Dynamics Of Vegetation Greenness Research Articles

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Hydrological Factor and Land Use/Land Cover Change Explain the Vegetation Browning in the Dosso Reserve, Niger

The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel.

The greening of vegetation on the Loess Plateau has resulted in a northward shift of the vegetation greenness line

Vegetation greenness is crucial for assessing vegetation dynamics and evaluating the effectiveness of ecological governance on the Loess Plateau. In this study, we utilized Google Earth Engine (GEE) platform and Landsat image to investigate the spatiotemporal evolution of vegetation greenness and the shift of vegetation greenness lines (VGLs) during the growing season on the Loess Plateau from 1987 to 2020. The driving factors were analyzed using a geographic detector. The results show that the average annual growth rates of NDVI and EVI in the Loess Plateau growing season from 1987 to 2020 were 0.0042/year (P < 0.01) and 0.0023/year (P < 0.01), respectively, with the growth rate after 2000 being three to four times higher. Spatially, significant increases of 78.8% in NDVI and 69.2% in EVI were observed across the entire area of the Loess Plateau. Furthermore, the average VGL based on NDVI and EVI on the Loess Plateau from 1987 to 2020 exhibited a northwestward shift at a rate of 9.79 km/year. This shift resulted in an average movement of 271.83 km over the period. The most significant shifts occurred between 2005 and 2010. Land use and precipitation emerged as the predominant driving factors influencing both the change in vegetation greenness and the shift of the VGLs. Land use and precipitation are the primary factors influencing changes in vegetation greenness and the shift of the VGLs. Land use explains 49.7% of the variation in vegetation greenness, while precipitation accounts for 43%. The combined influence of land use and precipitation explains 68% of the variation in vegetation greenness on the Loess Plateau. Prolonged growing seasons due to climate changes and the “Grain for Green” Project significantly contributed to increased vegetation greenness and northward VGL shifts. Our study offers valuable insights into the spatiotemporal dynamics of vegetation greenness and the shift of VGLs, enhancing our understanding and providing guidance for effective vegetation restoration efforts on the Loess Plateau.

Exploring the Driving Forces of Vegetation Greening on the Loess Plateau at the County Scale

China has implemented several ecological projects in the Loess Plateau region to address severe land degradation and soil erosion. Accurately assessing ecological restoration and its driving factors remains challenging. Previous studies in this area concentrated on driving factors have mainly focused on natural factors at the regional or watershed scale, with limited consideration of socioeconomic factors at the county scale. In this study conducted in Huanglong County on the Loess Plateau, the focus was to fill the gaps in previous research and provide insights into the socioeconomic driving forces behind vegetation greening. Remote sensing image data (NDVI) from 1999 to 2019 were used to analyze vegetation greenness dynamics in the region. Five socioeconomic variables were considered, including afforestation intensity, deforestation intensity, agricultural intensity, village intensity, and road intensity layers, to characterize the impact of afforestation, agriculture, and urbanization policies. The RESTREND (residual trends) method was employed to assess the relative importance of climate and human activities on vegetation dynamics. This study found that temperature–NDVI relationships are more suitable for building RESTREND models than precipitation–NDVI relationships. Human activity was the main driver of vegetation dynamics, contributing 62% compared to 38% from climate change. Agricultural practices and afforestation were found to have a positive impact on ecological restoration, while deforestation and urbanization had no significant impact. These findings highlight a conceptual framework for understanding the intricate relationship between ecological restoration, climatic factors, and human activity on the Loess Plateau. This study suggests that significant progress has been made in ecological restoration through human efforts in combating land degradation. However, it emphasizes the need to strengthen natural conservation efforts and gradually transition toward restoration processes driven by natural forces for sustainable socioeconomic development. The methodology used in this study can be applied to explore the driving forces of ecological restoration in other regions facing human-driven land degradation.

Does urban growth mean the loss of greenness? A multi-temporal analysis for Chinese cities

Urban growth is recognized as the conversion of vegetated surface to built-up surface. However, there is still no consensus about the urbanization-induced dynamic of vegetation greenness in view of existing literatures. In this study, we aimed to empirically investigate whether urban growth mean the loss of vegetation greenness. We selected 340 Chinese cities as the study areas, relied on consistent multi-temporal remotely sensed data and adopted linear regression analysis, annual growth area, Tail-Sen slope and Mann-Kendall models. Results show that although vegetation greening generally lagged behind urban growth in the monitoring period, a tendency of their consistent speeding up can be observed over time. By categorizing four forms and four trends of vegetation greenness dynamics related to urban growth, we revealed the diversity of Chinese cities. The former focused on the velocity of urban growth and vegetation greenness dynamics within newly urbanized area in three phases, i.e., 2003–2008, 2008–2013 and 2013–2018. The latter focused on the interannual trends of vegetation greenness dynamics among the previously existing and newly urbanized areas. The key finding is that, in over 85 % of the cities, we measured an increase of vegetation greenness along with urban growth. In addition, our detailed results allow quantifying the impact of urbanization in Chinese cities on vegetation protection and sustainable development.

