No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A systematic review of vegetation phenology in Africa
... The timing of a specific biological phase such as flowering, leaf growth, leaf fall (growth and senescence) and their causes can be described as phenology [1][2][3][4]. Vegetation phenology has a close relationship with climatic variability [5] which has influence on the timing of plant growth and development especially in the dry sub-humid region of Africa [4] Abrupt or seasonal change in vegetation phenology may have effect on water, carbon and energy cycle which might in-turn influence global climate change and net primary production [4,5]. However, human activities such as indiscriminate felling of trees, extensive agricultural practices, overgrazing and climate variability are some of the factors which might cause change or shift in vegetation phenology [1,[6][7][8]. ...
... The timing of a specific biological phase such as flowering, leaf growth, leaf fall (growth and senescence) and their causes can be described as phenology [1][2][3][4]. Vegetation phenology has a close relationship with climatic variability [5] which has influence on the timing of plant growth and development especially in the dry sub-humid region of Africa [4] Abrupt or seasonal change in vegetation phenology may have effect on water, carbon and energy cycle which might in-turn influence global climate change and net primary production [4,5]. However, human activities such as indiscriminate felling of trees, extensive agricultural practices, overgrazing and climate variability are some of the factors which might cause change or shift in vegetation phenology [1,[6][7][8]. ...
... The timing of a specific biological phase such as flowering, leaf growth, leaf fall (growth and senescence) and their causes can be described as phenology [1][2][3][4]. Vegetation phenology has a close relationship with climatic variability [5] which has influence on the timing of plant growth and development especially in the dry sub-humid region of Africa [4] Abrupt or seasonal change in vegetation phenology may have effect on water, carbon and energy cycle which might in-turn influence global climate change and net primary production [4,5]. However, human activities such as indiscriminate felling of trees, extensive agricultural practices, overgrazing and climate variability are some of the factors which might cause change or shift in vegetation phenology [1,[6][7][8]. ...
Time series data are of great importance for monitoring
vegetation phenology in the dry sub-humid regions
where change in land cover has influence on biomass productivity.
However few studies have inquired into examining
the impact of rainfall and land cover change on
vegetation phenology. This study explores Seasonal Trend
Analysis (STA) approach in order to investigate overall
greenness, peak of annual greenness and timing of annual
greenness in the seasonal NDVI cycle. Phenological
pattern for the start of season (SOS) and end of season
(EOS) was also examined across different land cover types
in four selected locations. A significant increase in overall
greenness (amplitude 0) and a significant decrease in
other greenness trend maps (amplitude 1 and phase 1) was
observed over the study period. Moreover significant positive
trends in overall annual rainfall (amplitude 0) was
found which follows similar pattern with vegetation trend.
Variation in the timing of peak of greenness (phase 1) was
seen in the four selected locations, this indicate a change
in phenological trend. Additionally, strong relationship
was revealed by the result of the pixel-wise regression between
NDVI and rainfall. Change in vegetation phenology
in the study area is attributed to climatic variability than
anthropogenic activities.
... In Africa, there have also been several phenological studies, both ground-based and satellite-based (Adole, Dash, & Atkinson, 2016). However, despite being home to 17% of the world's forest cover (Food and Agriculture Organization of the United Nations, 2010), approximately 12% of the world's tropical mangroves (Donato et al., 2011;Giri et al., 2010), and with a diverse range of vegetation types (Fig. 1), compared to other continents, the number of phenological studies in Africa is very limited (Adole et al., 2016). ...
... In Africa, there have also been several phenological studies, both ground-based and satellite-based (Adole, Dash, & Atkinson, 2016). However, despite being home to 17% of the world's forest cover (Food and Agriculture Organization of the United Nations, 2010), approximately 12% of the world's tropical mangroves (Donato et al., 2011;Giri et al., 2010), and with a diverse range of vegetation types (Fig. 1), compared to other continents, the number of phenological studies in Africa is very limited (Adole et al., 2016). Similarly, unlike other regions, there are no phenological networks in Africa (Adole et al., 2016). ...
... However, despite being home to 17% of the world's forest cover (Food and Agriculture Organization of the United Nations, 2010), approximately 12% of the world's tropical mangroves (Donato et al., 2011;Giri et al., 2010), and with a diverse range of vegetation types (Fig. 1), compared to other continents, the number of phenological studies in Africa is very limited (Adole et al., 2016). Similarly, unlike other regions, there are no phenological networks in Africa (Adole et al., 2016). A recent systematic review by Adole et al. (2016) revealed that of 9566 articles on vegetation phenology globally, only 130 focused on Africa. ...
Vegetation phenological studies at different spatial and temporal scales offer better understanding of the relationship between the global climate and the global distribution of biogeographical zones. These studies in the last few decades have focussed on characterising and understanding vegetation phenology and its drivers especially using satellite sensor data. Nevertheless, despite being home to 17% of the global forest cover, approximately 12% of the world's tropical mangroves, and a diverse range of vegetation types, Africa is one of the most poorly studied regions in the world. There has been no study characterising land surface phenology (LSP) of the major land cover types in the different geographical sub-regions in Africa, and only coarse spatial resolution datasets have been used for continental studies. Therefore, we aim to provide seasonal phenological pattern of Africa's vegetation and characterise the LSP of major land cover types in different geographical sub-regions in Africa at a medium spatial resolution of 500 m using MODIS EVI time-series data over a long temporal range of 15 years (2001–2015). The Discrete Fourier Transformation (DFT) technique was employed to smooth the time-series data and an inflection point-based method was used to extract phenological parameters such as start of season (SOS) and end of season (EOS). Homogeneous pixels from 12 years (2001–2012) MODIS land cover data (MODIS MCD12Q1) was used to describe, for the first time, the LSP of the major vegetation types in Africa. The results from this research characterise spatially and temporally the highly irregular and multi-annual variability of the vegetation phenology of Africa, and the maps and charts provide an improved representation of the LSP of Africa, which can serve as a pivot to filling other research gaps in the African continent.
... Vegetation phenology refers to the seasonal timing of recurring biological events, the effects of climate and environmental changes on the different phases of these events and the interrelationships among these phases either from the same or different species (Lieth, 1974;Verhegghen et al., 2014;Adole et al., 2016). Vegetation phenology, especially the beginning and end of the growing season, has been reported as one of the most effective indicators of vegetation dynamics and the long-term biological effects of climate change (Peng et al., 2017;Duarte et al., 2018). ...
... Vegetation phenology, especially the beginning and end of the growing season, has been reported as one of the most effective indicators of vegetation dynamics and the long-term biological effects of climate change (Peng et al., 2017;Duarte et al., 2018). Accurate characteristics of growing season are also important for carbon, energy and water fluxes between land and atmosphere in local, regional and global agriculture and ecosystems (Richardson et al., 2013;Adole et al., 2016;Chang et al., 2019). An increasing number of studies have suggested that global warming during recent decades has induced earlier starting, later ending and longer length of the growing season (Barichivich et al., 2013;Richardson et al., 2013;Verhegghen et al., 2014;Piao et al., 2015;Wu et al., 2018;Ren et al., 2019). ...
... An increasing number of studies have suggested that global warming during recent decades has induced earlier starting, later ending and longer length of the growing season (Barichivich et al., 2013;Richardson et al., 2013;Verhegghen et al., 2014;Piao et al., 2015;Wu et al., 2018;Ren et al., 2019). Consequently, the spatial and temporal pattern of vegetation growing season and its related environmental factors have attracted increasing attention during the past several decades (Richardson et al., 2013;Piao et al., 2015;Fu et al., 2015;Crabbe et al., 2016;Li et al., 2018), which can provide detailed characteristics of spatiotemporal changes of vegetation in terrestrial biogeochemical cycles and climate change mitigation strategies (Richardson et al., 2013;Adole et al., 2016). ...
The differences of three key growing season metrics, namely, the start of growing season (SOS), the end of growing season (EOS) and the length of growing season (LOS), derived from ground observations, satellite-derived normalized difference vegetation index (NDVI) data and air temperature were compared, and the spatial distribution and temporal trend of NDVI-based growing season metrics were analyzed in the arid and semi-arid areas of northern China. The results show that the growing season metrics obtained by three methods were quite different. The temperature-based SOS dates were earlier than those observed at the four phenological sites, while the NDVI-based SOS dates were the latest. The EOS (LOS) derived from NDVI and temperature were later (longer) than the observed values. At 240 meteorological stations, temperature-based SOS and EOS dates were generally earlier than the NDVI-based results, leading to longer temperature-based LOS than that derived from NDVI. From 1982 to 2015, the NDVI-based SOS was advanced by 2.3 days per decade, and the NDVI-based EOS was delayed by 9.5 days per decade, causing a prolonged LOS of 11.8 days per decade. Earlier SOS, later EOS and prolonged LOS were significantly appeared at 44.3%, 40.6% and 52.6% of the vegetated area respectively. The significant advanced SOS was concentrated in the central Inner Mongolia, northern Hebei, northern Shanxi and northwestern Xinjiang, while the significant delayed EOS was mainly distributed in the majority of western and northern Xinjiang and northern Inner Mongolia, resulting in the significant prolonged LOS in most of the study area. Each method has its distinct advantages and disadvantages. It is suggested to use high spatial and temporal resolution satellite images and appropriate methods to establish phenological models for different land cover types and to take into account the powerful vegetation indices and key climatic factors such as daytime temperature and precipitation and their interactions, which are of great significance for large-scale and accurate phenological monitoring and assessment.
... However, advances in satellite and other remotesensing measures are enabling improved quantification of historical vegetation change and variability across Africa (Pettorelli et al 2012, Adole et al 2016, Hawinkel et al 2016. These products have been used to evaluate vegetation sensitivities at large spatial scales and identify complex vegetation-climate interactions. ...
... While many African ecosystems are waterlimited (Nemani et al 2003, Zhang et al 2006, Ugbaje and Bishop 2020, recent studies have explored vegetation dependencies on other environment and climate variables and interactions between them (Ryan et al 2017, Adole et al 2018. For example, precipitation responses of African vegetation, even in water-limited ecosystems, may be strongly mediated through interactions with thermal plant-growth thresholds and temperature effects (Adole et al 2016(Adole et al , 2019, which warrant further investigation under changing climate conditions. While these water-temperature interactions are generally important across Africa, in East Africa, precipitation variability remains a key driver of vegetation change (Nicholson et al 2017, Wei et al 2018. ...
... Nevertheless, there remain outstanding research gaps to characterize long-term vegetation trends across the SNP at seasonal and monthly scales, and to identify how changing, interactive climate variables contribute to these trends (Adole et al 2016). Identifying these vegetation trends requires rigorous statistical methods, for prior work using simplified techniques (Chen et al 2019) has likely overestimated the prevalence of global greening (Cortés et al 2021). ...
While long-term vegetation greening trends have appeared across large land areas over the late 20th century, uncertainty remains in identifying and attributing finer-scale vegetation changes and trends, particularly across protected areas. Serengeti National Park (SNP) is a critical East African protected area, where seasonal vegetation cycles support vast populations of grazing herbivores and a host of ecosystem dynamics. Previous work has shown how non-climate drivers (e.g. land use) shape the SNP ecosystem, but it is still unclear to what extent changing climate conditions influence SNP vegetation, particularly at finer spatial and temporal scales. We fill this research gap by evaluating long-term (1982-2016) changes in SNP leaf area index (LAI) in relation to both temperature and moisture availability using Ensemble Empirical Mode Decomposition and Principal Component Analysis with regression techniques. We find that SNP LAI trends are nonlinear, display high sub-seasonal variation, and are influenced by lagged changes in both moisture and temperature variables and their interactions. LAI during the long rains (e.g. March) exhibits a greening-to-browning trend reversal starting in the early 2000s, partly due to antecedent precipitation declines. In contrast, LAI during the short rains (e.g. November, December) displays browning-to-greening alongside increasing moisture availability. Rising temperature trends also have important, secondary interactions with moisture variables to shape these SNP vegetation trends. Our findings show complex vegetation-climate interactions occurring at important temporal and spatial scales of the SNP, and our rigorous statistical approaches detect these complex climate-vegetation trends and interactions, while guarding against spurious vegetation signals.
... Vegetation phenology studies specific plant life cycle events such as bud burst, canopy growth, flowering, and senescence, and their relation to environmental factors. Phenology is a robust ecological indicator of climate change [2,3] and environmental variation impacts, across individual species to entire landscapes [4]. Most phenological events are receptive to temperature, rainfall, and human activities that affect vegetation growth and functions. ...
