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... Field observations of species-level phenophases have been successfully associated with local and regional climatic variations occurring over several decades (Beaubien & Freeland 2000;Fitter et al., 1995;Lechowicz & Koike 1995;Kramer 1996;Schnelle 1967). Vegetation phenology also differs between regional climate systems (D'Odorico et al., 2002;Ma et al., 2013;Menzel et al., 2005), and can also be influenced by distance from coasts or other bodies of water (Malmqvist et al., 2017). Spatial variations in spring phenology are strongly influenced by geographical factors such as elevation, latitude and longitude (Dunn & de Beurs 2011;Hopkins 1920;Ziello et al., 2009). ...
... The annual variability of phenology indicators can be calculated as the difference of dates when the EVI in a specific year reaches the same magnitude as its long-term mean (Fisher et al., 2006;Melaas et al., 2013). Interannual variability in SOS has been compared based on broad forest cover types and even species (Ma et al., 2013;. In this study interannual variability in SOS was calculated as the coefficient of variation of the mean leaf-on dates for each study area between 1985 and 2017 (Fisher et al., 2006;Melaas et al., 2013;. ...
... Interannual variability for LOD was greater for golf courses than for street trees or deciduous forest, which has also been shown in other studies (Ma et al., 2013). There was also more interannual variability for golf courses in cities than for golf courses outside of cities. ...
Thesis
Urban land cover contributes to higher temperatures in urban areas compared to adjacent rural areas, which can cause an earlier start of the growing season for urban vegetation. Variations in plant community characteristics between urban and rural areas also produce intra-urban differences in vegetation phenophases, although few studies have investigated differences in phenology between plant functional types in multiple urban environments. In this study I used an exploratory analysis based on the Landsat Phenology Algorithm and weather station data to quantify differences in leaf-onset dates for different plant functional types in the New York City Metropolitan Area. The results demonstrate that Landsat can be used to identify urban-rural variations in leaf-onset for different plant functional types, and that these variations are driven by different climate variables depending on plant functional type. Furthermore, results from such analyses suggest that long-term changes in leaf onset vary across different plant functional types—i.e., grasslands may be advancing at a slower rate than forests. Keywords: urban heat island; vegetation phenology; Landsat
... For example, in Australia's mesic savannas, fire usually only consumes the seasonal grassy understorey, whereas canopy trees mostly remain intact (Lehmann et al., 2014). By contrast, in Australia's tropical drylands, a highly resilient leaf phenology allows strong growth during wet years despite the absence of a growing season in previous dry years (Ma et al., 2013). Similarly, Australian tropical rainforest trees are considered to be somewhat resilient to high-temperature stress and heatwaves due to the very high temperature at which leaf dark respiration reaches a peak (60°C) (Weerasinghe et al., 2014), although they may be instead vulnerable to high VPD stresses (Fu et al., 2018). ...
... Similarly, Australian tropical rainforest trees are considered to be somewhat resilient to high-temperature stress and heatwaves due to the very high temperature at which leaf dark respiration reaches a peak (60°C) (Weerasinghe et al., 2014), although they may be instead vulnerable to high VPD stresses (Fu et al., 2018). However, a loss of resilience has been predicted for Australian drylands with the increased occurrence of future woody dieback and megadrought events (Ma et al., 2013), and the continued resilience of many ecosystems in Australia and New Zealand is not assured with global change . ...
... The MODIS GPP product estimated the annual amplitude of tower GPP fluxes quite well but performed less well in estimating the seasonal phase of variation (Leuning et al., 2005). These assessments with relatively high accuracy where ecosystem processes are phenologically driven, such as in Australian wet to dry tropical savannas, grasslands and croplands Glenn et al., 2011;Ma et al., 2013;Moore et al., 2017;Zhang et al., 2008). However, in temperate and Mediterranean evergreen Australian forests/woodlands, the VI and LAI products were seasonally out of phase with GPP and found to be better proxies of photosynthetic 'infrastructure' ...
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In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.
... As the impacts of climate change intensify, the need to identify vulnerable ecosystems has spurred scientific work to elucidate environmental controls on vegetation dynamics (Archibald and Scholes 2007). Patterns in vegetation growth are influenced by processes involving water, energy exchanges, and land and atmospheric systems (Ma et al. 2013). Evidence from studying short-and longterm vegetation dynamics across time and space confirms the effects of climate change on ecosystems (Cleland et al. 2007). ...
... In a study done by Potter et al. (2017), annual mean precipitation, mean annual temperature, and cloud cover amount differences are mapped across the globe revealing the importance of the connection between topography, ocean circulation, and the atmosphere in southern and eastern Africa. This complexity complicates spatial patterns of rainfall and makes savannas one of the most temporally and spatially dynamic global biomes (Ma et al. 2013, Guan et al. 2014. ...
... The spacing between the phenoregions' average NDVI profiles was greater for the end sites and showed more interannual variability, whereas the average NDVI profiles were mostly very similar for Ruaha and Luangwa, the middle-latitude parks. This maps on to the span of the ITCZ (Ma et al. 2013, Guan et al. 2014) and the seasonality of precipitation-the high-and low-latitude areas have more extreme intra-annual variation in precipitation, while the midlatitude areas are more consistent. ...
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Semiarid and savanna‐type (SAST) ecosystems in East Africa have unique plant species compositions and characteristics that make quantifying this biome's seasonality and interannual variability difficult. Phenoregion classification offers a way to use seasonality of vegetation growth to help understand the phenological spatial patterns of complex landscapes. Here, we used Normalized Difference Vegetation Index (NDVI) time series from Landsat 8 to map phenoregions in scenes centered around national parks from Mt. Kenya National Park (Kenya) to Limpopo National Park (Mozambique). We then assessed whether landscape‐scale controls on phenology are consistent across the region or whether they vary across this latitudinal gradient. We compared our phenoregion maps to MODIS Land Cover and geology, and we used multinomial logistic regression to determine the role that elevation, slope, aspect, and geology play in driving phenological differences. The sites' phenoregions showed no unique land cover composition, suggesting that MODIS land cover does not capture the subtle variations identified in phenoregion analysis. Multinomial logistic regression showed that geographic trend (x‐ and y‐directions) was a strong predictor in four of the five landscapes and that, depending on the scene, geology, elevation, or aspect was a strong secondary predictor. Using seasonality of the NDVI time series to generate phenoregions provides different and, in some cases, more ecologically relevant information, compared to vegetation maps that use only land cover from a single season or time period to generate ecoregions. With a significant population of humans and animals that live in and depend on SAST ecosystems, it is important to better understand vegetation processes and factors that affect them as climate change becomes an increasingly pertinent issue in dry systems.
... In addition, there are several strengths in studying phenology in agricultural ecosystems. Plant species and their growth are highly uniform in most croplands; therefore, the transition points between different phenological stages are more explicit in agricultural ecosystems than in natural systems, where several species at different phenological stages coexist (Cleland et al., 2007;D'Odorico et al., 2015;Helman, 2018;Kross et al., 2014;Ma et al., 2013). Moreover, satellite-based phenological data are of great importance for quantifying regional or global vegetation productivity. ...
... The proportion of time-series data discarded via QA filtering was low, as expected for a site with limited cloud cover and snow contamination. Screened low-quality observations were gap-filled to obtain a daily time-series using linear interpolation (Broich et al., 2015;Ma et al., 2013). A threshold value of 40 % was also used to produce another series of phenological metrics for comparison. ...
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Phenology-mainly associated with climatic factors-is crucial for the accurate estimation of cumulative annual carbon exchange between terrestrial ecosystems and the atmosphere. However, the effects of changes in phenology on annual vegetation productivity and its regulatory mechanisms remain unclear, particularly in agricultural ecosystems. Therefore, in this study, we examined the associations among cumulative net ecosystem productivity (NEP), phenological metrics, and climatic factors based on long-term (2005-2014) eddy covariance flux and meteorological observations in a maize cropland in Northeast China. The results showed that carbon uptake period (CUP) was mainly determined by the end date of CUP (ECUP) in autumn. Cumulative NEP from May to September (NEP 5− 9), a period generally corresponding to the growing season, significantly increased with NEP max (defined in this study as the 90th percentile of daily NEP during CUP) and CUP. NEP max explained greater interannual variation in NEP 5− 9 than CUP. The start date of CUP (SCUP) and ECUP were both advanced with increasing winter temperature, but ECUP was more temperature-sensitive than SCUP. Thus, CUP tended to shorten with increasing temperature, ultimately decreasing cumulative NEP. In addition, NEP max decreased with increasing precipitation in summer and autumn. The Greenup and MidGreendown dates from the MODIS Global Vegetation Phenology (MCD12Q2) product generally captured the interannual variation in the carbon flux-based SCUP and ECUP, respectively, well. The results of this study would be of great significance for predicting the response of ecosystem productivity to plant phenology shifts in agricultural ecosystems in future climate change scenarios.
... The vegetation varies from Eucalyptus and Corymbia savannas in the northern humid areas (above 700 mm annual precipitation) to hummock grasslands and Acacia savannas to the south arid areas (Walker et al., 1999;Hutley et al., 2011). More detailed description of the vegetation, climate, and soil of the NATT study area can be found in Ma et al. (2013) and Hutley et al. (2011). ...
... We applied Singular Spectrum Analysis (SSA) to decompose the EVI (t) into trend (EVI T ) and cyclic (EVI C ) signals (Alexandrov et al., 2012). Following Ma et al. (2013), we used the window length of 37 composite periods (37 × 16 / 365 ≈ 1.6 years) that was found to best capture the periodicity of the savanna vegetation dynamics over the NATT study area. With the window length set as 37, there were 37 components generated by the singular vector decomposition (SVD). ...
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Realistic representations and simulation of mass and energy exchanges across heterogeneous landscapes can be a challenge in land surface and dynamic vegetation models. For mixed life-form biomes such as savannas, plant function is very difficult to parameterise due to the distinct physiological characteristics of tree and grass plant functional types (PFTs) that vary dramatically across space and time. The partitioning of their fractional contributions to ecosystem gross primary production (GPP) remains to be achieved at regional scale using remote sensing. The objective of this study was to partition savanna gross primary production (GPP) into tree and grass functional components based on their distinctive phenological characteristics. Comparison of the remote sensing partitioned GPPtree and GPPgrass against field measurements from eddy covariance (EC) towers showed an overall good agreement in terms of both seasonality and magnitude. We found total GPP, as well as its tree and grass components, decreased dramatically with rainfall over the North Australian Tropical Transect (NATT), from the Eucalyptus forest and woodland in the northern humid coast to the grasslands, Acacia woodlands and shrublands in the southern xeric interior. Spatially, GPPtree showed a steeper decrease with precipitation along the NATT compared to GPPgrass, thus tree/grass GPP ratios also decreased from the northern mesic region to the arid south region of the NATT. However, results also showed a second trend at the southern part of the transect, where tree-grass ratios and total GPP increased with decreasing mean annual precipitation, and this occurred in the physiognomic transition from hummock grasslands to Acacia woodland savannas. Total GPP and tree-grass GPP ratios across climate extremes were found to be primarily driven by grass layer response to rainfall dynamics. The grass-containing xeric savannas exhibited a higher hydroclimatic sensitivity, whereas GPP in the northern mesic savannas was fairly stable across years despite large variations in rainfall amount. The pronounced spatiotemporal variations in savanna vegetation productivity encountered along the NATT study area suggests that the savanna biome is particularly sensitive and vulnerable to predicted future climate change and hydroclimatic variability.
... In dryland ecosystems low vegetation cover is the primary limitation in detecting land surface phenology [12]. High soil fractional cover can cause the growing season VI to be indistinguishable from the dormant season VI, making phenology extraction impossible. ...
... VI uncertainty from shadows, view angle, and atmospheric interference can increase the false positive and false negative rates in LSP detections and increase errors in resulting transition dates [12]. False negatives can occur when a VI time series does not meet the QA threshold when it otherwise would with zero uncertainty. ...
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Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.
... For these reasons, the savanna sites along the North Australian Tropical Transect (NATT) provide an excellent living laboratory (Hutley et al., 2011), especially as the strong rainfall gradient from north to south (approx. 1 mm mean annual rainfall per km south) provides different climatological circumstances, whereas other factors as topography and soils remain rather constant. Moreover, moisture availability is known to be one of the main drivers of vegetation behaviour in savannas (Scholes and Archer, 1997), which makes the transect well-suited for studies on 100 the effect of precipitation on vegetation (Hutley et al., 2011), either observation based (Ma et al., 2013;Moore et al., 2016) or model based (Haverd et al., 2013;Whitley et al., 2016). ...
... Nevertheless, the rooting depths of grasses were more or less constant 490 along the transect, which contrasts with the changing understorey species composition along the transect. Only at Howards Springs annual Sorghum grasses are dominant, whereas a reduction of Sorghum grasses and a shift towards more perennial grasses with deeper roots exists with declining precipitation amounts over the transect (Ma et al., 2013). It could be argued that the grass rooting depths at the wetter sites are too deep, as annual Sorghum generally reaches depths of around 0.15 m in stead of 0.20 m -0.60 m for especially the sites of Howard Springs, Adelaide River and Daly River (Fig. 8b). ...