Climatic Niche of Vegetation Greenness Is Likely to Be Conservative in Degraded Land

Satellite data have been widely used to study changes in vegetation greenness in geographical space; however, this change is rarely considered in climatic space. Here, the climatic niche dynamics of vegetation greenness, represented by the normalized difference vegetation index (NDVI), was quantified in the climate space of the Loess Plateau, a piece of degraded land greening significantly from 2000 to 2018. The niche similarity test was used to examine the niche conservatism of vegetation greenness during the 19 years of restoration. The results show that the climate niche of vegetation greenness is always more similar than expected. The stability niche occupied most parts (83–98%) of their climatic niche, and niche overlap reached 0.52–0.69. Climate niche conservatism suggests that potential greenness constructed by statistical methods could be used as a criterion or baseline for ecosystem function restoration on the Loess Plateau. The study also suggests that the integrated niche similarity test in decision-making for restoration of degraded land will clarify our understanding of the climatic niche dynamics of vegetation greenness and the making of forecasts.

Vegetation Greenness Dynamics in the Western Greater Khingan Range of Northeast China Based on Dendrochronology.

Simple SummaryThis study reconstructed 193 years of vegetation greenness dynamics in the study area based on the chronology of the tree rings of Pinus sylvestris var. mongolica Litv. in the western Greater Khingan Range, and analyzed the vegetation greenness dynamics pattern accordingly. After the 1950s, it was found that the scale and intensity of extreme changes in the vegetation greenness dynamics increased significantly, and the frequency of occurrence also increased significantly. The study also found that climate (precipitation) is the most important factor influencing the change of vegetation greenness dynamics in the western Greater Khingan Range; the influence of human activities is also important and rapid, and the degree of influence has gradually increased in recent years. Finally, the influence of human activities on the vegetation greenness dynamics was ranked in terms of importance, as follows: livestock > afforestation > population > farmland.Understanding the vegetation greenness dynamics in the forest–steppe transition zone is essential for ecosystem management, and in order to study ecological changes in the region. This study provides a valuable record of the vegetation greenness dynamics in the western Greater Khingan Range over the past 193 years (1826–2018) based on tree-ring data represented by the normalized difference vegetation index (NDVI). The reconstructed vegetation greenness dynamics record contains a total of 32 years of high vegetation greenness and 37 years of low vegetation greenness, together occupying 35.8% of the entire reconstructed period (193 years). Climate (precipitation) is the main influence on the vegetation greenness dynamics at this site, but human activities have also had a significant impact over the last few decades. The magnitude, frequency, and duration of extreme changes in vegetation greenness dynamics have increased significantly, with progressively shorter intervals. Analyses targeting human behavior have shown that the density of livestock, agricultural land area, and total population have gradually increased, encroaching on forests and grasslands and reducing the inter-annual variability. After 2002, the government implemented projects to return farmland to its original ecosystems, and for the implementation of new land management practices (which are more ecologically related); as such, the vegetation conditions began to improve. These findings will help us to understand the relationship between climate change and inter- and intra- annual dynamics in northeastern China, and to better understand the impact of human activities on vegetation greenness dynamics.

The trend shift caused by ecological restoration accelerates the vegetation greening of China’s drylands since the 1980s

Satellite observations since the early 1980s have revealed a trend of ‘Earth greening’ across global terrestrial ecosystems. Dryland vegetation is more sensitive to climate change and human activities. China’s drylands are among the largest in extent worldwide, and large-scale ecological restoration of these areas has been implemented since the late 1970s, which has resulted in more complicated but still poorly quantified vegetation dynamics. To figure out the vegetation dynamics and associated driving forces, we provide an assessment of the vegetation dynamics from 1982 to 2015 using the CO2 fertilization effect function, principal component regression, Residual Trend analysis, and Breaks For Additive Seasonal and Trend methods based on the ERA5 climate factors and GIMMS 3.1 normalized difference vegetation index datasets. This study shows that anthropogenic impacts and CO2 fertilization have jointly led to vegetation greening in China’s drylands since the 1980s, and ecological restoration has accelerated this greening since the 2000s. The results show that the vegetation greening in China’s drylands (41.51% of the study area, +0.60 × 10−3 yr−1) is mainly driven by CO2 fertilization (+0.55 × 10−3 yr−1) and anthropogenic activities (+0.12 × 10−3 yr−1). The anthropogenic effects are especially higher on the Loess Plateau (+1.01 × 10−3 yr−1) and the Three-North region (+0.23 × 10−3 yr−1). The vegetation dynamics shifts in 6.73% (31.64 Mha) of China’s drylands were directly attributed to anthropogenic impacts around the 2000s. When the anthropogenic effect was intensified, the vegetation dynamics shifted from no change to greening and vice versa, which significantly intensified the vegetation greening since the 1980s. These results capture the processes of ecological programs and provide an assessment of the effects of ecological restoration. This work provides a credible attribution of the vegetation greenness dynamics and trend shifts in China’s drylands, thus facilitating a better understanding of regional environmental change and management.

