Inter-annual NDVI Research Articles

Spatio-Temporal Dynamics of Normalized Difference Vegetation Index and Its Response to Climate Change in Xinjiang, 2000–2022

Based on the NDVI and climate data from 2000 to 2022, this study systematically investigated the spatial and temporal patterns, trend characteristics, and stability of the NDVI in Xinjiang using the one-way linear regression method, Theil–Sen Median trend analysis, the Mann–Kendall significance test, and the coefficient of variation. Meanwhile, the persistence of the NDVI distribution was analyzed by combining the trend results and Hurst index. Finally, partial correlation analysis was used to deeply explore the response mechanisms of interannual and seasonal-scale NDVI and climatic factors in Xinjiang, and the characteristics of multi-year vegetation distribution were comprehensively analyzed with the help of human footprint data. The findings indicate the following: (1) The NDVI of interannual and seasonal vegetation in Xinjiang showed a significant increasing trend during the 23-year period, but the spatial distribution was heterogeneous, and the improvement of the vegetation condition in the southern part of the region was remarkable. (2) The NDVI is relatively stable across the region. Unlike in other regions, in general, it is difficult to maintain the existing trend in NDVI in the study area for a long period of time, and the reverse trend is more persistent. (3) On the interannual scale, both precipitation and temperature are positively correlated with the NDVI, and the influence of temperature (80.94%) is greater than that of precipitation (63.82%). Precipitation was dominantly positively correlated with the NDVI in spring, summer, and the growing season, while it was negatively correlated with it in autumn. Temperature and NDVI were positively correlated, with the greatest influence in the spring. (4) Human activities had the greatest impact on the areas with low vegetation cover and areas with medium–low vegetation cover, and there was a high degree of overlap between the areas where the interannual human footprints and NDVI showed an increasing trend. The percentage of human footprints that significantly correlated with interannual NDVI was 34.79%. In the future, the protection and management of ecologically fragile areas should be increased to increase desert-vegetation cover.

Spatiotemporal Variation in Vegetation and Its Driving Mechanisms in the Southwest Alpine Canyon Area of China

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA.

Greater Greening Trend in the Loess Plateau of China Inferred from Long-Term Remote Sensing Data: Patterns, Causes and Implications

The Loess Plateau (LP) of China, which is the pilot region of the “Grain to Green Project” (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade−1p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems.

How to automate timely large-scale mangrove mapping with remote sensing

Mangrove forests have witnessed significant changes resulted from both anthropogenic and natural disturbances in the last four decades. Although a few attempts have been reported, effective methods that can repeatedly generate large-scale mangrove maps on a timely basis are still lacking due to the difficulty in gathering sufficient training samples in large geographical areas. In this study, we have addressed three objectives in the following manner: (1) we aim to develop a method to automatically collect ample mangrove training samples; Correspondingly, we developed an automatic training sample collection method which extracted unchanged mangrove samples from a historical mangrove map; In addition, we employed a region growing method to include more diversified training samples; (2) we strive to foster compatible classifiers that can leverage the collected one-class training samples; To this end, we came up with two representative one-class classifiers: the Support Vector Data Description (SVDD), and the Positive and Unlabeled Learning algorithm (PUL); (3) we endeavor to compare the effectiveness of various combinations of training samples, classifiers, and input images; As a result, we developed 32 classification models by varying four different variables: training samples (unchanged vs. expanded), input data (Landsat 8, Sentinel-1, and Sentinel-2), classifiers (SVDD vs. PUL), and study sites (Florida, the United States and Guangxi, China). We found that our developed automatic training sample collection methods performed well (user's accuracy >97%). Inter-annual NDVI combined with geometric restrictions warranted the effective extraction of unchanged training samples while the region growing method further reduced the omission due to its addition of recently emerged mangroves. In addition, PUL performed better than SVDD. This is attributed to the fact that PUL draws upon not only mangrove samples, but also unlabeled ones unaccounted for in SVDD. Lastly, the combination of Sentinel-1 and Sentinel-2 is recommended among all the compared models. In summary, we developed an effective method to automatically extract mangrove training samples, based on which a one-class classification method for large-scale mangrove mapping is made possible. We envision our methods will contribute to a wide spectrum of timely large-scale mangrove mapping tasks.

