the results suggest that feature detectors play a critical role in determining how vegetation is sampled by SFM. Future research should consider developing feature detectors that are optimized for vegetation mapping, including extracting elements like leaves and flowers. Features should be considered the fundamental unit of SFM mapping, like the pixel in optical imaging and the laser pulse of LIDAR. Under optimal conditions, SFM fusion accuracy exceeded that of TLS, and the two systems produced similar representations of the overall tree shape. SFM is the lower-cost solution for obtaining accurate 3D-RGB fusion measurements of the outer surfaces of vegetation, the critical zone of interaction between vegetation, light, and the atmosphere from leaf to canopy scales.
... Automated image analysis (greenness index, Sgreen) has provided reliable information on developmental stages of the dominant tree species through all seasons. Variations in plant phenology affect the phase, timing, and magnitude of ecosystem carbon sequestration and hydrological processes [17][18][19][20] . Therefore, the impact of vegetation phenology modification (the timing of leaf emergence, developmental, and senescence stages) on WUE is likely to be critical. ...
We calculated water use efficiency (WUE) using measures of gross primary production (GPP) and evapotranspiration (ET) from five years of continuous eddy covariance measurements (2009–2013) obtained over a primary subtropical evergreen broadleaved forest in southwestern China. Annual mean WUE exhibited a decreasing trend from 2009 to 2013, varying from ~2.28 to 2.68 g C kg H2O−1. The multiyear average WUE was 2.48 ± 0.17 (mean ± standard deviation) g C kg H2O−1. WUE increased greatly in the driest year (2009), due to a larger decline in ET than in GPP. At the diurnal scale, WUE in the wet season reached 5.1 g C kg H2O−1 in the early morning and 4.6 g C kg H2O−1 in the evening. WUE in the dry season reached 3.1 g C kg H2O−1 in the early morning and 2.7 g C kg H2O−1 in the evening. During the leaf emergence stage, the variation of WUE could be suitably explained by water-related variables (relative humidity (RH), soil water content at 100 cm (SWC_100)), solar radiation and the green index (Sgreen). These results revealed large variation in WUE at different time scales, highlighting the importance of individual site characteristics.
... White et al. 2014;Wang et al. 2015), webcam digital images (e.g. Mizunuma et al. 2012) and experimental warming (e.g. Wolkovich et al. 2012), but these approaches are not well suited for assessing individual tree responses or variations between provenances. ...
The widely observed advance in spring budburst across a range of temperate forest species due to climatic warming has received considerable attention. This change in forest phenology has important implications for the choice of species and provenances currently being planted, which need to be suited to both current and future climatic conditions. Using a provenance trial in the south of England, this study assessed variation in the timing of budburst across 23 different European provenances of oak (Quercus robur L. and Q. petraea (Matt.) Liebl.) over 8 years of observations. The order in which the different provenances reached budburst was related to provenance source latitude: the southern provenances were always earlier than those from more northerly latitudes. The statistical technique partial least squares regression was used to identify critical periods of both chilling and warming. A General Linear Model and three-dimensional temperature response surfaces were used to analyse the temporal trends in budburst. There was a negative correlation between the date of budburst and mean daily air temperature in both the chilling and warming periods for all provenances, which was statistically significant for a majority. Spring warming had a larger effect on budburst than winter chilling with a mean spring temperature-driven advance of 3.61 days/°C (standard error = 0.17 days/°C) and mean winter period temperature-driven advance of 0.99 days/°C (standard error = 0.17 days/°C). Surprisingly, there was no statistically significant interaction between mean air temperatures during the chilling and warming phases on the date of budburst.
... Such variations may be due to diurnal differences in illumination, since the white balance and exposure settings of the RGB cameras we used were fixed to automatic with no option available to change these settings. Therefore, where the equipment allows we recommend changing the white balance from automatic to a fixed setting as this can stabilise diurnal fluctuations in the indices calculated [51]. Nevertheless, our results support the findings of Motohka et al. [28] and von Bueren et al. [52] who found that the GRVI could be used to successfully monitor phenological change in vegetation. ...
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.
... 帽儿山站通量塔周围温带落叶阔叶林 BVI 日变化呈上午先降低后略升高，午后达到最 大值略微下降，日落前迅速增加的趋势。BVI 的午间最大值与太阳高度角和坡度的夹角的最 大值出现时间基本一致(图 2)。本研究中，BVI 日变化格局与芬兰南部松树林的 NDVI B 日变 化格局 [27] 基本一致，午后峰值时间略有差异。而 Huemmrich 等 [21] 发现，在地势平坦(坡度约 1°)的加拿大南部老龄黑云杉(Picea mariana)林，NDVI B 上午逐渐降低，中午基本不变，下午 略下降。不同站点 BVI 的日变化格局的差异可能是由于地理纬度(太阳高度)和地形因素(主 要是坡向)的不同造成的。以往无论是利用 BVI 还是数字相机绿度指数监测冠层动态通常选 择与卫星过境时间一致的瞬时数据 [21,27] ，或者是用中午前后(10:00-14:00)的数据的平均值 来减少太阳高度角的影响 [39][40][41] [42] ，r PAR 在一天内的太阳高度角最大时最小 [43][44] ，这与入射光的直 射与散射辐射比例的日变化有关，冠层对散射辐射的反射率比对直射辐射的大，当太阳高度 角较小时，入射 PAR 散射辐射所占比例比直射辐射大，随着太阳高度角的增大直射辐射所 占比例随之增加，直射 PAR 对冠层的穿透能力增强，反射率随之减小 [43] ；但 NIR 波段存在 反射和多次散射 [42] ，除与光线入射角度有关还受到水汽吸收带的影响。r NIR 与 r PAR 的日变化 格局不一致造成 BVI 的日变化。另外，也可能是不同时刻坡面上各方向的散射辐射不同导 致了 r NIR 和 r PAR 的日变化 [45] ，进而导致了 BVI 的日变化格局。黎明和傍晚的 BVI 噪音较大 且偏高，可能是由清晨或者傍晚的露水造成的 [46] 。此外，辐射表安装(水平或平行于坡面) 影响入射辐射 [47] [21] 和 Wilson [28] 两种计算方法，结果表明 Huemmrich [21] 的方法与卫星数据的相关性更高，更适合验证卫星 数据。本文对比了生长季 BVI 的 3 种计算方法，发现 Huemmrich [21] ...
Broadband vegetation index (BVI) derived from routine radiation measurements on eddy flux towers have the advantage of high temporal resolutions, and thus have potential to obtain detailed information of dynamics in canopy leaf area index (LAI). Taking the temperate broadleaved deciduous forest around the Maoershan flux tower in Northeast China as a case, we investigated the controlling factors and smoothing method of four BVI time-series, i.e., broadband normalized difference vegetation index (NDVIB), broadband enhanced vegetation index (EVIB), the ratio of the near-infrared radiation reflectance to photosynthetically active radiation reflectance (SRNP), and the ratio of the shortwave radiation reflectance to photosynthetically active radiation
reflectance (SRSP). We compared the seasonal courses of the BVIs with the LAI based on litterfall collection method. The values for each BVI were slightly different among the three calculation methods by Huemmrich, Wilson, and Jenkins, but all four BVIs showed similar seasonal patterns. The diurnal variations in BVIs were mainly influenced by the solar elevation and the angle between the solar elevation and slope, but the BVIs were relative stable around 12:30. The noise of daily BVI time-series could be effectively smoothed by a threshold of clearness index (K). The seasonal courses of BVIs for each time of day around the noon had similar patterns, but their thresholds of K and the percentages of remaining data were different. Therefore, the daily values
of BVIs might be optimized based on the smoothing and the proportion of remaining data. The NDVIB was closely correlated linearly with the LAI derived from the litterfall collection method, while the EVIB, SRNP, and SRSP had a logarithmic relationship with the LAI. The NDVIB had the advantage in tracking the seasonal dynamics in LAI and extrapolating LAI to a broader scale. Given that most eddy flux towers had equipped with energy balance measurements, a network of monitoring canopy LAI could be readily achieved if the reflectance of photosynthetically active radiation was measured synchronously.
... Similarly, chromatic coordinates of red and blue (R CC and B CC ) are also computed. Several indices based on RGB colours have been developed in the last years, including for example the green excess index (GEI) (Woebbecke et al., 1995;Mizunuma et al., 2013). Some authors also used a combination of G CC and R CC to extract autumn phenophases (e.g. ...
n this paper we extensively describe new software available as a R package that allows for the extraction of phenological information from time-lapse digital photography of vegetation cover. The phenopix R package includes all steps in data processing. It enables the user to: draw a region of interest (ROI) on an image; extract red green and blue digital numbers (DN) from a seasonal series of images; depict greenness index trajectories; fit a curve to the seasonal trajectories; extract relevant phenological thresholds (phenophases); extract phenophase uncertainties.