... In this study, we aimed to investigate the influence of climate on rubber phenological changes over the past ten years using remote sensing datasets. The specific objectives of the paper are three-fold: (1) to demonstrate the applicability of remote sensing data to extract the rubber phenology of SOS and EOS; (2) to examine the trend and changes of two major climatic data; and (3) to assess SOS/EOS response to rainfall and temperature. This information is essential for differentiating natural leaf drop during wintering with abnormal leaf drop. ...
Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (Hevea brasiliensis) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 °C advanced SOS by ~25 days and EOS by ~14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.
... Moreover, earlier studies are mostly performed at a relatively coarse resolution, e.g. pixel-size greater than 1 km, and fail to capture complexities that occur at finer scales 34 . ...
... inDVi -climate residual trends. In order to estimate climate change impacts on crop production, most crop models account for rainfall, temperature, solar radiation and CO 2 , while climate change is usually expressed in terms of change in rainfall and temperature as a result of changes in CO 2 concentrations 1,34,79,80 . As such, we focus on establishing the pixel-wise relationship between crop iNDVI, land surface temperature, solar radiation and total annual precipitation using partial correlations 81 . ...
The climate of West Africa is expected to become more arid due to increased temperature and uncertain rainfall regimes, while its population is expected to grow faster than the rest of the world. As such, increased demand for food will likely coincide with declines in agricultural production in a region where severe undernutrition already occurs. Here, we attempt to discriminate between the impacts of climate and other factors (e.g. land management/degradation) on crop production across West Africa using satellite remote sensing. We identify trends in the land surface phenology and climate of West African croplands between 2000 and 2018. Using the combination of a an attribution framework and residual trend anlaysis, we discriminate between climate and other impacts on crop productivity. The combined effect of rainfall, land surface temperature and solar radiation explains approximately 40% of the variation in cropland productivity over West Africa at the 95% significance level. The largest proportions of croplands with greening trends were observed in Mali, Niger and Burkina Faso, and the largest proportions with browning trends were in Nigeria, The Gambia and Benin. Climate was responsible for 52% of the greening trends and 25% of the browning trends. Within the other driving factors, changes in phenology explained 18% of the greening and 37% of the browning trends across the region, the use of inputs and irrigation explained 30% of the greening trends and land degradation 38% of the browning trends. These findings have implications for adaptation policies as we map out areas in need of improved land management practices and those where it has proven to be successful.
... While small satellite constellation (e.g. Planet-Scope) providing daily 3 m spatial resolution have been providing unparalleled opportunities for local-to-global scale monitoring (Houborg and Mccabe, 2018;Vrieling et al., 2018;Adole et al., 2016). ...
... As phenology information aggregated from multispecies introduce uncertainty for LSP. The majority of LSP studies focus on phenological patterns and their relationship with climatic variability (Adole et al., 2016), while most of species-specific vegetation phenology studies focus on crop growth stages of, which is commonly used in monitoring of crop conditions, yield estimation and precision farming management. ...
... Moreover, it is generally used to determine the land cover over extensive areas and retrieve time series of optical remote sensing data. Different available techniques and indices are reviewed by [21,23]. ...
... In Africa, over 70% of all phenological studies are satellite-based remote sensing phenological estimates [23]. In fact, these approaches are effective in retrieving detailed spatial patterns of vegetation phenology for semi-arid rangelands with short vegetation cycles [24][25][26], and in detecting invasive plants [27][28][29]. ...
According to the Intergovernmental Panel on Climate Change, the Horn of Africa is getting drier. This research aims at assessing browning and/or greening dynamics and the suitability of Sentinel-2 satellite images to map changes in land cover in a semiarid area. Vegetation dynamics are assessed through a remote sensing approach based on densely vegetated areas in a pilot area of North Horr Sub-County, in northern Kenya, between 2016–2020. Four spectral vegetation indices are calculated from Sentinel-2 images to create annual multi-temporal images. Two different supervised classification methods—Minimum Distance and Spectral Angle Mapper—are then applied in order to identify dense vegetated areas. A general greening is found to have occurred in this period with the exception of the year 2020, with an average annual percentage increase of 19%. Results also highlight a latency between climatic conditions and vegetation growth. This approach is for the first time applied in North Horr Sub-County and supports local decision-making processes for sustainable land management strategies.
... Remote-sensing-derived vegetation productivity metrics are key indicators of ecosystem function and resilience in semiarid savanna woodlands (Wang and Fensholt, 2017;. Globally, there are increasingly intense, frequent and extensive extreme climate events whose magnitude and direction of impact on ecosystem function and resilience remain uncertain and only partially documented (Adole et al., 2016;Park et al., 2016;Wang and Fensholt, 2017). Semiarid savanna woodlands of interior southern Africa are affected by differences in land cover and changing climate conditions, particularly droughts and floods (Olsen et al., 2015;More et al., 2016). ...
... Measurement and monitoring of vegetation need to be carried out extensively in the wild in GNP, which is an important wildlife habitat in south-eastern Zimbabwe, to improve wildlife conservation. In southern Africa, there is a lack of consistent up-to-date data on vegetation productivity (Jeganathan et al., 2010;Adole et al., 2016) on protected areas like the semi-arid savanna woodlands of GNP. However, recent research and innovation towards the use of earth observation techniques help in carrying out such studies easily and successfully. ...
Spatial and temporal patterns of vegetation productivity in semi-arid savanna national parks are influenced by differences in land cover and changes in time series trends. The main purpose of this paper is to analyse patterns of vegetation productivity metrics of base value, peak value, amplitude, and small and large integrals in Gonarezhou National Park (GNP) in south-eastern Zimbabwe from 1981 to 2015. Three sample sites comprising shrublands, deciduous broadleaved forested woodlands and mixed cover (shrublands, broadleaved deciduous forested woodlands and grasslands) were selected to show existing patterns of vegetation productivity for GNP. We used remotely sensed Normalised Difference Vegetation Index (NDVI) data which was further processed in the TIMESAT 3.3 program to derive productivity metrics. We then tested differences in land cover using analysis of variance and changes in time-series trends using Mann–Kendall and Theil–Sen’s tests. We note significant differences in land cover (P < 0.01) in selected samples. There are significant downward trends in the base value in shrublands (P < 0.01) and broadleaved deciduous forested woodlands (P = 0.04). Significant upward trends in the amplitude in the shrublands (P < 0.01) and mixed cover areas (P = 0.01) were noted. However, there are no changes in vegetation productivity, as indicated by the peak value and large and small integral indices. Shrublands are becoming vulnerable in terms of energy and vegetation productivity and need constant monitoring. Long-span coarse-resolution images are important stepping stones in providing a baseline for further studies from moderate and fine-resolution imagery. Research on vegetation productivity using fine-resolution imagery is more suitable for GNP.
... Mendoza, Peres, and Morellato (2017) in their review of 218 phenology datasets from the Neotropics report that only 10 sites have studies that lasted for more than 10 years. But, long-term studies are increasing, along with studies using automated monitoring systems, such as satellites or fixed-point cameras (Adole, Dash, & Atkinson, 2016;Alberton et al., 2014;Moore, Beringer, Evans, Hutley, & Tapper, 2017). It is still challenging to apply remote-sensing technologies to evergreen forest canopies with little variations in color spectra, compared to similar efforts to analyze temperate grasslands and deciduous forests, but newly developed methodologies have improved monitoring techniques (Alberton et al., 2017;Albrecht, Riesen, & Schmid, 2010;Nagai et al., 2016;Wu et al., 2018). ...
Tropical phenology is characterized by its high diversity. Lacking a cool season that restricts growth, phenological cycles vary from species that reproduce multiple times per year to those that reproduce only once in several years even within a community. As such, environmental cues of phenological events are more diverse among species and communities of tropical organisms compared with those in higher latitudes. Community‐wide phenological patterns differ among regions that differ in climate patterns and biogeographical backgrounds. These patterns are increasingly revealed as long‐term phenology data accumulate especially for tree species at long‐term monitoring sites. Advances in analytical methods applied to sufficiently long‐term data sets generate novel insights. Long‐term data are also critically important for understanding how climate changes affect phenological patterns and consequently species interactions and biological diversity. Particularly important is to understand how changes in drought regimes, both in terms of frequency and intensity, may affect plant phenology, and consequently have cascading impacts on tropical forest communities. To effectively link phenology studies and management of tropical forests and their ecosystem services in future studies, we should not only continue observation at existing sites, but also expand monitoring sites across regions, including ecosystems modified by human activities.
... While the importance of climate change and its consequences with respect to primary productivity and overall biogeochemical cycles are well known (Myneni et al. 1997;Imhoff et al. 2000;Bonan 2002), more studies are required to fully understand how climate change affects vegetation phenology. Especially for African countries that contribute about 17% of the global carbon budget, these regions have been identified as one of the most vulnerable regions to climate change impacts (Adole et al. 2016). Despite this, a limited number of studies has addressed the phenology and climate trends across Africa, a region which has a diverse range of vegetation types (Favier et al. 2012). ...
Operational monitoring of vegetation and its response to climate change involves the use of vegetation indices (VIs) in relation to relevant climatic data. This study analyses the temporal variations of vegetation indices in response to climatic data (temperature and precipitation) to better understand the phenological changes in the Wa-West and Tolon districts of Ghana during 1999-2011. This study also examines the inter-annual variation of vegetation indices and lag effects of climate variables (temperature and precipitation) using simple regression and correlation approaches. Results indicate that the mean Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) were significantly correlated with the mean temperature, whereby the value of NDVI increases with a decrease in temperature and value of NDSI increases with an increase in temperature. On examining seasonal variations, our findings indicated that the months of August and September have the highest mean NDVI values. This study confirms that consistently rising temperature and altered precipitation patterns have exerted a strong influence on temporal distributions and productivities of the terrestrial ecosystems of the Tolon and Wa-West districts of Ghana. Furthermore, this research demonstrates how vegetation indices can be used as an indicator to monitor phenological changes in the terrestrial ecosystem.
... Some studies started to explore topic frequencies and concept trends in phenological studies, but only for a specific country or a specific subject area. For instance, Adole et al. [26] examined all peer-reviewed literature on Africa's vegetation phenology to provide a synthesis of such studies and classified them based on the methods and techniques used in order to identify major research gaps; Nagai et al. [27] reviewed remote sensing phenological studies in Japan and discussed current knowledge, problems, and future developments based on these studies; Uribe-Toril et al. [28] in the bibliometric overview of the Forests journal aimed at highlighting the state of the art of forestry, cited as a major research cluster the integration of MODIS and Landsat imagery for mapping forest biomass and phenology. Other bibliometric studies analyzed major topics and concepts in remote sensing research fields either in general [29,30] or based on specific issues, like crop growth monitoring [31], water research [32], archaeology [33], etc. ...
As an interdisciplinary field of research, phenology is developing rapidly, and the contents of phenological research have become increasingly abundant. In addition, the potentiality of remote sensing technologies has largely contributed to the growth and complexity of this discipline, in terms of the scale of analysis, techniques of data processing, and a variety of topics. As a consequence, it is increasingly difficult for scientists to get a clear picture of remotely sensed phenology (rs+pheno) research. Bibliometric analysis is increasingly used for the study of a discipline and its conceptual dynamics. This review analyzed the last 40 years (1979–2018) of publications in the rs+pheno field retrieved from the Scopus database; such publications were investigated by means of a text mining approach, both in terms of bibliographic and text data. Results demonstrated that rs+pheno research is exponentially growing through time; however, it is primarily considered a subset of remote sensing science rather than a branch of phenology. In this framework, in the last decade, agriculture is becoming more and more a standalone science in rs+pheno research, independently from other related topics, e.g., classification. On the contrary, forestry struggles to gain its thematic role in rs+pheno studies and remains strictly connected with climate change issues. Classification and mapping represent the major rs+pheno topic, together with the extraction and the analysis of phenological metrics, like the start of the growing season. To the contrary, forest ecophysiology, in terms of ecosystem respiration and net ecosystem exchange, results as the most relevant new topic, together with the use of the red edge band and SAR (Synthetic Aperture Radar) data in rs+pheno agricultural studies. Some niche emerging rs+pheno topics may be recognized in the ocean and arctic investigations linked to phytoplankton blooming and ice cover dynamics. The findings of this study might be applicable for planning and managing remotely sensed phenology research; scientists involved in such discipline might use this study as a reference to consider their research domain in a broader dynamical network.
... While there have been many studies on the environmental drivers on vegetation phenology in the northern hemisphere, few detailed studies quantifying vegetation phenological responses to these environmental drivers have been undertaken in Africa 10,11 . Indeed, the triggers of onset of vegetation growth and the beginning of dormancy in Africa are poorly understood. ...