Rewilding of large herbivore communities in high elevation Puna: geographic segregation and no evidence of positive effects on peatland productivity

During the past decades, the Argentine Puna (a subtropical high elevation desert of c. 14 million hectares) has been going through a process of “rewilding” of large herbivore communities. In response to rural population outmigration and resulting livestock reduction accompanied by protection measures, large wild herbivores such as vicunas increased. To explore the ecological consequences of this transition, we analysed the spatial patterns of wild herbivores and livestock in relation to human settlements, and we estimated the changes in vegetation productivity of peatlands, using NDVI derived from MODIS satellite images between 2001 and 2012. Peatlands are key ecosystems in high elevation deserts; they regulate water and carbon fluxes and provide most forage for livestock and wildlife. A field survey of 50 peatlands revealed that travel time from human settlements was positively correlated with the wild herbivore abundance, and negatively correlated with livestock abundance (especially sheep and goats). Thus, it is a good indicator of livestock use. At the regional scale, after controlling for the physical and climatic variables of the 8134 peatlands, accessibility from human settlements explained less than 0.1% of mean and interannual NDVI variability. These results provide no evidence of livestock-induced degradation. Furthermore, likely due to rainfall increase, peatlands showed an overall positive NDVI trend between 2001 and 2012, but isolated peatlands showed comparatively more negative NDVI trends during the same period, possibly associated with increased wildlife herbivory. Similar rewilding processes could occur in many mountain regions undergoing decreasing human population and marginal land uses. Our study suggests that traditional management could contribute to the productivity, stability, and resilience of peatlands.

Convergence between satellite information and farmers’ perception of drought in rangelands of North-West Patagonia, Argentina

Drought is a complex natural hazard with social and environmental implications. Satellite information is increasingly used to support decision-makers in preventing or coping with the negative impacts of drought. The integration of local and scientific knowledge to support drought monitoring is still far from being the main procedure in the development of drought monitoring and early warning systems. This study aimed at assessing the degree of convergence between satellite information on the effect of droughts on rangeland vegetation, from time series analysis, and farmers’ perception of drought in North-West Patagonia, Argentina. We characterised the scientific evidence of drought in terms of duration, spatial distribution, most severe years and recovery for the period 2000–2018 by identifying inter-annual NDVI changes. Farmers’ perceptions and experiences of drought were studied with open-ending interviews, with respect to occurrence, duration and recovery for that period. Satellite information matched farmers’ perception of drought at a regional scale, emphasising the value of remote sensing tools in supporting regional policy decision-making. However, farmers’ perceptions and recall of past drought impacts were more diverse than satellite information at a local level, highlighting the need for knowledge integration at finer scales.

Trends in evapotranspiration and their responses to climate change and vegetation greening over the upper reaches of the Yellow River Basin

This study comprehensively analyzed long-term (1960–2014) variations of potential and actual evapotranspiration (ET) and quantified their inter-annual and seasonal sensitivity and attribution to climate and vegetation growth changes in the upper reaches of the Yellow River Basin (UYRB). Satellite-derived vegetation greenness and in situ measured hydrometeorological datasets were used. Average annual reference evapotranspiration (ET0) showed significant decreasing trend (p < 0.05) by 0.32 mm year−1 over the past 55 years. Results obtained from recovered stationary series and sensitivity analyses showed that reduction of wind speed was the major driving force for decreasing trends in annual and seasonal ET0. Water budget-derived actual evapotranspiration (ETa) increased dramatically during the study period, whereas potential evapotranspiration (ETp) calculated from Penman equation and pan evaporation (ETpan) decreased significantly, suggesting the existence of evaporation paradox over the UYRB. ETa and ETp or ETpan exhibited complementary behavior, while the complementary relationship was asymmetric. During the period 1982–2014, vegetation greening occurred in most areas (65.6%) of the UYRB and was mediated by elevation. Interestingly, non-uniform climate warming imposed opposite impacts on vegetation growth, namely inter-annual and seasonal NDVI was stimulated by daytime temperature but suppressed by nighttime temperature. Furthermore, the positive impacts imposed by daytime temperature on NDVI was much larger in spring than in autumn and summer. Large-scale coherent vegetation greening would be the primary factor affecting inter-annual ETa in comparison with climate factors. These results help in deep understanding the hydrological response to climate change in cold mountain regions.