The software capabilities are illustrated by analyzing one year of data from a selection of seven sites
belonging to the PhenoCam network (http://phenocam.sr.unh.edu/), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel- based image analysis available in the phenopix R package.
... The chlorophyll concentration has high relationship with the photosynthesis rate and the gross primary productivity (GPP) (Field andMooney 1986, Morecroft et al. 2003). Therefore, the peak GPP may not synchronize with the peak of the greenness indices, such as Gcc and ExG, during the growing season (Richardson et al. 2007, Ahrends et al. 2009, Mizunuma et al. 2013). On the other hand, Richardson et al. (2007) evaluated the relationship between broadband normalized difference vegetation index (NDVI) and the greenness index in the canopy scale, and showed that the peak of NDVI and Gcc, ExG have highly time-serial synchronization (Richardson et al. 2007). ...
Plant phenology has a significant impact on the forest ecosystem carbon balance. Detecting plant phenology by capturing the time-series canopy images through digital camera has become popular in recent years. However, the relationship between color indices derived from camera images and plant physiological characters are elusive during the growing season in temperate ecosystems. We collected continuous images of forest canopy, leaf size, leaf area index (LAI) and leaf chlorophyll measured by a soil plant analysis development (SPAD) analyzer in a northern subtropical oak forest in China. Our results show that (1) the spring peak of color indices, Gcc (Green Chromatic Coordinates) and ExG (Excess Green), was 18 days earlier than the 90% maximum SPAD value; (2) the 90% maximum SPAD value coincided with the change point of Gcc and ExG immediately after their spring peak; and (3) the spring curves of Gcc and ExG before their peaks were highly synchronous with the expansion of leaf size and the development of LAI value. We suggest it needs to be adjusted if camera-derived Gcc or ExG is used as a proxy of chlorophyll or gross primary productivity, and images observation should be complemented with field phenological and physiological information to interpret the physiological meaning of leaf seasonality.
... 4), although the effect of colour of branches and stems on GEI values remained (Inoue et al., 2014a). To explain the effect of background phenology through the analysis of downwardslooking images taken by fish-eye lens, sideways-looking images, which tend not to capture forest floor vegetation and are little influenced by background phenology, may be a useful addition (Inoue Table 3 Quantities and proportions of leaf litter contributed by each tree species during the study period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) et al., 2014a;Mizunuma et al., 2012), as GEI values are strongly affected by illumination conditions. The timing of ELF detected by means of threshold values and inflection points (Henneken et al., 2013) through the analysis of seasonal patterns of GEI values may include uncertainty caused by differences in the patterns and timing of leaf-colouring and -fall among species. ...
We evaluated the uncertainty in the estimation of year-to-year variability in the timing of leaf fall detected by the analysis of red, green and blue (RGB) values extracted from daily phenological images in a deciduous broad-leaved forest in Japan. We examined (1) the spatial distribution of individual tree species within a 1-ha permanent plot and the spatio-temporal variability of leaf litter of various species for 8 years; and (2) the relationship between the year-to-year variability of leaf fall detected by leaf litter and that detected by phenological images of various species. Uncertainties were caused by (1) the heterogeneous distribution of each species within the whole forest community; (2) the year-to-year variability of the timing of leaf fall among species; and (3) differences in leaf colouring and leaf fall patterns among species. Our results indicate the importance of integrating RGB analysis of each species and of the whole canopy on the basis of spatial locations of individuals and proportions of tree species within a forest to reduce uncertainty.
... For one site in our study, UK-Ham, we analyzed estimates from two ground camera systems: an RGB camera with oblique view and a hemispherical ('fisheye') lens mounted on a downward-viewing camera. Over the period 2009-2012, we found that SOS estimates from the downward-viewing hemispherical camera were consistently earlier than from the oblique-viewing camera system (results not shown), confirming findings by Mizunuma et al. (2013). These results might be explained by the understory vegetation development captured by the nadir view, which however is not comparable to the satellite nadir view integrating over a much larger portion of the canopy at coarser spatial resolution. ...
... Vegetation colour, in particular the ratio of green to red and/or blue, has been shown to vary seasonally and be useful as a proxy for vegetation phenology (Richardson et al., 2007, Migliavacca et al., 2011 and health (Mizunuma et al., 2013). Downward facing true colour photographs of the vegetated NEE plots (n = 36) were collected on ten occasions between 20/06/2012 and 25/10/2012 (n = 282). ...
Peatlands are recognized as important carbon stores; despite this, many have been drained for agricultural improvement. Drainage has been shown to lower water tables and alter vegetation composition, modifying primary productivity and decomposition, potentially initiating peat loss. To quantify CO 2 fluxes across whole landscapes, it is vital to understand how vegetation composition and CO 2 fluxes vary spatially in response to the pattern of drainage features. However, Molinia caerulea ‐dominated peatlands are poorly understood despite their widespread extent.
Photosynthesis (P G600 ) and ecosystem respiration (R Eco ) were modelled (12 °C, 600 µmol photons m ⁻² s ⁻¹ , greenness excess index of 60) using empirically derived parameters based on closed‐chamber measurements collected over a growing season. Partitioned below‐ground fluxes were also collected. Plots were arranged ⅛, ¼ and ½ the distance between adjacent ditches in two catchments located in Exmoor National Park, southwest England.
Water table depths were deepest closest to the ditch and non‐significantly ( p = 0·197) shallower further away. Non‐ Molinia species coverage and the Simpson diversity index significantly decreased with water table depth ( p < 0·024) and increased non‐significantly ( p < 0·083) away from the ditch. No CO 2 fluxes showed significant spatial distribution in response to drainage ditches, arguably due to insignificant spatial distribution of water tables and vegetation composition. Whilst R Eco showed no significant spatial variation, P G600 varied significantly between sites ( p = 0·012), thereby controlling the spatial distribution of net ecosystem exchange between sites. As P G600 significantly co‐varied with water table depths ( p = 0·034), determining the spatial distribution of water table depths may enable CO 2 fluxes to be estimated across M. caerulea ‐dominated landscapes. © 2015 The Authors. Ecohydrology published by John Wiley & Sons, Ltd.
... In provenance trials, for which a highly significant negative correlation between frost damage and bud break date was revealed (Gömöry and Paule, 2011), the later flushing populations from warmer origins may show 112 the least damage (Chmura and Rozkowski, 2002). Equally, juveniles seem to be more affected by lateMizunuma et al. (2013) were able to record the effects of a late frost on canopy greenness, which led ...
Damage by late spring frost is a risk deciduous trees have to cope with in order to optimize the length of their growing season. The timing of spring phenological development plays a crucial role, not only at the species level, but also at the population and individual level, since fresh new leaves are especially vulnerable. For the pronounced late spring frost in May 2011 in Germany, we studied the individual leaf development of 35 deciduous trees (mainly European beech Fagus sylvatica L.) at a mountainous forest site in the Bayerischer Wald National Park using repeated digital photographs. Analyses of the time series of greenness by a novel Bayesian multiple change point approach mostly revealed five change points which almost perfectly matched the expected break points in leaf development: (i) start of the first greening between day of the year (DOY) 108–119 (mean 113), (ii) end of greening, and (iii) visible frost damage after the frost on the night of May 3rd/4th (DOY 123/124), (iv) re-sprouting 19–38 days after the frost, and (v) full maturity around DOY 178 (166–184) when all beech crowns had fully recovered. Since frost damage was nearly 100%, individual susceptibility did not depend on the timing of first spring leaf unfolding. However, we could identify significant patterns in fitness linked to an earlier start of leaf unfolding. Those individuals that had an earlier start of greening during the first flushing period had a shorter period of recovery and started the second greening earlier. Thus, phenological timing triggered the speed of recovery from such an extreme event. The maximum greenness achieved, however, did not vary with leaf unfolding dates. Two mountain ashes (Sorbus aucuparia L.) were not affected by the low temperatures of -5°C. Time series analysis of webcam pictures can thus improve process-based knowledge and provide valuable insights into the link between phenological variation, late spring frost damage, and recovery within one stand.
... At the forested Alice Holt site, the annual cycle is dominated by the balance between photosynthesis and respiration (Fig. 4c). Net uptake in the daytime by the understorey is evident early in the year (Wilkinson et al., 2012), followed by a sudden increase in net uptake which coincides with leaf-out of the oak canopy (Mizunuma et al., 2013). During the night and in winter, the forest acts as a small source of CO 2 . ...