Vegetation phenology is driven by environmental factors such as photoperiod, precipitation, temperature, insolation, and nutrient availability. However, across Africa, there’s ambiguity about these drivers, which can lead to uncertainty in the predictions of global warming impacts on terrestrial ecosystems and their representation in dynamic vegetation models. Using satellite data, we undertook a systematic analysis of the relationship between phenological parameters and these drivers. The analysis across different regions consistently revealed photoperiod as the dominant factor controlling the onset and end of vegetation growing season. Moreover, the results suggest that not one, but a combination of drivers control phenological events. Consequently, to enhance our predictions of climate change impacts, the role of photoperiod should be incorporated into vegetation-climate and ecosystem modelling. Furthermore, it is necessary to define clearly the responses of vegetation to interactions between a consistent photoperiod cue and inter-annual variation in other drivers, especially under a changing climate.
... They enable us to monitor more sites, more remote places, larger areas, and to improve standardization between sites (Alberton et al. 2017, Richardson et al. 2013. New data are now coming from studies designed to understand biome-level responses to climate change, rather than the responses of individual plants (Adole et al. 2016, Alberton et al. 2014, Moore et al. 2017, Streher et al. 2017, Wu et al. 2016. In 2013, Pereira and colleagues included remotely sensed land-surface leaf phenology as one of the six Essential Biodiversity Variables recommended to monitor global change (Pereira et al. 2013). ...
... Over the last five decades, Earth-observation (EO) data have increasingly been used to map and monitor land cover (Adole et al., 2016;Woodcock et al., 2008;Wulder et al., 2012). In particular, the Landsat archive provides open-access, long-term data, with 30-metre spatial resolution and six spectral bands that are well suited for vegetation mapping. ...
The Ngorongoro Conservation Area (NCA) of Tanzania, is globally significant for biodiversity conservation due to the presence of iconic fauna, and, since 1959 has been managed as a unique multiple land‐use areas to mutually benefit wildlife and indigenous residents. Understating vegetation dynamics and ongoing land cover change processes in protected areas is important to protect biodiversity and ensure sustainable development. However, land cover changes in savannahs are especially difficult, as changes are often long‐term and subtle. Here, we demonstrate a Landsat‐based monitoring strategy incorporating (i) regression‐based unmixing for the accurate mapping of the fraction of the different land cover types, and (ii) a combination of linear regression and the BFAST trend break analysis technique for mapping and quantifying land cover changes. Using Google Earth Pro and the EnMap‐Box software, the fractional cover of the main land cover types of the NCA were accurately mapped for the first time, namely bareland, bushland, cropland, forest, grassland, montane heath, shrubland, water and woodland. Our results show that the main changes occurring in the NCA are the degradation of upland forests into bushland: we exemplify this with a case study in the Lerai Forest; and found declines in grassland and co‐incident increases in shrubland in the Serengeti Plains, suggesting woody encroachment. These changes threaten the wellbeing of livestock, the livelihoods of resident pastoralists and of the wildlife dependent on these grazing areas. Some of the land cover changes may be occurring naturally and caused by herbivory, rainfall patterns and vegetation succession, but many are linked to human activity, specifically, management policies, tourism development and the increase in human population and livestock. Our study provides for the first time much needed and highly accurate information on long‐term land cover changes in the NCA that can support the sustainable management and conservation of this unique UNESCO World Heritage Site. The Ngorongoro Conservation Area (NCA), a UNESCO World Heritage Site, is globally important for biodiversity conservation due to the presence of iconic megafauna. For decades now, the NCA is experiencing a number of notoriously difficult to address challenges; understanding its land cover dynamics is therefore increasingly important to improve habitat monitoring, preserve biodiversity and ensure sustainable development. We used multi‐temporal Landsat data spanning 35 years and a combination of regression‐based unmixing, linear regression and a trend break analysis to map and quantify the land cover dynamics in the area. We found a decrease in forest and grassland cover as well as a significant amount of woody encroachment which is often linked to land degradation in African savannahs. These changes are consistent with other savannah ecosystems and pose a threat to the wellbeing of livestock, the livelihoods of the pastoralist communities, and the wildlife of the NCA.
... In addition to the above-mentioned gaps in research methodology, substantial gaps exist with respect to the study of the vegetation phenology of Africa (Adole, Dash, and Atkinson 2016;IPCC 2014). While it has been shown that other factors besides climate are responsible for some variation in phenology and increases in greenness in different regions of the African continent (Herrmann, Anyamba, and Tucker 2005;Martínez et al. 2011;Polansky and Boesch 2013), studies investigating this phenomenon across the whole of Africa are limited. ...
Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more ‘longer season’ LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both.
... They enable us to monitor more sites, more remote places, larger areas, and to improve standardization between sites (Alberton et al. 2017, Richardson et al. 2013. New data are now coming from studies designed to understand biome-level responses to climate change, rather than the responses of individual plants (Adole et al. 2016, Alberton et al. 2014, Moore et al. 2017, Streher et al. 2017, Wu et al. 2016. In 2013, Pereira and colleagues included remotely sensed land-surface leaf phenology as one of the six Essential Biodiversity Variables recommended to monitor global change (Pereira et al. 2013). ...
We retrace the development of tropical phenology research, compare temperate phenology study to that in the tropics and highlight the advances currently being made in this flourishing discipline. The synthesis draws attention to how fundamentally different tropical phenology data can be to temperate data. Tropical plants lack a phase of winter dormancy and may grow and reproduce continually. Seasonal patterns in environmental parameters, such as rainfall, irradiance or temperature, do not necessarily coincide temporally, as they do in temperate climes. We review recent research on the drivers of phenophase cycles in individual trees, species and communities and highlight how significant innovations in biometric tools and approaches are being driven by the need to deal with circular data, the complexity of defining tropical seasons and the myriad growth and reproductive strategies used by tropical plants. We discuss how important the use of leaf phenology (or remotely-sensed proxies of leaf phenophases) has become in tracking biome responses to climate change at the continental level and how important the phenophase of forests can be in determining local weather conditions. We also highlight how powerful analyses of plant responses are hampered at many tropical sites by a lack of contextual data on local environmental conditions. We conclude by arguing that there is a clear global benefit in increasing long term tropical phenology data collection and improving empirical collection of local climate measures, contemporary to the phenology data. Directing more resources to research in this sector will be widely beneficial.
... Dozens of vegetation indexes available in the literature can be used in several vegetation studies, among which is the Normalized Difference Vegetation Index (NDVI) (Rouse, Hass, Schell, & Deering, 1973). NDVI is one of the most used vegetation indexes (Adole, Dash, & Atkinson, 2016;Matsushita, Yang, Chen, Onda, & Qiu, 2007) because it is highly related to several biophysical 1519 Semina: Ciências Agrárias, Londrina, v. 41, n. 5, p. 1517-1534, set./out. 2020 ...
Crop phenology knowledge is relevant to a series of actions related to its management and can be accessed through vegetation indexes. Thus, this study aimed to evaluate the use of the Normalized Difference Vegetation Index (NDVI), from images of OLI and MODIS sensors, to obtain phenological information from corn crops. To this end, we evaluated two corn cropping areas, irrigated by a central pivot, and located western Bahia state, Brazil. These areas were managed with high technology and had no record of biotic and abiotic stresses. NDVI showed a well-defined temporal pattern throughout the corn cycle, with a rapid increase at the beginning, stabilization at intermediate stages, and decreases at the end of the cycle. Excellent fits for polynomial equations were obtained to estimate NDVI as a function of days after sowing (DAS), with R² values of 0.96 and 0.95 for images of OLI and MODIS sensors, respectively. This demonstrates that both sensors could characterize corn canopy changes over time. NDVI ranges were correlated with the main phenological stages (PE), using the direct relationship between both variables (NDVI and PE) with days after sowing (DAS). For the beginning and end of each phenological stage, NDVI ranges were validated through model identity testing. NDVI proved to be a suitable parameter to assess corn phenology accurately and remotely. Finally, NDVI was also an important tool for detecting biotic and abiotic stresses throughout the crop cycle, and hence for decision making based on corn phenology.
... It is apparent that such 'sticking power' remains a challenge in the tropics. Even among science-led monitoring programs, there is little coordination of recording effort across multiple sites (Adole et al. 2016, Morellato et al. 2016. Fieldwork is often remote, and logistically challenging and financial resources for long-term monitoring are extremely limited meaning that few sites can be considered long-term (e.g., >10-yr continuous monitoring; Mendoza et al. 2017, Adamescu et al. 2018). ...
Phenology is a key component of ecosystem function and is increasingly included in assessments of ecological change. We consider how existing, and emerging, tropical phenology monitoring programs can be made most effective by investigating major sources of noise in data collection at a long-term study site. Researchers at Lopé NP, Gabon, have recorded monthly crown observations of leaf, flower and fruit phenology for 88 plant species since 1984. For a subset of these data, we first identified dominant regular phenological cycles, using Fourier analysis, and then tested the impact of observation uncertainty on cycle detectability, using expert knowledge and generalized linear mixed modeling (827 individual plants of 61 species). We show that experienced field observers can provide important information on major sources of noise in data collection and that observation length, phenophase visibility and duration are all positive predictors of cycle detectability. We find that when a phenological event lasts >4 wk, an additional 10 yr of data increases cycle detectability by 114 percent and that cycle detectability is 92 percent higher for the most visible events compared to the least. We also find that cycle detectability is four times as high for flowers compared to ripe fruits after 10 yr. To maximize returns in the short-term, resources for long-term monitoring of phenology should be targeted toward highly visible phenophases and events that last longer than the observation interval. In addition, programs that monitor flowering phenology are likely to accurately detect regular cycles more quickly than those monitoring fruits, thus providing a baseline for future assessments of change.
... All these analyses are needed to support management policies and for biodiversity conservation actions and are discussed in a historical perspective in the closing synthesis article by Abernethy et al. (2018). The short time-span of many ground-based data sets on tropical phenology remains an obstacle for understanding the current velocity of change in tropical ecosystems (Chambers et al. 2013, Adole et al. 2016, Mendoza et al. 2017. Indeed, we have knowledge of only 12, 14, and six study sites with more than 10-yr phenological monitoring in tropical America, Africa, and Asia, respectively (see Fig. 2 in Abernethy et al. 2018). ...
Here, we introduce the Special Section (SS) on long‐term monitoring and new analytical methods in tropical phenology. The SS puts together nine original papers plus a synthesis, bringing significant advances and new insights into our understanding of tropical phenology across Africa and tropical America. The papers address environmental cues, methodological shortcomings, and provide innovative analytical approaches, opening new pathways, perspective and applications of tropical phenology for forest management and environmental monitoring. The SS is a substantial step toward a more comprehensive overview of trends in tropical phenology, as seven of nine studies evaluate >10‐yr data sets applying new methods of analysis such as hierarchical Bayesian models, generalized additive models, and Fourier analysis. We argue that it is essential to maintain ongoing monitoring programs and build a tropical phenology network at least for long‐term (>10 yr) study sites, providing the means for national and international financial support. Cross‐continental comparisons are now a primary goal, as we work toward a global vision of trends and shifts in tropical phenology in the Anthropocene.
... Indeed, African vegetation contributes 38% of the global climate-carbon cycle feedback (Friedlingstein, Cadule, Piao, Ciais, & Sitch, 2010). In spite of this, African vegetation is relatively understudied (Adole, Dash, & Atkinson, 2016), and the few existing vegetation models are associated with significant uncertainties (Hemming, Betts, & Collins, 2013;Scheiter & Higgins, 2009). Another fundamental concern is the vulnerability of African vegetation to climate change, further worsened by interactions between changes in climatic drivers and anthropogenic land use, which puts at risk both the condition and the amount of overall vegetation cover (IPCC, 2014). ...
Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall‐driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of pre‐rain flush effects in some parts of Africa. The spatial extent of this pre‐rain green‐up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to‐date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa, and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of pre‐rain green‐up over Africa than previously reported, with pre‐rain green‐up being the norm rather than the exception. We also show the relative sparsity of post‐rain green‐up, confined largely to the Sudano‐Sahel region. While the pre‐rain green‐up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling.
... These changes were linked to climatic variations and especially to increasing temperature and higher precipitation [7, 8]. For example, responses of the phenology to climate change have been explored in United States [9, 10], Europe [11][12][13], Africa [14,15], and parts of China [16,17]. However, evidence of plant phenology and its relations with climatic variables mainly relies on in situ records that provide accurate phenological information at the species level but are limited in their spatial scope [16]. ...
Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.
... The indices used were: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Normalized Vegetation Index (gNDVI) and Normalized Difference Water Index (NDWI). The NDVI is perhaps the most widely employed index in vegetation phenology (Adole et al., 2016;Julien and Sobrino, 2009), which allows comparison with previous studies. Although NDVI is used widely it tends to saturate at high biomass or at high chlorophyll concentration, which is especially likely for mangroves, and it is affected by soil background, atmospheric effects and aerosols. ...