Spatio‐temporal effects of inundation and climate on vegetation greenness dynamics in dryland floodplains

AbstractMonitoring floodplain vegetation response to water availability is essential for sustainable water resource management. However, the interpretation of these responses through space and time has been challenging due to the limitation of satellite‐derived inundation extent, which is restricted by the return period of imagery and prevalence of clouds affected images during high flow events. We address this issue by utilising a predictive inundation model capable of reporting inundation at daily temporal resolution. Spatio‐temporal patterns in vegetation greenness of four floodplain vegetation communities (Eucalyptus camaldulensis, Eucalyptus largiflorens, Eucalyptus coolabah and Duma florulenta) were quantified using Landsat‐derived vegetation index and a combination of time series of floodplain inundation pattern, rainfall and soil moisture for the years 1989–2016. The results show that vegetation dynamics were highly variable between years and are closely associated with irregular rainfall patterns and overbank flooding. Linear mixed‐effect models explained between 38% and 67% of the variability in vegetation productivity, with all involved variables being significant predictors (p &lt; 0.05). The effects of rainfall, soil moisture and inundation were diverse and depended on vegetation type. Higher inundation frequency and low interflood period often corresponded to lower vegetation responses, which may relate to a mixed surface water and vegetation signal, or a short‐term inhibitive effect of surface water on growth. These results highlight the importance of groundwater and lagged effects of rainfall and flow in explaining patterns of vegetation condition in semi‐arid floodplains and should be considered in the design of environmental water monitoring programmes.

Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions.

Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

Modeling vegetation greenness and its climate sensitivity with deep-learning technology.

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.

Directional Climate Trend, Intensified Intraannual Variability, and Changes in Land Cover Drive the Dynamics of Vegetation Greenness in Peri‐Urban China During 2001–2015

AbstractSpatiotemporal dynamics of remote‐sensed vegetation indices and derived variables such as land surface phenology have often been studied as a function of climate with implied assumption of negligible land‐cover change. However, this assumption is not valid for areas with intensive human activities such as fast‐developing countries. To address the impact of land cover on vegetation greenness, we analyzed the trend of EVI59, enhanced vegetation index in May–September, within the 25‐km2 patches centered at 674 meteorological stations located mostly in peri‐urban areas of China, and the impacts of land cover and intraannual climate variability on the EVI, during 2001–2015 by means of linear mixed‐effect model. Impacts of land cover were assessed with the enhancement of sum of the squared difference with respect to the climate model. The climate models explained on average 60% sum of the squared difference of the full models (climate plus land cover), and this proportion reached 83% and 94% for the temperate grassland and the high‐cold Tibet, respectively. Including land cover enhanced on average 40% of the sum of the squared difference, and the enhancement is over 60% for the dense‐populated warm‐temperate‐deciduous forest and subtropical‐evergreen forest zones. The impact of land cover not only depends on the intensity but also on the type of land‐cover change. Forest regrowth in east China and vegetation growth from bare land in temperate desert significantly enhanced greenness but the shift from agriculture to shrubs in temperate grassland may not significantly alter the greenness. We showed that climate variability is important for EVI in all zones except Tibet, and the climate variability contributed on average 64% of climate impacts.

Hydrological Control of Vegetation Greenness Dynamics in Africa: A Multivariate Analysis Using Satellite Observed Soil Moisture, Terrestrial Water Storage and Precipitation

Vegetation activity in many parts of Africa is constrained by dynamics in the hydrologic cycle. Using satellite products, the relative importance of soil moisture, rainfall, and terrestrial water storage (TWS) on vegetation greenness seasonality and anomaly over Africa were assessed for the period between 2003 and 2015. The possible delayed response of vegetation to water availability was considered by including 0–6 and 12 months of the hydrological variables lagged in time prior to the vegetation greenness observations. Except in the drylands, the relationship between vegetation greenness seasonality and the hydrological measures was generally strong across Africa. Contrarily, anomalies in vegetation greenness were generally less coupled to anomalies in water availability, except in some parts of eastern and southern Africa where a moderate relationship was evident. Soil moisture was the most important variable driving vegetation greenness in more than 50% of the areas studied, followed by rainfall when seasonality was considered, and by TWS when the monthly anomalies were used. Soil moisture and TWS were generally concurrent or lagged vegetation by 1 month, whereas precipitation lagged vegetation by 1–2 months. Overall, the results underscore the pre-eminence of soil moisture as an indicator of vegetation greenness among satellite measured hydrological variables.