Anthropogenic and biogenic controls on the surface-atmosphere exchange of CO2 are explored for three different environments. Similarities are seen between suburban and woodland sites during summer, when photosynthesis and respiration determine the diurnal pattern of the CO2 flux. In winter, emissions from human activities dominate urban and suburban fluxes; building emissions increase during cold weather, while traffic is a major component of CO2 emissions all year round. Observed CO2 fluxes reflect diurnal traffic patterns (busy throughout the day (urban); rush-hour peaks (suburban)) and vary between working days and non-working days, except at the woodland site. Suburban vegetation offsets some anthropogenic emissions, but 24-h CO2 fluxes are usually positive even during summer. Observations are compared to estimated emissions from simple models and inventories. Annual CO2 exchanges are significantly different between sites, demonstrating the impacts of increasing urban density (and decreasing vegetation fraction) on the CO2 flux to the atmosphere.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
... Image series (ISeries) are of special interest because they provide the ability to reanalyze data that has already been collected and can improve spatial and temporal resolution of long-term monitoring variables while at the same time reduce labor (Ide and Oguma, 2010). Plant phenology which is an observable trait impacted by climatic variations (Badeck et al., 2004; Høye et al., 2007) vital for understanding species responses, ecosystem function and the effects of climate (Wright et al., 1999) has been detailed by ISeries (Graham et al., 2009; Richardson et al., 2007) and has been related to measurements such as carbon dioxide uptake (Mizunuma et al., 2013) and gross primary production (Saitoh et al., 2012). In general, ISeries are generated from cameras placed in housing platforms designed to provide stability, power and protection. ...
Abstract
Image series are increasingly being used to output ecological indicators because they provide the ability to reanalyze data that has already been collected and they improve temporal as well as spatial resolution. We address both the increased utilization and the need to diversify the way they are produced by introducing an open source Python (www.python.org) library called EcoIS that creates image series from unaligned pictures of specially equipped plots. We use EcoIS to sample flowering phenology plots in a high arctic environment and create image series that later generate phenophase counts and automatically estimate phenological dates of interest. Our results exhibit one day difference between EcoIS estimations of local indicators and the ones calculated with the established field-based process. We show that EcoIS’ error is similar to the one of image series generated with fixed camera setups. We see that EcoIS processes an image in 3.8 seconds and show how it is equipped to handle data intensive scenarios. We additionally identify in-camera automatic image formatting and image acquiring oversight as contributing factors for increasing the overall error. Our main conclusion is that EcoIS creates usable image series that maintain the spatiotemporal qualities of the original images and can successfully be utilized to generate ecological indicators. EcoIS is relevant as a replacement for traditional image series infrastructure where the cost of deploying EcoIS is smaller or less intrusive to the ecosystem.
... The images captured by these traffic cameras offer extended analysis possibilities, particularly when coupled with similar resolution observations of precipitation and temperature, carbon flux measurements and soil properties [47]. This will benefit the understanding of budgets of carbon and water exchange (e.g., gross primary productivity (GPP) [50,51]) and moreover shed light on any interannual variability in the growing season caused by weather conditions (e.g., windstorms [52]) and other occasional but often severe biotic stresses that influence the physiological status of trees [45,53]. Beyond this there is scope for the data to be used in the understanding of ecosystem interactions, food chains, pollination, breeding and migration [54,55]. ...
Phenological metrics are of potential value as direct indicators of climate change. Usually they are obtained via either satellite imaging or ground based manual measurements; both are bespoke and therefore costly and have problems associated with scale and quality. An increase in the use of camera networks for monitoring infrastructure offers a means of obtaining images for use in phenological studies, where the only necessary outlay would be for data transfer, storage, processing and display. Here a pilot study is described that uses image data from a traffic monitoring network to demonstrate that it is possible to obtain usable information from the data captured. There are several challenges in using this network of cameras for automatic extraction of phenological metrics, not least, the low quality of the images and frequent camera motion. Although questions remain to be answered concerning the optimal employment of these cameras, this work illustrates that, in principle, image data from camera networks such as these could be used as a means of tracking environmental change in a low cost, highly automated and scalable manner that would require little human involvement.
Light is a fundamental attribute and key abiotic driver in forest ecosystems. Although the ecological effects of light itself is well studied, capturing the complex parameters that constitute the whole light environment remain an intricate research endeavor. Here, we apply the newly introduced environmental light field (ELF) technique in Kibale National Park, Uganda. We captured whole light scenes with repeat photography and processed it to measure both the spectral composition of light in the red‐green‐blue range, as well as its variation, or “contrast‐span”, using the newly introduced International System of Units (SI); “lit”. We compare across major and globally common utilized forest types—primary, secondary, and selectively logged areas, as well as a completely cleared area as a control. We find that the ELF system is able to effectively capture key aspects of the local light environment across the range of forest types. The distribution of light intensity and its spectral composition across our study is hardly uniform, with primary forest and a clearing showing two orders of magnitude difference in light. Blue light predominates the sky areas of the clearing, indicating the Rayleigh scattering of sunlight in the atmosphere. In general, radiance decrease with increasing intactness of the forest, and selectively logged and primary forest show the most similar environmental light characteristics. Owing to its ability to capture fine scale variations in light across elevation gradients, their spectral characteristics, as well as their intensities, the ELF system should become a useful tool in better quantifying light in ecology. In particular, we discuss its potential use in restoration ecology.
We evaluated the usability of the red (R), green (G), and blue (B) digital numbers (DNRGB) extracted from daily phenological images of a tropical rainforest in Malaysian Borneo. We examined temporal patterns in the proportions of DNR, DNG, and DNB as percentages of total DN (denoted as %R, %G and %B), in the hue, saturation, and lightness values in the HSL color model, and in a green excess index (GEI) of the whole canopy and of individual trees for 2 years. We also examined temporal patterns in the proportions of the red, green, and blue reflectance of the whole canopy surface as percentages of total reflectance (denoted as %ref_R, %ref_G, and %ref_B), and vegetation indices (the normalized-difference vegetation index, enhanced vegetation index, and green–red vegetation index) of the whole canopy by using daily measurements from quantum sensors. The temporal patterns of %RGB and saturation of individual trees revealed the characteristics of tree phenology caused by flowering, coloring, and leaf flushing. In contrast, those of the whole canopy did not, nor did those of %ref_R, %ref_G, or %ref_B, or the vegetation indices. The temporal patterns of GEI, however, could detect differences among individual trees caused by leaf flushing and coloring. Our results show the importance of installing multiple time-lapse digital cameras in tropical rainforests to accurately evaluate the sensitivity of tree phenology to meteorological and climatic changes. However, more work needs to be done to adequately describe whole-canopy changes.
Clarification of species-specific year-to-year variations of the timings of the start of leaf-expansion (SLE) and the end of leaf-fall (ELF) is an important and challenging task because these timings may alter spatial and temporal variations in ecosystem services such as carbon stock and climate control. Although many previous studies have applied automatically captured digital camera images to observe the timings of SLE and ELF, the evaluation of the long-term variation in both timings of each tree species based on image analysis has not yet been sufficiently investigated. In this study, we investigated the year-to-year variation in the timings of SLE and ELF for multiple deciduous broad-leaved tree species in a cool-temperate deciduous broad-leaved forest in Japan by using long-term and daily hemispherical (“fish-eye”) canopy surface images from 2004 to 2013. We found that (1) differences in the characteristics of year-to-year variations in the timing of ELF among the tree species were more apparent than those of the timing of SLE among the tree species, (2) the threshold value of the camera-based index (green excess index) for detecting the timing of ELF varied depending on the spatial and temporal distribution of understories and the visual distortion of the fish-eye images, and (3) the phenological sensitivity of the timing of ELF to air temperature was lower than that of the timing of SLE. Our results indicate that it might be helpful for ecologists to use daily continuous canopy surface images for monitoring of species-specific characteristics of spatial and temporal changes in foliage phenology in mixed-species deciduous broad-leaved forests.
This rigorous yet accessible text introduces the key physical and biochemical processes involved in plant interactions with the aerial environment. It is designed to make the more numerical aspects of the subject accessible to plant and environmental science students, and will also provide a valuable reference source to practitioners and researchers in the field. The third edition of this widely recognised text has been completely revised and updated to take account of key developments in the field. Approximately half of the references are new to this edition and relevant online resources are also incorporated for the first time. The recent proliferation of molecular and genetic research on plants is related to whole plant responses, showing how these new approaches can advance our understanding of the biophysical interactions between plants and the atmosphere. Remote sensing technologies and their applications in the study of plant function are also covered in greater detail.
Remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) have been used to scale up flux-based gross primary production (GPP) measurements. Recently, the use of visible-band (VIS) indices for estimation of GPP has been proposed, and VIS_indices derived from digital cameras have been used for detecting phenological changes. To confirm the utility of remotely sensed VIS_indices for the evaluation of GPP in a Japanese larch forest , we investigated the relationships between flux-based GPP measurements and indices derived from both moderate resolution imaging spectroradiometer (MODIS) data and tower-mounted digital camera images. We evaluated the suitability of both traditional (NDVI and EVI) and VIS_indices (the green-red vegetation index (GRVI) and green ratio (GR)) at both satellite and near-surface scales for GPP estimation. We also used the MODIS data to evaluate the sensitivity of the indices to the effects of a severe forest disturbance. The results showed that VIS_indices had several advantages over the traditional indices: (1) seasonal variations in VIS_indices were more strongly correlated with GPP variations; (2) the vegetation growing season could be easily discriminated from the winter dormant period, because ground surface conditions affect VIS_indices less than they affect traditional indices; (3) the seasonal dynamics of vegetation could be determined at a satellite scale from MODIS data, and possibly even at a canopy scale from digital camera images; and (4) inter-annual variations of VIS_indices were likely to be more sensitive to vegetation changes after a disturbance. These results demonstrate the utility of VIS_indices for estimating GPP at satellite scales and possibly at the canopy scale. We suggest that multi-scale visible-band remote sensing could help our understanding of the ecosystem by improving the temporal and spatial resolutions of satellite data.
the purpose of understanding pat-terns and processes controlling carbon budgets across a broad range of scales, explicit activities to assess the impact of phenol-ogy on ecosystem carbon bal-ance are still somewhat lacking within the carbon cycle commu-nity. The reasons are clear: long-term observations, otherwise called 'monitoring' are not popu-lar with those that sponsor re-search in this area; three or five year projects are the norm, when in practice much longer records are required to detect long-term trends and their rela-tionships to climatic drivers. There is however, evidence for a shift in attitudes. Keeling's meas-urements of atmospheric CO 2 concentrations, that began in 1958, are an outstanding exam-ple of the value long-term moni-toring represents in the context of a changing world (Nisbet, 2007). Moreover, continuous eddy covariance measurements of CO 2 fluxes began in the early 1990s at a handful of sites. Every year, more and more sites have been added to FLUXNET, and many of these are now providing useful long term data not only with regard to spatial patterns of carbon uptake and release, but also in relation to the influence of phenology on carbon seques-tration. One example of a synergy be-tween phenology and flux moni-toring networks in Europe has Why observe phenology within FLUXNET? Phenology is the study of the timing of lifecycle events, espe-cially as influenced by the sea-sons and by the changes in weather patterns from year to year. The oldest phenological records, observations of cherry flowering at the Royal Court in Kyoto date back to 705 AD, and are still maintained to this day across Japan where the Japanese Meteorological Agency use these data to provide weekly forecast maps of expected blooming dates (http: Marsham, the father of modern phenological recording, was a wealthy landowner and amateur naturalist who recorded "Indications of spring" in Nor-folk, England, beginning in 1736. His family maintained these re-cords until the 1950s. In the modern era, phenology has gained a new impetus, as people realize that such records, if sus-tained over many years, can reveal how plants and animals respond to climate change. Moreover, phenological events such as the spring leaf-out and the autumn fall exert a strong control on both spatial and tem-poral patterns of the carbon cycle. Phenology also influences hydrologic processes, as spring leaf-out is accompanied by a marked increase in evapotranspi-ration, and nutrient cycling as autumn senescence results in a flush of fresh litter (nutrient) input to the forest floor. Phenology is a robust integrator of the effects of climate change on natural systems (Schwartz et al., 2006; IPCC 2007), and it is recognized that improved moni-toring of phenology on local-to-continental scales is needed. Historically, phenological obser-vations were a pastime of ama-teur naturalists (e.g. the Mar-sham family) and reliable records were often dependent on the skills and effort of the observer. The increased demand for inter-national co-operation and stan-dardisation in this area led to the creation of many large-scale phenological monitoring net-works such as the International Phenology Garden (IPG) pro-gram (http: (established in 1998) as well as the recently-established USA-National Phenology Network (U S A -N P N) (http://www.usanpn.org) and associated regional networks (e.g., http://www.nerpn.org). These networks have focused on developing standardized proto-cols for phenological observa-tions, and ensuring overlap be-tween plant species found across locations. Although there are obvious advantages in creating explicit linkages between these
The carbon balance of an 80 yr old deciduous oak plantation in the
temperate oceanic climate of the south-east of Britain was measured by
eddy covariance over 12 yr (1999-2010). The mean annual net ecosystem
productivity (NEP) was 486 g C m-2 y-1 (95% CI of
±73 g C m-2 y-1), and this was partitioned
into a Gross Primary Productivity (GPP) of 2034 ± 145 g C
m-2 y-1, over a 165 (±6) day growing
season, and an annual loss of carbon through respiration and
decomposition (ecosystem respiration, Reco) of 1548 ±
122 g C m-2 y-1. The interannual variation of NEP
was large (coefficient of variation (CV) 23%), although the variation
for GPP and Reco was smaller (12%) and the ratio of
Reco/GPP was relatively constant (0.76 ± 0.02 CI).
Some anomalies in the annual patterns of the carbon balance could be
linked to particular combinations of anomalous weather events, such as
high summer air temperature and low soil moisture content. The
Europe-wide heat-wave and drought of 2003 had little effect on the C
balance of this woodland on a surface water gley soil. Annual variation
in precipitation (CV 18%) was not a main factor in the variation in NEP.
The inter-annual variation in estimated intercepted radiation only
accounted for ~ 47% of the variation in GPP, although a significant
relationship (p<0.001) was found between peak leaf area index and
annual GPP which in turn played an important role in modifying the
efficiency with which incident radiation was used in net CO2
uptake. Whilst the spring start and late autumn end of the net
CO2 uptake period varied substantially (range of 24 and 27
days, respectively), annual GPP was not related to growing season
length. Severe outbreaks of defoliating moth caterpillars, mostly
Tortrix viridana L. and Operophtera brumata L., caused considerable
damage to the forest canopy in 2009 and 2010, resulting in reduced GPP
in these years.
Variations in the amplitude and timing of the seasonal cycle of atmospheric CO2 have shown an association with surface air temperature consistent with the hypothesis that warmer temperatures have promoted increases in plant growth during summer1 and/or plant respiration during winter2 in the northern high latitudes. Here we present evidence from satellite data that the photosynthetic activity of terrestrial vegetation increased from 1981 to 1991 in a manner that is suggestive of an increase in plant growth associated with a lengthening of the active growing season. The regions exhibiting the greatest increase lie between 45°N and 70°N, where marked warming has occurred in the spring time3 due to an early disappearance of snow4. The satellite data are concordant with an increase in the amplitude of the seasonal cycle of atmospheric carbon dioxide exceeding 20% since the early 1970s, and an advance of up to seven days in the timing of the drawdown of CO2 in spring and early summer1. Thus, both the satellite data and the CO2 record indicate that the global carbon cycle has responded to interannual fluctuations in surface air temperature which, although small at the global scale, are regionally highly significant.
Probably the single most fundamental measure of “global change” of highest practical interest to humankind is the change in terrestrial biological productivity. Biological productivity is the source of all the food, fiber, and fuel by which humans survive, and so defines most fundamentally the habitability of Earth. The spatial variability of net primary productivity (NPP) over the globe is enormous, from about 1000 g Cm-2 for evergreen tropical rain forests to less than 30 g Cm-2 for deserts (Scurlock et al. 1999). With increased atmospheric carbon dioxide (CO2) and global climate change, NPP over large areas may be changing (Myneni et al. 1997a, VEMAP 1995, Melillo et al. 1993). Understanding regional variability in carbon cycle processes requires a more spatially detailed analysis of global land surface processes. Since December 1999, the U.S. National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) produces a regular global estimate of (gross primary productivity, GPP) and annual NPP of the entire terrestrial earth surface at 1-km spatial resolution, 150 million cells, each having GPP and NPP computed individually.
Vegetation phenology has a strong influence on the timing and phase of global terrestrial carbon and water exchanges and is an important indicator of climate change and variability. In this study we tested the application of inexpensive digital visible-light cameras in monitoring phenology. A standard digital camera was mounted on a 45 m tall flux tower at the Lägeren FLUXNET/CarboEuropeIP site (Switzerland), providing hourly images of a mixed beech forest. Image analysis was conducted separately on a set of regions of interest representing two different tree species during spring in 2005 and 2006. We estimated the date of leaf emergence based on the levels of the extracted red, green and blue colors. Comparisons with validation data were in accordance with the phenology of the observed trees. The mean error of observed leaf unfolding dates compared with validation data was 3 days in 2005 and 3.6 days in 2006. An uncertainty analysis was performed and demonstrated moderate impacts on color values of changing illumination conditions due to clouds and illumination angles. We conclude that digital visible-light cameras could provide inexpensive, spatially representative and objective information with the required temporal resolution for phenological studies.