Spatial and temporal changes in inter-annual and seasonal NDVI in the Qinling mountains of China

As an important ecological barrier in central China, the Qinling Mountains have received more attention in response to global or regional climate change. Spatial and temporal changes of inter-annual and seasonal NDVI were analysed after extraction of the vegetation growth season, based on MODIS NDVI images and linear trend analysis. The results showed that: (1) the NDVI value and vegetation cover level of the Qinling Mountains were high. The NDVI showed significant annual and seasonal increases, in which the summer value contributed the most to the inter-annual value, followed by spring and autumn. In terms of the change trend, the NDVI showed a linear increase in annual and seasonal data, in which the spring growth rate contributed the most to the inter-annual value, followed by autumn and summer. (2) The spatial distribution of the linear change trend of the NDVI in the inter-annual and seasonal data was mainly increased, but the area of reduction was also large. The area of reduced NDVI was mainly distributed in the middle and western area with middle and high elevations, as well as urban neighbourhoods with intensive human activities, such as the Hanzhong basin, the Ankang basin, and the Shangluo basin. The rate of change in NDVI in human activity areas was higher than in the middle and higher elevation areas. The change may be driven by human activities in the former and by climate change in the latter. Monitoring of the vegetation ecological system in the Qinling Mountains should be strengthened to understand the change process, as well as to provide a scientific basis when making policies for regional ecological environmental protection.

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

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.

Design and characterization of spatial units for monitoring global impacts of environmental factors on major crops and food security

AbstractThis paper describes the design and characterization of a limited number of areas to be used as spatial monitoring and reporting units (MRU) by CropWatch, the global crop monitoring system ( http://www.cropwatch.com.cn/) hosted by the Chinese Academy of Sciences. The MRUs are modified and adapted from an existing standard vegetation zoning map. They are designed to be areas of uniform vulnerability as assessed by three sets of variables: environmental (such as climate and NDVI), seasonality (such as intra‐annual climate variations and interannual NDVI variability) and agronomy (such as presence/absence of major crops and their yield). The paper pays due attention to within‐zone spatial variability for each of the three groups of variables: in general, the variability measured as the average of the spatial coefficient of variation inside MRUs, is much larger for agronomic variables (330%) than for environmental variables (38%) and seasonality (55%). The MRUs provide a rather coherent picture of the variations of global agriculture, for instance, the links between the distribution of crops, agricultural production potential, and environmental variability (over space and time). They closely delineate the distribution of the major nonsugar food crops (barley, cassava, maize, potatoes, rice, soybean, and wheat). The discussion focuses on the relations between the size of the MRUs and the within‐MRU variability (spatial heterogeneity) of cropping, and environmental conditions including seasonality. The conclusion stresses that spatial variability in agriculture is bound to be larger than the variability in the environmental variables used to define the units, regardless of the units’ size. The size of the spatial units (and therefore their number) is not a very critical constraint for operational impact reporting, especially if impact indicators focus on agricultural areas inside MRUs.

Shifts in vegetation growth in response to multiple factors on the Mongolian Plateau from 1982 to 2011