Mangrove forest phenology at the regional scale have been poorly investigated and its driving factors remain unclear. Multi-temporal remote sensing represents a key tool to investigate vegetation phenology, particularly in environments with limited accessibility and lack of in situ measurements. This paper presents the first characterisation of mangrove forest phenology from the Yucatan Peninsula, south east Mexico. We used 15-year time-series of four vegetation indices (EVI, NDVI, gNDVI and NDWI) derived from MODIS surface reflectance to estimate phenological parameters which were then compared with in situ climatic variables, salinity and litterfall. The Discrete Fourier Transform (DFT) was used to smooth the raw data and four phenological parameters were estimated: start of season (SOS), time of maximum greenness (Max Green), end of season (EOS) and length of season (LOS). Litterfall showed a distinct seasonal pattern with higher rates during the end of the dry season and during the wet season. Litterfall was positively correlated with temperature (r=0.88, p<0.01) and salinity (r=0.70, p<0.01). The results revealed that although mangroves are evergreen species the mangrove forest has clear greenness seasonality which is negatively correlated with litterfall and generally lagged behind maximum rainfall. The dates of phenological metrics varied depending on the choice of vegetation indices reflecting the sensitivity of each index to a particular aspect of vegetation growth. NDWI, an index associated to canopy water content and soil moisture had advanced dates of SOS, Max Green and EOS while gNDVI, an index primarily related to canopy chlorophyll content had delayed dates. SOS ranged between day of the year (DOY) 144 (late dry season) and DOY 220 (rainy season) while the EOS occurred between DOY 104 (mid-dry season) to DOY 160 (early rainy season). The length of the growing season ranged between 228 and 264 days. Sites receiving a greater amount of rainfall between January and March showed an advanced SOS and Max Green. This phenological characterisation is useful to understand the mangrove forest dynamics at the landscape scale and to monitor the status of mangrove. In addition the results will serve as a baseline against which to compare future changes in mangrove phenology due to natural or anthropogenic causes.
... Currently, no comprehensive database exists for African plant phenology and vegetation data (Adole et al. 2016). The African tropical rainforest belt, which has high occurrence probability for Ebola virus disease emergence (Pigott et al. 2014), is characterised by a regional, seasonal pattern of greening, fruiting and flowering linked to the cyclical West African monsoon (Cornforth 2013). ...
Ebola virus disease outbreaks in animals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are little explored. Filoviridae viruses have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report, we outline a proof of concept that temporal fluctuations of a set of ecological and environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We compiled a data set of climate and plant phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural network-based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for cost-effective transect surveys to supply phenology data for predictive modelling efforts.
Electronic supplementary material
The online version of this article (10.1007/s10393-017-1288-z) contains supplementary material, which is available to authorized users.
... Therefore, the higher values for the SAVI and EVI compared with the NDVI may be the result of the advantages cited above. However, despite these positive points for the SAVI and EVI, the NDVI is the most widely used VI for the retrieval of vegetation canopy biophysical properties (Adole et al., 2016;Jiang et al., 2006;Matsushita et al., 2007). ...
Crop biomass (Bio) is one of the most important parameters of a crop, and knowledge of it before harvest is essential to help farmers in their decision making. Both green and dry Bio can be estimated from vegetation spectral indices (VIs) because they have a close relationship with accumulated absorbed photosynthetically active radiation (APAR), which is proportional to total Bio. The aims of this study were to analyze the potential capacity of spectral vegetation indices in estimating corn green biomass based on their relationship with the photosynthetic vegetation sub-pixel fraction derived from spectral mixture analysis and to analyze the best interval of VI accumulation (days) for corn grain yield estimation. Field data of center pivots cultivated with corn during the irrigation seasons of 2015 and 2018 and Landsat 8 and Sentinel 2 images were used. The EVI produced the best results; Pearson's correlation coefficient, RMSE and Willmott’s index reached 0.99, 6.5%, and 0.948, respectively. Among the nine potential VIs analyzed, the EVI, SAVI and OSAVI were considered the first, second and third best performing for corn green Bio estimation, respectively, based on their comparison to the photosynthetic vegetation sub-pixel fraction (fPV), and the time intervals that extended until 120 days after sowing showed the best results for corn grain yield estimation.
... Similarly, one gets 259,673 documents containing the keyword "climate change" for the period of 1910 to 2019 and 16,361 "phenology" as well. Donnelly and Yu (2017) while The impact of warming on bud breaking is the most frequently studied event in climate change studies (Badeck et al. 2004;Bertin 2008;Penuelas et al. 2009;Vitasse et al. 2011Vitasse et al. , 2018Shen 2011;Shen et al. 2015;Fitchett et al. 2015;Parmesan and Hanley 2015;Adole et al. 2016;Gray and Brady 2016;Bussotti and Pollastrini 2017;Dahlin et al. 2017;Gerst et al. 2017;Scranton and Amarasekare 2017;Kumar and Chopra 2018;Maurya et al. 2018;Chmura et al. 2019;Gillison 2019;Wang et al. 2019a). The advancement of bud breaking event is not only a prominent indicator of the overall warming in high latitudes and high elevations but also of changes in the seasonality in tropical and equatorial regions. ...
The rate at which climate change is influencing the living organisms and ecosystem is considered as a major threat to sustaining the resources that are required for the human survival in the future. Environmental impacts on the life stages and functioning of organisms have been a major area of study over the last century. The information available from these studies has provided some insights to the impact of climate change on various phenophases of organisms. Individuals in a vegetation community being fixed to a location have to withstand the environmental variation as compared to animals which had the opportunity to move to favourable environments. Thus, plant phenology has greater potential value to understand the impact of climate change on organisms. Plant phenology studies traditionally provided information from ground-based studies. However, with the use of remote sensing technology and climate models, predicting the future plant community structure and functions also started. Validation of such model results using controlled condition experiments will lead to a greater understanding of the influence of climate change on the vegetation phenology. This chapter provides a summary of published information on the impact of climate change on the plant phenology.
... Estimates of vegetation phenology have been frequently obtained for the African continent, or parts thereof (for a review see Adole et al., 2016), using vegetation index series derived from various moderate and coarse resolution sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Satellite Pour l'Observation de la Terre (SPOT) Vegetation, and the Advanced Very High Resolution Radiometer (AVHRR) series. Estimates of vegetation phenological timings have various practical applications. ...
The short revisit times afforded by recently-deployed optical satellite sensors that acquire 3–30 m resolution imagery provide new opportunities to study seasonal vegetation dynamics. Previous studies demonstrated a successful retrieval of phenology with Sentinel-2 for relatively stable annual growing seasons. In semi-arid East Africa however, vegetation responds rapidly to a concentration of rainfall over short periods and consequently is subject to strong interannual variability. Obtaining a sufficient density of cloud-free acquisitions to accurately describe these short vegetation cycles is therefore challenging. The objective of this study is to evaluate if data from two satellite constellations, i.e., PlanetScope (3 m resolution) and Sentinel-2 (10 m resolution), each independently allow for accurate mapping of vegetation phenology under these challenging conditions. The study area is a rangeland with bimodal seasonality located at the 128-km² Kapiti Farm in Machakos County, Kenya. Using all the available PlanetScope and Sentinel-2 imagery between March 2017 and February 2019, we derived temporal NDVI profiles and fitted double hyperbolic tangent models (equivalent to commonly-used logistic functions), separately for the two rainy seasons locally referred to as the short and long rains. We estimated start- and end-of-season for the series using a 50% threshold between minimum and maximum levels of the modelled time series (SOS50/EOS50). We compared our estimates against those obtained from vegetation index series from two alternative sources, i.e. a) greenness chromatic coordinate (GCC) series obtained from digital repeat photography, and b) MODIS NDVI. We found that both PlanetScope and Sentinel-2 series resulted in acceptable retrievals of phenology (RMSD of ~8 days for SOS50 and ~15 days for EOS50 when compared against GCC series) suggesting that the sensors individually provide sufficient temporal detail. However, when applying the model to the entire study area, fewer spatial artefacts occurred in the PlanetScope results. This could be explained by the higher observation frequency of PlanetScope, which becomes critical during periods of persistent cloud cover. We further illustrated that PlanetScope series could differentiate the phenology of individual trees from grassland surroundings, whereby tree green-up was found to be both earlier and later than for grass, depending on location. The spatially-detailed phenology retrievals, as achieved in this study, are expected to help in better understanding climate and degradation impacts on rangeland vegetation, particularly for heterogeneous rangeland systems with large interannual variability in phenology and productivity.
... Países como China, Canadá, Estados Unidos, Reino Unido, Alemania o Austria poseen importantes redes de observación (Chen y Yang, 2020). Sin embargo, este tipo de registros fenológicos son escasos o inexistentes en múltiples regiones del planeta (Adole et al., 2016). ...
Phenological dynamics of vegetation is considered as an important biological indicator for understanding the functioning of terrestrial ecosystems. Land surface phenology (LSP), the study of vegetation phenology from time series of vegetation indices (IV), has provided a comprehensive overview of ecosystem dynamics. Iberian Peninsula is one of the regions with the greatest diversity of ecosystems in European continent. It is therefore an excellent study area for monitoring phenological dynamics of vegetation. The aim of this study is to analyse the spatial variability of the phenology of the vegetation of the Iberian Peninsula and Balearic Islands for the period 2001-2017. NDVI (Normalized Difference Vegetation Index) time series were generated from the surface reflectance product MOD09Q1 at a spatial resolution of 250 meters and with a composite period of 8 days. Atmospheric disturbances and noise were reduced using a Savitzky-Golay smoothing filter. Different phenological metrics or phenometrics were extracted using a threshold-based method. Results showed the existence of a different behaviour between spring and autumn phenophases in the Atlantic and Mediterranean biogeographic regions. The Mediterranean mountainous areas showed a similar phenological behaviour to the Atlantic vegetation. Biogeographic regions showed an internal variability, which may be derived from the different behaviour of land covers (e.g., natural vegetation vs. crops).
... Several VIs are calculated based upon different spectral bands and, therefore, evidence of different components of the environment [19]. The Enhanced Vegetation Index (EVI) has been widely used to characterise vegetation phenology [20,21] due to its sensitivity to high biomass and reduced atmospheric and soil effects. EVI is calculated from the near-infrared (NIR), Red, and Blue bands and can be derived for different satellite platforms, such as Landsat, Sentinel, and MODIS. ...
Citation: Medeiros, R.; Andrade, J.; Ramos, D.; Moura, M.; Pérez-Marin, A.M.; dos Santos, C.A.C.; Silva, B.; Cunha, J. Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers. Remote Sens. 2022, 14, 2637. https://doi.
... Our observation on the response of vegetative heterogeneity to seasonality can be associated with the phenological patterns of vegetation and the relationship between climate and vegetation (Adole et al., 2016, Wessels et al., 2011. The primary productivity of plants has been observed to peak during the wet season and to decrease during the dry season, owing to limited water and nutrient availability (Byrne et al., 2013, Prevéy andSeastedt, 2014). ...
Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, the in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI). The coefficient of variation was used to measure spectral heterogeneity, which is a function of environmental heterogeneity. Heterogenous environments are more diverse, compared to homogenous environments, and the vegetation heterogeneity can be inferred from the heterogeneity of a landscape. The coefficient of variation was used to calculate the α- and β measures of vegetation heterogeneity (the Shannon–Weiner Index and the Rao's Q, respectively), whilst the monotonic trends in the spatio–temporal variation (January–December) of vegetation heterogeneity were derived by using the Mann–Kendall non–parametric test. Lastly, to explain the spatio–temporal variations of vegetation heterogeneity, a set of environmental variables were used, along with a machine-learning algorithm (random forest). The vegetation heterogeneity was observed to be relatively high during the wet season and low during the dry season, and these changes were mainly driven by landcover- and climate–related variables. More specifically, significant changes in vegetation heterogeneity were observed around natural water pans, along roads and rivers, as well as in cropping areas. Furthermore, these changes were better predicted by the Rao's Q (MAE = 5.81, RMSE = 6.63 and %RMSE = 42.41), than by the Shannon–Weiner Index (MAE = 30.37, RMSE = 33.25 and %RMSE = 63.94). These observations on the drivers and changes in vegetation heterogeneity provide new insights into the possible effects of future landcover changes and climate variability on GDEs. This information is imperative, considering that these environments are biodiversity hotspots that are capable of supporting many livelihoods. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored to achieve Sustainable Development Goal (SDG) Number 15.
... Our observation on the response of vegetative diversity to seasonality can be associated with the phenological patterns of the vegetation and the relationship between climate and vegetation (Adole et al., 2016, Wessels et al., 2011. The primary productivity of plants has been observed to peak during the wet season and to decrease during the dry season, owing to limited water and nutrient availability (Byrne et al., 2013, Prevéy andSeastedt, 2014). ...
