Summer Normalized Difference Vegetation Index Research Articles

NDVI-based vegetation dynamics and their response to recent climate change: a case study in the Tianshan Mountains, China

Mountains form distinct geographical units with complex topographic and climatic features. Mountain ecosystems, especially those in arid and semi-arid regions, are likely to be strongly influenced by climate change. The NDVI-based vegetation response to climate change was analyzed in the Tianshan Mountains in China, one of the largest mountain systems of central Asia. Datasets, including the Normalized Difference Vegetation Index (NDVI), precipitation, soil moisture, and snow cover, were used to analyze spatial patterns of NDVI during 2001–2013. A trend test and correlation analysis were used to verify the results. Results showed that: (1) Spatial patterns of NDVI in the Tianshan Mountains revealed significant differences during 2001–2013. A decreasing trend appeared mainly in the Ili River Valley (<−0.005 NDVI/year), the Kaidu River (−0.01 to −0.005 NDVI/year), and Bogda Shan (−0.005 to 0 NDVI/year). NDVI in the western Tianshan Mountains, eastern and western Bogda Shan showed an increasing trend. (2) Spring NDVI in the Tianshan Mountains decreased, while summer NDVI increased during 2001–2013. (3) Spatial variations in vegetation dynamics were attributed to the interaction of the four spheres of the earth’s system, hydrosphere–pedosphere–atmosphere–biosphere. The main contributors including temperature, precipitation, and soil moisture had a notable effect on variations in vegetation. (4) The snow cover in the mountains was crucial for vegetation growth, especially in the winter half of the year. Understanding the spatial characteristics of NDVI in mountains under the effects of climate change will underpin further study in this ecological environment.

Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive

Abstract Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~ 30 years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM +, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM + and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.

Sources of bias and variability in long-term Landsat time series over Canadian boreal forests

Abstract A variety of evidence suggests that the boreal forests of Canada are responding to climate change. Specifically, several studies have inferred that widespread browning trends detected in time series of the Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) reflect the response of boreal forests to longer growing seasons, increased summer drought stress, and higher frequency of fires. Data from the Thematic Mapper (TM5) and Enhanced Thematic Mapper Plus (ETM+) sensors onboard Landsat 5 and 7, respectively, span essentially the same time period as the AVHRR record, but provide data with substantially higher radiometric and spatial fidelity, and by extension, a much improved basis for evaluating decadal-scale trends in spectral vegetation indices such as the NDVI. However, detection of trends, which are often subtle, requires careful attention to ensure that artifacts associated with the quality and stability of inter- and intra-sensor calibration do not lead to spurious conclusions in results from time series analyses. In this paper, we use time series of TM5 and ETM+ images for fifteen sites distributed across the Canadian boreal forest zone to explore if and how sensor geometry and inter- and intra-sensor calibration affect trends in spectral vegetation indices derived from multi-decadal Landsat time series. To do this, we created annual cloud-free composites for each Landsat spectral band based on peak summer NDVI at each site from 1984 to 2011 using all available TM5 and ETM+ data. To distinguish trends arising from long term climate change from those related to disturbance, we isolated areas within each site that were undisturbed during the Landsat record, and used these locations to analyze sources of variance in time series of red reflectance, near-infrared (NIR) reflectance, the NDVI, and the Enhanced Vegetation Index (EVI). Our results highlight the challenges involved in distinguishing trends in surface properties from data artifacts caused by undetected atmospheric effects, changes in sensor view angles, and subtle radiometric differences between the TM5 and ETM+ sensors. In particular, differences in sensor view geometry across adjacent overlapping Landsat scenes cause vegetated pixels in the eastern portion of Landsat scenes to have higher reflectances in the red and NIR bands (by 5 and 6 percent, respectively) than pixels in the western portion of scenes. While this effect does not significantly change NDVI values, it does affect EVI values. We also found modest, but potentially significant, differences between the red band reflectance of each sensor, with TM5 data having 14 percent higher red reflectance on average for vegetated pixels, which can introduce spurious trends in time series that combine TM5 and ETM+ data. More generally, the results from this work demonstrate that while the 30 + year Landsat archive provides unprecedented opportunities for studying changes to the Earth's terrestrial biosphere over the last three decades, care must be taken when inferring trends in these data without considering how sources of variance unrelated to surface processes affect the integrity of Landsat time series.