The Mongolian Plateau (MP) steppe is one of the largest steppe environments in the world. To monitor the terrestrial vegetation dynamics on the MP and to ascertain what the driving forces, this study examined the vegetation dynamics in Republic of Mongolia (M) and the Inner Mongolia Autonomous Region (IM) of China from the period 1982 to 2011, based on the satellite-derived GIMMS NDVI3g (Normalized Difference Vegetation Index) data across three biomes (desert, grassland and forest). The results are as followed: (1) Vegetation coverage in IM was generally greater than that in M. Before 2002, time series of NDVI over the MP increased at an average rate of 0.05%yr−1. Additionally, after 2002, the NDVI increased at a rate of 0.21%yr−1. From 1982 to 2011, the area of IM and M with positive anomalies in the NDVI increased at a separate rate of 1.82%yr−1 and 1.76%yr−1, respectively. (2) At the biome scale, the inter-annual forest NDVI variation in IM and desert NDVI for the entire MP had a significant increasing trend (0.06%yr−1 and 0.04%yr−1, respectively). (3) Climate forcing was a dominant controlling factor affecting the vegetation, and the anthropogenic behavior exhibited no significant value in the whole region. However, overgrazing was the most important reason for the regional degradation, particularly in IM. (4) In the future, the forest biome will go to recovery, whereas both the grassland and desert biomes are predicted to degrade continuously.

Monitoring vegetation change and dynamics on U.S. Army training lands using satellite image time series analysis

Given the significant land holdings of the U.S. Department of Defense, and the importance of those lands to support a variety of inherently damaging activities, application of sound natural resource conservation principles and proactive monitoring practices are necessary to manage military training lands in a sustainable manner. This study explores a method for, and the utility of, analyzing vegetation condition and trends as sustainability indicators for use by military commanders and land managers, at both the national and local levels, in identifying when and where vegetation-related environmental impacts might exist. The BFAST time series decomposition method was applied to a ten-year MODIS NDVI time series dataset for the Fort Riley military installation and Konza Prairie Biological Station (KPBS) in northeastern Kansas. Imagery selected for time-series analysis were 16-day MODIS NDVI (MOD13Q1 Collection 5) composites capable of characterizing vegetation change induced by human activities and climate variability. Three indicators related to gradual interannual or abrupt intraannual vegetation change for each pixel were calculated from the trend component resulting from the BFAST decomposition. Assessment of gradual interannual NDVI trends showed the majority of Fort Riley experienced browning between 2001 and 2010. This result is supported by validation using high spatial resolution imagery. The observed versus expected frequency of linear trends detected at Fort Riley and KPBS were significantly different and suggest a causal link between military training activities and/or land management practices. While both sites were similar with regards to overall disturbance frequency and the relative spatial extents of monotonic or interrupted trends, vegetation trajectories after disturbance were significantly different. This suggests that the type and magnitude of disturbances characteristic of each location result in distinct post-disturbance vegetation responses. Using a remotely-sensed vegetation index time series with BFAST and the indicators outlined here provides a consistent and relatively rapid assessment of military training lands with applicability outside of grassland biomes. Characterizing overall trends and disturbance responses of vegetation can promote sustainable use of military lands and assist land managers in targeting specific areas for various rehabilitation activities.

Mapping Potential Groundwater‐Dependent Ecosystems for Sustainable Management

Ecosystems which rely on either the surface expression or subsurface presence of groundwater are known as groundwater-dependent ecosystems (GDEs). A comprehensive inventory of GDE locations at an appropriate management scale is a necessary first-step for sustainable management of supporting aquifers; however, this information is unavailable for most areas of concern. To address this gap, this study created a two-step algorithm which analyzed existing geospatial and remote sensing data to identify potential GDEs at both state/province and aquifer/basin scales. At the state/province scale, a geospatial information system (GIS) database was constructed for Texas, including climate, topography, hydrology, and ecology data. From these data, a GDE index was calculated, which combined vegetative and hydrological indicators. The results indicated that central Texas, particularly the Edwards Aquifer region, had highest potential to host GDEs. Next, an aquifer/basin scale remote sensing-based algorithm was created to provide more detailed maps of GDEs in the Edwards Aquifer region. This algorithm used Landsat ETM+ and MODIS images to track the changes of NDVI for each vegetation pixel. The NDVI dynamics were used to identify the vegetation with high potential to use groundwater--such plants remain high NDVI during extended dry periods and also exhibit low seasonal and inter-annual NDVI changes between dry and wet seasons/years. The results indicated that 8% of natural vegetation was very likely using groundwater. Of the potential GDEs identified, 75% were located on shallow soil averaging 45 cm in depth. The dominant GDE species were live oak, ashe juniper, and mesquite.