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Most terrestrial biosphere models (TBMs) rely on more or less detailed information about the properties of the local vegetation. In contrast, optimality-based models require much less information about the local vegetation as they are designed to predict vegetation properties based on general principles related to natural selection and physiological limits. Although such models are not expected to reproduce current vegetation behaviour as closely as models that use local information, they promise to predict the behaviour of natural vegetation under future conditions, including the effects of physiological plasticity and shifts of species composition, which are difficult to capture by extrapolation of past observations. A previous model intercomparison using conventional terrestrial biosphere models (TBMs) revealed a range of deficiencies in reproducing water and carbon fluxes for savanna sites along a strong precipitation gradient of the North Australian Tropical Transect (Whitley et al., 2016). Here we examine the ability of an optimality-based model (the Vegetation Optimality Model, VOM) predict vegetation behaviour for the same savanna sites. The VOM optimizes key vegetation properties such as foliage cover, rooting depth and water use parameters in order to maximize the Net Carbon Profit (NCP), defined here as the difference between total carbon taken up by photosynthesis minus the carbon invested in construction and maintenance of plant organs. Despite a reduced need for input data, the VOM performed similarly or better than the conventional TBMs in terms of reproducing the seasonal amplitude and mean annual fluxes recorded by flux towers at the different sites. It had a relative error of 0.08 for the seasonal amplitude in ET, and was among the best three models tested with the smallest relative error in the seasonal amplitude of gross primary productivity (GPP). Nevertheless, the VOM displayed some persistent deviations from observations, especially for GPP, namely an underestimation of dry season evapo-transpiration at the wettest site, suggesting that the hydrological assumptions (free drainage) have a strong influence on the results. Furthermore, our study exposes a persistent overprediction of vegetation cover and carbon uptake during the wet seasons by the VOM. Our analysis revealed several areas for improvement in the VOM, including a better representation of the hydrological settings, as well as the costs and benefits related to plant water transport and light capture by the canopy. The results of this study imply that vegetation optimality is a promising approach to explain vegetation dynamics and the resulting fluxes. It provides a way to derive vegetation properties independently from observations, and allows for a more insightful evaluation of model shortcomings as no calibration or site-specific information is required.
... Time series analyses of remote sensing data (Moreno-de Las Heras et al. 2015;Vladimir et al. 2016;Rapinel 2019;Zhang et al. 2003) allow for phenological studies of vegetation to be carried out in a landscape (e.g. peak of the greening period, senescence, length of growing season, etc.), continental and global scales (Huete et al. 2002;Zhang et al. 2003;Edward et al. 2008;Ma et al. 2013). Space borne optical sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) provide regular (daily to biweekly) measurements of a variety of biophysical and biochemical parameters of the land surface (Tucker, Townshend, and Goff 1985;Huete et al. 2002;Ma et al. 2013). ...
... peak of the greening period, senescence, length of growing season, etc.), continental and global scales (Huete et al. 2002;Zhang et al. 2003;Edward et al. 2008;Ma et al. 2013). Space borne optical sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) provide regular (daily to biweekly) measurements of a variety of biophysical and biochemical parameters of the land surface (Tucker, Townshend, and Goff 1985;Huete et al. 2002;Ma et al. 2013). The use of remote sensing data (e.g. ...
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Phenology is an important component in the climate system, and play a key role in controlling many feedbacks of vegetation to the climate systems. Differences in phenology of plant functional types (PFTs) due to the variation in seasonal cycles (e.g. changes in weather variability), the impact from land-use activities (e.g. fire) and their mechanisms for adaptation (e.g. climate change, regrowth/post-fire regeneration) have major implications in conservation planning and monitoring actions. Detecting phenological variations in PFTs is therefore of great importance to quantitative remote sensing applications, especially in a biome as diverse and complex as savannah. In this study, we implement Savitzky-Golay filtering and the Breaks For Additive Seasonal and Trend (BFAST) algorithms to detect changes in PFTs based on their phenological events using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data from 2001 to 2018. In this region, PFTs present distinct seasonal, annual and interannual variability. Woody phenology presents early green-up dates with a prolonged senescence period and invariably observed the longest growing season length (GSL). The relationship between the start of season (SOS) or end of season (EOS) was assessed for each PFT to find out the extent to which they determine GSL for different PFTs. GSL is mostly determined by the SOS in woody savannah (coefficient of determination (R²) =0.41, p <0.01), open shrubs and (R² =0.79, p <0.001), grassland (R² =0.35, p <0.01) while EOS determined the GSL for both dryland crops (R² =0.75, p <0.001) and paddy rice (R² =0.69, p <0.001). We compared the interannual variability of woody savannah and other PFTs by measuring the differences of their phenological indicators using Welch's t-test. All PFTs show statistically significant difference with the GSL of woody savannah except open shrubs (p = 0.23). The abrupt vegetation changes estimated with BFAST varied by PFT. Some PFTs are more resilient to harsh environmental conditions than others. While woody species present a few abrupt changes, grass phenology is more vulnerable to disturbance, seasonally as well as ARTICLE HISTORY in the trend components (large number of abrupt changes) with browning trend following an abrupt negative change. In all PFTs, breakpoint (disturbance) assessed using BFAST negatively correlate with precipitation data which means the magnitude of disturbance decreases with increasing precipitation. Woody species had an r (correlation coefficient) value of-0.5 while grassland had r =-0.7 which is a further indication that grass phenology responds more strongly to annual precipitation than the woody species. These results show that MODIS NDVI time-series data can be used to distinguish the phenological events of different PFTs in West African savannah dominated landscape.
... Geographic location, climate features, vegetation type, data range and daytime energy balance closure ratio at the six selected eddy covariance flux sites located along the North Australian Tropical Transect, Northern Territory, Australia, after Hutley et al. (2011) and Ma et al. (2013b). The MAP is the mean annual precipitation (mm). ...
... The X(t) timeseries were reconstructed with seasonal-trend decomposition based on the locally weighted regression (LOESS) smoother in Lu et al. (2003). Singular Spectrum Analysis (SSA) was robust to noise caused by cloud or aerosol contaminations (Alexandrov, 2009;Ma et al., 2013b). Thus, SSA was used to retrieve the signal's long-term trend X A and annual amplitude X A as in Ma et al. (2013a) in our study. ...
Article
Savannas, occupying a fifth of the global land surface, are characterized by the coexistence of trees and grasses. Accurate estimation of savanna evapotranspiration (ET) is vital for understanding the regional and global water balance and its feedback to climate. However, the overlapping phenology and different water-use patterns of trees and grasses constitute a major challenge for modeling efforts. To estimate savanna ET, we used a three-source ET model, partitioning ET among soil, trees, and grasses. To represent legacy effects of precipitation on ecosystem water use, the Normalized Ecosystem Drought Index (NEDI, i.e. a function of precipitation and potential evapotranspiration) was included to limit canopy conductances in the model and also in two other classic two-layer models (Shuttleworth-Wallace model and Penman-Monteith-Leuning model). The results of our model and the other models were tested and compared using tower-based eddy covariance flux data collected at six sites (including four savanna sites, one pasture site, and one grassland site) along a precipitation gradient in northern Australia, together with satellite-derived leaf area index, which was partitioned to represent the canopy dynamics of trees and grasses. Inclusion of NEDI significantly reduced seasonal biases in ET estimation results for all models compared with observations at savanna sites (fitted slopes were closer to unity by 0.08–0.10, R² increased by 0.03–0.04, and RMSE decreased by 0.07–0.09 mm d⁻¹). The three-source model provides insights into simulation of water fluxes over vegetated areas of complex composition. Our work makes a contribution to savanna research by determining a flexible indicator defining the seasonal water availability limitation on savanna ET. The inclusion of NEDI in ET models could guide future research on modeling ecosystem water and carbon fluxes in response to seasonal droughts.
... In dryland ecosystems, low vegetation cover is the primary limitation in detecting land surface phenology [12]. High soil fractional cover can cause the growing season VI to be indistinguishable from the dormant season VI, making phenology extraction impossible. ...
... On VI Uncertainty. VI uncertainty from shadows, view angle, and atmospheric interference can increase the falsepositive and false-negative rates in LSP detections and increase errors in resulting transition dates [12]. False negatives can occur when a VI time series does not meet the minimum threshold when it otherwise would with zero 7 Journal of Remote Sensing uncertainty. ...
Article
Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.
... This study was conducted at a sub-continental scale between 10 • S to 26 • S and 113 • E to 138 • E, which encompassed northern and central Australia by a relatively constant decrease in rainfall with distance inland (Figure 1). This region, particularly for northern Australia, has a classic monsoon climate pattern, which receives more than 80% of annual precipitation during November to April [42]. From northern mesic tropics to the xeric central Australia, mean annual rainfall ranges from 1700 mm to approximately 300 mm (Bureau of Meteorology, Available online: https://www.bom.gov.au ...
... (accessed on 20 January 2020)), in line with the aridity index (AI) decreasing from 0.8 to 0.1 ( Figure 1b). Correspondingly, the vegetation follows a wet-dry gradient that shifts from Eucalyptus dominated forests, open forests, and woodlands in the coastal northern areas to Acacia-dominated open woodlands, scattered shrubs, and hummock grassland into the vast inland [42] (Figure 1a). ...
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Satellite-estimated solar-induced chlorophyll fluorescence (SIF) is proven to be an effective indicator for dynamic drought monitoring, while the capability of SIF to assess the variability of dryland vegetation under water and heat stress remains challenging. This study presents an analysis of the responses of dryland vegetation to the worst extreme drought over the past two decades in Australia, using multi-source spaceborne SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) and TROPOspheric Monitoring Instrument (TROPOMI). Vegetation functioning was substantially constrained by this extreme event, especially in the interior of Australia, in which there was hardly seasonal growth detected by neither satellite-based observations nor tower-based flux measurements. At a 16-day interval, both SIF and enhanced vegetation index (EVI) can timely capture the reduction at the onset of drought over dryland ecosystems. The results demonstrate that satellite-observed SIF has the potential for characterizing and monitoring the spatiotemporal dynamics of drought over water-limited ecosystems, despite coarse spatial resolution coupled with high-retrieval noise as compared with EVI. Furthermore, our study highlights that SIF retrieved from TROPOMI featuring substantially enhanced spatiotemporal resolution has the promising capability for accurately tracking the drought-induced variation of heterogeneous dryland vegetation.
... The SSA-Pheno (singular spectrum analysis for phenology) algorithm, graphically depicted by Figure 4, was used to retrieve the phenological metrics from VI time series [68,69]. Six phenological metrics, including the start of growing season (SGS), peak of growing season (PGS), end of growing season (EGS), length of growing season (LGS), seasonal maximum VI (VImax) and annual integrated VI (IntVI), were retrieved from the time series of H-8 AHI NDVI and EVI time series. ...
... Phenological metrics retrieved from time series of VIs normalised to different SZAs will be indicated using the subscripts, e.g., SGSS-40 and IntNDVIS-20 means the SGS and IntNDVI retrieved from time series of NDVIS-40. The SSA-Pheno algorithm has been tested over Australia across a wide-range of vegetation types and has been demonstrated to be a robust and reliable method for extracting phenological metrics from noisy VI time series [68,69]. The SSA (Singular Spectrum Analysis) is a data-adaptive method that has been found to be well-suited to the analysis of nonlinear dynamics in geophysical datasets [70][71][72]. ...
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Satellite remote sensing of vegetation at regional to global scales is undertaken at considerable variations in solar zenith angle (SZA) across space and time, yet the extent to which these SZA variations matter for the retrieval of phenology remains largely unknown. Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) across a study area in southeastern Australia encompassing forest, woodland, and grassland sites. The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite. The semi-empirical RossThick-LiSparse-Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was inverted for each spectral band on a daily basis using 10-minute reflectances acquired by H-8 AHI at different sun-view geometries for each site. The inverted RTLSR model was then used to forward calculate surface reflectance at three constant SZAs (20°, 40°, 60°) and one seasonally varying SZA (local solar noon), all normalised to nadir view. Time series of NDVI and EVI adjusted to different SZAs at nadir view were then computed, from which phenological metrics such as start and end of growing season were retrieved. Results showed that NDVI sensitivity to SZA was on average nearly five times greater than EVI sensitivity. VI sensitivity to SZA also varied among sites (biome types) and phenological stages, with NDVI sensitivity being higher during the minimum greenness period than during the peak greenness period. Seasonal SZA variations altered the temporal profiles of both NDVI and EVI, with more pronounced differences in magnitude among NDVI time series normalised to different SZAs. When using VI time series that allowed SZA to vary at local solar noon, the uncertainties in estimating start, peak, end, and length of growing season introduced by local solar noon varying SZA VI time series, were 7.5, 3.7, 6.5, and 11.3 days for NDVI, and 10.4, 11.9, 6.5, and 8.4 days for EVI respectively, compared to VI time series normalised to a constant SZA. Furthermore, the stronger SZA dependency of NDVI compared with EVI, resulted in up to two times higher uncertainty in estimating annual integrated VI, a commonly used remote-sensing proxy for vegetation productivity. Since commonly used satellite products are not generally normalised to a constant sun-angle across space and time, future studies to assess the sun-angle effects on satellite applications in agriculture, ecology, environment, and carbon science are urgently needed. Measurements taken by new-generation geostationary (GEO) satellites offer an important opportunity to refine this assessment at finer temporal scales. In addition, studies are needed to evaluate the suitability of different BRDF models for normalising sun-angle across a broad spectrum of vegetation structure, phenological stages and geographic locations. Only through continuous investigations on how sun-angle variations affect Remote Sens. 2020, 12, 1339 2 of 23 spatiotemporal vegetation dynamics and what is the best strategy to deal with it, can we achieve a more quantitative remote sensing of true signals of vegetation change across the entire globe and through time.