Empirical Relationships of Sea Surface Temperature and Vegetation Activity with Summer Rainfall Variability over the Sahel*

Abstract Regional land surface and remote ocean variables have been considered as primary forcings altering the variability of summer rainfall over the Sahel. However, previous studies usually examined the two components separately. In this study, the authors apply statistical methods including correlation, multivariate linear regression, and Granger causality analyses to investigate the relative roles of spring–summer sea surface temperature (SST) and vegetation activity in explaining the Sahel summer rainfall variability from 1982 to 2006. The remotely sensed normalized difference vegetation index (NDVI) is used as an indicator of land surface forcing. This study shows that spring and summer SSTs over the subtropical North Atlantic have significant positive correlations with summer rainfall. The spring and summer NDVIs over the Sahel have significant negative and positive correlations, respectively, with summer rainfall. Based on the multivariate linear regression analysis, the adjusted R2 for the integrated model with both the land and ocean variables is 0.70. It is around 2 times larger than the model with SST alone (adjusted R2 = 0.36). To further investigate the causal relationships of summer rainfall with the SST and NDVI variables selected in the integrated multivariate model, the authors perform a Granger causality test. This study finds that summer NDVI over the Sahel does Granger cause summer rainfall over the Sahel, while the summer SST over the subtropical North Atlantic does not Granger cause the summer rainfall. The results indicate that the regional land surface forcing has a relatively strong contribution to Sahel summer rainfall, compared to the remote ocean forcing, during the recent decades.

Interactions between river stage and wetland vegetation detected with a Seasonality Index derived from LANDSAT images in the Apalachicola delta, Florida

The distribution of swamp floodplain vegetation and its evolution in the lower non-tidal reaches of the Apalachicola River, Florida USA, is mapped using Landsat Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) images captured over a period of 29 years. A newly developed seasonality index (SI), the ratio of the NDVI in winter months to the summer months, shows that the hardwood swamp, dominated by bald cypress and water tupelo, is slowly replaced by bottomland hardwood forest. This forest shift is driven by lower water levels in the Apalachicola River in the last 30 years, and predominantly occurs in the transitional area between low floodplains and high river banks. A negative correlation between maximum summer NDVI and water levels in winter suggests the growth of more vigorous vegetation in the vicinity of sloughs during years with low river flow. A negative correlation with SI further indicates that these vegetation patches are possibly replaced by species typical of drier floodplain conditions.

Spatiotemporal changes of vegetation on the Tibetan Plateau and relationship to climatic variables during multiyear periods from 1982–2012

Understanding the dynamics of vegetation system change is often limited by relatively brief data sequences and the shortage of comparable analyses of variation over longer periods. In this study, GIMMS NDVI and MODIS NDVI datasets were integrated to establish a consistent NDVI time series from 1982 to 2012 on vegetation of the Tibetan Plateau in China. The spatiotemporal patterns of change in seasonal NDVI and their linkage with climatic variables were analyzed at regional and pixel scales over 14 periods ranging from 18 to 31 years and beginning in 1982. On a regional scale, positive trends of growing season and seasonal NDVI were observed during the 14 periods, and the increases were statistically significant for growing season NDVI during all periods, and for summer and autumn NDVI during only the last four and the last two periods, respectively. The rates of NDVI increase in growing season and spring significantly decreased over the 14 periods. NDVI rates slightly decrease in summer and significantly increase in autumn. At a pixel scale, areas with significant greening or significant browning significantly increased over the 14 periods during the growth season and during all seasons except spring, in which the proportion of vegetated area with greening rapidly decreased. Temperature was the primary climatic driver for the observed vegetation changes during multiple periods while precipitation and sunshine duration had significant impacts on vegetation growth only in limited parts of the study area or during spring. Vegetation growth response to climate change varied across seasons and regions. Trend analysis during the multiple nested time series provides a better understanding of NDVI dynamics and may help to forecast future changes. Spring NDVI is likely to continue decreasing, autumn NDVI will continue increasing, and the magnitude of the NDVI increase during growing season will decline in the future.