The northern high latitudes have warmed by about 0.8°C since the early 1970s, but not all areas have warmed uniformly [Hansen et al., 1999]. There is warming in most of Eurasia, but the warming rate in the United States is smaller than in most of the world, and a slight cooling is observed in the eastern United States over the past 50 years. These changes beg the question, can we detect the biotic response to temperature changes? Here we present results from analyses of a recently developed satellite-sensed normalized difference vegetation index (NDVI) data set for the period July 1981 to December 1999: (1) About 61% of the total vegetated area between 40°N and 70°N in Eurasia shows a persistent increase in growing season NDVI over a broad contiguous swath of land from central Europe through Siberia to the Aldan plateau, where almost 58% (7.3×106km2) is forests and woodlands; North America, in comparison, shows a fragmented pattern of change in smaller areas notable only in the forests of the southeast and grasslands of the upper Midwest, (2) A larger increase in growing season NDVI magnitude (12% versus 8%) and a longer active growing season (18 versus 12 days) brought about by an early spring and delayed autumn are observed in Eurasia relative to North America, (3) NDVI decreases are observed in parts of Alaska, boreal Canada, and northeastern Asia, possibly due to temperature-induced drought as these regions experienced pronounced warming without a concurrent increase in rainfall [Barber et al., 2000]. We argue that these changes in NDVI reflect changes in biological activity. Statistical analyses indicate that there is a statistically meaningful relation between changes in NDVI and land surface temperature for vegetated areas between 40°N and 70°N. That is, the temporal changes and continental differences in NDVI are consistent with ground-based measurements of temperature, an important determinant of biological activity. Together, these results suggest a photosynthetically vigorous Eurasia relative to North America during the past 2 decades, possibly driven by temperature and precipitation patterns. Our results are in broad agreement with a recent comparative analysis of 1980s and 1990s boreal and temperate forest inventory data [United Nations, 2000].
Digital control of color television monitors—in particular, via frame buffers—has added precise control of a large subset of human colorspace to the capabilities of computer graphics. This subset is the gamut of colors spanned by the red, green, and blue (RGB) electron guns exciting their respective phosphors. It is called the RGB monitor gamut. Full-blown color theory is a quite complex subject involving physics, psychology, and physiology, but restriction to the RGB monitor gamut simplifies matters substantially. It is linear, for example, and admits to familiar spatial representations. This paper presents a set of alternative models of the RGB monitor gamut based on the perceptual variables hue (H), saturation (S), and value (V) or brightness (L). Algorithms for transforming between these models are derived. Particular emphasis is placed on an RGB to HSV non-trigonometric pair of transforms which have been used successfully for about four years in frame buffer painting programs. These are fast, accurate, and adequate in many applications. Computationally more difficult transform pairs are sometimes necessary, however. Guidelines for choosing among the models are provided. Psychophysical corrections are described within the context of the definitions established by the NTSC (National Television Standards Committee).
Vegetation distribution and state have been measured since 1981 by the AVHRR (Advanced Very High Resolution Radiometer) instrument through satellite remote sensing. In this study a correction method is applied to the Pathfinder NDVI (Normalized Difference Vegetation Index) data to create a continuous European vegetation phenology dataset of a 10-day temporal and 0.1° spatial resolution; additionally, land surface parameters for use in biosphere–atmosphere modelling are derived. The analysis of time-series from this dataset reveals, for the years 1982–2001, strong seasonal and interannual variability in European land surface vegetation state. Phenological metrics indicate a late and short growing season for the years 1985–1987, in addition to early and prolonged activity in the years 1989, 1990, 1994 and 1995. These variations are in close agreement with findings from phenological measurements at the surface; spring phenology is also shown to correlate particularly well with anomalies in winter temperature and winter North Atlantic Oscillation (NAO) index. Nevertheless, phenological metrics, which display considerable regional differences, could only be determined for vegetation with a seasonal behaviour. Trends in the phenological phases reveal a general shift to earlier (−0.54 days year−1) and prolonged (0.96 days year−1) growing periods which are statistically significant, especially for central Europe.
Background: To understand how forests and woodland respond to global climate change, phenological observations are being made at a number of sites worldwide. Recently, digital cameras have been deployed as part of the existing network of ecosystem CO2 flux towers to provide a time-series of canopy images, and various numerical indices have so far been used by different authors.Aims: To identify which are the most effective colour indices to calculate from the signals extracted from digital cameras, in order to provide recommendations to the scientific community.Methods: Sample images of a Japanese beech (Fagus crenata) forest on Mt. Tsukuba (Japan) were used to define and calculate 12 colour signals and vegetation indices.Results: Although the strength of green signal and green excess index were reliable indicators for estimating foliage growth period, the indices were susceptible to low-visibility weather conditions and distance from the camera. Hue provided a robust metric, showing much less scatter during the vegetative period and a good indication of spring bud break. The bud break dates derived from the indices were slightly earlier than those assessed by visual observation, while the abscission dates were later.Conclusions: We propose that of all the candidate colour indices, hue is the most promising for the detection of bud break as it was least affected by atmospheric conditions.
Agricultural and Forest Meteorology j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g r f o r m e t Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function a b s t r a c t We developed and tested the use of light emitting diodes (LEDs) to monitor vegetation reflectance in narrow spectral bands as a tool suitable for quantifying and monitoring ecosystem structure, function and metabolism. LEDs are appealing because they are inexpensive, small and reliable light sources that, when used in reverse mode, can measure spectrally selective radiation. We selected LEDs in red and near-infrared wavebands as they are used to calculate the normalized difference vegetation index (NDVI). The lab experiments revealed that the LEDs showed linear relation with a hyper-spectral spectroradiometer (R 2 > 0.94 and 0.99 for red and NIR, respectively) and marginal sensitivity to temperature. To test the efficacy of this novel sensor, we measured spectral reflectance with LEDs over an annual grassland in California over 3.5 years. The LED-sensor captured daily to interannual variation of the spectral reflectance at the two bands with reliable and stable performance. The spectral reflectance in the two bands and NDVI proved to be useful to identify the leaf-on and leaf-off dates (mean bias errors of 5.3 and 4.2 days, respectively) and to estimate canopy photosynthesis (R 2 = 0.91). We suggest that this novel instrument can monitor other structural and functional (e.g. leaf area index, leaf nitrogen) variables by employing LEDs that have other specific wavelengths bands. Considering that off-the-shelf LEDs cover a wide range of wavebands from the ultraviolet to near-infrared regions, we believe that the research community could explore a range of similar instruments across a range of bands for a variety of ecological applications.
There are thousands of outdoor webcams which offer live images freely over the Internet. We report on methods for discovering and organizing this already existing and massively distributed global sensor, and argue that it provides an interesting alternative to satellite imagery for global-scale remote sensing applications. In particular, we characterize the live imaging capabilities that are freely available as of the summer of 2009 in terms of the spatial distribution of the cameras, their update rate, and characteristics of the scene in view. We offer algorithms that exploit the fact that webcams are typically static to simplify the tasks of inferring relevant environmental and weather variables directly from image data. Finally, we show that organizing and exploiting the large, ad-hoc, set of cameras attached to the web can dramatically increase the data available for studying particular problems in phenology.
Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric-atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005-2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.
An eddy covariance system is described which has been developed jointly at a number of European laboratories and which was used widely in HAPEX-Sahel. The system uses commercially available instrumentation: a three-axis sonic anemometer and an IR gas analyser which is used in a closed-path mode, i.e. air is brought to the optical bench by being ducted down a sampling tube from a point near the sonic anemometer. The system is controlled by specially written software which calculates the surface fluxes of momentum, sensible and latent heat and carbon dioxide, and displays them in real time. The raw turbulent records can be stored for post-processing. Up to five additional analogue instruments can be sampled at up to 10 Hz and digitised by the sonic anemometer. The instruments are described and details of their operation and connection are presented. The system has relatively low power consumption and can operate from appropriate solar cells or rechargeable batteries. Calibration of the gas analyser needs to be performed typically every 2 or 3 days, and, given that the system requires minimal maintenance and is weather insensitive, it can be operated for the routine collection of surface flux data for extended periods. There are a number of corrections which have to be applied in any eddy covariance system and we describe the system of transfer functions which define our system. Some representative results showing the potential of the system are presented.
The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO(2) uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO(2) uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle-climate models.