... The strength of the rainfall effect on canopy cover is surprising, given the limited temporal data points and minimal interannual variation in phenology in mesic eucalypt savannas (Ma et al. 2013). For example, low sensitivity of canopy cover to water availability was demonstrated experimentally by Myers et al. (1998), who found dry season irrigation produced only a minimal increase in canopy cover and leaf retention in a similar mesic savanna near Darwin. ...
Article
Previous analyses of historical aerial photography and satellite imagery have shown thickening of woody cover in Australian tropical savannas, despite increasing fire frequency. The thickening has been attributed to increasing precipitation and atmospheric CO2 enrichment. These analyses involved labour‐intensive, manual classification of vegetation, and hence were limited in the extent of the areas and the number of measurement times used. Object‐based, semi‐automated classification of historical sequences of aerial photography and satellite imagery has enabled the spatio‐temporal analysis of woody cover over entire landscapes, thus facilitating measurement, monitoring and attribution of drivers of change. Using this approach, we investigated woody cover change in 4000 ha of intact mesic savanna in the Ranger uranium lease and surrounding Kakadu National Park, using imagery acquired on 10 occasions between 1950 and 2016. Unlike previous studies, we detected no overall trend in woody cover through time. Some variation in cover was related to rainfall in the previous 12 months, and there were weak effects of fire in the year of image acquisition and the antecedent 4 years. Our local‐scale study showed a mesic eucalypt savanna in northern Australia has been resilient to short‐term variation in rainfall and fire activity; however, changes in canopy cover could have occurred in other settings. When applying this semi‐automated approach to similar studies of savanna dynamics, we recommend maximising the time depth and number of measurement years, standardising the time of year for image acquisition and using many plots of 1 ha in area, rather than fewer, larger plots.
... Among them, climate variability affects many species (MacArthur, 1972), and increasing climate variability due to climate change is a threat for biodiversity (Thuiller et al., 2005;Zhang et al., 2018). Changes in climate can cause changes in the phenology of vegetation greenness (Ma et al., 2013) and in the seasonality of temperature (Mann and Park, 1996). Such changes can lead to phenological mismatches between wildlife species and the resources that they rely on for food, reproduction, and habitat features (Harrington et al., 1999;Menzel et al., 2006;Schwartz et al., 2006). ...
Article
Over the course of a year, vegetation and temperature have strong phenological and seasonal patterns, respectively , and many species have adapted to these patterns. High inter-annual variability in the phenology of vegetation and in the seasonality of temperature pose a threat for biodiversity. However, areas with high spatial variability likely have higher ecological resilience where inter-annual variability is high, because spatial variability indicates presence of a range of resources, microclimatic refugia, and habitat conditions. The integration of inter-annual and spatial variability is thus important for biodiversity conservation. Areas where spatial variability is low and inter-annual variability is high are likely to limit resilience to disturbance. In contrast, areas of high spatial variability may be high priority candidates for protection. Our goal was to develop spatio-temporal remotely sensed indices to identify hotspots of biodiversity conservation concern. We generated indices that capture the inter-annual and spatial variability of vegetation greenness and land surface temperature and integrated them to identify areas of high, medium, and low biodiversity conservation concern. We applied our method in Argentina (2.8 million km 2), a country with a wide range of climates and biomes. To generate the inter-annual variability indices, we analyzed MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) time series from 2001 to 2018, fitted curves to obtain annual phenological and seasonal metrics, and calculated their inter-annual variability. To generate the spatial variability indices, we calculated standard deviation image texture of Landsat 8 EVI and LST. When we integrated our inter-annual and spatial variability indices, areas in the northeast and parts of southern Argentina were the hotspots of highest conservation concern. High inter-annual variability poses a threat in these areas, because spatial variability is low. These are areas where management efforts could be valuable. In contrast, areas in the northwest and central-west are where protection should be strongly considered because the high spatial variability may confer resilience to disturbance , due to the variety of conditions and resources within close proximity. We developed remotely sensed indices to identify hotspots of high and low conservation concern at scales relevant to biodiversity conservation, = which can be used to target management actions in order to minimize biodiversity loss.
... was calculated. Meanwhile, we used the time lag spectrum (Equation (5)) to estimate the corresponding time lag (from the value of phase difference) with the same frequency (T represents the cycle) [44,45]. ...
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In this paper, cross-spectrum analysis was used to verify the agreement of periodicity between the global LAI (leaf area index) and climate factors. The results demonstrated that the LAI of deciduous forests and permanent wetlands have high agreement with temperature, rainfall and radiation over annual cycles. A low agreement between the LAI and seasonal climate variables was observed for some of the temperate and tropical vegetation types including shrublands and evergreen broadleaf forests, possibly due to the diversity of vegetation and human activities. Across all vegetation types, the LAI demonstrated a large time lag following variation in radiation (> 1 month), whereas relatively short lag periods were observed between the LAI and annual temperature (around 2 weeks)/rainfall patterns (less than 10 days), suggesting that the impact of radiation on global vegetation growth is relatively slow, which is in accord with the results of previous studies. This work can provide a benchmark of the phenological drivers in global vegetation, from the perspective of periodicity, as well as helping to parameterize and refine the DGVMs (Dynamic Global Vegetation Models) for different vegetation types.
... Despite the free availability of these MRSI, their usage for vegetation cover mapping in Nigeria is comparatively low to developed countries (Ibrahim and Kuta, 2015;Labib and Harris, 2018). Over 40 years, Landsat (a medium spatial resolution satellite imagery) has been widely used in vegetation cover mapping (Cord et al., 2013;Jordan et al., 2014;Ma et al., 2013). Also, Sentinel 2A was a recently launched satellite in 2015 that provides 10m resolution for visible and near-infrared (NIR) bands (Labib and Harris, 2018). ...
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In recent years, the high cost and non-affordability of high-resolution satellite imageries had caused high reliability on the medium resolution satellite imageries (MRSI) in developing countries such as Nigeria. Also, the Normalized Difference Vegetation Index (NDVI) had been the most commonly used vegetation index for vegetation cover mapping (VCM). It is pertinent to determine the most accurate vegetation index for VCM of a degraded forest environment in Nigeria despite the varied spatial and spectral resolutions of the MRSI. This study determined the appropriate vegetation indices from Landsat 8 and Sentinel 2A of degraded tropical forest in Ise Forest Reserve, Southwest Nigeria. Recent Landsat 8 and Sentinel 2A satellite imageries were acquired and pre-processed. The earlier was downscaled to ensure uniformity in spatial resolution with Sentinel 2A. Seven vegetation indices: NDVI, Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), Pigment Specific Simple Ratio (PSSR), Soil Adjusted Vegetative Index (SAVI), Modified Soil Adjusted Vegetative Index (MSAVI) and Transformed Soil Adjusted Vegetative Index (TSAVI), were extracted from the datasets. Both Landsat 8 and Sentinel 2A have the same overall accuracy (98.00%) and kappa coefficients (0.96) in NDVI, SAVI and transformed SAVI. However, EVI (97.69% and 0.854), followed by GNDVI (91.50% and 0.802) extracted from Sentinel 2A outperformed NDVI (a common and widely used vegetation index) based on their overall accuracy and kappa coefficients respectively. The high performance of EVI (0.06 – 0.32) derived from Sentinel 2A despite the downscaling of Landsat 8 makes the consideration of spectral surface reflectance consequential. The enhanced capability and performance of Sentinel 2A in vegetation cover mapping should be more explored in developing economies with low affordability of commercial geospatial data.
... Advancing the understanding of the phenological response to climate change is therefore important for forecasts of the impact of future climate change on terrestrial ecosystems (Cleland et al., 2007;Nord and Lynch, 2009). With the climate change observed over recent years, advances of spring phenology and delays of autumn phenology have been reported worldwide such as in Europe (Menzel and Fabian, 1999;Menzel et al., 2001;Fu et al., 2014a), North America (Schwartz and Reiter, 2000;Jeong et al., 2011;Fridley, 2012), the Southern Hemisphere (Chambers et al., 2013;Ma et al., 2013), and China (Ge et al., 2014;Ge et al., 2015;Zheng et al., 2016). This variation was attributed to prevailing climate warming trends (Cleland et al., 2007;Bertin, 2008). ...
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Climate models often predict that more extreme precipitation events will occur in arid and semiarid regions, where plant phenology is particularly sensitive to precipitation changes. To understand how increases in precipitation affect plant phenology, this study conducted a manipulative field experiment in a desert ecosystem of northwest China. In this study, a long-term in situ water addition experiment was conducted in a temperate desert in northwestern China. The following five treatments were used: natural rain plus an additional 0, 25, 50, 75, and 100% of the local mean annual precipitation. A series of phenological events, including leaf unfolding (onset, 30%, 50%, and end of leaf unfolding), cessation of new branch elongation (30, 50, and 90%), and leaf coloration (80% of leaves turned yellow), of the locally dominant shrub Nitraria tangutorum were observed from 2012 to 2018. The results showed that on average, over the seven-year-study and in all treatments water addition treatments advanced the spring phenology (30% of leaf unfolding) by 1.29–3.00 days, but delayed the autumn phenology (80% of leaves turned yellow) by 1.18–11.82 days. Therefore, the length of the growing season was prolonged by 2.11–13.68 days, and autumn phenology contributed more than spring phenology. In addition, water addition treatments delayed the cessation of new branch elongation (90%) by 5.82–12.61 days, and nonlinear relationships were found between the leaves yellowing (80% of leaves) and the amount of watering. Linear relationships were found between the cessation of new branch elongation (90%), the length of the growing season, and amount of water addition. The two response patterns to water increase indicated that predictions of phenological events in the future should not be based on one trend only.
... For instance, preceding rainfall has stronger relationship with SOS than EOS in a tropical deciduous forest of Mexico, because expansion of leaves is greatly influenced by water availability, while leaf abscission depends on multiple environmental factors (Bullock and Solis-Magallanes 1990). In north Australian tropical savanna, leaf flushing and leaf fall dates have good temporal coherency with rainy season (Williams et al. 1997), and rainfall variability is a major environmental cue of vegetation phenology variability in the North Australian Tropical Transect (Ma et al. 2013). In addition, Bobée et al. (2012) revealed that the first rain event has high synchronization with vegetation SOS in Sahelian environments, while lack of rainfall at the beginning of rainy season may delay vegetation emergence phase. ...
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Using leaf area index (LAI) data from 1981 to 2014 in the tropical moist forest eco-zone of South America, we extracted start (SOS) and end (EOS) dates of the active growing season in forest and savanna at each pixel. Then, we detected spatiotemporal characteristics of SOS and EOS in the two vegetation types. Moreover, we analyzed relationships between interannual variations of SOS/EOS and climatic factors, and simulated SOS/EOS time series based on preceding mean air temperature and accumulated rainfall. Results show that mean SOS and EOS ranged from 260 to 330 day of year (DOY) and from 150 to 260 DOY across the study region, respectively. From 1981 to 2014, SOS advancement is more extensive than SOS delay, while EOS advancement and delay are similarly extensive. For most pixels of forest and savanna in tropical moist forest eco-zone, preceding rainfall correlates predominantly negatively with SOS but positively with EOS, while the relationship between preceding temperature and phenophases is location-specific. In addition, preceding rainfall is more extensive than preceding temperature in simulating SOS, while both preceding rainfall and temperature play an important role for simulating EOS. This study highlights the reliability of using LAI data for long-term phenological analysis in the tropical moist forest eco-zone.
... The EVI of different spatial scales showed strong relationships with CO 2 fluxes (GPP and NEE, Fig. 12). Because of strong correspondence with measured eddy fluxes, remotely-sensed EVI data have been widely used by several satellite-based or empirical models to estimate CO 2 fluxes and ET (Nagler et al., 2005;Sims et al., 2008;Wagle et al., 2017;Wu et al., 2014;Xiao et al., 2004), and to better understand spatial and temporal variations in ecosystem carbon uptake and ET (Churkina et al., 2005;Ma et al., 2013;Wagle et al., 2015). The MODIS-derived EVI at 500 m and 250 m resolutions explained 51% and 72%, respectively, of variability in NEE. ...