Remote sensing of interannual boreal forest NDVI in relation to climatic conditions in interior Alaska

Climate has warmed substantially in interior Alaska and several remote sensing studies have documented a decadal-scale decline in the normalized difference vegetation index (NDVI) termed a ‘browning trend’. Reduced summer soil moisture due to changing climatic factors such as earlier springs, less snowpack, and summer drought may reduce boreal productivity and NDVI. However, the relative importance of these climatic factors is poorly understood in boreal interior Alaska. In this study, I used the remotely sensed peak summer NDVI as an index of boreal productivity at 250 m pixel size from 2000 to 2014. Maximum summer NDVI was related to last day of spring snow, early spring snow water equivalent (SWE), and a summer moisture index. There was no significant correlation between early spring SWE and peak summer NDVI. There was a significant correlation between the last day of spring snow and peak summer NDVI, but only for a few higher elevation stations. This was likely due to snowmelt occurring later at higher elevations, thus having a greater effect on summer soil moisture relative to lower elevation sites. For most of boreal interior Alaska, summer drought was likely the dominant control on peak summer NDVI and this effect may persist for several years. Peak summer NDVI declined at all 26 stations after the 2004 drought, and the decline persisted for 2 years at all stations. Due to the shallow rooting zone of most boreal plants, even cool and moist sites at lower elevations are likely vulnerable to drought. For example the peak summer NDVI response following the 2004 drought was similar for adjacent cold and warm watershed basins. Thus, if frequent and severe summer droughts continue, moisture stress effects are likely to be widespread and prolonged throughout most of interior boreal Alaska, including relatively cool, moist sites regardless of spring snowpack conditions or spring phenology.

Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery

Drought is a rapidly rising environmental issue that can cause hardly repaired or unrepaired damages to the nature and socio-economy. This is especially true for a region that features arid/semi-arid climate, including the Turkey's most important agricultural district — Southeast Anatolia. In this area, we examined the uncertainties of applying Landsat 8 Operational Land Imager (OLI) NDVI data to estimate meteorological drought – Standardized Precipitation Index (SPI) — measured from 31 in-situ agro-meteorological monitoring stations during spring and summer of 2013 and 2014. Our analysis was designed to address two important, yet under-examined questions: (i) how does the co-existence of rainfed and irrigated agriculture affect remote sensing drought monitoring in an arid/semi-arid region? (ii) What is the role of spatial scale in drought monitoring using a GEOBIA (geographic object-based image analysis) framework? Results show that spatial scale exerted a higher impact on drought monitoring especially in the drier year 2013, during which small scales were found to outperform large scales in general. In addition, consideration of irrigated and rainfed areas separately ensured a better performance in drought analysis. Compared to the positive correlations between SPI and NDVI over the rainfed areas, negative correlations were determined over the irrigated agricultural areas. Finally, the time lag effect was evident in the study, i.e., strong correlations between spring SPI and summer NDVI in both 2013 and 2014. This reflects the fact that spring watering is crucial for the growth and yield of the major crops (i.e., winter wheat, barley and lentil) cultivated in the region.

The diversity and abundance of North American bird assemblages fail to track changing productivity.

Plant biomass or productivity and the species richness of birds are associated across a range of spatial scales. Species-energy theory is generally assumed to explain these correlations. If true, bird richness should also track productivity temporally, and there should be spatial and temporal relationships between productivity and both bird abundance and bird richness. Using the summer normalized difference vegetation index (NDVI) for 1982-2006 and the North American Breeding Bird Survey, we evaluated the response of avian richness and abundance to interannual changes in plant biomass or productivity. We found positive spatial relationships between richness and NDVI for all 25 years. Temporally, however, richness and NDVI were positively associated at 1579 survey sites and negatively associated at 1627 sites (mean r2 = 0.09). Further, total abundance and NDVI were unrelated spatially (r2 values spanning < 0.01 and 0.03) and weakly related temporally (mean r2 = 0.10). We found no evidence that productivity drives bird richness beyond the spatial correlations, and neither prediction arising from species-energy theory was confirmed. Spatial relationships between productivity and bird richness may thus be largely spurious, arising via covariance between plant biomass or productivity and vegetation structural complexity, and the latter may be driving bird communities. This is consistent with the MacArthurs' classic hypothesis that the vertical profile of foliage drives bird species diversity.