There have been increasing calls to monitor Groundwater-Dependent Ecosystems (GDEs) more effectively, since they are biodiversity hotspots that provide several ecosystem services. The accurate monitoring of GDEs is an indispensable under Sustainable Development Goal (SDG) 15, because it promotes the existence of phreatophytes. It is imperative to monitoring GDEs, since their ecological significance (e.g., as biodiversity hotspots) is not well understood in most environments they exist. For example, vegetation diversity in GDEs requires routine monitoring, to conserve their biodiversity status and to preserve the ecosystem services in these environments. Such monitoring requires robust measures and techniques, particularly in arid environments threatened by groundwater over-abstraction, landcover and climate change. Although in-situ methods are reliable, they are challenging to use in extensive transboundary groundwater resources such as the Khakea-Bray Transboundary Aquifer. To avoid these setbacks, remote sensing technologies have spatially explicit landscape-scale capabilities for characterising vegetation diversity in GDEs. Remotely-sensed data and the Spectral Variation Hypothesis (SVH) have the inherent capability to provide a unique opportunity to monitor the vegetation diversity of GDEs, and their response to seasonal or intra-annual environmental stressors. Therefore, this research seeks to review the trends and milestones in using remote sensing for characterising vegetation diversity in GDEs, and use satellite remote sensing data (i.e., Sentinel-2 MSI and Landsat 8 OLI) to characterise the vegetation diversity in the Khakea-Bray Transboundary Aquifer. In addition, this thesis aims to monitor the spatio-temporal variations of vegetation diversity in the Khakea-Bray Transboundary Aquifer. Overall, the remote sensing data demonstrated the potential of characterising vegetation diversity in the Khakea-Bray Transboundary Aquifer (R 2 = 0.61 and p = 0.0003). It was observed that the vegetation diversity in the Khakea-Bray Transboundary Aquifer was concentrated more around natural pans and along roads, fence lines and rivers, and that the changes in vegetation diversity within these areas was driven mainly by land conversion and climate variability. These findings are imperative for natural resource managers seeking to conserve the Khakea-Bray Transboundary Aquifer and to achieve the national or regional biodiversity targets. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored in semi-arid environments, to achieve the SDGs.
... Tian et al., 2018), and early diagnosis of climate-induced forest mortality . The majority of vegetation remote sensing studies focusing on Africa are based on image acquisitions from polar orbiting satellites like MODIS (Adole et al., 2016), while only a few studies are based on vegetation indices derived from the geostationary satellite Meteosat Second Generation (MSG; e.g., Yan et al., 2017). Geostationary satellite based vegetation indices are available in daily temporal resolution, which is their biggest advantage compared to polar orbiting satellites where such high resolution in time is not possible. ...
Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi‐arid landscapes. This along with data scarcity poses challenges for large‐scale modeling of vegetation‐water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co‐variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter‐intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide‐spread occurrence of the “inverse texture effect” on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water‐use strategy. Thus, our observation‐based products have large potential for better understanding complex vegetation‐water interactions from regional to continental scales.
... NDVI was used in this study as it is a reliable ecological indicator. Due to its spectral characteristics, as well as to its rather long history, NDVI has become the most commonly used tool for assessing forest and non-forest vegetation worldwide (Adole et al., 2016;Huang et al., 2021;Soubry et al., 2021). While this satellite index may feature some technical disadvantages (shortcomings), which include the saturation phenomenon or certain sensor issues and which were discussed in depth in a recent study (Huang et al., 2021), NDVI was used in this study due to the higher number of technical and practical advantages it delivers. ...
Forests have become increasingly stressed by climate change, including in Romania over recent decades, but their response to climate dynamics has not yet been analysed in this country. This study aims to investigate, for the first time, recent ecological changes in forests across Romania, in relation to climate dynamics that affected the country from 1987 to 2018. To this end, countrywide remote sensing (Landsat) data were processed for forest boundaries over the 32 years, in order to compute annual (summer season data) Normalized Difference Vegetation Index (NDVI) datasets, which were subsequently investigated as trends using the Mann-Kendall test and Sen's slope estimator. Simultaneously, various climatic data (temperature, precipitation and reference evapotranspiration) were processed through interpolation techniques and via the same two statistical tools, and were subsequently used for exploring the impact of climate change on Romanian forestlands after 1987. The results highlighted general greening (increasing NDVI) trends of forests nationally (65% of all NDVI changes, which total over 30.000 km² across Romania), which were dominated by widespread positive NDVI trends detected in the Carpathians region of Romania. This general ecological dynamic suggests a possible enhancement in vegetation productivity in the country’s high-altitude areas. Contrasting browning (decreasing NDVI) trends were found for 35% of Romanian forestlands marked by NDVI changes, especially apparent in the Extra-Carpathians (lowland) region, which indicates that in these cases forests were degraded or devitalized. However, the statistical significance of both greening and browning trends is limited across the country. The analysis of climatic trends and of correlations between annual NDVI and climate data indicated that recent warming throughout Carpathians may be an important driving force of forest greening in temperature-limited mountain regions. This finding is supported by an at least moderate intensity of air temperature – NDVI relationships (r correlation coefficient values of ∼50%, the highest of all eco-climatic relationships analysed), generally detected throughout mountain environments. At the same time, it seems that evapotranspiration increase accounted at least in part for forest browning in lowland areas, while the impact of precipitation in forest ecological dynamics remains unclear. All these findings can be useful for a better forest management under the future climate change conditions in Romania.
... Regarding publication year, 78% were published between 2011 and 2018, while the remaining 22% were published between 2003 and 2010 ( Fig. 4). This significant increase in publications in the research field of phenology can be attributed to the demand for climate change indicators (Adole et al. 2016) or to the necessity of comparisons with in situ observations, remote sensing data, and VIs that have been developed to estimate the phenological cycle of plant species. MODIS (Moderate-Resolution Imaging Spectroradiometer) was the most commonly used satellite for generating phenological parameters (78% of the publications), followed by Landsat (19%), and AVHRR (Advanced Very High-Resolution Radiometer, 13%). ...
Phenology has been useful to better understand the climate vegetation relationship, and it is considered an indicator of climate change impact. Phenological data have been generated through multiple remote sensing techniques and ground-based observations through professional or citizen science. The combination of both techniques is known as cross-scale phenological monitoring. However, no comparative analysis has been carried out to assess the advantages and disadvantages of each of these techniques to characterize the phenological cycle of forest ecosystem species. This work is a content-analysis-based review of scientific literature published between 2000 and 2018 related to cross-scale monitoring methods, to estimate the phenological variation in different forest ecosystems worldwide. For this study, 97 publications related to cross scale phenological monitoring were selected. We found that 71% of the articles aimed to corroborate the data generated through satellite imagery using surface data from either phenocams, flux towers or from citizen science networks. More publications were published by authors in the United States (30%), Canada (8%), and China (7%). The most commonly used vegetation index was the normalized difference vegetation index (65%). Some deficiencies in the evaluation of the phenological phases of autumn when compared with surface observations were found. Flux towers and phenocams were included as alternatives for ground-based monitoring. Cross-scale phenological monitoring has the potential to characterize the phenological response of vegetation accurately due to data combinations at two different observation scales. This work contributes to specifying the methodologies used in gathering phenological parameters of the world's forest ecosystems.
... Currently, no comprehensive database exists for African plant phenology and vegetation data (Adole et al. 2016). The African tropical rainforest belt, which has high occurrence probability for Ebola virus disease emergence (Pigott et al. 2014), is characterised by a regional, seasonal pattern of greening, fruiting and flowering linked to the cyclical West African monsoon (Cornforth 2013). ...
Ebola virus disease outbreaks in mammals (including humans and great apes) start with sporadic host switches from unknown reservoir species. The factors leading to such spillover events are not clearly understood. Filoviridae have a wide range of natural hosts and are unstable once outside hosts. Spillover events, which involve the physical transfer of viral particles across species, could therefore be directly promoted by conditions of host ecology and environment. In this report we outline a proof of concept that temporal fluctuations of a set of eco-environmental variables describing the dynamics of the host ecosystem are able to predict such events of Ebola virus spillover to humans and animals. We newly compiled a dataset of climate and phenology variables and Ebola virus disease spillovers in humans and animals. We identified critical biotic and abiotic conditions for spillovers via multiple regression and neural networks based time series regression. Phenology variables proved to be overall better predictors than climate variables. African phenology variables are not yet available as a comprehensive online resource. Given the likely importance of phenology for forecasting the likelihood of future Ebola spillover events, our results highlight the need for more phenology monitoring to supply data for predictive modelling efforts.
... Different literature reviews on vegetation phenology with a different focus have been published in recent years. Adole et al. (2016) synthesised the state of the vegetation phenology research in the African continent from species-specific phenological observations and satellite remote sensing. Donnelly and Yu (2017) and Piao et al. (2019) analysed the findings in the study of the relationship between vegetation phenology and climate change considering different observational approaches (e.g. ...
Vegetation phenology is considered an important biological indicator in understanding the behaviour of ecosystems and how it responds to environmental cues. Changes in vegetation dynamics have been strongly linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems, such as the land surface-atmosphere exchange of water and carbon, energy flows and interaction between different species. Land surface phenology (LSP) is the study of seasonal patterns in plant phenophases based on time series from vegetation indices (VI) or biophysical variables derived from satellite data, and has played an essential role in monitoring the response of terrestrial ecosystems to environmental changes from local to global scales. The goal of this systematic literature review is to provide a detailed synthesis of the main contributions of the global LSP research to the development of environmental knowledge and remote sensing science and technology, identifying possible gaps that could be addressed in the coming years.
This systematic review found that the number of LSP studies has grown exponentially since the 1980s, although the analysis of phenological metrics or phenometrics derived from satellite data (i.e. proxies for the biological phenophases of plants) has focused specifically on ecosystems located in the mid- and high-altitude in the Northern Hemisphere (e.g. boreal forest/taiga, evergreen, deciduous or mixed temperate forest). LSP studies use different satellite dataset and methods to estimate phenometrics. These studies identified an advance in spring and a delay in autumn phenophases as general trends. Although these trends were associated mainly to changes in temperature and precipitation, phenological cycle dynamics might be related to other drivers, such as photoperiod, soil moisture or organic carbon content, among others. Therefore, this interaction between different climatic and non-climatic drivers make phenology modelling a difficult task.
Hence, in the coming years, a greater integration of LSP data into ecological process modelling could provide a more complete overview on the terrestrial ecosystems functioning. Furthermore, different technical and methodological aspects (e.g. greater temporal coverage of recent high-spatial-resolution satellites, advances in remote-sensing technology or improved efficiency in the computational processing of geospatial data) may also contribute to improve our understanding of Earth’s ecosystem dynamics and their environmental drivers.
... Key drivers of phenology include temperature and photoperiod in the mid-and high-latitudes [2,[31][32][33], while precipitation is regarded as the dominant driver in the tropics [34,35]. Satellite-based observations have been widely used to characterize the evolution of vegetation in the Northern Hemisphere, but other regions have received far less attention: A systematic review of phenology in Africa over the last decade reveals a recent increase in the number of phenological studies based on remote sensing but suggests that further investigations are needed across this continent, especially to focus on the relationship between climate and vegetation phenological changes [36]. ...
Mathematical models, such as the logistic curve, have been extensively used to model the temporal evolution of biological processes, though other similarly shaped functions could be (and sometimes have been) used for this purpose. Most previous studies focused on agricultural regions in the Northern Hemisphere and were based on the Normalized Difference Vegetation Index (NDVI). This paper compares the capacity of four parametric double S-shaped models (Gaussian, Hyperbolic Tangent, Logistic, and Sine) to represent the seasonal phenology of an unmanaged, protected savanna biome in South Africa’s Lowveld, using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) generated by the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) processing system on the basis of data originally collected by National Aeronautics and Space Administration (NASA)’s Multi-angle Imaging SpectroRadiometer (MISR) instrument since 24 February 2000. FAPAR time series are automatically split into successive vegetative seasons, and the models are inverted against those irregularly spaced data to provide a description of the seasonal fluctuations despite the presence of noise and missing values. The performance of these models is assessed by quantifying their ability to account for the variability of remote sensing data and to evaluate the Gross Primary Productivity (GPP) of vegetation, as well as by evaluating their numerical efficiency. Simulated results retrieved from remote sensing are compared to GPP estimates derived from field measurements acquired at Skukuza’s flux tower in the Kruger National Park, which has also been operational since 2000. Preliminary results indicate that (1) all four models considered can be adjusted to fit an FAPAR time series when the temporal distribution of the data is sufficiently dense in both the growing and the senescence phases of the vegetative season, (2) the Gaussian and especially the Sine models are more sensitive than the Hyperbolic Tangent and Logistic to the temporal distribution of FAPAR values during the vegetative season, and, in particular, to the presence of long temporal gaps in the observational data, and (3) the performance of these models to simulate the phenology of plants is generally quite sensitive to the presence of unexpectedly low FAPAR values during the peak period of activity and to the presence of long gaps in the observational data. Consequently, efforts to screen out outliers and to minimize those gaps, especially during the rainy season (vegetation’s growth phase), would go a long way to improve the capacity of the models to adequately account for the evolution of the canopy cover and to better assess the relation between FAPAR and GPP.