Article
Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO2 fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO2 fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.
... Satellite image-derived fractional ground cover mapping has proven to be an essential source of information for applications, including analysis of spatial and temporal vegetation dynamics [1], monitoring urban greenness [2], mapping bushfire burn severity levels [3], forest cover change [4], and deforestation [5]. Algorithms, including spectral mixture analysis [6][7][8], multiple endmember spectral mixture analysis [9], and relative spectral mixture analysis [10], are used to produce fractional cover (FC) maps. ...
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The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective sampling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point sampling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point sampling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover sampling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products.
... The higher NDVI values correspond to locations where the more photosynthetically active vegetation is present, with NDVI > 0.6 typically highlighting areas of dense forests or tropical rainforests [34,35]. Various ecosystem changes have been studied at local and global scales such as plant growth [34], greening and browning patterns in specific areas [36][37][38], seasonal vegetation productivity [39], detecting and predicting vegetation anomalies [40], assessment of human impact on vegetation [41], monitoring of forest conditions [42], drought monitoring [43] and changes in length of growing seasons [44]. Known limitations of the satellite-derived NDVI are cloud cover, presence of very steep topographic features, presence of snow or ice and adverse atmospheric conditions [25]. ...
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There is a growing interest for scientists and society to acquire deep knowledge on the impacts from environmental disasters. The present work deals with the investigation of vegetation dynamics in the Chernobyl area, a place widely known for the devastating nuclear disaster on the 26th of April 1986. To unveil any possible long-term radiation effects on vegetation phenology, the remotely sensed normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) was analyzed within the 30 km Exclusion Zone, where all human activities were ceased at that time and public access and inhabitation have been prohibited ever since. The analysis comprised applications of seasonal trend analysis using two techniques, i.e., pixel-wise NDVI time series and spatially averaged NDVI time series. Both techniques were applied in each one of the individual land cover types. To assess the existence of abnormal vegetation dynamics, the same analyses were conducted in two broader zones, i.e., from 30 to 60 km and from 60 to 90 km, away from Chernobyl area, where human activities were not substantially altered. Results of both analyses indicated that vegetation dynamics in the 30 km Exclusion Zone correspond to increasing plant productivity at a rate considerably higher than that of the other two examined zones. The outcome of the analyses presented herein attributes greening trends in the 30 km and the 30 to 60 km zones to a combination of climate, minimized human impact and a consequent prevalence of land cover types which seem to be well adapted to increased radioactivity. The vegetation greening trends observed in the third zone, i.e., the 90 km zone, are indicative of the combination of climate and increasing human activities. Results indicate the positive impact from the absence of human activities on vegetation dynamics as far as vegetation productivity and phenology are concerned in the 30 km Exclusion Zone, and to a lower extent in the 60 km zone. Furthermore, there is evidence that land cover changes evolve into the prevalence of woody vegetation in an area with increased levels of radioactivity.
... The enhanced vegetation index (EVI) correlates well in Australia with phenology (Ma et al., 2013;Restrepo-Coupe et al., 2016), whilst also minimizing the effect of bare soil on the calculation of the index (Huete et al., 2002). Consequently, EVI was used in the present study to assess phenology. ...
Article
The southern hemisphere and especially Australian arid and semi-arid ecosystems played a significant role in the 2011 global land carbon sink anomaly. Arid and semi-arid regions occupy 70% of the Australian land surface, dominated by two biomes: Mulga woodlands and spinifex grasslands or savannas. We monitored carbon and water fluxes in two of these characteristic ecosystems: a Mulga woodland (2010–2017) and a Corymbia savanna dominated by spinifex grasses (2012–2017). The aims of this study were to compare net ecosystem productivity (NEP) and evapotranspiration (ET) of these two ecosystems and to identify precipitation thresholds at which these ecosystems switched from being a C source to a C sink. Annual NEP in the Mulga woodland ranged from −47 to 217 gC m−2 y−1 (2010–2017), with the second largest positive NEP observed during the global C sink anomaly (162 gC m−2 y−1, 2010–2011). By contrast in the Corymbia savanna, annual NEP ranged from −190 to 115 gC m−2 y−1, with frequent occurrences of negative NEP and larger ET rates than for the Mulga woodland. Precipitation thresholds were identified at 262 mm y−1 and 507 mm y−1 in the Mulga woodland and the Corymbia savanna, respectively. Soil water content (SWC), along with air temperature and vapour pressure deficit, was a significant driver for water fluxes in both ecosystems (SWC–ET correlation of 0.5–0.56) and for carbon fluxes in the woodland (SWC–NEP and SWC–GPP correlation of −0.51 and −0.41, respectively). Arid and semi-arid ecosystems have dominated the inter-annual variability of the global terrestrial C sink, thus identifying precipitation thresholds at which ecosystems switch from being a C source to a C sink is important for furthering our understanding of the global C and water budget and for modelling of future climate.
... Drought or water stress can result in reduced g 1 for some, but not all, species (Heroult, Lin, Bourne, Medlyn, & Ellsworth, 2013;Zhou, Duursma, Medlyn, Kelly, & Prentice, 2013). We similarly found g 1-flux to decline in the Mulga woodland with time since rainfall, during the dry sea- We found g 1-leaf to show this late-season response much sooner than seen in g 1-flux , consistent with an increasing stomatal limitation to photosynthesis in Mulga at the conclusion of the wet season (Ma et al., 2013;Restrepo-Coupe et al., 2016). This late-wet-season difference between g 1-leaf and g 1-flux may be further explained by (a) the sub-daily pattern of leaf-level photosynthetic assimilation which is not captured by g 1-leaf , (b) variability in photosynthetic activity across leaves and trees which is under sampled by g 1-leaf and (c) continued stomatal and photosynthetic activity into the late dry season by understorey grasses and shrubs which was observed only by g 1-flux . ...
Article
As the ratio of carbon uptake to water use by vegetation, water‐use efficiency (WUE) is a key ecosystem property linking global carbon and water cycles. It can be estimated in several ways, but it is currently unclear how different measures of WUE relate, and how well they each capture variation in WUE with soil moisture availability. We evaluated WUE in an Acacia‐dominated woodland ecosystem of central Australia at various spatial and temporal scales using stable carbon isotope analysis, leaf gas exchange and eddy covariance fluxes. Semi‐arid Australia has a highly variable rainfall pattern, making it an ideal system to study how WUE varies with water availability. We normalised our measures of WUE across a range of vapour pressure deficits using g1, which is a parameter derived from an optimal stomatal conductance model and which is inversely related to WUE. Continuous measures of whole‐ecosystem g1 obtained from eddy covariance data were elevated in the 3 days following rain, indicating a strong effect of soil evaporation. Once these values were removed, a close relationship of g1 with soil moisture content was observed. Leaf‐scale values of g1 derived from gas exchange were in close agreement with ecosystem‐scale values. In contrast, values of g1 obtained from stable isotopes did not vary with soil moisture availability, potentially indicating remobilisation of stored carbon during dry periods. Our comprehensive comparison of alternative measures of WUE shows the importance of stomatal control of fluxes in this highly variable rainfall climate and demonstrates the ability of these different measures to quantify this effect. Our study provides the empirical evidence required to better predict the dynamic carbon‐water relations in semi‐arid Australian ecosystems.
... Vegetation indices obtained by various satellites have been developed to calculate vegetation phenological periods, such as normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) [33]. Ma et al. [34] investigated the relationship in spatial and temporal variability for rainfall, phenology and vegetation with EVI data in the North Australian Tropical Transect. Xiao et al. [35] used time-series data from EVI production to analyze tropical evergreen forest leaf phenology over a long-term time scale in South America. ...
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Global climate change, especially the snow drought events, is causing extreme weather events influencing regional vegetation growth and terrestrial ecosystem stability in a long-term and persistent way. In this study, the Sanjiang Plain was selected, as this area has been experiencing snow drought in the past two decades. Logistic models, combined with multisource remote sensing and unmanned aerial vehicle (UAV) data, as well as the meteorological data over the past 20 years, were used to calculate sixteen phenological periods and biomass. The results show that (1) over the past two decades, snow drought has been based on the snow accumulation and has been occurring more frequently, wider-ranging and more severely; (2) snow drought has advanced the forest start of season (SOS)/end of season (EOS) by 6/5 days, respectively; (3) if the snowfall is greater than 80% of a normal year, the SOS/EOS of grass is postponed by 8/6 days; conversely, if it is less than 80%, the SOS/EOS are advanced by 7/5 days; and (4) biomass decreased approximately 0.61%, compared with an abundant snowfall year. Overall, this study is the first to explore how snow drought impacts the phenological period in a mid-high latitude area, and more attention should be paid to these unknown risks to the ecosystem.
... The date reaching the minimum EVI2 before the growing season plus 10% of the amplitude is SGS, while the date reaching the minimum after the growing season plus 10% of the amplitude is EGS, and the time duration between SGS and EGS is LGS. Further details regarding the SSA-Pheno algorithm can refer to Ma et al. (2013). ...
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Sensors onboard the new generation of geostationary (GEO) satellites launched over past few years have much improved radiometric and temporal resolutions. As one of the latest GEO sensors, the Japanese Himawari-8 Advanced Himawari Imager (hereafter H8/AHI) has temporal resolution of 10 minutes and spectral bands similar to MODIS. Observations from H8/AHI therefore have the potential to improve the precision of vegetation phenology monitoring by significantly shortening the compositing period. In this study, we evaluated methods for compositing daily 2-band Enhanced Vegetation Index (EVI2) from 10-minutes H8/AHI observations at six study sites in northern China encompassing forest, cropland, and grassland. After screening the cloudy observations by a physics-based cloud detection algorithm, key phenological metrics were retrieved from the composited AHI EVI2. H8/AHI 10-min raw EVI2 showed significant diurnal variations that are mainly associated with sun angle variations. By calculating the daily average EVI2 values in different time intervals, we concluded that the mean value using a fixed time window (10:30-13:30) centered around local solar noon effectively reduced the anisotropy effect from the diurnal sun-angle variations, leading to stable EVI2 time series suitable for extracting phenological metrics. On average, the amount of the composited H8/AHI EVI2 time-series data was 5.3 times more than the Terra/Aqua combined MODIS EVI2 product per year and the average size (width) of the gaps in H8/AHI EVI2 time series was 75% smaller than that of the MODIS. Our results demonstrated the promising capability of the new generation GEO satellite for generating time series vegetation index with fewer cloud-induced gaps and higher temporal resolution, hence improving the monitoring of global vegetation phenology and ecosystem responses to climate change.
... There have also been many attempts to estimate GPP based solely on remote sensing inputs, thereby minimizing or eliminating the need for meteorological and LUE information. Spectral VIs were found to accurately estimate GPP across a wide range of North American ecosystems (Wylie et al., 2003;Rahman et al., 2005), African tropical savannas (Sjostrom et al., 2011), Australian mesic and xeric tropical savannas (Ma et al., 2013), various terrestrial ecosystems in China (Xiao, Zhou, et al., 2015), and dry to humid tropical forests in Southeast Asia and the Amazon (Huete et al., 2006;Huete et al., 2008). A more recent study examined the relationship between satellite-derived VIs and flux tower GPP for 121 FLUXNET sites encompassing a wide range of biomes across the globe and assessed how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect (Huang et al., 2019). ...
Article
Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence (SIF), land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of soil organic carbon. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50 years of remote sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking.
... Human population growth increasingly poses a threat to savanna ecosystems due to land use, land cover changes, and management policies [6] Climate change, such as prolonged droughts and erratic rainfalls, along with government policies for reforestation and afforestation, continue to threaten the resilience of savanna ecosystems [7,8]. As such, savannas have witnessed extended land clearing in the past three centuries which threaten the ability of savannas to continue serving as a carbon sink [9]. ...
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Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing. Citation: Nghiyalwa, H.S.; Urban, M.; Baade, J.; Smit, I.P.J.; Ramoelo, A.; Mogonong, B.; Schmullius, C. Spatio-Temporal Mixed Pixel
... carotenoids and chlorophyll); time series of eddy covariance (EC) flux tower measurements (Nasahara and Nagai, 2015;Peng et al., 2017); multispectral images from time-lapse cameras located in carbon flux measurement sites (Peichl et al., 2015;Moore et al., 2016;Richardson et al., 2018a); photosynthetically active radiation; shortwave radiation sensor measurements; airborne hyperspectral/multispectral measurements; and citizen science observations (Zhang et al., 2018b). Satellite greenness indices have been directly related to EC tower carbon flux measurements across a wide range of ecosystems (Rahman et al., 2005;Gitelson et al., 2006;Ma et al., 2013). EC data represent fluxes which encompass diurnal and seasonal ecosystem processes, whilst satellite greenness measures operate at coarser time scales, but the two data sources are related and comparable. ...