Analysis on spatio-temporal trends and drivers in vegetation growth during recent decades in Xinjiang, China

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. In this study, a non-stationary 1982–2012 AVHRR NDVI3g time series, the newest dataset, were used to evaluate spatio-temporal patterns of seasonal vegetation changes in Xinjiang province of China at regional, biome and pixel scales over progressively longer periods from 18 to 31 years, starting in 1982, and their linkages to climatic factors and human activities were analyzed. At regional scale, the increases were statistically significant for autumn NDVI during fourteen periods, for growing season and summer NDVI during the most periods, and for spring only during the first four periods. The rates of NDVI increase in growing season and all seasons significantly decreased over fourteen periods. At pixel scale, areas with significant browning rapidly increased over fourteen periods for growing season and all seasons, and these areas were mainly concentrated in northern desert of Xinjiang. Vegetation growth in Xinjiang was regulated by both moisture and thermal conditions: the response of NDVI in spring and autumn was more sensitive to thermal factors, such as temperature and potential evapotranspiration, and correlations between NDVI and precipitation and between NDVI and humidity index were stronger in summer and growing season. Extensive use of fertilizers and expanded farmland irrigated area increased vegetation growth for cropland. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation may reduce spring NDVI in the part of farmlands. Trend analysis during the multiple nested time series may contribute to a better and deep understanding of NDVI dynamic and foreseeing changes in the future. Accordingly, NDVI in Xinjiang will continuously increase at regional scale and the areas showing significant browning will also furthermore grow.

Assessing vegetation dynamics and their relationships with climatic variability in northern China

In this study, the vegetation dynamics and their correlations with climate variability in northern China were evaluated based on the normalized difference vegetation index (NDVI) and meteorological datasets from 1982 to 2006. The NDVI showed that vegetation cover had a tiny increasing trend for whole study area in the past 25 years. However, the interannual changes of NDVI were different in each season. The part of spring and autumn NDVI values increased significantly, while the summer NDVI increased no significantly. And the interannual variations of the NDVI showed obvious spatial differentiations. The annual max NDVI increased were mainly distributed in most areas of grassland and farmland, whereas the annual max NDVI decreased were mainly distributed in forest areas. The annual NDVI and temperature had more important relationships. Thus, as compared to precipitation, the correlation between NDVI with temperature was stronger than the precipitation in northern China. NDVI and climatic variables were different in each season. The NDVI trends exhibited a close correspondence to climatological variations in region and season. In Addition, human activities also had profound effect to the NDVI trends in some regions. All these findings will make humans know more about the knowledge of the natural forces that influence vegetation change and supply a scientific basic resource to for the environmental management in northern China.

Change in NDVI of forest ecosystems in Northern Caucasus as a function of topography and climate

A grid of long-term average July temperature (TJUL) with a grid spacing 500 m was developed to analyze associations between the NDVI vegetation index of mountain forests and climate and topography in the Kuban river basin. The detail of a map obtained based on this grid is substantiated (validation is a special term also used in this paper) by the close association (R2 = 0.978) of temperature on weather stations with topography. It was demonstrated that the long-term average July temperature is significantly determined not only by an altitude (above sea level) but also by the slope insolation (terminology) and valley characteristics. A close nonlinear association with TJUL was detected for the NDVI of mountain forests in this basin. Samples of pine forests close to glaciers reveal close nonlinear associations between the summer NDVI and topography and distance from glaciers. Ecological optima of TJUL were detected for broad-leaved, small-leaved, and pine types of forest in the basin; such an optimum was not established for dark coniferous forests. The average value of NDVI can decrease in predicted NDVI estimations of broad-leaved forests, according to the climatic E GISS scenario, by 2050, since the temperature optimum of forests will move to the area of high-altitude topography, which is characterized by less favorable topographical conditions for the development of forests.

Is Forest Restoration in the Southwest China Karst Promoted Mainly by Climate Change or Human-Induced Factors?