The photosynthetic development of pedunculate oak (Quercus robur L.) sun leaves in a mature woodland canopy in Oxfordshire, southern England, was investigated in situ during 3 years with contrasting weather conditions. Development of full photosynthetic capacity (indicated by light-saturated net assimilation rates, A
max, typical of the summer period) took between approximately 50 and 70 days after budbreak in different years. This slow development means that these leaves do not utilise a substantial fraction of the seasonal peak of solar irradiance. During the late autumn senscence period the photosynthetic capacity declined over a 2-week period, but as this is a time of low irradiance, the loss of potential photosynthesis was relatively small. The consequences of these developmental changes and differences in bud break dates for daily and seasonal leaf carbon balance were investigated through a simple light-response photosynthetic model. Seasonal changes in photosynthetic capacity would decrease annual carbon uptake per unit leaf area by about 23% compared to that potentially possible if leaves photosynthesised at peak rates throughout the growing season. This difference is likely to be up to 30% larger in years with late budburst and as low as 18% in years with early budburst.
We tested the hypothesis that the date of the onset of net carbon uptake by temperate deciduous forest canopies corresponds with the time when the mean daily soil temperature equals the mean annual air temperature. The hypothesis was tested using over 30 site-years of data from 12 field sites where CO(2) exchange is being measured continuously with the eddy covariance method. The sites spanned the geographic range of Europe, North America and Asia and spanned a climate space of 16 degrees C in mean annual temperature. The tested phenology rule was robust and worked well over a 75 day range of the initiation of carbon uptake, starting as early as day 88 near Ione, California to as late as day 147 near Takayama, Japan. Overall, we observed that 64% of variance in the timing when net carbon uptake started was explained by the date when soil temperature matched the mean annual air temperature. We also observed a strong correlation between mean annual air temperature and the day that a deciduous forest starts to be a carbon sink. Consequently we are able to provide a simple phenological rule that can be implemented in regional carbon balance models and be assessed with soil and temperature outputs produced by climate and weather models.
Phenological eyes network (PEN) is designed as a validation campaign of satellite remote sensing data for terrestrial ecosystem observation. This project has started in several carbon-flux observatories of AsiaFlux projects. Main part of PEN consists of three core instruments : Automatic-capturing Digital Fisheye Camera (ADFC), HemiSpherical Spectro-Radiometer (HSSR) and SunPhotometer (SP). ADFC serves images mainly for a phenological study, sky condition and leaf area index (LAI). HSSR provides vegetation spectral parameters and a photosynthetically active radiation. SP provides atmospheric parameters for an atmospheric correction of satellite images. In combination with these PEN observations and flux or ecosystem research, validation study of ecosystem remote sensing will be enhanced in various aspects, namely, radiative quality check as well as ecological standpoints. PEN is an open project and welcomes involvements from various groups.
An improved light sensor with a spectral response based on photon absorption between 400 and 700 nm was developed for both field and environmental chamber use. The indicated sensor response was selected because it approximates the photosynthetic response of plants for which data are available. A silicon photo cell with high response in the visible range was used as a sensor. The spectral response was controlled by use of a Kodak Gelatin Wratten Filter. A visible bandpass interference filter produced a sharp cutoff at 700 nm. Heat-absorbing glass eliminated transmission beyond 1,000 nm, and a diffusing plastic and filters were mounted in a miniaturized cosine-corrected head that was fitted with a collimating system to eliminate spectral shifts which arise when light enters the interference filter at oblique angles. Error calculations indicate that under sun-and-sky radiation and various artificial light sources the errors are smaller than those associated with available standard calibration lamps. The device also includes a battery-operated meter circuit suitable for making measurements over a wide range of intensities.
When analysing the woodland light climate, the effect of the canopy on diffuse light from the sky and on direct sunlight must be considered separately. Instantaneous measurements can be made to estimate the percentage of diffuse light cut off, the `day-light factor', but problems of spectral composition and instrument response, of unequal distribution of light over the sky, and of short-term fluctuations of light in the open are all liable to bias such estimates. Integrated measurements of light totals at any considerable number of sites are costly, require considerable maintenance, and do not permit prediction of light condition at other times of year. A reasonably accurate estimate of the mean percentage of diffuse and direct light cut off by the canopy can be obtained from hemispherical photographs, and from these percentages, the actual total of light received over any desired period may be calculated. The photographs, taken with a special camera, cover a whole hemisphere. On the circular image grids can be placed to estimate the light conditions, and details of grid construction are given. These photographic estimates compare well with estimates made from the partial regression of daily or hourly totals of light at three sites, in a deciduous wood in east England, on diffuse and direct light over equivalent periods in the open nearby. The evidence suggests that the percentage reduction of diffuse and direct light can be treated as constant over a period of a month, but these are only averages, and over shorter periods discrepancies might appear. Figures for the mean percentage reduction of diffuse light over an hour for the month may be slightly biased by unequal light distribution over the sky. As `daylight factor' has often been misapplied by biologists, and has also an architectural definition, the term `site factor', with appropriate qualifications as to type of light and time, has been adopted instead for the percentage reduction of light. When presenting results, however, these should as far as possible be given as absolute amounts of light, not as percentage reductions. Light conditions in the open vary greatly with climate and latitude, and equal figures for the percentage reduction of light from two different areas may well represent quite different absolute quantities.
Color slide images of weeds among various soils and residues were digitized and analyzed for red, green, and blue (RGB) color content. Red, green, and blue chromatic coordinates (rgb) of plants were very different from those of background soils and residue. To distinguish living plant material from a nonplant background, several indices of chromatic coordinates were studied, tested, and were successful in identifying weeds. The indices included r-g, g-b, (g-b)||r-g|, and 2g-r-b. A modified hue was also used to distinguish weeds from non-plant surfaces. The modified hue, 2g-r-b index, and the green chromatic coordinate distinguished weeds from a nonplant background (0.05 level of significance) better than other indices. However, the modified hue was the most computationally intense. These indices worked well for both nonshaded and shaded sunlit conditions. These indices could be used for sensor design for detecting weeds for spot spraying control.
We examined the relationship between four vegetation indices and tree canopy phenology in an evergreen coniferous forest in Japan based on observations made using a spectral radiometer and a digital camera at a daily time step during a 4 year period. The colour of the canopy surface of Japanese cedar Cryptomeria japonica changed from yellowish-green to whitish-green from late May to July and turned reddish-green in winter. The normalized difference vegetation index NDVI, enhanced vegetation index EVI and plant area index PAI showed no seasonality. In contrast, the green–red ratio vegetation index GRVI increased from March to June and then decreased gradually from July to December, resulting in a bell-shaped curve. GRVI revealed seasonal changes in the colour of the canopy surface. GRVI correlated more positively with the evaluated maximum photosynthetic rate for the whole forest canopy, A max, than did NDVI or EVI. These results suggest the possibility that GRVI is more useful than NDVI and EVI for capturing seasonal changes in photosynthetic capacity, as the green and red reflectances are strongly influenced by changes in leaf pigments in this type of forest.
Recent studies have reported that seasonal variation in camera-based indices that are calculated from the digital numbers of the red, green, and blue bands (RGB_DN) recorded by digital cameras agrees well with the seasonal change in gross primary production (GPP) observed by tower flux measurements. These findings suggest that it may be possible to use camera-based indices to estimate the temporal and spatial distributions of photosynthetic productivity from the relationship between RGB_DN and GPP. To examine this possibility, we need to investigate the characteristics of seasonal variation in three camera-based indices (green excess index [GE], green chromatic coordinate [rG], and HUE) and the robustness of the relationship between these indices and tower flux-based GPP and how it differs among ecosystems. Here, at a daily time step over multiple years in a deciduous broad-leaved and an evergreen coniferous forest, we examined the relationships between canopy phenology assessed by using the three indices and GPP determined from tower CO2 flux observations, and we compared the camera-based indices with the corresponding spectra-based indices estimated by a spectroradiometer system. We found that (1) the three camera-based indices and GPP showed clear seasonal patterns in both forests; (2) the amplitude of the seasonal variation in the three camera-based indices was smaller in the evergreen coniferous forest than in the deciduous broad-leaved forest; (3) the seasonal variation in the three camera-based indices corresponded well to seasonal changes in potential photosynthetic activity (GPP on sunny days); (4) the relationship between the three camera-based indices and GPP appeared to have different characteristics at different phenological stages; and (5) the camera-based and spectra-based HUE indices showed a clear relationship under sunny conditions in both forests. Our results suggest that it might be feasible for ecologists to establish comprehensive networks for long-term monitoring of potential photosynthetic capacity from regional to global scales by linking satellite-based, in situ spectra-based, and in situ camera-based indices.