Article
Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at five OzFlux sites showed that our algorithm was robust to view angle-induced sensitivity of the input data and showed similar performance when using EVI data calculated using MODIS Nadir BRDF-Adjusted Reflectance product. Our retrieved LSP metrics revealed that vegetation growth in arid ecosystems is highly irregular and can occur at any time of the year, more than once in a year, or can skip a year. The proportion of pixels with two growing seasons was found to be correlated with the average annual precipitation of the study area (p < 0.01), providing an estimation approach of LSP via rainfall. Our study improves the detection and measurement of vegetation phenology in arid and semi-arid regions by improving the spatial extend of LSP retrievals, which contributes to studies on LSP variations and dryland ecosystem resilience to climate change. More evaluation is planned for future work to assess and further improve the accuracy of the retrieved LSP metrics.
... Forest expansion appeared indeed as a continuous and steady process in the MDNP and if we extrapolate the observed fairly constant rate of forest gain into savanna, the area will lose all its savanna in less than 30 years. This is even more certain when we consider the rise in CO2 concentration and climate change predictions for tropical Africa that expect a rise in precipitations over the next decades (Pachauri and Meyer, 2014) favouring forest expansion (Higgins and Scheiter, 2012;Ma et al., 2013;Stevens et al., 2016Stevens et al., , 2017. Local factors including fire management, soil fertility and herbivory pressure are expected to mediate this general prediction. ...
Thesis
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Understanding the effects of global change (combining anthropic and climatic pressures) on biome distribution needs innovative approaches allowing to address the large spatial scales involved and the scarcity of available ground data. Characterizing vegetation dynamics at landscape to regional scale requires both a high level of spatial detail (resolution), generally obtained through precise field measurements, and a sufficient coverage of the land surface (extent) provided by satellite images. The difficulty usually lies between these two scales as both signal saturation from satellite data and ground sampling limitations contribute to inaccurate extrapolations. Airborne laser scanning (ALS) data has revolutionized the trade-off between spatial detail and landscape coverage as it gives accurate information of the vegetation’s structure over large areas which can be used to calibrate satellite data. Also recent satellite data of improved spectral and spatial resolutions (Sentinel 2) allow for detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. Another major obstacle comes from the lack of a temporal perspective on dynamics and disturbances. Growing satellite imagery archives over several decades (45 years; Landsat) and available computing facilities such as Google Earth Engine (GEE) provide new possibilities to track long term successional trajectories and detect significant disturbances (i.e. fire) at a fine spatial detail (30m) and relate them to the current structure and composition of the vegetation. With these game changing tools our objective was to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon with induced changes in the vegetatio structure and composition within two contrasted scenarios of anthropogenic pressures: 1) the Nachtigal area which is targeted for the dam construction and subject to intense agricultural activities and 2) the Mpem et Djim National Park (MDNP) which has no management plan. The maximum likelihood classification of the Spot 6/7 image aided with the information from the canopy height derived from ALS data discriminated the vegetation types within the Nachtigal area with good accuracy (96.5%). Using field plots data in upscaling aboveground biomass (AGB) form field plots estimates to the satellite estimates with model-based approaches lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha−1 (90%), whereas calibration with ALS data (AGBALS) lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha−1, 58%) with little effect of the satellite sensor used. However, these results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixel wise predictions, because of large relative RMSPE, especially above (200–250 Mg.ha−1). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples. AGB and species diversity measured within 74 field inventory plots (distributed along a savanna to forest successional gradient) were higher for the vegetation located in the MDNP compared to their pairs in the Nachtigal area. The automated unsupervised long-term (45 years) land cover change monitoring from Landsat image archives based on GEE captured a consistent and regular pattern of forest progression into savanna at an average rate of 1% (ca. 6 km².year-1). No fire occurrence was captured for savanna that transited to forest within five years of monitoring. Distinct assemblages of spectral species are apparent in forest vegetation which is consistent with the age of transition. As forest gets older AGBALS recovers at a rate of 4.3 Mg.ha-1.year-1 in young forest stands (< 20 years) compared to 3.2 Mg.ha-1.year-1 recorded for older forest successions (≥ 20 years). In savannas, two modes could be identified along the gradient of spectral species assemblage, corresponding to distinct AGBALS levels, where woody savannas with low fire frequency store 50% more carbon than open grassy savannas with high fire frequency. At least two fire occurrences in five years is found to be the fire regime threshold below which woody savannas start to dominate over grassy ones. Four distinct plant communities were found distributed along a fire frequency gradient. However the presence of fire-sensitive pioneer forest species in all scenarios of fire frequencies (from low to high fire frequencies) would suggest that the limiting effect of fire on woody vegetation is not sufficient to hinder woody encroachment in the area bringing therefore sufficient humidity required for the establishment of pioneer forest saplings within open savannas. These results have implications for carbon sequestration and biodiversity conservation policies. The maintenance of the savanna ecosystem in the region would require active management actions, and contradicts reforestation goals (REDD+, Bonn challenge, etc.).
... Changes in crop types and agricultural management also contributed greatly to the temporal variation in LSP in recent decades across the USA Liang et al., 2021). With regard to savannas, the effects of tree cover and tree grass ratio on the spatial patterns of LSP have been observed (Ma et al., 2013;Guan et al., 2014;Cho et al., 2017). For example, Cho et al. (2017) revealed significant spatial differences in phenological metrics among different tree cover levels across southern African savannas. ...
Article
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Changes in climate and land cover are potential drivers of land surface phenology changes. Here, we investigate whether subpixel percent tree cover (PTC) change is an important driver of trends in satellite derived vegetation spring greenup date (GUD) across the Hulunbuir temperate forest-grassland ecotone in northeastern China. GUD was estimated using the MODIS-derived enhanced vegetation index time series during 2001–2020 with a spatial resolution of 500 m. To understand the influential mechanisms of PTC on GUD, we examined relationships between the spatial variations in GUD and PTC at multiple spatio-temporal extents. Forested pixels with greater PTC were found to have generally earlier GUDs for all forest types. The GUD of forests was also generally earlier than that of grassland. On the other hand, we observed approximately 23.7% and 1.2% significantly earlier and later trends in GUD across the region, respectively. Meanwhile, widespread increases in preseason land surface temperature (LST) and PTC were detected. Both increases in LST and PTC contributed to the earlier GUD in the forested region. Specifically, we found negative correlations (Spearman correlation coefficient -0.17 to -0.55) between the change slopes of GUD and PTC in every forest and grassland type. The results highlight the important impacts of subpixel PTC on GUD variations, and improve the understanding of ecosystem changes under the effects of climate and human activities (e.g., afforestation) over the Hulunbuir temperate forest-grassland ecotone.
... We speculate that this delayed correlation is the result of plant growth and leaf development continuing throughout the summer season (i.e., continuing after peak radiation), with increased precipitation later in the season leading to a spike in photosynthetic activity. However, phenology and increase in foliage can be highly variable in tropical ecosystems, even within the same biome (Bie et al., 1998;Guan et al., 2013;Ma et al., 2013;Monasterio & Sarmiento, 1976;Moore et al., 2018). Particularly in savannas, phenology and vegetation growth are influenced by plant composition (i.e., tree and grass fraction covers) and fire seasonality (Guan et al., 2014;Williams et al., 2005). ...
Article
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Tropical ecosystems strongly influence Earth's climate and weather patterns. Most tropical ecosystems remain warm year‐round; nonetheless, their plants undergo seasonal cycles of carbon and water exchange. Previous research has shown the importance of precipitation and radiation as drivers of the seasonality of photosynthetic activity in the tropics. Although data are scarce, field‐based studies have found that seasonal cycles at a handful of tropical sites do not match those in the land surface model (LSM) simulations. A comprehensive understanding and model comparison of how seasonal variations in tropical photosynthetic activity relate to climate is lacking. Here, we identify the relationships of precipitation and radiation with satellite‐based proxies for photosynthetic activity (e.g., GOME‐2 SIF, MAIAC EVI) for the pantropical region. Three dominant and spatially distinct seasonal relationships emerge: photosynthetic activity that is positively correlated with both drivers (36% of tropical pixels), activity that increases following rain but decreases with radiation (28%), and activity that increases following bright seasons but decreases with rain (14%). We compare distributions of these observed relationships with those from LSMs. In general, compared to satellite‐based proxies of photosynthetic activity, model simulations of gross primary productivity (GPP) overestimate the extent of positive correlations of photosynthetic activity with water and underestimate positive correlations with radiation. The largest discrepancies between simulations and observations are in the representation of regions where photosynthetic activity increases with radiation and decreases with rain. Our clear scheme for representing the relationship between climate and photosynthetic activity can be used to benchmark tropical seasonality of GPP in LSMs.
... The seasonal development of grassland ecosystems is generally driven by phenological transitions closely linked to temperature and water availability (Bolton et al., 2020;Jin et al., 2019). Variations of water availability and temperature during droughts or heatwaves consequently alter the standard phenological trajectory of grasslands depending on the ecosystem's sensitivity to such changes (de Beurs et al., 2018;Ma et al., 2013). Additionally, management events such as mowing, or grazing represent short-term disturbances of the grassland seasonality. ...
Article
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Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.
... The SSA-Pheno (Singular Spectrum Analysis for Phenology retrieval) algorithm, graphically depicted by Fig. 2, was used to retrieve the phenological metrics from EVI time series [5]. Six phenological metrics, including the start of growing season (SGS), peak of growing season (PGS), end of growing season (EGS), and length of growing season (LGS) were retrieved from the time series of H8/AHI and MODIS EVI time series. ...
Conference Paper
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Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.
... Satellite remote sensing studies of vegetation dynamics in Australia's aeolian landscapes and the broader arid zone have commonly employed vegetation indices such as the Enhanced Vegetation Index (EVI), or Normalised Difference Vegetation Index (NDVI) (e.g. Lu et al., 2003;Donohue et al., 2009;Lawley et al., 2011Lawley et al., , 2016Ma et al., 2013Ma et al., , 2020Burrell et al., 2017). Australian arid vegetation studies using fractional cover have been relatively few (e.g. ...
Article
Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous. A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of fPV and fNPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data. The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (fBS), 6.5% (fPV) and 15.8% (fNPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as fPV, and 3–9 months later as a peak in fNPV. Components of fPV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of fNPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).
... Several previous works have analyzed vegetation phenological indices such as Normalized Difference Vegetation Index (NDVI) to assess vegetation dynamics either using in-situ observations or remotely sensed data or a combination of both (Reed et al., 1994). A common finding is that besides the great value of in-situ observations (Ma et al., 2013), the spatial coverage and temporal resolution of remotely sensed observations cannot be achieved by ground data alone (Bajocco et al., 2015a(Bajocco et al., , 2015b. Consequently, remote sensing information has been used to monitor vegetation dynamics and productivity at the local, regional and global scale Gemitzi et al., 2019;Mishra and Chaudhuri, 2015). ...
Article
Fire events are among the natural disasters that affect both ecosystem functioning but also human lives. The present work deals with the assessment of the properties of vegetation phenology in areas affected by fire events during a 10-year period in the Peloponnese, Greece. The main objective is to identify special patterns in NDVI time series of fire-affected areas, that can be used either as precursory indicators or as a tool for assessment of regeneration patterns. Therefore, we constructed and analyzed time series of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m spatial resolution with a 16-day time step for affected and non-affected areas surrounding the burnt sites, where the majority of the area refers to the same land cover type. Analysis of changes of NDVI time series of both burnt and unburnt areas was conducted and results indicated that all fire events in the burnt sites caused breaks in the NDVI time series, demonstrated either as NDVI shifts to the lower and/or as changes in the trend and seasonality of the time series. Trends of the NDVI time series of the affected sites were almost doubled in the post fire period compared to the period before the event, indicating the evolution of the regenerating process. The basic statistics of the NDVI time series of the affected and non-affected areas correspond to greater minimum NDVI values of the affected areas throughout the study period, except of the two years after the fire event. Mean NDVI values were almost equal in burnt and unburnt areas before the events and reached the same values approximately seven years after the fire. Maximum NDVI values of the affected and non-affected sites were almost equal before the event but were abruptly reduced in the affected areas after the events and revert to their original state approximately ten years after. An alarming finding is the considerably lower standard deviation of the fire affected areas in comparison to non-fire-affected ones, during all years of the study period, except of a short period immediately after the fire event. It seems thus that areas covered by homogeneous vegetation with no fire breaks or other landforms to act as barriers to fire are the most vulnerable and most severely affected.
... In an ecosystem with adequate amounts of water, plant phenology is primarily driven by temperature. For example, climate warming led to the advanced phenology of many woody plants in the spring and postponed phenology in the autumn in Europe [6][7][8], North America [9][10][11], China [12][13][14], the southern hemisphere [15,16] and other areas. However, in arid and semiarid ecosystems, the availability of water plays a key role in the regulation of plant phenology [17,18]. ...