... However, to portray the spatial patterns of vegetation change over larger areas, these techniques are time consuming, limited in extent and expensive, and therefore, inefficient [19,20]. In the last five decades, satellite Earth observation (EO) data are increasingly used to map and monitor vegetation cover and its characteristics [21][22][23]. The use of EO technologies with open-access data archives provide the opportunity to study inaccessible areas and to assess the vegetative cover and its evolution through time [19]. ...
Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.
... To our knowledge, this is the first study to map the canopy phenology of tropical dry forests and its uncertainty, as well as the spatial variation in deciduousness at coarse spatial scales, using satellite information at a fine spatial resolution (10 m). Most studies mapping deciduousness have used satellite imagery data with coarse spatial resolutions such as 250 m, 500 m, and 1 km [45]. A major finding of this research is that spectral bands, vegetation indices, spectral unmixing fractions, and texture metrics from high-resolution imagery are good predictors of tree species deciduousness of tropical dry forests along an environmental gradient that covers the most important forest ecosystems in the Yucatan Peninsula. ...
In tropical dry forests, deciduousness (i.e., leaf shedding during the dry season) is an important adaptation of plants to cope with water limitation, which helps trees adjust to seasonal drought. Deciduousness is also a critical factor determining the timing and duration of carbon fixation rates, and affecting energy, water, and carbon balance. Therefore, quantifying deciduousness is vital to understand important ecosystem processes in tropical dry forests. The aim of this study was to map tree species deciduousness in three types of tropical dry forests along a precipitation gradient in the Yucatan Peninsula using Sentinel-2 imagery. We propose an approach that combines reflectance of visible and near-infrared bands, normalized difference vegetation index (NDVI), spectral unmixing deciduous fraction, and several texture metrics to estimate the spatial distribution of tree species deciduousness. Deciduousness in the study area was highly variable and decreased along the precipitation gradient, while the spatial variation in deciduousness among sites followed an inverse pattern, ranging from 91.5 to 43.3% and from 3.4 to 9.4% respectively from the northwest to the southeast of the peninsula. Most of the variation in deciduousness was predicted jointly by spectral variables and texture metrics, but texture metrics had a higher exclusive contribution. Moreover, including texture metrics as independent variables increased the variance of deciduousness explained by the models from R 2 = 0.56 to R 2 = 0.60 and the root mean square error (RMSE) was reduced from 16.9% to 16.2%. We present the first spatially continuous deciduousness map of the three most important vegetation types in the Yucatan Peninsula using high-resolution imagery.
... Uncertainties are therefore particularly high in these regions [34]. Some progress has been made in the Amazon [35][36][37][38][39] and African forests [40][41][42]; however, the Southern China region has typically been neglected in remote sensing phenology studies [43][44][45][46]. Consequently, the phenological character of forests in this region remains uncertain, with additional complexity driven by fragmentation [47], high species diversity, and complex topography [48]. ...
Leaf area is a key parameter underpinning ecosystem carbon, water and energy exchanges via photosynthesis, transpiration and absorption of radiation, from local to global scales. Satellite-based Earth Observation (EO) can provide estimates of leaf area index (LAI) with global coverage and high temporal frequency. However, the error and bias contained within these EO products and their variation in time and across spatial resolutions remain poorly understood. Here, we used nearly 8000 in situ measurements of LAI from six forest environments in southern China to evaluate the magnitude, uncertainty, and dynamics of three widely used EO LAI products. The finer spatial resolution GEOV3 PROBA-V 300 m LAI product best estimates the observed LAI from a multi-site dataset (R2 = 0.45, bias = −0.54 m2 m−2, RMSE = 1.21 m2 m−2) and importantly captures canopy dynamics well, including the amplitude and phase. The GEOV2 PROBA-V 1 km LAI product performed the next best (R2 = 0.36, bias = −2.04 m2 m−2, RMSE = 2.32 m2 m−2) followed by MODIS 500 m LAI (R2 = 0.20, bias = −1.47 m2 m−2, RMSE = 2.29 m2 m−2). The MODIS 500 m product did not capture the temporal dynamics observed in situ across southern China. The uncertainties estimated by each of the EO products are substantially smaller (3–5 times) than the observed bias for EO products against in situ measurements. Thus, reported product uncertainties are substantially underestimated and do not fully account for their total uncertainty. Overall, our analysis indicates that both the retrieval algorithm and spatial resolution play an important role in accurately estimating LAI for the dense canopy forests in Southern China. When constraining models of the carbon cycle and other ecosystem processes are run, studies should assume that current EO product LAI uncertainty estimates underestimate their true uncertainty value.
... Bajocco et al. (2019) provided a bibliometric overview of what was explored up to 2018 in terms of remote sensing phenology at a global level. Some authors focused on reviewing the advances of ground and remote sensing phenology within specific regions, such as Australia , Japan (Nagai et al., 2015), Africa (Adole et al., 2016) and South and Central America (Morellato et al., 2013). ...
Vegetation phenology is the study of recurring plant life cycle stages, seasonality which is linked to many ecosystem processes and is an important proxy of climate and environmental change. Remote sensing has been playing an important and increasing role in the monitoring and assessment of vegetation phenology. The aim of this review is to critically examine key studies related to remote sensing of vegetation phenology, with a special focus on temperate and boreal forests. Specifically, we focus on how the latest ground, near-surface and aerial data have been used to assess the satellite-derived Land Surface Phenology (LSP) metrics and the agreements that has been achieved in the last 15 years. Results demonstrated that the timing of satellite-derived LSP events can be detected, in the best-case scenarios, with a certainty of around half-week for spring metrics (e.g. Day of Year -DOY- of start of growing season) and around one week for autumn metrics (e.g. DOY of end of growing season). With expected shifts in plant phenology averaging <1 day per decade, such LSP uncertainties (in terms of absolute phenological dates) could greatly over- or under-estimate these species-level shifts; but the spatial variation in phenology can be consistently monitored. An increasing number of studies have investigated autumn phenology in the last decade, but autumn phenological dates continue to be more challenging to retrieve and interpret than spring dates. Emerging opportunities to further advance remote sensing of forest phenology is presented that includes synergetic use of multiple orbital sensors and its LSP evaluation with data from new sensors at a ground, near-surface and airborne level; yet traditional ground-based observations will continue to be highly useful to accurately record the timing of species-specific phenological events. This review might provide a guide for planning and managing remote sensing of forest phenology.
Numerous studies have shown that plant phenology has advanced due to urbanization. However, large uncertainties remain because different indicators and methods have been used to characterize plant phenology. In this study, by means of photograph identification and logistic curve fitting, the phenophases of leaves and male cones of Chinese pine (Pinus tabuliformis Carr.) were divided into seven and six phases, respectively. The rural-urban gradient in phenology of leaves and male cones varied significantly in ranges of 0.36–1.92 and 0.41–0.66 days/km, respectively, depending on the phenophase defined. Interestingly, the rural-urban gradient of leaf phenology increased with leaf development. Thus, fine separation of phenophases is necessary to provide more precise criterion for assessing urbanization-induced plant phenology across different studies or regions. For Chinese pine, the timings of maximum leaf elongation rate achievement and male cones maturation were key phenophases for investigating the effect of urbanization.
Coastal habitats, such as mangrove, seagrass, and salt marsh, are termed “blue carbon” and have recently gained much attention due to their high carbon sequestration capacities. Although the global area of blue carbon ecosystem is much smaller than the terrestrial ecosystem, they sequestrated carbon in a much greater amount in their living biomass, as well as in the sediment. Land Use/Land Cover has affected these productive ecosystems globally. Recent estimates suggest that over one‐third of its cover has vanished due to coastal development, deforestation, expanding agriculture, aquaculture, and pollution. Thus, conservation and restoration of these coastal ecosystems is a growing concern. For conservation of these productive ecosystems (such as mangrove, seagrass, and salt marsh), adequate knowledge of their spatial distribution and total cover, standing biomass, change detection, as well as the distinctive mechanisms governing carbon sequestration strategies, are important. This will help in making protection policies and management guidelines. Nowadays, remote sensing and geospatial technologies are emerging widely, which are very effective for mapping and monitoring the coastal ecosystems. These technologies provide real‐time large data in a smaller time, which will be helpful for future research by minimizing the information gap. In the present study, detailed description of blue carbon ecosystems, their importance, distribution, carbon sequestration potential, degradation, and loss rate have been discussed. Further, a comprehensive description of various branches of remote sensing (such as optical, hyperspectral, and microwave) and its applications for the management of blue carbon ecosystems has been discussed.
The Gauteng City-Region in the northern interior of South Africa hosts one of the world’s largest and most densely vegetated urban forests. The tree species, distributed across pavements, parks and suburban gardens, comprise a range of indigenous and alien species. The most aesthetically distinct of these is Jacaranda mimosifolia (Bignoniaceae), a purple-blossoming tree introduced from Brazil in the 1800s to beautify the cities. The distinct appearance during flowering and their abundance in the Gauteng City-Region has resulting in reporting of peak flowering events in local newspapers throughout the past century. This provides a valuable phenological record, particularly in southern Africa where phenology is seldom recorded. Analysing these reports of Jacaranda mimosifolia flowering, an advance of 2.1 days per decade is calculated for the period 1927–2019. This occurs against a backdrop of statistically significant annual and monthly temperature increases of ∼0.1−0.2 °C/decade for Tmax and ∼0.2−0.4 °C/decade for Tmin, and non-uniform change in rainfall. This phenological advance is most significantly related to winter climatic conditions, including Tmax, rainfall and frost occurrence. The strongest phenological driver is June Tmax, at a rate of 4.3–5.3d/°C across the City-Region. This advance reflects the response of the tree to regional climate warming, which poses threats to the species and the urban forest in the long term when thresholds for adaptation are surpassed.
Wind energy has attracted worldwide attention as a clean energy source and wind farms are rapidly increasing in number. However, operation of wind farms can affect the local climate and, consequently, local vegetation phenology. Hence, the influence of wind farms on phenology needs to be understood. In this paper, we use remote sensing MOD09GQ data to calculate phenological indexes of vegetation near a large wind farm in a semi-arid grassland area of Inner Mongolia, China. The vegetation phenology before and wind farm construction is compared, with a control area used to account for long-term climate change. The results show that the wind farm extended the growing season of vegetation in areas upwind and downwind of the wind farm. In the prevailing wind direction, the growing season was extended by 11.7 days within 4 km of the wind farm in the upwind area, by 10.0 days within the wind farm, and by 5.5 days within 4 km of the wind farm in the downwind area. The extension of the growing season is due to an earlier start of the growing season, which was mainly influenced by increases in local land surface temperatures. And such an extension will increase the evaporation from vegetation transpiration in study area, which is very likely to bring about decreases in soil moisture here. Such effects should be considered when assessing the ecological impacts of planned wind farms.
El empleo de la información satelital para el estudio de los manglares cubanos ha sido muy limitado ya que los métodos más generalizados se enfocan en el empleo de datos de campo restringidos espacialmente a pequeñas unidades que proveen perspectivas locales y de amplitud temporal restringida. Estos datos no son suficientes para describir bosques que se distribuyen por cientos de kilómetros de zonas costeras con alta variabilidad espacio temporal. En el presente trabajo se evalúan las características espectrales de los bosques de mangles en Cuba, a partir de sensores remotos, y se describe su variabilidad espacial, a partir de imágenes del Landsat 8 del año 2017, como línea base para futuros estudios. Con las imágenes procesadas se crearon mosaicos de diez índices espectrales de vegetación. La variabilidad espacial se muestreó estadísticamente a partir de 11 584 puntos que permitieron caracterizar las distribuciones de valores entre regiones, zonas costeras y los principales humedales de Cuba. Los índices mostraron patrones específicos de correlaciones entre ellos, siendo las máximas entre TDVI, SAVI y EVI, así como entre GVI con RDVI y DVI (mayores al 90%). También se correlacionaron con la cobertura arbórea y con distancias a factores de influencia potencial como el mar, cuerpos de agua y poblaciones humanas. Los dos primeros componentes principales, obtenidos para reducir el número de dimensiones, explicaron el 80% de la varianza y permitieron detectar diferencias globales en las distribuciones de puntajes entre regiones. Los manglares de los cuatro sistemas de humedales más extensos del país mostraron patrones particulares de índices espectrales, posiblemente relacionados a sus características geomorfológicas y estructurales. Se discuten las aplicaciones de estas variables en el estudio y monitoreo de este importante ecosistema cubano y se describen sus potencialidades. La capacidad para el estudio regional y global de la dinámica y propiedades de los manglares y de la vegetación, en general, se incrementará a medida que estas modernas herramientas se comiencen a utilizar con más frecuencia entre botánicos y ecólogos en Cuba.