The Southwest China Karst, the largest continuous karst zone in the world, has suffered serious rock desertification due to the large population pressure in the area. Recent trend analyses have indicated general greening trends in this region. The region has experienced mild climate change, and yet significant land use changes, such as afforestation and reforestation. In addition, out-migration has occurred. Whether climate change or human-induced factors, i.e., ecological afforestation projects and out-migration have primarily promoted forest restoration in this region was investigated in this study, using Guizhou Province as the study area. Based on Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data, we found general greening trends of the forest from 2000 to 2010. About 89% of the forests have experienced an increase in the annual NDVI, and among which, about 41% is statistically significant. For the summer season, more than 65% of the forests have increases in summer NDVI, and about 16% of the increases are significant. The strongest greening trends mainly occurred in the karst areas. Meanwhile, annual average and summer average temperature in this region have increased and the precipitation in most of the region has decreased, although most of these changes were not statistically significant (p &gt; 0.1). A site-based regression analysis using 19 climate stations with minimum land use changes showed that a warming climate coupled with a decrease in precipitation explained some of the changes in the forest NDVI, but the results were not conclusive. The major changes were attributed to human-induced factors, especially in the karst areas. The implications of an ecological afforestation project and out-migration for forest restoration were also discussed, and the need for further investigations at the household level to better understand the out-migration–environment relationship was identified.

Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere.

Over the last century the Northern Hemisphere has experienced rapid climate warming, but this warming has not been evenly distributed seasonally, as well as diurnally. The implications of such seasonal and diurnal heterogeneous warming on regional and global vegetation photosynthetic activity, however, are still poorly understood. Here, we investigated for different seasons how photosynthetic activity of vegetation correlates with changes in seasonal daytime and night-time temperature across the Northern Hemisphere (>30°N), using Normalized Difference Vegetation Index (NDVI) data from 1982 to 2011 obtained from the Advanced Very High Resolution Radiometer (AVHRR). Our analysis revealed some striking seasonal differences in the response of NDVI to changes in day- vs. night-time temperatures. For instance, while higher daytime temperature (Tmax) is generally associated with higher NDVI values across the boreal zone, the area exhibiting a statistically significant positive correlation between Tmax and NDVI is much larger in spring (41% of area in boreal zone--total area 12.6×10(6) km2) than in summer and autumn (14% and 9%, respectively). In contrast to the predominantly positive response of boreal ecosystems to changes in Tmax, increases in Tmax tended to negatively influence vegetation growth in temperate dry regions, particularly during summer. Changes in night-time temperature (Tmin) correlated negatively with autumnal NDVI in most of the Northern Hemisphere, but had a positive effect on spring and summer NDVI in most temperate regions (e.g., Central North America and Central Asia). Such divergent covariance between the photosynthetic activity of Northern Hemispheric vegetation and day- and night-time temperature changes among different seasons and climate zones suggests a changing dominance of ecophysiological processes across time and space. Understanding the seasonally different responses of vegetation photosynthetic activity to diurnal temperature changes, which have not been captured by current land surface models, is important for improving the performance of next generation regional and global coupled vegetation-climate models.

Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011

Recent warming has stimulated the productivity of boreal and Arctic vegetation by reducing temperature limitations. However, several studies have hypothesized that warming may have also increased moisture limitations because of intensified summer drought severity. Establishing the connections between warming and drought stress has been difficult because soil moisture observations are scarce. Here we use recently developed gridded datasets of moisture variability to investigate the links between warming and changes in available soil moisture and summer vegetation photosynthetic activity at northern latitudes (&gt;45°N) based on the Normalized Difference Vegetation Index (NDVI) since 1982. Moisture and temperature exert a significant influence on the interannual variability of summer NDVI over about 29% (mean r2 = 0.29 ± 0.16) and 43% (mean r2 = 0.25 ± 0.12) of the northern vegetated land, respectively. Rapid summer warming since the late 1980s (~0.7 °C) has increased evapotranspiration demand and consequently summer drought severity, but contrary to earlier suggestions it has not changed the dominant climate controls of NDVI over time. Furthermore, changes in snow dynamics (accumulation and melting) appear to be more important than increased evaporative demand in controlling changes in summer soil moisture availability and NDVI in moisture-sensitive regions of the boreal forest. In boreal North America, forest NDVI declines are more consistent with reduced snowpack rather than with temperature-induced increases in evaporative demand as suggested in earlier studies. Moreover, summer NDVI variability over about 28% of the northern vegetated land is not significantly associated with moisture or temperature variability, yet most of this land shows increasing NDVI trends. These results suggest that changes in snow accumulation and melt, together with other possibly non-climatic factors are likely to play a significant role in modulating regional ecosystem responses to the projected warming and increase in evapotranspiration demand during the coming decades.

NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas

Considerable researches during the past several decades have focused on monitoring changes in vegetation growth due to its important role in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of the satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of vegetation growth in 1982–2006 in the Koshi River Basin (KRB) in the middle Himalayas. In addition, the dataset from the global land surface satellite sensor from SPOT-4's Vegetation instrument in 1998–2011 was used to further verify the results of this study. At the regional scale, although a statistically significant increase in average growing season NDVI was observed (0.0008yr−1, P=0.03) during the entire study period, there existed three distinct periods with opposing trends. Growing season NDVI significantly increased in 1982–1994 (0.0019yr−1, P=0.03), and then decreased in 1994–2000 (−0.0058yr−1, P<0.001), while increased again in 2000–2006 (0.0049yr−1, P<0.001) and in 2000–2011 (0.0034yr−1, P<0.001). A sudden drop in NDVI in 1994–2000 largely contributed to these fluctuations in growing season NDVI over the KRB. Spring, summer and autumn NDVI significantly decreased in 1994–2000 (−0.0077yr−1, P<0.001; −0.0067yr−1, P<0.001; and −0.0155yr−1, P<0.001; respectively). Our further spatial analyses supported the existence of the sudden decrease in spring, summer and autumn NDVI in 1994–2000.

Assessing woody biomass in African tropical savannahs by multiscale remote sensing

Woody biomass production is a critical indicator in evaluation of land use management and the dynamics of the global carbon cycle (sequestration/emission) in terrestrial ecosystems. The objective of the present study was to develop, through a case study in Sudan, an operational multiscale remote-sensing-based methodology for large-scale estimation of woody biomass in tropical savannahs. Woody biomass estimation models obtained by different authors from destructive field measurements in different tropical savannah ecosystems were expressed as functions of tree canopy cover (CC). The field-measured CC data were used for developing regression equations with atmospherically corrected and reflectance-based vegetation indices derived from Landsat ETM+ (Enhanced Thematic Mapper Plus) imagery. Among a set of vegetation indices, the normalized difference vegetation index (NDVI) provided the best correlation with CC (R2 = 0.91) and was hence selected for woodland woody biomass estimation. After validation of the CC-NDVI model and its applicability to Moderate Resolution Imaging Spectroradiometer (MODIS) data, time-series MODIS NDVI data (MOD13Q1) were used to partition the woody component from the herbaceous component for sparse woodlands, woodlands and forests defined by the Food and Agriculture Organization (FAO) of the United Nations Land Cover Map. Following the weighting of the estimation models based on the dominant woody species in each vegetation community, NDVI-based woody biomass models were applied according to their weighted ratios to the decomposed summer and autumn woody NDVI images in all vegetation communities in the whole of Sudan taking the year 2007, for example. The results were found to be in good agreement with those from other authors obtained by either field measurements or other remote sensing methods using MODIS and lidar data. It is concluded that the proposed approach is operational and can be applied for a reliable large-scale assessment of woody biomass at a ground resolution of 250 m in tropical savannah woodlands of any month or season.