Article
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Plant phenology is the most sensitive biological indicator that responds to climate change. Many climate models predict that extreme precipitation events will occur frequently in the arid areas of northwest China in the future, with an increase in the quantity and unpredictability of rain. Future changes in precipitation will inevitably have a profound impact on plant phenology in arid areas. A recent study has shown that after the simulated enhancement of precipitation, the end time of the leaf unfolding period of Nitraria tangutorum advanced, and the end time of leaf senescence was delayed. Under extreme climatic conditions, such as extremely dry or wet years, it is unclear whether the influence of the simulated enhancement of precipitation on the phenology of N. tangutorum remains stable. To solve this problem, this study systematically analyzed the effects of the simulated enhancement of precipitation on the start, end and duration of four phenological events of N. tangutorum, including leaf budding, leaf unfolding, leaf senescence and leaf fall under extremely dry and wet conditions. The aim of this study was to clarify the similarities and differences of the effects of the simulated enhancement of precipitation on the start, end and duration of each phenological period of N. tangutorum in an extremely dry and an extremely wet year to reveal the regulatory effect of extremely dry and excessive amounts of precipitation on the phenology of N. tangutorum. (1) After the simulated enhancement of precipitation, the start and end times of the spring phenology (leaf budding and leaf unfolding) of N. tangutorum advanced during an extremely dry and an extremely wet year, but the duration of phenology was shortened during an extremely wet year and prolonged during an extremely drought-stricken year. The amplitude of variation increased with the increase in simulated precipitation. (2) After the simulated enhancement of precipitation, the start and end times of the phenology (leaf senescence and leaf fall) of N. tangutorum during the autumn advanced in an extremely wet year but was delayed during an extremely dry year, and the duration of phenology was prolonged in both extremely dry and wet years. The amplitude of variation increased with the increase in simulated precipitation. (3) The regulation mechanism of extremely dry or wet years on the spring phenology of N. tangutorum lay in the different degree of influence on the start and end times of leaf budding and leaf unfolding. However, the regulation mechanism of extremely dry or wet years on the autumn phenology of N. tangutorum lay in different reasons. Water stress caused by excessive water forced N. tangutorum to start its leaf senescence early during an extremely wet year. In contrast, the alleviation of drought stress after watering during the senescence of N. tangutorum caused a delay in the autumn phenology during an extremely dry year.
... Previous studies suggested that VIs response to climate differed at different timescales across the year's seasons [31][32][33]. A high correlation between precipitation and NDVI has been reported at a yearly scale [6]. ...
Article
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Multiple studies revealed that pasture grasslands are a time-varying complex ecological system. Climate variables regulate vegetation growing, being precipitation and temperature the most critical driver factors. This work aims to assess the response of two different Vegetation Indices (VIs) to the temporal dynamics of temperature and precipitation in a semiarid area. Two Mediterranean grasslands zones situated in the center of Spain were selected to accomplish this goal. Correlations and cross-correlations between VI and each climatic variable were computed. Different lagged responses of each VIs series were detected, varying in zones, the year's season, and the climatic variable. Recurrence Plots (RPs) and Cross Recurrence Plots (CRPs) analyses were applied to characterise and quantify the system's complexity showed in the cross-correlation analysis. RPs pointed out that short-term predictability and high dimensionality of VIs series, as well as precipitation, characterised this dynamic. Meanwhile, temperature showed a more regular pattern and lower dimensionality. CRPs revealed that precipitation was a critical variable to distinguish between zones due to their complex pattern and influence on the soil's water balance that the VI reflects. Overall, we prove RP and CRP's potential as adequate tools for analysing vegetation dynamics characterised by complexity.
Article
This paper analyzes inconsistencies in remotely sensed land surface phenology (LSP) reported by AVHRR GIMMS3g and MODIS datasets across land cover types of the Northern Hemisphere. We extracted the start of the growing season (SOS) and the end of the growing season (EOS) from the AVHRR GIMMS3g (1982–2015) and MODIS (2000–2015) datasets, and weekly GPP mean data from the Fluxnet2015 dataset to compare spatial patterns and trends of the phenological indicators in the Northern Hemisphere. We used the same method to fit the time series curves and extract phenological parameters from the two datasets to avoid uncertainties caused by differences in fitting and extraction approach. The results showed that (1) The multi-year means extracted from the GIMMS3g (1982–2015 and 2000–2015) and MODIS (2000–2015) datasets in the Northern Hemisphere display greater differences in the spatial distribution of SOS than EOS; (2) Under the 95% confidence level, GIMMS3g showed a significantly delayed (0.1716 days/year) SOS and advanced (0.9172 days/year) EOS in most parts of the Northern Hemisphere between 2000 and 2015. The SOS and EOS extracted from the MODIS dataset exhibited significantly advanced (0.5861 days/year) and delayed (0.6305 days/year) trends, respectively; (3) From 2000 to 2015, the same significant trends were observed in the SOS and EOS from two datasets, accounting for 1.33% and 1.17% of the total pixels in the Northern Hemisphere, respectively. (4) From 2000 to 2015, a significantly advanced trends in SOS extracted from MODIS (M_SOS) was more frequent than for GIMMS3g (G_SOS) in different land cover types at high latitude. EOS extracted from GIMMS3g (G_EOS) had a significantly advanced trend, and EOS extracted from MODIS (M_EOS) had a significantly delayed trend in different land cover types. The phenological parameters obtained from GIMMS3g are closer to ground phenology than those from MODIS. The results suggest that the phenological parameters derived from different datasets have different effects on the LSP trend.
Article
Tracking dryland vegetation phenology under a changing climate is of great concern because dryland ecosystems have broad spatial coverage and are important drivers of global carbon cycles. However, dryland ecosystems often consist of two or more different vegetation types with divergent phenological patterns and are characterized by relatively low vegetation greenup signal compared to noise present in satellite imagery. Thus, accurately characterizing dryland phenology – including the start, peak, and end of season (SOS, POS, and EOS, respectively) – across large temporal and spatial scales with satellite vegetation indices (VIs) has proven challenging. Working across six dryland flux tower sites in the United States from 2003 to 2014, we asked: 1) How well do satellite VIs explain GPP? 2) How accurately can satellite VIs characterize the number and timing of phenological transition dates? and 3) Why does this accuracy vary across VIs? To address these questions, we used daily GPP data from the FLUXNET2015 dataset. We computed four daily VIs from the Moderate Resolution Imaging Spectrometer (MODIS) sensor including two greenness indices (i.e., NDVI and EVI), and two water-related indices (i.e., Land Surface Water Index (LSWI) and NDVI-LSWI difference). We derived phenological transition dates from spline smoothed VI and GPP time series. We found that all major phenological patterns apparent in GPP time series were present in VI data across sites and VIs. Among the four VIs, EVI explained the most variance in daily GPP (R² = 0.65). Between 88 and 90% of phenological events found in GPP time series – especially recurring, large magnitude events but also some sporadic, small ephemeral events – were detected from MODIS VI data. The rate of omission, when an event was detected from GPP but not from VI data, was similar across VIs (10–12% of all GPP events). Commissions, when an event was detected from VI but not from GPP data, varied more widely from 18% for NDVI to 30% for LSWI. Transition date accuracy as measured by mean absolute difference (MAD) between GPP- and VI-derived SOS and POS ranged from 13 to 15 days across VIs but was lower for EOS (17–26 days). EOS from LSWI most accurately characterized GPP-derived EOS. This may be because LSWI is sensitive to leaf water content, which declines in certain dryland vegetation even when leaves remain green. Our results highlight the potential for satellite VI-derived metrics to accurately track spatio-temporal variation in the phenological event occurrence and timing in dryland ecosystems.
Article
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District Jhelum is located in the extremely diverse province of Punjab, Pakistan, and flowering event in plants is always influenced by the environment. This study was conducted during 2018 to 2020 to investigate the climatic effects on flowering cycle of plants. The main focus of the study was to find out the particular association between flowering phenology of plants and climatic variables. Month-wise phenological response of plants was recorded during frequent field visits at multiple representative microhabitats. The response data is saved as binary data matrix, and mean monthly climatic data is obtained through remote sensing, and analysed by using multivariate analyses like canonical correspondence analysis, hierarchical classification and pseudo-canonical correlation. CCA and Hierarchical classification were applied to assess the importance climatic variations towards the flowering phenological response and potential groups respectively. A total of 404 plant species of 223 genera belonging to 75 plant families were examined. Majority of plant species were found in flowering during the month of March (174 spp.) followed by April (159 spp.) and August (158 spp.), similarly, Summer was the leading season (208 spp.) followed by Monsoon (203 spp.), Spring (181 spp.) and Autumn (157 spp.). CCA results depicted that total variations in the flowering phenology response data were 3.45084, and about 45.6% were explained by the explanatory climatic variables. Wind speed, mean monthly maximum temperature and soil moisture were detected as most influential drivers of flowering phenology in the study area. The current study will be useful for researchers as a major source of knowledge for the conservation of valuable species. Such type of attempts will be supportive to explore the phenological response of plants in various habitats such as forest, hilly, riverine, desert and range lands flora in their future projects.
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The sustainability of the global savanna ecosystem is currently under threat from climate and anthropological change. Despite the immense threats, the existence of the savanna ecosystem is undervalued and understudied. This study examined the dynamics of the savanna ecosystem in the southern part of Southeast Asia (SEA) using MODIS leaf area index (LAI) data (MOD15A3H) with 500-m spatial resolution and 4-day data, from 2002 to 2020. The annual phenological metrics comprising the start of season (SOS), end of season (EOS), length of season (LOS), rate of greening, rate of browning, and the maximum peak values were derived from the daily interpolated data using the spline function. Additional oscillation and trend analysis using empirical ensemble decomposition methods (EEMD) was conducted to derive the nonlinear trend dynamics of the savanna ecosystem in East Nusa Tenggara (ENT). We found that the SOS at the Savanna ecosystem in SEA is 253.76 ± 2.1 days, the EOS is 161.12 ± 4.0 days, and the average LOS is 170.68 ± 6.5 days. The rainfall variabilities can explain around 35% of the variability in the LAI of the savanna ecosystem. Our EEMD analysis captured the decreasing LAI trend, showing a net change between 2002 and 2015 from 1.08 LAI units (scale of 10−2) to − 0.17 LAI units (scale of 10−2) from 2015 onwards. The result indicated a declining trend of LAI values of savanna ecosystem in ENT, thus requiring further monitoring to ensure the sustainability of this ecosystem.
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Objective Climate change has long been recognized as a significant driver of dietary diversity and dietary quality. An often overlooked aspect of climate change are shifts in fire regimes, which have the potential to drastically affect landscape diversity, species distributions, and ultimately, human diets. Here, we investigate whether the fire regimes shaped by Indigenous Australians change landscape diversity in ways that improve dietary quality, considering both the diversity and the quantity of traditional foods in the diet. Methods We use structural equation modeling to explore two causal models of dietary quality, one focused on the direct effects of climate change and resource depression, the other incorporating the dietary effects of landscape diversity, itself a product of fire‐created patchiness. We draw on a focal camp dataset covering 10 years of observations of Martu foraging income in the Western Desert of Australia. Results We find strong support for the hypothesis that fire‐created patchiness improves diet quality. Climate change (cumulative 2‐year rainfall) has only an indirect effect on dietary quality; the availability of traditional foods is mediated primarily through the landscape diversity shaped by fire. Conclusions Our model suggests that the loss of the indigenous fire mosaic may lead to worsening availability of traditional foods, measured as both caloric intake and diet diversity. Because the effects of rainfall are mediated through landscape diversity, increased rainfall may not compensate for the recent changes in fire regimes resulting from the loss of Aboriginal fire from the landscape.
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PlanetScope satellite data with a 3-m resolution and near-daily global coverage have been increasingly used for land surface monitoring, ranging from land cover change detection to vegetative biophysics characterization and ecological assessments. Similar to other satellite data, effective screening of clouds and cloud shadows in PlanetScope images is a prerequisite for these applications, yet remains challenging as PlanetScope has 1) fewer spectral bands than other satellites hindering the use of traditional methods, and 2) inconsistent radiometric calibration across satellite sensors making the cloud/shadow detection using fixed thresholds unrealistic. To address these challenges, we developed a SpatioTemporal Integration approach for Automatic Cloud and Shadow Screening (‘STI-ACSS’), including two steps: (1) generating initial masks of clouds/shadows by integrating both spatial (i.e. cloud/shadow indices of an individual PlanetScope image) and temporal (i.e. reflectance outliers in PlanetScope image time series) information with an adaptive threshold approach; (2) a two-step fine-tuning on these initial masks to derive final masks by integrating morphological processing with an object-based cloud and cloud shadow matching. We tested STI-ACSS at six tropical sites representative of different land cover types (e.g. forest, urban, cropland, savannah, and shrubland). For each site, we evaluated the performance of STI-ACSS with reference to the manual masks of clouds/shadows, and compared it with four state-of-the-art methods, namely Function of mask (Fmask), Automatic Time-Series Analysis (ATSA), Iterative Haze Optimized Transformation (IHOT) and the default PlanetScope quality control layer. Our results show that, across all sites, STI-ACSS 1) has the highest average overall accuracy (98.03%), 2) generates an average producer accuracy of 95.53% for clouds and 89.48% for cloud shadows, and 3) is robust across sites and seasons. These results suggest the effectiveness of using STI-ACSS for cloud/shadow detection for PlanetScope satellites in the tropics, with potential to be extended to other satellite sensors with limited spectral bands.