Sources of variation among the chemical and spectral properties of tropical forest canopies are poorly understood, yet chemical traits reveal potential ecosystem and phylogenetic controls, and spectral linkages to chemical traits are needed for remote sensing of functional and biological diversity. We analyzed 21 leaf traits in 395 fully sunlit canopies, representing 232 species and multiple growth forms, in a lowland mixed dipterocarp forest of Sarawak, Malaysia. Leaf traits related to light capture and growth (for example, photosynthetic pigments, nutrients) were up to 55% lower, and defense traits (for example, phenols, lignin) were 15–40% higher, in the dominant family Dipterocarpaceae and in its genus Shorea, as compared to all other canopy species. The chemical variation within Dipterocarpaceae and Shorea was equivalent to that of all other canopy species combined, highlighting the role that a single phylogenetic branch can play in creating canopy chemical diversity. Seventeen of 21 traits had more than 50% of their variation explained by taxonomic grouping, and at least 16 traits show a connection to remotely sensed spectroscopic signatures (RMSE < 15%). It is through these chemical-to-spectral linkages that studies of functional and biological diversity interactions become possible at larger spatial scales, thereby improving our understanding of the role of species in tropical forest ecosystem dynamics.
Keeping an eye on the carbon balance: linking canopy development and net ecosystem exchange using an international webcam network.
Phenology is the study of reoccurring life-cycle events that are initiated and driven by environmental factors. It is an inter-disciplinary and integrative field that presents some key cross-cutting challenges. However, some recent advances in data and technology can help rise to these challenges. With these advances, phenology is poised to help address some critical ecological issues. This article presents an overview of phenology and some of its cross-cutting challenges. It then reviews some advances being made in the field and how these connect to land management, climate change, and climate modeling studies.
A hue image derived from three multi-spectral image bands by a RGB-IHS (red, green and blue to intensity, hue and saturation) transformation is shadow free because the pixel values in a hue image are independent of illumination and are related to only the shapes of the spectral signatures of the three bands. Three hue images derived from three different band triplets of a multi-spectral image, when displayed as a colour composite, form a strongly coloured, shadow-free image termed a hue red/green/blue (HRGB) colour composite image. The colours in this image are determined by the shapes of the spectral signatures of all the bands used and the spectral information of up to nine bands can be encoded into one colour image. Field checks and comparison with a published geological map have shown that geological interpretation of a HRGB image derived from a Landsat Thematic Mapper (TM) image of a semi-arid area in south east Spain accurately reveals the distribution of major rock types and hydrothermal alteration zones associated with a gold deposit.
Published eddy covariance measurements of carbon dioxide (CO2) exchange between vegetation and the atmosphere from a global network are distilled, synthesised and reviewed according to time scale, climate and plant functional types, disturbance and land use. Other topics discussed include history of the network, errors and issues associated with the eddy covariance method, and a synopsis of how these data are being used by ecosystem and climate modellers and the remote-sensing community. Spatial and temporal differences in net annual exchange, FN, result from imbalances in canopy photosynthesis (FA) and ecosystem respiration (FR), which scale closely with one another on annual time scales. Key findings reported include the following: (1) ecosystems with the greatest net carbon uptake have the longest growing season, not the greatest FA; (2) ecosystems losing carbon were recently disturbed; (3) many old-growth forests act as carbon sinks; and (4) year-to-year decreases in FN are attributed to a suite of stresses that decrease FA and FR in tandem. Short-term flux measurements revealed emergent-scale processes including (1) the enhancement of light use efficiency by diffuse light, (2) dynamic pulses in FR following rain and (3) the acclimation FA and FR to temperature. They also quantify how FA and FR respond to droughts and heat spells.
Background: Recent studies have described a technique that incorporates a digital camera to observe aspects of tree phenology such as leaf expansion and leaf fall. This technique has shown that seasonal patterns of red, green, and blue digital numbers (RGB_DN) extracted from digital images differ between species.Aims: To identify the different characteristics of phenology between species by examining RGB_DN, the relationship between the seasonal patterns of RGB_DN and ecological characteristics for various species need to be evaluated throughout the year.Methods: The relationship between the normalised RGB_DN values extracted from digital images and in situ leaf area index (LAI) and leaf chlorophyll content (indicated by soil and plant analyser development; SPAD) was examined for three dominant species for multiple years in a cool-temperate, deciduous, broad-leaved forest in Japan.Results: The RGB_DN values in spring were not useful in detecting the different characteristics of leaf-flush patterns between species. In contrast, RGB_DN values in autumn showed differences in leaf-colouring as well as in leaf-fall patterns and timings between species.Conclusion: Differences in autumn phenology between tree species can be detected by using the normalised RGB_DN technique while the technique cannot be applied in spring.
THROUGHOUT the Northern Hemisphere the concentration of atmospheric carbon dioxide rises in winter and declines in summer, mainly in response to the seasonal growth in land vegetation1–4. In the far north the amplitude of the seasonal cycle, peak to trough, is between 15 and 20 parts per million by volume5. The annual amplitude diminishes southwards to about 3 p.p.m. near the Equator, owing to the diminishing seasonally of plant activity towards the tropics. In spite of atmospheric mixing processes, enough spatial variability is retained in the seasonal cycle of CO2 to reveal considerable regional detail in seasonal plant activity6. Here we report that the annual amplitude of the seasonal CO2 cycle has increased by 20%, as measured in Hawaii, and by 40% in the Arctic, since the early 1960s. These increases are accompanied by phase advances of about 7 days during the declining phase of the cycle, suggesting a lengthening of the growing season. In addition, the annual amplitudes show maxima which appear to reflect a sensitivity to global warming episodes that peaked in 1981 and 1990. We propose that the amplitude increases reflect increasing assimilation of CO2 by land plants in response to climate changes accompanying recent rapid increases in temperature.
Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small-scale, high precision measurements or by satellite remote sensing for large-scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet-connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite-based methodologies. We collected twice-daily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring ‘green-up’ with that from co-occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green-up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite-based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera-based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large-scale ecological and environmental monitoring.
In the spring of 2010, temperatures averaged ~3 °C above the long-term mean (March–May) across the northeastern United States. However, in mid-to-late spring, much of this region experienced a severe frost event. The spring of 2010 therefore provides a case study on how future spring temperature extremes may affect northeastern forest ecosystems. We assessed the response of three northern hardwood tree species (sugar maple, American beech, yellow birch) to these anomalous temperature patterns using several different data sources and addressed four main questions: (1) Along an elevational gradient, how was each species affected by the late spring frost? (2) How did differences in phenological growth strategy influence their response? (3) How did the late spring frost affect ecosystem productivity within the study domain? (4) What are the potential long-term impacts of spring frost events on forest community ecology? Our results show that all species exhibited early leaf development triggered by the warm spring. However, yellow birch and American beech have more conservative growth strategies and were largely unaffected by the late spring frost. In contrast, sugar maples responded strongly to warmer temperatures and experi- enced widespread frost damage that resulted in leaf loss and delayed canopy development. Late spring frost events may therefore provide a competitive advantage for yellow birch and American beech at the expense of sugar maple. Results from satellite remote sensing confirm that frost damage was widespread throughout the region at higher elevations (>500 m). The frost event is estimated to have reduced gross ecosystem productivity by 70–153 g C m␣2, or 7–14% of the annual gross productivity (1061 ± 82 g C m␣2) across 8753 km2 of high-elevation forest. We conclude that frost events following leaf out, which are expected to become more common with climate change, may influence both forest composition and ecosystem productivity.
Conventional enhancements for the color display of multispectral images are usually based on independent contrast modifications (“stretches”) of three image channels. This approach generally does not produce colorful pictures if the image channels are strongly correlated. Procedures that selectively emphasize the weakly correlated component of the image data may better utilize the full range of colors. In Part I of this series of articles, two such methods were discussed that exaggerated color saturation without greatly modifying hue. These methods utilized principal-component analysis and HSI transformation of the image data. Herein we discuss two other methods, based on ratioing of data from different image channels. In the first approach, images of the ratioed data are “stretched” and assigned primary colors for display as color pictures. An alternative ratio method uses the difference, normalized to the intensity, instead of the ratio itself. Hues in pictures thus enhanced bear no resemblance to those in the unenhanced picture: hence, interpretation may be difficult. In the second approach, the intensity for each of three image channels is ratioed, pixel by pixel, to the sum of the intensities. Thus the data are transformed to image-domain “chromaticity” coordinates. These data are next “stretched” and then multiplied by the summed intensities to return them to the original coordinates for display. This technique is similar to those of Part I in that the hues need not be greatly altered.
Plant phenology relates strongly to primary productivity and the energy that enters into ecological food webs, and thus is vital in understanding ecosystem function and the effects of climate and climate change. The manual collection of phenological data is labor-intensive and not easily scalable, thus the ability to quantify leaf flush and other parameters at many locations requires innovative new methodologies such as the use of visible light digital cameras. Improved imaging performance was obtained by using a cabled, mobile camera system that allowed a repeated image census of branches of Rhododendron occidentale in the understory along a 30 m transect during leaf flush. Automatic division of acquired images into areas of interest (leaves) and background for calculating leaf area was accomplished by thresholding images in different color spaces. Transformation of the color space into the hue, saturation, and luminance (HSL) color space before thresholding resulted in a mean RMS error of 21.2 cm2 compared to hand-counts of leaf area. Thresholding in the native red, green, and blue (RGB) color space to isolate leaves resulted in a larger error, as did using algebraic combinations of the color components or color ratios. Relating physiological function to images, as for sap flow for branches of R. occidentale, indicates that the greening and calculated leaf area of a species as detected by imagers requires additional meteorological sensor data for interpretation.