Trends in vegetation greenness dynamics in protected areas across borders: what are the environmental controls?

ABSTRACTMonitoring temporal trends in ecosystem functioning of protected areas (PAs) provides important knowledge of local and regional ecological responses to both environmental and anthropogenic drivers of change. Understanding such changes constitute a great global effort to preserve biodiversity and ecosystem services. We characterized the inter-annual trends of the enhanced vegetation index (EVI) seasonal dynamics in the PAs of southern Spain and northern Morocco. We used a time series of satellite images of the Moderate Resolution Imaging Spectroradiometer Terra sensor from January 2001 to December 2012. The EVI inter-annual trends were grouped into four patterns of trends. Patterns of trends in the EVI seasonal dynamics may be climate driven in the PAs of both countries. However, socioecological processes might be an additional underlying cause of the patterns of trends in the EVI seasonal dynamics of Moroccan PAs and Spanish wetland PAs. The use of remote sensing to monitor temporal trends in ecosystem functioning should be considered to understand the ecological responses to drivers of change in natural ecosystems.

Understanding the Spatial Temporal Vegetation Dynamics in Rwanda

Knowledge of current vegetation dynamics and an ability to make accurate predictions of ecological changes are essential for minimizing food scarcity in developing countries. Vegetation trends are also closely related to sustainability issues, such as management of conservation areas and wildlife habitats. In this study, AVHRR and MODIS NDVI datasets have been used to assess the spatial temporal dynamics of vegetation greenness in Rwanda under the contrasting trends of precipitation, for the period starting from 1990 to 2014, and for the first growing season (season A). Based on regression analysis and the Hurst exponent index methods, we have investigated the spatial temporal characteristics and the interrelationships between vegetation greenness and precipitation in light of NDVI and gridded meteorological datasets. The findings revealed that the vegetation cover was characterized by an increasing trend of a maximum annual change rate of 0.043. The results also suggest that 81.3% of the country’s vegetation has improved throughout the study period, while 14.1% of the country’s vegetation degraded, from slight (7.5%) to substantial (6.6%) deterioration. Most pixels with severe degradation were found in Kigali city and the Eastern Province. The analysis of changes per vegetation type highlighted that five types of vegetation are seriously endangered: The “mosaic grassland/forest or shrubland” was severely degraded, followed by “sparse vegetation,” “grassland or woody vegetation regularly flooded on water logged soil,” “artificial surfaces” and “broadleaved forest regularly flooded.” The Hurst exponent results indicated that the vegetation trend was consistent, with a sustainable area percentage of 40.16%, unsustainable area of 1.67% and an unpredictable area of 58.17%. This study will provide government and local authorities with valuable information for improving efficiency in the recently targeted countrywide efforts of environmental protection and regeneration.

Identifying environmental controls on vegetation greenness phenology through model–data integration

Abstract. Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.

Phenology-based, remote sensing of post-burn disturbance windows in rangelands

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.

NDVI Variability in North East India

The variability of the Normalized Difference Vegetation Index (NDVI) in north east India in response to rainfall and temperature was analysed using twice-monthly NOAA/AVHRR satellite imagery acquired during 1982–2002 from the GIMMS (Global Inventory Modeling and Mapping Studies) data-set. Corresponding rainfall and temperature estimates were extracted from the Climate Research Unit's CR TS 2.1 data-set for 34 study sites, chosen using the GLC 2000 land use categories. The selected sites represent nine land use categories under differing institutional frameworks. Results showed a weak linear relationship between the growing season rainfall and NDVI range when plotted in a scatter diagram. The negative correlation between NDVI and rainfall and temperature and NDVI in the study area was accentuated during the growing season and a one- and two-month lag for rainfall and temperature respectively was operating. A gain coefficient image to determine the temporal change in NDVI during the 21-year period indicated a consistent decline for much of the study area. Among the study sites those under state protection fared better than sites under other institutional frameworks. Along with rainfall and temperature, land use and institutional frameworks emerged as causative factors in the dynamics of vegetation greenness in north east India.

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem–atmosphere CO 2 exchange in the Amazon region.