Long-term sustainability of the hydrology and vegetation of Cienega de Santa Clara, an anthropogenic wetland created by disposal of agricultural drain water in the delta of the Colorado River, Mexico

The Ciénega of Santa Clara is a valuable coastal wetland sustained almost entirely by discharge of brackish agricultural drain water from the U.S. and Mexico. In other locations, agricultural drain water has been problematic in supporting wetlands due to problems of salinity buildup, toxic substances and undesirable plant succession processes. We studied the development of the Cienega de Santa Clara from its creation in 1977 to the present to determine if it is on a sustainable trajectory in terms of vegetation, hydrology and habitat value. We used Landsat NDVI imagery from 1975 to 2011 to determine the area and intensity of vegetation and to estimate evapotranspiration (ET) to construct a water balance. Remote sensing data were combined with hydrological data, site surveys and other sources of information on the Cienega. The vegetated area increased from 1978 to 1995 and has been constant at about 4200ha since then. The dominant vegetation type is Typha domingensis (southern cattail), and peak summer NDVI since 1995 has been stable at 0.379 (SD=0.016), about half of NDVIMax. Flows into the marsh have been stable both month-to-month and year-to-year, with a mean annual value of 4.74m3s−1 (SD=1.03). Salinity has been stable with a mean value of 2.09gL−1 TDS (SD=0.13). About 37% of the inflow water is consumed in ET, with the remainder exiting the Cienega as outflow water, mainly during winter months when T. domingensis is dormant. The sustainability of the Cienega is attributed to: stable inflow rates; salinities within the tolerance limit of the dominant vegetation; and tidal flushing which maintains the wetland as an open system.

Vegetation dynamics and their response to freshwater inflow and climate variables in the Yellow River Delta, China

Based on SPOT Vegetation NDVI data, streamflow data and meteorological data, the variation of vegetation cover, measured by the Normalized Difference Vegetation Index (NDVI), and its response to freshwater inflow, precipitation and temperature in the Yellow River Delta and its buffer zones have been investigated for the period 1998–2009. The results show that NDVI has a remarkable regional and seasonal difference. The farther from the Yellow River Channel and the nearer to the Bohai Sea Coastline, the smaller the NDVI value, as influenced by the interaction between freshwater and saltwater on vegetation. Seasonally, high NDVI values appear in summer (August) and low in spring (April). From 1998 to 2009, growing season NDVI significantly increases in the Yellow River Delta. Summer and autumn NDVI have a similar trend pattern to growing season NDVI, while spring NDVI significantly decreases. NDVI shows different strengths of correlation with freshwater inflow, precipitation and temperature respectively and these correlations vary in different seasons and months. Freshwater inflow is a key factor for vegetation dynamics and NDVI variation. Climate features play a dominant role in seasonal variation in vegetation cover. However, the impacts of freshwater inflow and climate variables on vegetation have been greatly modified by a range of human activities such as land use pattern and land use change as well as water diversion from the Yellow River. Overall, the results of this study can be helpful for decision-making of regional ecological protection and economic development.

Drought and spring cooling induced recent decrease in vegetation growth in Inner Asia

The response of vegetation growth to current climate change in Inner Asia (35–55°N, 45–120°E) was investigated by analyzing time series of the Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2009. We found that at the regional scale, the greening trend observed during the 1980s was stalled in the 1990s. Different seasons, however, show different changes and mechanisms. Among the three seasons (spring, summer and autumn), summer has the earliest turning point (from greening to non-greening) in the early 1990s, as a result of summertime droughts, as indicated by Palmer Drought Severity Index (PDSI). Consistent with the change in summer NDVI, summer PDSI and precipitation significantly increased in the 1980s, but strongly decreased since the early 1990s at the regional scale. The negative effect of summer drought is particularly significant over dry regions such as eastern Kazakhstan, Mongolia and Inner Mongolia. However, in high altitude or high latitude regions (>50°N), summer vegetation growth is more strongly correlated with summer temperature rather than with summer PDSI. In spring, changes in vegetation growth are closely linked with temperature changes rather than droughts. Both spring temperature and spring NDVI, for instance, increased until the late 1990s and then decreased. Statistical analyses also show that spring NDVI is significantly correlated with spring temperature at the regional scale (P<0.05), implying that temperature is the dominant limiting factor for spring vegetation growth in most regions of Inner Asia except Turkmenistan and Uzbekistan, where spring PDSI shows significant positive correlation with spring NDVI. Further analyses of the response of vegetation to extreme high spring temperature in 1997 and extreme summer drought in 2001 exhibit a highly heterogeneous pattern.