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Vegetation phenology is an integrated indicator of vegetation growth and development in response to climate, and it is one of the important variables regulating carbon flux in forest ecosystems. Land surface phenology derived from different remote sensing products and their relationships with flux tower gross primary production (GPP) data have been paid much attention. Remotely sensed phenology was retrieved based on greenness, while GPP-based phenology was extracted based on photosynthesis. However, exploring the differences between remotely sensed and GPP-based phenology are limited. In this study, we compared phenological metrics obtained from flux tower GPP of a mixed plantation with phenological metrics derived using MODIS EVI and MCD12Q2 from 2006 to 2017. We also explored phenological transitions and the response of phenology to climatic variables. The results showed that the 16-day composited EVI time series was in agreement with the 16-day composited GPP time series (R² = 0.86, p < 0.001). The root mean squared deviation values between flux tower GPP- and the MODIS EVI- and MCD12Q2-retrieved phenology dates were 8, 13, 34, and 16 days and 8, 19, 28, and 18 days for the start of the growing season (SOS), end of the growing season (EOS), peak of the growing season (POS), and growing season length (GSL), respectively, suggesting that MODIS EVI and MCD12Q2 were valuable products for retrieving the vegetation phenological dynamics, except for POS. The partial correlation analysis showed that temperature had a more important role in phenophase than precipitation. Rising temperatures in spring triggered earlier SOS. Significant correlations were found between EOSEVI, EOSMCD12Q2, EOSGPP, and preseason temperature (60 d prior to EOS) (p < 0.05), indicating that warming over the preseason resulted in earlier EOS. These findings help to understand the discrepancies between MODIS EVI, MCD12Q2, and flux tower GPP-based phenologies, their responses to climatic factors, and further confirm that precipitation should be taken into account to improve the accuracy of phenology models.
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Studies about fauna diversity, conducted in agricultural landscapes, have not greatly face the relationship between environmental variables and the diversity of order Chiroptera, so in this article we evaluate seasonally effects on the abundance, richness and structure of the bat community throughout an agricultural landscape located in the northwest of Ecuador. As long as four years, we collected bats in agrosystems inside an agricultural landscape, we calculate abundance, richness, and diversity index which grouped in two seasons, we searching the differences between seasons and months with the non-parametric Kruskal- Wallis test. We registered 343 individuals belonging to 20 species of bats, alpha diversity does not showed significant differences between seasons or months. Richness and relative abundance of trophic guilds were significantly higher in October (dry seasons) and significantly lower in February (rainy season). Rankabundance curves showed that dry season’s assemblage exhibited highest richness, equitability, and number of rare species. Despite indices that represent the structure of the bats’ community did not change significantly between seasons or months of the year, rank-abundance curves showed that dry season’s assemblage exhibited highest diversity of bats. We concluded that at the assemblage level, dry season exhibits heights bat diversity than the rainy season in the agricultural landscape of western Ecuador.
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Abstract. Understanding of human-nature interactions is critical for global sustainability, but one of its frontier branches, regarding intentionally-positive anthropogenic feedbacks to environment at the macroecosystem scale, has been less studied. A concrete open question is whether people can break those chain -like macro-ecospatial transition zones. Based on remote sensing data and integrative data analysis, we examined this issue in the case of China, which both owns a macro-ecospatial transition zone top-ranked in the world – HU Line and has made massive environmental restoration efforts such as the Grain for Green Program (GGP). Literature reviews of the causes of HU Line revealed its natural formation, and spatiotemporal tests of its statuses indicated its contemporary stability, both telling the inherent difficulty of shaking macro-ecospatial chains . What's worse, the limited durations of those GGP-kind endeavors led to a debate on whether human will eventually exert positive or negative eco-effect on the evolution of HU Line. To handle this gap, we proposed using biogeographic, bioclimatology, and Earth system models in a simulation way and overviewed their potentials of reflecting the complex internal, external, and integral eco-functions in human deliberately improving nature. In all, the conclusion and proposal of this work are of fundamental implications for projecting the future of macro-ecospatial chains and pre-making polices for anthropogenically coping with global changes in land, environment, biology, ecology, and sustainability.
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Most terrestrial biosphere models (TBMs) rely on more or less detailed information about the properties of the local vegetation. In contrast, optimality-based models require much less information about the local vegetation as they are designed to predict vegetation properties based on general principles related to natural selection and physiological limits. Although such models are not expected to reproduce current vegetation behaviour as closely as models that use local information, they promise to predict the behaviour of natural vegetation under future conditions, including the effects of physiological plasticity and shifts of species composition, which are difficult to capture by extrapolation of past observations. A previous model intercomparison using conventional TBMs revealed a range of deficiencies in reproducing water and carbon fluxes for savanna sites along a precipitation gradient of the North Australian Tropical Transect (Whitley et al., 2016). Here, we examine the ability of an optimality-based model (the Vegetation Optimality Model, VOM) to predict vegetation behaviour for the same savanna sites. The VOM optimizes key vegetation properties such as foliage cover, rooting depth and water use parameters in order to maximize the net carbon profit (NCP), defined as the difference between total carbon taken up by photosynthesis minus the carbon invested in construction and maintenance of plant organs. Despite a reduced need for input data, the VOM performed similarly to or better than the conventional TBMs in terms of reproducing the seasonal amplitude and mean annual fluxes recorded by flux towers at the different sites. It had a relative error of 0.08 for the seasonal amplitude in ET and was among the three best models tested with the smallest relative error in the seasonal amplitude of gross primary productivity (GPP). Nevertheless, the VOM displayed some persistent deviations from observations, especially for GPP, namely an underestimation of dry season evapotranspiration at the wettest site, suggesting that the hydrological assumptions (free drainage) have a strong influence on the results. Furthermore, our study exposes a persistent overprediction of vegetation cover and carbon uptake during the wet seasons by the VOM. Our analysis revealed several areas for improvement in the VOM and the applied optimality theory, including a better representation of the hydrological settings as well as the costs and benefits related to plant water transport and light capture by the canopy. The results of this study imply that vegetation optimality is a promising approach to explain vegetation dynamics and the resulting fluxes. It provides a way to derive vegetation properties independently of observations and allows for a more insightful evaluation of model shortcomings as no calibration or site-specific information is required.
Article
The Vegetation Optimality Model (VOM, Schymanski et al., 2009, 2015) is an optimality-based, coupled water–vegetation model that predicts vegetation properties and behaviour based on optimality theory rather than calibrating vegetation properties or prescribing them based on observations, as most conventional models do. Several updates to previous applications of the VOM have been made for the study in the accompanying paper of Nijzink et al. (2022), where we assess whether optimality theory can alleviate common shortcomings of conventional models, as identified in a previous model inter-comparison study along the North Australian Tropical Transect (NATT, Whitley et al., 2016). Therefore, we assess in this technical paper how the updates to the model and input data would have affected the original results of Schymanski et al. (2015), and we implemented these changes one at a time. The model updates included extended input data, the use of variable atmospheric CO2 levels, modified soil properties, implementation of free drainage conditions, and the addition of grass rooting depths to the optimized vegetation properties. A systematic assessment of these changes was carried out by adding each individual modification to the original version of the VOM at the flux tower site of Howard Springs, Australia. The analysis revealed that the implemented changes affected the simulation of mean annual evapotranspiration (ET) and gross primary productivity (GPP) by no more than 20 %, with the largest effects caused by the newly imposed free drainage conditions and modified soil texture. Free drainage conditions led to an underestimation of ET and GPP in comparison with the results of Schymanski et al. (2015), whereas more fine-grained soil textures increased the water storage in the soil and resulted in increased GPP. Although part of the effect of free drainage was compensated for by the updated soil texture, when combining all changes, the resulting effect on the simulated fluxes was still dominated by the effect of implementing free drainage conditions. Eventually, the relative error for the mean annual ET, in comparison with flux tower observations, changed from an 8.4 % overestimation to an 10.2 % underestimation, whereas the relative errors for the mean annual GPP remained similar, with an overestimation that slightly reduced from 17.8 % to 14.7 %. The sensitivity to free drainage conditions suggests that a realistic representation of groundwater dynamics is very important for predicting ET and GPP at a tropical open-forest savanna site as investigated here. The modest changes in model outputs highlighted the robustness of the optimization approach that is central to the VOM architecture.
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It is possible to construct a representative structural forma for a plant community from a set of functionally-oriented attributes, here proposed as: leaf size, leaf angle, leaf type and life form. A subtropical semi-arid woodland ecosystem in southern Queensland is used as a field example. -P.J.Jarvis
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The degree of isolation following the break-up of the Gondwanan super continent has been a major influence contributing to the uniqueness of the Australian flora. This uniqueness does not hold for community structural development; Australian savannas are broadly similar to those on other southern continents but they diverge at finer levels of structure. At a broad scale the composition and structure of the savannas are related to climatic and soil variables which have generated a wide range of savanna types. The soils are generally low in nutrients being derived from an extensively preserved and deeply weathered Tertiary mantle. The frequent and large-scale fires and, until the early 1800s, the low level of herbivore intervention and use by man have been formative. Savanna lands are widespread and cover approximately 53% or 4,1 × 106 km2 of the Australian continent. Over the past 180 years the less xeric savannas have been developed for grazing by domestic livestock through tree clearing and pasture seeding.
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The wet–dry tropics of northern Australia are characterised by extreme seasonal variation in rainfall and atmospheric vapour pressure deficit, although temperatures are relatively constant throughout the year.This seasonal variation is associated with marked changes in tree canopy cover, although the exact determinants of these changes are complex. This paper reports variation in microclimate (temperature, vapour pressure deficit (VPD)), rainfall, soil moisture, understorey light environment (total daily irradiance), and pre-dawn leaf water potential of eight dominant tree species in an area of savanna near Darwin, Northern Territory, Australia. Patterns of canopy cover are strongly influenced by both soil moisture and VPD. Increases in canopy cover coincide with decreases in VPD, and occur prior to increases in soil moisture that occur with the onset of wet season rains. Decreases in canopy cover coincide with decreases in soil moisture following the cessation of wet season rains and associated increases in VPD. Patterns of pre-dawn water potential vary significantly between species and between leaf phenological guilds. Pre-dawn water potential increases with decreasing VPD towards the end of the dry season prior to any increases in soil moisture. Decline in pre-dawn water potential coincides with both decreasing soil moisture and increasing VPD at the end of the dry season. This study emphasises the importance of the annual transition between the dry season and the wet season, a period of 1–2 months of relatively low VPD but little or no effective rainfall, preceded by a 4–6 month dry season of no rainfall and high VPD. This period is accompanied by markedly increased canopy cover, and significant increases in pre-dawn water potential, which are demonstrably independent of rainfall. This finding emphasises the importance of VPD as a determinant of physiological and phenological processes in Australian savannas.
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Until recently, advanced very high-resolution radiometer (AVHRR) observations were the only viable source of data for global land cover mapping. While many useful insights have been gained from analyses based on AVHRR data, the availability of moderate resolution imaging spectroradiometer (MODIS) data with greatly improved spectral, spatial, geometric, and radiometric attributes provides significant new opportunities and challenges for remote sensing-based land cover mapping research. In this paper, we describe the algorithms and databases being used to produce the MODIS global land cover product. This product provides maps of global land cover at 1-km spatial resolution using several classification systems, principally that of the IGBP. To generate these maps, a supervised classification methodology is used that exploits a global database of training sites interpreted from high-resolution imagery in association with ancillary data. In addition to the IGBP class at each pixel, the MODIS land cover product provides several other parameters including estimates for the classification confidence associated with the IGBP label, a prediction for the most likely alternative class, and class labels for several other classification schemes that are used by the global modeling community. Initial results based on 5 months of MODIS data are encouraging. At global scales, the distribution of vegetation and land cover types is qualitatively realistic. At regional scales, comparisons among heritage AVHRR products, Landsat TM data, and results from MODIS show that the algorithm is performing well. As a longer time series of data is added to the processing stream and the representation of global land cover in the site database is refined, the quality of the MODIS land cover product will improve accordingly.
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The majority of data sets in the geosciences are obtained from observations and measurements of natural systems, rather than in the laboratory. These data sets are often full of gaps, due to to the conditions under which the measurements are made. Missing data give rise to various problems, for example in spectral estimation or in specifying boundary conditions for numerical models. Here we use Singular Spectrum Analysis (SSA) to fill the gaps in several types of data sets. For a univariate record, our procedure uses only temporal correlations in the data to fill in the missing points. For a multivariate record, multi-channel SSA (M-SSA) takes advantage of both spatial and temporal correlations. We iteratively produce estimates of missing data points, which are then used to compute a self-consistent lag-covariance matrix; cross-validation allows us to optimize the window width and number of dominant SSA or M-SSA modes to fill the gaps. The optimal parameters of our procedure depend on the distribution in time (and space) of the missing data, as well as on the variance distribution between oscillatory modes and noise. The algorithm is demonstrated on synthetic examples, as well as on data sets from oceanography, hydrology, atmospheric sciences, and space physics: global sea-surface temperature, flood-water records of the Nile River, the Southern Oscillation Index (SOI), and satellite observations of relativistic electrons.