Multi-pronged assessment of land degradation in West Africa to assess the importance of atmospheric fertilization in masking the processes involved

Separating human-induced land degradation from that caused by natural processes in the world of global climate and atmospheric change is a challenging task, but important for developing mitigation strategies. Current remote-sensing data and spatio-temporal analyses allow the distinction of climate and human-induced land degradation on a sub-continental scale, but the underlying processes cannot be discerned at this scale. This study is conducted at a river-basin scale to (1) identify land degradation hotspots in a basin or sub-basin, and (2) assess the correspondence and divergence of land degradation assessed by NDVI shifts with and without accounting for atmospheric fertilization with that based on soil erosion assessment at a sub-basin scale. Long-term remote sensing (NDVI) and rainfall data were used to identify human-induced land degradation hotspot areas in the Volta basin. The results were compared with the critical zone of soil loss in the White Volta sub-basin derived from a spatially distributed soil erosion model, validated by field-measured data. A spatial comparison of the above studies revealed that the biomass productivity (NDVI)-based land degradation assessment grossly underestimated the extent to which soil is being lost, unless a correction was included to account for atmospheric fertilization. Based on inter-annual NDVI signals land degradation was evident in about 8% of the Volta basin's landmass, but when accounting for atmospheric fertilization, as much as 65% of the land is losing some of its vital attributes such as soil quality or vegetation productivity. The study demonstrates the need for using a multi-pronged assessment strategy in land degradation assessment that offers an insight of the processes involved in land degradation.

Impacts of climate warming on vegetation in Qaidam Area from 1990 to 2003

The observed warming trend in the Qaidam area, an arid basin surrounded by high mountains, has caused land surface dynamics that are detectable using remotely sensed data. In this paper, we detected land-cover changes in the Qaidam Area between 1990 and 2003 in attempt to depict its spatial variability. The land-cover changes were categorized into two trends: degradation and amelioration, and their spatial patterns were examined. Then we estimated the correlation coefficients between growing-season NDVI and several climatic factors with the consideration of duration and lagging effects. The results show that the inter-annual NDVI variations are positively correlated with May to July precipitations, but not significantly correlated with sunshine duration. We observed no obvious trend in precipitation or sunshine duration from 1990 to 2003. Thus, the authors suggest that their slight fluctuations may not be responsible to the decade-scaled land-cover changes. However, our results indicate a good positive relationship between the NDVI trend and climate warming in the ameliorated areas, but a negative one in the degraded areas. By statistical analyses, we found that degradations mainly occurred at the oasis boundaries and at lower elevations in the non-oasis regions where effective soil moisture might have been reduced by the warming-caused increase in evapotranspiration. At higher elevations where thermal condition acts as a major limiting factor, ameliorations were unequivocally detected, which is attributable to the direct facilitation by temperature increases. We suggest that the impacts of the observed climate warming on vegetation are spatially heterogeneous, depending on the combinations of thermal condition and moisture availability.

Interannual variability of NDVI and species richness in Kenya

Ecologists have long recognized the spatial variability of species richness. In an attempt to identify the factors responsible for this variability, ecologists have traditionally used environmental data obtained from sparse point samples (such as meteorological stations). However, remotely sensed data also provide a means of estimating relevant environmental factors and thereby improving predictions of species richness. The Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index (AVHRR NDVI) has been shown to be related to net primary productivity (NPP) and actual evapotranspiration (AET) for many vegetation types. NPP and AET have frequently been used as surrogate measures for species richness. Local spatial variability of NPP and AET, indicating habitat heterogeneity, is hypothesized as another influence on species richness. We examined the relationship between interannual maximum NDVI parameters and species richness of vascular plants and mammals. The study was done at a landscape scale, which matches the scale of data collection. Statistical analyses revealed that higher average NDVI results in lower species richness, whereas standard deviation and coefficient of variation correlated positively with species richness. Thus, NDVI parameters appear to represent environmental factors influencing species richness. Hence, by utilizing remote sensing, our understanding of the spatial variability of species richness was improved.