The use of remote sensing information for study of Cuban mangroves have been very limited because generalized methods focus on field data that are spatially restricted to small units providing local perspectives with restricted temporal amplitude. This data are not enough to fully describe forest distribute over thousands of squared kilometers of coastal zones with high spatial and temporal variability. In current paper we assess spectral characteristics of Cuban mangrove forest in Cuba, from remote sensing and describe it spatial variability. We use Landsat 8 imagery from 2017, as baseline for future studies. With processed imagery we create nation-wide mosaic for ten spectral vegetation indexes. Spatial variability was statistically sampled from 11 584 points to characterize values distribution among regions, coastal zones and main wetland systems of Cuba. Indexes showed specific correlation patterns, either among them as with tree cover and distances to potential drivers such as sea line, water bodies or rivers and human populations. The two first principal components, computed to reduce dimensionality in analysis explained 80% of variance and lead to detection of global differences in scores distribution among regions. Mangrove forest of the four main wetland systems of the country showed specific spectral response patterns according to spectral indexes. Applications of this type of data in the study and monitoring of this important Cuban ecosystem were discussed and its potential described. The capacity for regional studies of properties and dynamic of mangroves, as well as other vegetation types, will increase when these modern tools began to be used more frequently among Cuban botanists and ecologists.
El empleo de la información satelital para el estudio de los manglares cubanos ha sido limitado. Los métodos generalizados que obtienen datos de campo proveen perspectivas locales y de amplitud temporal restringida, insuficientes para generalizarse a bosques que se distribuyen por cientos de kilómetros de zonas costeras con alta variabilidad espacio temporal. En el presente trabajo se describe y aplica el método para evaluar las características espectrales y su variabilidad espacial de los bosques de mangles en Cuba, a partir de imágenes del Landsat 8 del año 2017, como línea base para futuros estudios. Con las imágenes procesadas se crearon mosaicos de diez índices espectrales de vegetación. La variabilidad espacial se muestreó estadísticamente a partir de 11 584 puntos que permitieron caracterizar las distribuciones de valores entre regiones, zonas costeras y los principales humedales de Cuba. Los índices se correlacionaron entre ellos, con la cobertura arbórea y con distancias a factores de influencia potencial como el mar, cuerpos de agua y poblaciones humanas. Los dos primeros componentes principales, explicaron el 80% de la varianza y permitieron detectar diferencias globales en las distribuciones de puntajes entre regiones. Los manglares de los cuatro sistemas de humedales más extensos del país mostraron patrones particulares de índices espectrales, posiblemente relacionados a sus características geomorfológicas y estructurales. Se discuten las aplicaciones de estas variables en el estudio y monitoreo de este importante ecosistema cubano y se describen sus potencialidades. ecología espacial, humedales costeros, índices espectrales de vegetación
ABSTRACT
The uses of remote sensing information for study of Cuban mangroves have been limited. Generalized methods focused on field data provide local perspectives with restricted temporal amplitude, not enough to fully describe forest distribute over thousands of squared kilometers of coastal zones with high spatial and temporal variability. In current paper we describe and apply the method to assess spectral characteristics and describe it spatial variability of Cuban mangrove forest in Cuba, from Landsat 8 imagery in 2017, as baseline for future studies. With processed imagery we create nationwide mosaic for ten spectral vegetation indexes. Spatial variability was statistically sampled from 11 584 points to characterize values distribution among regions, coastal zones and main wetland systems of Cuba. Indexes were correlated among them, with tree cover and with distances to potential drivers such as sea line, water bodies or rivers and human populations. The two first principal components explained 80% of variance and lead to detection of global differences in scores distribution among regions. Mangrove forest of the four main wetland systems of the country showed specific spectral response patterns according to spectral indexes. Applications of this type of data in the study and monitoring of this important Cuban ecosystem were discussed and its potential described. coastal wetlands, spatial ecology, spectral vegetation indexes
The characterization of leaf phenology in tropical forests is of major
importance for forest typology as well as to improve our understanding of
earth–atmosphere–climate interactions or biogeochemical cycles. The
availability of satellite optical data with a high temporal resolution has
permitted the identification of unexpected phenological cycles, particularly
over the Amazon region. A primary issue in these studies is the relationship
between the optical reflectance of pixels of 1 km or more in size and ground
information of limited spatial extent. In this paper, we demonstrate that
optical data with high to very-high spatial resolution can help bridge this
scale gap by providing snapshots of the canopy that allow discernment of the
leaf-phenological stage of trees and the proportions of leaved crowns within
the canopy. We also propose applications for broad-scale forest
characterization and mapping in West-Central Africa over an area of
141 000 km2.
Eleven years of the Moderate Resolution Imaging Spectroradiometer (MODIS)
Enhanced Vegetation Index (EVI) data were averaged over the wet and dry
seasons to provide a data set of optimal radiometric quality at a spatial
resolution of 250 m. Sample areas covered at a very-high (GeoEye) and high
(SPOT-5) spatial resolution were used to identify forest types and to
quantify the proportion of leaved trees in the canopy. The dry-season EVI
was positively correlated with the proportion of leaved trees in the canopy.
This relationship allowed the conversion of EVI into canopy deciduousness at
the regional level. On this basis, ecologically important forest types could
be mapped, including young secondary, open Marantaceae, Gilbertiodendron dewevrei and swamp forests. We show
that in West-Central African forests, a large share of the variability in
canopy reflectance, as captured by the EVI, is due to variation in the
proportion of leaved trees in the upper canopy, thereby opening new
perspectives for biodiversity and carbon-cycle applications.
Identification of clouds, cloud shadows and snow in optical images is often a necessary step toward their use. Recently a new program (named Fmask) designed to accomplish these tasks was introduced for use with images from Landsats 4–7 (Zhu & Woodcock, 2012). In this paper, there are the following: (1) improvements in the Fmask algorithm for Landsats 4–7; (2) a new version for use with Landsat 8 that takes advantage of the new cirrus band; and (3) a prototype algorithm for Sentinel 2 images. Though Sentinel 2 images do not have a thermal band to help with cloud detection, the new cirrus band is found to be useful for detecting clouds, especially for thin cirrus clouds. By adding a new cirrus cloud probability and removing the steps that use the thermal band, the Sentinel 2 scenario achieves significantly better results than the Landsats 4–7 scenario for all 7 images tested. For Landsat 8, almost all the Fmask algorithm components are the same as for Landsats 4–7, except a new cirrus cloud probability is calculated using the new cirrus band, which improves detection of thin cirrus clouds. Landsat 8 results are better than the Sentinel 2 scenario, with 6 out of 7 test images showing higher accuracies.
Tropical forests provide important ecosystem services in maintaining biodiversity, sequestering carbon and regulating climate regionally and globally. Climate triggers the seasonal transitions of vegetation structure and function in tropical forests. In turn, the seasonal cycles of structure and function in tropical forests feed back to the climate system through the control of land-atmosphere exchange of carbon, water and energy fluxes. Large uncertainties exist in the carbon and water budgets of tropical forests, and environmental controls on phenology are among the least understood factors. Although field studies have identified patterns in the environmental controls on local-scale species-level phenology in the tropics, there is little consensus on large-scale top-down environmental controls on whole-ecosystem seasonality. In this paper, we use both optical and microwave remote sensing to investigate the seasonality of vegetation canopy structure and function in three distinct tropical African forest types, and identify environmental triggers or controls of their variability. For most tropical forests that have a closed canopy and high leaf biomass, optical remote sensing (e.g., vegetation indices) captures canopy photosynthetic capacity (i.e., canopy function), while small-wavelength microwave remote sensing characterizes the leaf biomass and leaf water content of the upper canopy (i.e., canopy structure). Our results reveal a strong coupling of canopy structure with canopy function in the tropical deciduous forests and woody savannas, and their seasonalities are both controlled by precipitation rather than solar radiation. By contrast, tropical evergreen forests in Africa exhibit a decoupling of canopy structure from canopy function revealed by different sensors: canopy photosynthetic capacity shown by the optical remote sensing is linked to the seasonal variation of precipitation, while microwave remote sensing captures semi-annual leaf-flushing that is synchronous with peak insolation intensity at the top of the atmosphere, which is bimodal. The differential coupling of canopy structure and function in tropical forests observed from remote sensing highlights differences inherent in distinct vegetation types within the tropics that may originate in the different life histories of their respective floras. This satellite-based finding encourages more field-based studies to clarify the interpretation of these large scale patterns.
Read More: http://www.esajournals.org/doi/abs/10.1890/ES12-00232.1
[1] Using improved metrics and recently available MODerate resolution Imaging Spectrometer (MODIS) data, we examined the magnitude, extent, and nature of changes in photosynthetic activity and its timing across Sub-Saharan Africa. Changes in overall vegetation activity and shifts in its timing have considerable implications for a variety of processes including surface-atmosphere energy exchanges, terrestrial sources and sinks of carbon, the contribution of local evapotranspiration to the water cycle, disturbance regimes such as fires and pests, and the food security of societies using these ecosystems. While previous studies have examined broad-scale trends in phenology or provided more detailed estimates of land-cover conversion in the tropics, less is known of the year-to-year dynamics of vegetation. Here we quantified the overall changes in vegetation activity for each year between 2000 and 2004 and examined the proportion linked to differences in phenology and overall photosynthetic activity. In addition, we examined the nature of these changes in terms of frequency and duration, the proportion per ecosystem, identified areas of intensive change, and discuss the potential consequences of these changes. We found that most interannual change was not from shifts in timing or phenology, but rather largely due to differences in the amount of annual photosynthetic activity. In fact, there was as much as a 5% annual difference in vegetation activity across the continent. The changes were likely climate driven with particular vegetation types most susceptible to interannual change with high spatial and temporal variability found across the continent.
In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESS Remote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.
Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.
Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We used phenological variables from a MODIS Land Cover Dynamics (MCD12Q2) product and examined whether they reproduced the spatio-temporal variability of crop phenological stages in Southern Mali. Furthermore, a validated cereal crop growth model for this region, SARRA-H (System for Regional Analysis of Agro-Climatic Risks), provided precise agronomic information. Remotely-sensed green-up, maturity, senescence and dormancy MODIS dates were extracted for areas previously identified as crops and were compared with simulated leaf area indices (LAI) temporal profiles generated using the SARRA-H crop model, which considered the main cropping practices. We studied both spatial (eight sites throughout South Mali during 2007) and temporal (two sites from 2002 to 2008) differences between simulated crop cycles and determined how the differences were indicated in satellite-derived phenometrics. The spatial comparison of the phenological indicator observations and simulations showed mainly that (i) the satellite-derived start-of-season (SOS) was detected approximately 30 days before the model-derived SOS; and (ii) the satellite-derived end-of-season (EOS) was typically detected 40 days after the model-derived EOS. Studying the inter-annual difference, we verified that the mean bias was globally consistent for different climatic conditions. Therefore, the land cover dynamics derived from the MODIS time series can reproduce the spatial and temporal variability of different start-of-season and end-of-season crop species. In particular, we recommend simultaneously using start-of-season phenometrics with crop models for yield forecasting to complement commonly used climate data and provide a better estimate of vegetation phenological changes that integrate rainfall variability, land cover diversity, and the main farmer practices.
Monitoring vegetation conditions is a critical activity for assessing food security in the Horn of Africa. Remote sensing from space offers a unique opportunity to obtain consistent and timely information over large and often inaccessible areas where field observations are scattered, non-homogenous, or frequently unavailable. In this study we outline a method to assess objectively the performance and characteristics of seasonal vegetation development solely on the basis of time series of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from Satellite Pour l’Observation de la Terre SPOT-VEGETATION (SPOT-VGT) imagery. Key phenological indicators such as the start and end of growing periods are derived from a statistical analysis of the time series to characterize the spatial and temporal evolution of successive seasons. These indicators are then utilized to compute a proxy of the seasonal gross primary production (GPP) as the cumulative FAPAR during the growing season. Vegetation condition and associated risk of food deficit for specific seasons and locations are finally derived from the comparison of the seasonal GPP proxy with its average value computed over the whole time series. The impact on vegetation of the severe drought experienced by the Horn of Africa between late 2010 and late 2011 is discussed.