Recent advances in understanding relationships between spectral reflectance of vegetation canopies and the structural and physiological drivers of canopy-atmosphere carbon dioxide exchange highlight the potential for using narrowband spectral vegetation indices to spatially scale CO2 fluxes beyond the area of a tower footprint. However, ground reference observations of narrowband spectral reflectance in support of satellite observations can be challenging to obtain because (1) automated sampling of both upwelling and downwelling radiation is required over extended time periods to characterize diurnal and seasonal variability, (2) hyperspectral spectroradiometer data and hardware can be sensitive to environmental factors such as temperature and moisture, and (3) hyperspectral spectroradiometers are expensive, greatly limiting prospects for widespread automated sampling. We have therefore developed the QuadPod: a simple, lightweight, relatively low cost and low power sensor capable of continuously measuring upwelling and downwelling radiation in 10 nm wavebands centered at 532 nm, 568 nm, 676 nm, and 800 nm. QuadPod measurements can be combined to calculate spectral reflectance indices (e.g., the photochemical reflectance index, PRI; and the normalized difference vegetation index, NDVI) useful for modeling canopy-atmosphere carbon exchange. The basic QuadPod instrument design described here can be implemented using any combination of optical filters in order to calculate other spectral vegetation indices.
Vegetation phenology such as the onset of green-up and senescence is strongly controlled by climate and other environmental factors, and in turn affects the terrestrial carbon balance. Therefore, phenological observation is important as an indicator of global warming and for estimation of the terrestrial carbon balance. Because phenological responses differ from species to species, precise monitoring from the species scale to the global scale is required. In this study, we analyzed images from digital cameras, which have proliferated in recent years, to investigate their utility as remote sensors. We collected daily images taken by digital cameras in national parks across Japan over 8years in wetland mixed deciduous forest, and evergreen broadleaved forest. Values of red, green, and blue (RGB) channels in each pixel within images were extracted, and a vegetation green excess index (2G-RBi) was calculated to detect phenology. The time series of 2G-RBi showed clear phenological patterns of each vegetation type in each year at the species or community scale. Even physiological damage due to a typhoon was detected. The dates of green-up were estimated easily and objectively from the second derivative of 2G-RBi, and a trend in yearly green-up dates of various types of vegetation was demonstrated. Furthermore, a strong correlation between interannual variations in green-up dates and local spring temperature was found, and the sensitivity of green-up date to temperature was revealed. The results suggest the utility of digital cameras for phenological observations at precise temporal and spatial resolutions, despite a year-to-year drift of color balance of camera as a technical device. As a form of near-surface remote sensing, digital cameras could obtain significant ecological information. Establishing camera networks could help us understand phenological responses at a wide range of scales.
Digital repeat photography has the potential to become an important long-term data source for phenological research given its advantages in terms of logistics, continuity, consistency and objectivity over traditional assessments of vegetation status by human observers. Red-green-blue (RGB) color channel information from digital images can be separately extracted as digital numbers, and subsequently summarized through color indices such as excess green (ExG = 2G − [R + B]) or through nonlinear transforms to chromatic coordinates or other color spaces. Previous studies have demonstrated the use of ExG and the green chromatic coordinate (gcc = G/[R + G + B]) from digital landscape image archives for tracking canopy development but several methodological questions remained unanswered. These include the effects of diurnal, seasonal and weather-related changes in scene illumination on ExG and gcc, and digital camera and image file format choice.
The continuous and automated monitoring of canopy phenology is of increasing scientific interest for the multiple implications of vegetation dynamics on ecosystem carbon and energy fluxes. For this purpose we evaluated the applicability of digital camera imagery for monitoring and modeling phenology and physiology of a subalpine grassland over the 2009 and 2010 growing seasons.
We tested the relationships between color indices (i.e. the algebraic combinations of RGB brightness levels) tracking canopy greenness extracted from repeated digital images against field measurements of green and total biomass, leaf area index (LAI), greenness visual estimation, vegetation indices computed from continuous spectroradiometric measurements and CO2 fluxes observed with the eddy covariance technique. A strong relationship was found between canopy greenness and (i) structural parameters (i.e., LAI) and (ii) canopy photosynthesis (i.e. Gross Primary Production; GPP). Color indices were also well correlated with vegetation indices typically used for monitoring landscape phenology from satellite, suggesting that digital repeat photography provides high-quality ground data for evaluation of satellite phenology products.
We demonstrate that by using canopy greenness we can refine phenological models (Growing Season Index, GSI) by describing canopy development and considering the role of ecological factors (e.g., snow, temperature and photoperiod) controlling grassland phenology. Moreover, we show that canopy greenness combined with radiation use efficiency (RUE) obtained from spectral indices related to photochemistry (i.e., scaled Photochemical Reflectance Index) or meteorology (i.e., MOD17 RUE) can be used to predict daily GPP.
Building on previous work that has demonstrated that seasonal variation in the structure and function of plant canopies can be quantified using digital camera imagery, we have highlighted the potential use of these data for the development and parameterization of phenological and RUE models, and thus point toward an extension of the proposed methodologies to the dataset collected within PhenoCam Network.
Revealing the seasonal and interannual variations in forest canopy photosynthesis is a critical issue in understanding the ecological mechanisms underlying the dynamics of carbon dioxide exchange between the atmosphere and deciduous forests. This study examined the effects of temporal variations of canopy leaf area index (LAI) and leaf photosynthetic capacity [the maximum velocity of carboxylation (V
cmax)] on gross primary production (GPP) of a cool-temperate deciduous broadleaf forest for 5 years in Takayama AsiaFlux site, central Japan. We made two estimations to examine the effects of canopy properties on GPP; one is to incorporate the in situ observation of V
cmax and LAI throughout the growing season, and another considers seasonality of LAI but constantly high V
cmax. The simulations indicated that variation in V
cmax and LAI, especially in the leaf expansion period, had remarkable effects on GPP, and if V
cmax was assumed constant GPP will be overestimated by 15%. Monthly examination of air temperature, radiation, LAI and GPP suggested that spring temperature could affect canopy phenology, and also that GPP in summer was determined mainly by incoming radiation. However, the consequences among these factors responsible for interannual changes of GPP are not straightforward since leaf expansion and senescence patterns and summer meteorological conditions influence GPP independently. This simulation based on in situ ecophysiological research suggests the importance of intensive consideration and understanding of the phenology of leaf photosynthetic capacity and LAI to analyze and predict carbon fixation in forest ecosystems.
Plant leaf colour is commonly used as an index for nutrient diagnosis. We have developed a low-cost diagnostic method that is easy to use to assess the nutrient status of plants, based on the estimation of chlorophyll content of leaves using a portable colour video camera and a personal computer. Relationships between chlorophyll content and various functions derived from red, green and blue wavelengths are examined. Although red-blue and green-blue wavelengths show the highest correlation with chlorophyll content under a limited range of meteorological conditions, the normalized difference (red-blue)/(red+blue) is the most applicable function which can use data collected under different meteorological conditions. The accuracy in estimating chlorophyll content from video images could be improved by correcting with solar radiation data. It is shown that, for practical purposes, the chlorophyll content of leaves can be estimated with sufficient accuracy using a portable video camera. Copyright 1998 Annals of Botany Company
Normal human color perception is a product of three independent sensory systems. By mirroring this mechanism, full-color display devices create colors as mixtures of three primaries. Any displayable color can be described by the corresponding values of these primaries. Frequently it is more convenient to define various other color spaces, or coordinate systems, for color representation or manipulation. Several such color spaces are presented which are suitable for applications involving user specification of color, along with the defining equations and illustrations. The use of special color spaces for particular kinds of color computations is discussed.
There is a need to document how plant phenology is responding to global change factors, particularly warming trends. "Near-surface" remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras ("webcams") can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for "regions of interest" within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface-atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux.
This paper examines the mean flowering times of 11 plant species in the British Isles over a 58-year period, and the flowering times of a further 13 (and leafing time of an additional 1) for a reduced period of 20 years. Timings were compared to Central England temperatures and all 25 phenological events were significantly related (P<0.001 in all but 1 case) to temperature. These findings are discussed in relation to other published work. The conclusions drawn from this work are that timings of spring and summer species will get progressively earlier as the climate warms, but that the lower limit for a flowering date is probably best determined by examining species phenology at the southern limit of their distribution.