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Drought-induced tree mortality is occurring across all forested continents and is expected to increase worldwide during the coming century. Regional-scale forest die-off influences terrestrial albedo, carbon and water budgets, and landsurface energy partitioning. Although increased temperatures during drought are widely identified as a critical contributor to exacerbated tree mortality associated with "global-change-type drought", corresponding changes in vapor pressure deficit (D) have rarely been considered explicitly and have not been disaggregated from that of temperature per se. Here, we apply a detailed mechanistic soil-plant-atmosphere model to examine the impacts of drought, increased air temperature (+2°C or +5°C), and increased vapor pressure deficit (D; +1 kPa or +2.5 kPa), singly and in combination, on net primary productivity (NPP) and transpiration and forest responses, especially soil moisture content, leaf water potential, and stomatal conductance. We show that increased D exerts a larger detrimental effect on transpiration and NPP, than increased temperaturealone, with or without the imposition of a 3-month drought. Combined with drought, the effect of increased D on NPP was substantially larger than that of drought plus increased temperature. Thus, the number of days when NPP was zero across the 2-year simulation was 13 or 14 days in the control and increased temperature scenarios, but increased to approximately 200 days when D was increased. Drought alone increased the number of days of zero NPP to 88, but drought plus increased temperature did not increase the number of days. In contrast, drought and increased D resulted in the number of days when NPP = 0 increasing to 235 (+1 kPa) or 304 days (+2.5 kPa). We conclude that correct identification of the causes of global change-type mortality events requires explicit consideration of the influence of D as well as its interaction with drought and temperature.
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Metabolism and phenology of Amazon rainforests significantly influence global dynamics of climate, carbon and water, but remain poorly understood. We analyzed Amazon vegetation phenology at multiple scales with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements from 2000 to 2005. MODIS Enhanced Vegetation Index (EVI, an index of canopy photosynthetic capacity) increased by 25% with sunlight during the dry season across Amazon forests, opposite to ecosystem model predictions that water limitation should cause dry season declines in forest canopy photosynthesis. In contrast to intact forests, areas converted to pasture showed dry-season declines in EVI-derived photosynthetic capacity, presumably because removal of deep-rooted forest trees reduced access to deep soil water. Local canopy photosynthesis measured from eddy flux towers in both a rainforest and forest conversion site confirm our interpretation of satellite data, and suggest that basin-wide carbon fluxes can be constrained by integrating remote sensing and local flux measurements.
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The "cerrado" biome in central Brazil is rapidly being converted into pasture and agricultural crops with important consequences for local and regional climate change and regional carbon fluxes between the atmosphere and land surface. Satellite remote sensing provides an opportunity to monitor the highly diverse and complex cerrado biome, encompassing grassland, shrubland, woodland and gallery forests, and converted areas. In this study, the potential of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data is analyzed to discriminate among these diverse cerrado physiognomies and converted pastures based on their seasonal dynamics and phenology. Four years (2000-03) of MODIS 16-day composited, 250-m resolution vegetation index (VI) data were extracted over a series of biophysically sampled field study sites representing the major cerrado types. The temporal VI profiles over the cerrado formations exhibited high seasonal contrasts with a pronounced dry season from June to August and a wet growing season from November to March. The converted pasture areas showed the highest seasonal contrasts while the gallery forest formation had the lowest contrast. Seasonal VI variations were negatively correlated with woody canopy crown cover and provided a method to discriminate among converted cerrado areas, gallery forests, and the woody and herbaceous cerrado formations. The grassland and shrub cerrado formations, however, were difficult to separate based on their seasonal VI profiles. Maximum discrimination among the cerrado types occurred during the dry season where a positive linear relationship was found between VI and green cover. The annual integrated VI values showed the gallery forests and cerrado woodland as having the highest, and hence most annual productivity, while the more herbaceous shrub and grassland cerrado types were least productive. The cumulative VI profiles of converted cerrado, pasture areas varied distinctly in shape due to their strong dry season inactivity. Furthermore, the annual integrated VI values of the converted pastures differed significantly between the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) MODIS VI products, resulting in large discrepancies in productivity estimates relative to the native cerrado sites. This study shows that the MODIS seasonal-temporal VI profiles are highly useful in monitoring the cerrado biome and conversion-related activities.
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Savanna communities dominate the wet–dry tropical regions of the world and are an important community type in monsoonal northern Australia. As such they have a significant impact on the water and carbon balance of this region. Above the 1200-mm isohyet, savanna’s are dominated by Eucalyptus miniata–E. tetrodonta open forests. We have described in detail the composition and structure as well as seasonal patterns of leaf area index and above-ground biomass in the E. miniata–E. tetrodonta open forests of the Gunn Point region near Darwin in the Northern Territory of Australia. In all, 29 tree species from four phenological guilds were recorded in these forests. Stand structure suggests that the forests were still recovering from the impacts of cyclone Tracy and subsequent frequent fires. Eucalyptus miniata and E. tetrodonta were significant contributors to overstorey leaf area index and standing biomass (>70%), and both leaf area index and biomass were strongly correlated to basal area. Leaf area index was at a maximum (about 1.0) at the end of the wet season and declined over the dry season by about 30–40%. There were proportionally greater changes in the understorey reflecting the greater contribution of deciduous and semi-deciduous species in this strata. Standing biomass was about 55 t ha –1 . Detailed descriptions of leaf area index and biomass are important inputs into the development of a water and carbon balance for the savanna’s of northern Australia.
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Introduction If you leave the Wet Tropics around Cairns, and head west by car for an hour or so, the road goes up and over the mountains that lie behind the coast. On the other side, the rainfall drops off quickly, and you enter the great ‘sea’ of savanna that stretches across Northern Australia. Still heading west, several long days of driving later, you will reach Broome on the edge of the Indian Ocean. For all this time, for all the 3000 or more kilometres of travel, you will have been among vast areas of eucalypt savannas and native grasslands, broken only by an occasional cleared paddock, a scattering of small towns, and the rivers and wetlands that give life to the country. This landscape of savanna and rainforest, rivers and wetlands, is of great significance. On a global scale, such large natural areas are now very rare. Northern Australia stands out as one of the few very large natural areas remaining on Earth: alongside such global treasures as the Amazon rainforests, the boreal conifer forests of Alaska, and the polar wilderness of Antarctica. Unlike much of southern and eastern Australia, nature remains in abundance in the North. Great flocks of birds still move over the land searching for nectar, seeds and fruit. Rivers still flow naturally. Floods come and go. In fertile billabongs, thousands of Magpie Geese, brolgas, egrets and other water birds still congregate. The intact nature of the North provides a basis for much of the economic activity and the general quality of life for residents of the area. Most of the major industries – tourism, pastoralism, Indigenous economies – rely on productive, functioning and healthy natural ecosystems. Across the North, recreational activities such as fishing, four-wheel driving and visiting beautiful country depend on the opportunities provided by a largely intact and natural landscape. Being in and among nature remains a normal part of life for people in the North, in contrast to the situation for those living in the now highly transformed, cleared and urbanised areas of southern Australia. For the high proportion of Northern Australian residents who are Indigenous, country is part of the essence of life. Knowledge of and links to the land remain strong, and there remains an enduring responsibility to look after the land, and its plants and animals.
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This paper discusses the relationship of vegetation phenology and three climate variables (rainfall, minimum and maximum temperature) at 17 savanna sites in Botswana and Zambia. Interactions among climate variables were responsible for the largest variation in vegetation canopy phenology measured by the Normalized Difference Vegetation Index (NDVI) and tree shoot extension from ground observations. The most important determinant of savanna phenology in southern Africa was the interaction between minimum and maximum temperature. This observation indicates the need to incorporate minimum and maximum temperature in modelling the impact of climate change on savanna vegetation. Cluster analysis, using five NDVI metrics, separated the study sites into four savanna types with distinct tree:grass ratios. The semi-arid sites were characterized by a co-dominance of the tree and grass components while at more mesic sites, the significance of the grass component was lowest on sites with kalahari sand and mopane woodland. The use of NDVI metrics applied in the study provides an additional technique, that explicitly takes into account the tree:grass ratio during the growing season, for the classification of savanna vegetation types at local scale.
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The fraction of photosynthetically active radiation absorbed by plant canopies (fAPAR) is a critical biophysical variable for extrapolating ecophysiological measurements from the leaf to landscape scale. Quantification of fAPAR determinants at the landscape level is needed to improve the interpretation of remote sensing data, to facilitate its use in constraining ecosystem process models, and to improve synoptic-scale links between carbon and nutrient cycles. Most canopy radiation budget studies have focused on light attenuation in plant canopies, with little regard for the importance of the scaledependent biophysical and structural factors (e.g., leaf and stem optical properties, leaf and stem area, and extent of vegetation structural types) that ultimately determine fAPAR at canopy and landscape scales. Most studies have also assumed that nonphotosynthetic vegetation (litter and stems) contributes little to fAPAR. Using a combined field measurement and radiative transfer modeling approach, we quantified (a) the relative role of the leaf-, canopy-, and landscape-level factors that determine fAPAR in terrestrial ecosystems and (b) the magnitude of PAR absorption by grass litter and woody plant stems. Variability in full spectral-range (400-2500 nm) reflectance/transmittance and PAR (400-700 nm) absorption at the level of individual leaf, stem, and litter samples was quantified for a wide array of broadleaf arborescent and grass species along a 900-km north-south Texas savanna transect. Among woody growth forms, leaf reflectance and transmittance spectra were statistically comparable between populations, species within a genus, and functional types (deciduous vs. evergreen, legume vs. nonlegume). Within the grass life-form, spectral properties were statistically comparable between species and C,/C, physiologies. We found that tissue-level PAR absorption among species, genera, functional groups, and growth forms and between climatologically diverse regions was statistically similar, and for fresh leaves, it represented the most spectrally similar region of the shortwave spectrum. Subsequent modeling analyses indicated that the measured range of leaf, woody stem, and litter optical properties explained only a small proportion of the variance in tree and grass canopy fAPAR. However, the presence of nonphotosynthetic vegetation (e.g., stem and litter) had a significant effect on canopy fAPAR. In trees with a leaf area index (LAI) <3.0, stem surfaces increased canopy fAPAR by 10-40%. Standing grass litter canopies absorbed almost as much PAR as green grass canopies. Modeling the radiation regime in plant canopies should therefore account for the absorption of PAR by nonphotosynthetic plant components. Failure to do so may lead to overestimates of primary production, especially in woodlands, savannas, and shrublands dominated by species with optically thin canopies and in grasslands that accumulate senescent material. Further sensitivity analyses revealed that the extent and LA1 of vegetation structural types (trees and grasses) were the dominant controls on savanna landscape-level fAPAR, accounting for 60-80% of the total variation. Variation in leaf-level and all other canopylevel factors contributed individually to explain only a small proportion (<11%) of the variance in landscape fAPAR; however, when considered as a group, they accounted for 20-40% of the variation in landscape fAPAR. These results emphasize the need for more mechanistic analyses of canopy-level radiative transfer, and subsequent carbon flux and trace gas processes, in plant canopies and across landscapes comprising heterogeneous mixtures of plant growth forms and life-forms.
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Climate change is predicted to increase both drought frequency and duration, and when coupled with substantial warming, will establish a new hydroclimatological model for many regions. Large-scale, warm droughts have recently occurred in North America, Africa, Europe, Amazonia and Australia, resulting in major effects on terrestrial ecosystems, carbon balance and food security. Here we compare the functional response of above-ground net primary production to contrasting hydroclimatic periods in the late twentieth century (1975-1998), and drier, warmer conditions in the early twenty-first century (2000-2009) in the Northern and Southern Hemispheres. We find a common ecosystem water-use efficiency (WUE(e): above-ground net primary production/evapotranspiration) across biomes ranging from grassland to forest that indicates an intrinsic system sensitivity to water availability across rainfall regimes, regardless of hydroclimatic conditions. We found higher WUE(e) in drier years that increased significantly with drought to a maximum WUE(e) across all biomes; and a minimum native state in wetter years that was common across hydroclimatic periods. This indicates biome-scale resilience to the interannual variability associated with the early twenty-first century drought-that is, the capacity to tolerate low, annual precipitation and to respond to subsequent periods of favourable water balance. These findings provide a conceptual model of ecosystem properties at the decadal scale applicable to the widespread altered hydroclimatic conditions that are predicted for later this century. Understanding the hydroclimatic threshold that will break down ecosystem resilience and alter maximum WUE(e) may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands.
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