Tropical forests are global epicentres of biodiversity and important modulators of climate change, and are mainly constrained by rainfall patterns. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees, may be more tolerant to short-term rainfall reduction than are wetter tropical forests, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species.
Land surface phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstrated to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This study detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examined across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and GSL varied considerably during 1982-2010 across the globe. Generally, the interannual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative GSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982-1999) to prolonged trends (2000-2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than 1 % in most of climate regions although it could be as large as 10 %.
The seasonal cycle of tree leaf display in the savannas and woodlands of the seasonally dry tropics is complex, and robust observations are required to illuminate the processes at play. Here, we evaluate three types of data for this purpose, comparing scatterometry (QuikSCAT σ0) and optical/near-infrared MODIS EVI remotely sensed data against field observations. At a site in Mozambique, the seasonal cycles from both space-borne sensors are in close agreement with each other and with estimates of tree plant area index derived from hemispherical photography . This agreement results in similar estimates of the start of the growing season across different data types (range 13 days). Ku-band scatterometry may therefore be a useful complement to vegetation indices such as EVI for estimating the start of the growing season for trees in tropical woodlands. More broadly, across southern tropical Africa there is close agreement between scatterometry and EVI time series in woody ecosystems with 25 % tree cover, but in areas of 25 % tree cover, the two time series diverge and produce markedly different start of season (SoS) dates (difference 50 days). This is due to increases in σ0 during the dry season, not matched by increase in EVI. The reasons for these increases are not obvious, but might relate to soil moisture, flowering, fruiting, or grass dynamics. Further observations and modeling of this phenomenon is warranted to understand the causes of these dry season changes in σ0. Finally, three different definitions of the SoS were examined and found to produce only small differences in estimated dates, across all types of data.
Wetlands represent 6%of the Earth’s land cover surface. They are of crucial importance in the global water cycle and climatic dynamics. Nowadays, wetlands are the most threatened land cover type, nevertheless their spatial distribution and ecological functions are poorly documented. Despite the need for more detailed information, wetland mapping is a rare activity. Few data are available mainly because of the complexity of obtaining good field data. We therefore propose a method based on multisensor imagery analysis to characterize land cover patterns of the second largest wetland area of the world (The Cuvette Centrale of
the Congo River Basin). The time series of moderate resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) images are used to map land cover types based on their
phenological differences. Flooded areas in the Congo basin have been mapped during different seasons using -band synthetic aperture radar (PALSAR) imagery. The associated model has
been improved upon by the addition of elevation data as well as mean canopy heights acquired with light detection and ranging (LIDAR) data. The result of this study is the first detailed spatial
distribution of four forested wetland types within the Cuvette Centrale of the Congo River Basin. This study demonstrates that the spatial organization of the floodplain landscape depends on the
extent of flooding. The results also show that land cover phenology is closely related to the time period of flooding and solar intensity for this region, similarly to what is observed in the extensive
floodplain of the Amazon basin. Index Terms—Congo River Basin, ICESat/GLAS, landscape,
large river floodplain forest, PALSAR, moderate resolution imaging spectroradiometer (MODIS), remote sensing
The characterization of leaf phenology in tropical forests is of major importance and improves our understanding of earth-atmosphere-climate interactions. The availability of satellite optical data with a high temporal resolution has permitted the identification of unexpected phenological cycles, particularly over the Amazon region. A primary issue in these studies is the relationship between the optical reflectance of pixels of 1 km or more in size and ground information of limited spatial extent. In this paper, we demonstrate that optical data with high to very-high spatial resolution can help bridge this scale gap by providing snapshots of the canopy that allow discernment of the leaf-phenological stage of trees and the proportions of leaved crowns within the canopy. We also propose applications for broad-scale forest characterization and mapping in West Central Africa over an area of 141 000 km2. Eleven years of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data were averaged over the wet and dry seasons to provide a dataset of optimal radiometric quality at a spatial resolution of 250 m. Sample areas covered at a very-high (GeoEye) and high (SPOT-5) spatial resolution were used to identify forest types and to quantify the proportion of leaved trees in the canopy. The dry season EVI was positively correlated with the proportion of leaved trees in the canopy. This relationship allowed the conversion of EVI into canopy deciduousness at the regional level. On this basis, ecologically important forest types could be mapped, including young secondary, open Marantaceae, Gilbertiodendron dewevrei and swamp forests. We show that in west central African forests, a large share of the variability in canopy reflectance, as captured by the EVI, is due to variation in the proportion of leaved trees in the upper canopy, thereby opening new perspectives for biodiversity and carbon-cycle applications.
Changes in growing seasons for 2041–2060 across Africa are projected using a regional climate model at 90-km resolution, and confidence in the predictions is evaluated. The response is highly regional over West Africa, with decreases in growing season days up to 20% in the western Guinean coast and some regions to the east experiencing 5–10% increases. A longer growing season up to 30% in the central and eastern Sahel is predicted, with shorter seasons in parts of the western Sahel. In East Africa, the short rains (boreal fall) growing season is extended as the Indian Ocean warms, but anomalous mid-tropospheric moisture divergence and a northward shift of Sahel rainfall severely curtails the long rains (boreal spring) season. Enhanced rainfall in January and February increases the growing season in the Congo basin by 5–15% in association with enhanced southwesterly moisture transport from the tropical Atlantic. In Angola and the southern Congo basin, 40–80% reductions in austral spring growing season days are associated with reduced precipitation and increased evapotranspiration. Large simulated reductions in growing season over southeastern Africa are judged to be inaccurate because they occur due to a reduction in rainfall in winter which is over-produced in the model. Only small decreases in the actual growing season are simulated when evapotranspiration increases in the warmer climate. The continent-wide changes in growing season are primarily the result of increased evapotranspiration over the warmed land, changes in the intensity and seasonal cycle of the thermal low, and warming of the Indian Ocean.
Canopy phenology of Acacia tortilis ssp. raddiana, a dominant semi-deciduous species of the northern Sahelian zone, was monitored for 39 mature individuals, each month and bi-weekly in the rainy season, over a 5.5-year period in North Senegal. To investigate the relationships between leaf phenology and environmental variables, soil water availability and several climatic variables were monitored.Over six rainy seasons, annual rainfall ranged between 146 and 367mm. The full canopy stage lasted between 5 and 8 months, broadly including the rainy season (July–September) and the “cool” dry season (November–January). A significant inter-annual variation, up to 2.0 months, affects both the timing of the peaks of leaf flush and leaf fall. The canopy was maintained during the dry season despite low upper soil water availability and tree roots had access to a deep water table (31m). These results support the current view that in the dry tropics, groundwater availability is the major environmental variable controlling leaf phenology. However, inter-annual variation in the peaks of leaf flush and leaf fall could not be explained by ground water, genetics or day length. In such water-controlled biome, we focused on a comparison between two additional drivers, upper soil water availability and climatic variables which contribute to evaporative demand. Models predicting changes in canopy fullness from environmental variables were investigated by polynomial logistic regression. We considered each tree and pooled all the years, distinguishing periods of leaf flush (April–August) and leaf fall (January–April). Then, the ability of such models to predict inter-annual variation in the timing of peaks of leaf flush and leaf fall was tested.Inter-annual variation in the timing of leaf flush peak was well predicted by models based on air relative humidity or vapour pressure deficit or global radiation (root mean square error=0.5 month and R2=0.8). Inter-annual variation of leaf fall peak was also significantly predicted by models based on atmospheric variables (temperatures or maximum value of vapour pressure deficit) however with weaker relationships (root mean square error=0.7 month and R2=0.7). By contrast, models based on upper soil water availability or rainfall did not predict either leaf flush or leaf fall inter-annual variation. It appears that inter-annual variation of canopy phenology is mainly tuned to atmospheric conditions. Such behaviour maximizes the duration of high photosynthetic activity below a threshold of evaporative demand.
Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the Envisat MERIS terrestrial chlorophyll index (MTCI) data set for monitoring vegetation phenology, via its estimates of chlorophyll content. The MTCI was used to construct the phenological profile of and extract key phenological event dates from woodland and grass/heath land in Southern England as these represented a range of chlorophyll contents and different phenological cycles. The period 2003–2008 was selected as this was known to be a period with temperature and phenological anomalies. Comparisons of the MTCI-derived phenology data were made with ground indicators and climatic proxy of phenology and with other vegetation indices: MERIS global vegetation index (MGVI), MODIS normalized difference vegetation index (NDVI) and MODIS enhanced vegetation index (EVI). Close correspondence between MTCI and canopy phenology as indicated by ground observations and climatic proxy was evident. Also observed was a difference between MTCI-derived phenological profile curves and key event dates (e.g. green-up, season length) and those derived from MERIS MGVI, MODIS NDVI and MODIS EVI. The research presented in this article supports the use of the Envisat MTCI for monitoring vegetation phenology, principally due to its sensitivity to canopy chlorophyll content, a vegetation property that is a useful proxy for the canopy physical and chemical alterations associated with phenological change.
Variations in the amplitude and timing of the seasonal cycle of atmospheric CO2 have shown an association with surface air temperature consistent with the hypothesis that warmer temperatures have promoted increases in plant growth during summer1 and/or plant respiration during winter2 in the northern high latitudes. Here we present evidence from satellite data that the photosynthetic activity of terrestrial vegetation increased from 1981 to 1991 in a manner that is suggestive of an increase in plant growth associated with a lengthening of the active growing season. The regions exhibiting the greatest increase lie between 45°N and 70°N, where marked warming has occurred in the spring time3 due to an early disappearance of snow4. The satellite data are concordant with an increase in the amplitude of the seasonal cycle of atmospheric carbon dioxide exceeding 20% since the early 1970s, and an advance of up to seven days in the timing of the drawdown of CO2 in spring and early summer1. Thus, both the satellite data and the CO2 record indicate that the global carbon cycle has responded to interannual fluctuations in surface air temperature which, although small at the global scale, are regionally highly significant.
The interannual and intraseasonal variability of West African vegetation over the period 1982–2002 is studied using the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR).
The novel independent component analysis (ICA) technique is applied to extract the main modes of the interannual variability of the vegetation, among which two modes are worth describing. The first component (IC1) describes NDVI variability over the Sahel from August to October. A strong photosynthetic activity over the Sahel is related to above-normal convection and rainfall within the intertropical convergence zone (ITCZ) in summertime and is partly associated with colder (warmer) SST in the eastern tropical Pacific (the Mediterranean). The second component (IC2) depicts a dipole pattern between the Sahelian and Guinean regions during the northern summer followed by a southward-propagating signal from October to December. It is associated with a north–south dipole in convection and rainfall induced by variations in the latitudinal location of the ITCZ as a response to the occurrence of the tropical Atlantic dipole.
The analysis of the intraseasonal variability of the Sahelian vegetation relies on the analysis of the seasonal marches and their main phenological stages. Green-up usually starts in early July and shows a very low year-to-year variability, while senescence ends by mid-November and is prone to larger interannual variability. Six types of vegetative seasonal marches are discriminated according to variations in the timing of phenological stages as well as in the greening intensity. These types appear to be strongly dependent on rainfall distribution and amount, particularly those recorded in late August. Finally, year-to-year memory effects are highlighted: NDVI recorded during the green-up phase in year j appears to be strongly related to the maximum NDVI value recorded at year j − 1.
Changes in net area of tropical forest are the sum of several processes: deforestation, regeneration of previously deforested areas, and the changing spatial location of the forest-savanna boundary. The authors conducted a long-term (1986-2006) quantification of vegetation change in a 5400 km(2) forest-savanna boundary area in central Cameroon. A cross-calibrated normalized difference vegetation index (NDVI) change detection method was used to compare three high-resolution images from 1986, 2000, and 2006. The canopy dimensions and locations of over 1000 trees in the study area were measured, and a very strong relationship between canopy area index (CAI) and NDVI was found. Across 5400 km(2) 12.6% of the area showed significant positive change in canopy cover from 1986 to 2000 (0.9% yr(-1)) and 7.8% from 2000 to 2006 (1.29% yr(-1)), whereas <0.4% of the image showed a significant decrease in either period. The largest changes were in the lower canopy cover classes: the area with <0.2 m(2) m(-2) CAI decreased by 43% in 20 years. One cause may be a recent reduction in fire frequency, as documented by Along Track Scanning Radiometer-2/Advanced ATSR (ATSR-2/AATSR) data on fire frequency over the study area from 1996 to 2006. The authors suggest this is due to a reduction in human pressure caused by urbanization, as rainfall did not alter significantly over the study period. An alternative hypothesis is that increasing atmospheric CO2 concentrations are altering the competitive balance between grasses and trees. These data add to a growing weight of evidence that forest encroachment into savanna is an important process, occurring in forest savanna boundary regions across tropical Africa.