Central Asia is one of the most vulnerable regions on the planet earth to global climate change, depending on very fragile natural resources. The Soviet legacy has left the five countries (Kazakhstan, Tajikistan, Kyrgyzstan, Turkmenistan, and Uzbekistan) with a highly integrated system but they are facing great challenges with tensions that hinder regional coordination of food and water resources. With increasing climate variability and warming trend in the region, food and water security issues become even more crucial now and, if not addressed properly, could affect the regional stability. The long-term drivers of these two most critical elements, food and water, are climate change; the immediate and probably more drastic factors affecting the food and water security are land uses driven by institutional change and economic incentives. As a feedback, changes in land use and land cover have directly implications on water uses, food production, and lifestyles of the rural community in the region. Regional and international efforts have been made to holistically understand the cause, extent, rate and societal implications of land use changes in the region. Much of these have been understood, or under investigation by various projects, but solutions or research effort to develop solutions, to these urgent regional issues are lacking. This article, serves as an introduction to the special issue, provides a brief overview of the challenges facing the Central Asian countries and various international efforts in place that resulted in the publications of this special issue.

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 land-cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.

Sigificant dependence from climate and anthropogenic influences characterize ecological systems of Kazakhstan. As result of the geographical location of the republic and ecological situation vegetative degradation sites exist throughout the territory of Kazakhstan. The major process of desertification takes place in the arid and semi-arid areas. To allocate spots of stable degradation of vegetation, the transition zone was first identified. Productivity of vegetation in transfer zone is slightly dependent on climate conditions. Multi-year digital maps of vegetation index were generated with NOAA satellite images. According to the result, the territory of the republic was zoned by means of vegetation productivity criterion. All the arable lands in Kazakhstan are in the risky agriculture zone. Estimation of the productivity of agricultural lands is highly important in the context of risky agriculture, where natural factors, such as wind and water erosion, can significantly change land quality in a relatively short time period. We used an integrated vegetation index to indicate land degradation measures to assess the inter-annual features in the response of vegetation to variations in climate conditions from low-resolution satellite data for all of Kazakhstan. This analysis allowed a better understanding of the spatial and temporal variations of land degradation in the country.

Inland lakes are the major surface water resource in the arid regions of Central Asia. Therefore, the surface area changes in inland lakes have been a sensitive indicator of climate changes and human activities, and have often been the focus of ecological and environmental research. This study aimed to monitor the changes in surface area of nine major lakes over a 32-year period. The water body was extracted from MSS images from the mid-1970s, TM images from the early 1990s, ETM+ images in the late 1990s, and TM images in 2007. The results indicated that the total surface area of these nine lakes had decreased over time to 50.38% of the area, from 91402.06 km² in 1975 to 46049.23 km² in 2007. As the surface area of lakes in the western part of Central Asia was larger than that in the eastern part, the shrinking trend of lake area was more significant in the west than in the east. There was a varied reduction of closed lakes in flat regions. The most substantial decrease was in the surface area of closed lakes in flat regions. Most significantly, the area of the Aral Sea was reduced by 75.7% from its original area in 1975. The area of alpine lakes remained relatively stable; the change in surface area was less than 0.7% during the period 1975–2007. The area change in opened lakes with outlets was notably different from the other two types. The area of Zaysan had increased sharply by 5.85%, and that of Bosten had decreased by 9.1%. Sasykkol had hardly any changes in this period. Due to global climate warming, vapor transfer to the south via westerly winds had been blocked, resulting in a decrease of much-needed precipitation in the western parts of Turkmenistan, Uzbekistan, and Kazakhstan between 1970 and 2000. The decrease in precipitation and the increase in water consumption for agricultural irrigation resulted in the decrease of river runoff. Consequently, the area of inland lakes in Central Asia shrank over the past 32 years.

Grain production in the countries of the former USSR sharply declined during the past two decades and has only recently started to recover. In the context of the current economic and food-price crisis, Russia, Ukraine, and Kazakhstan might be presented with a window of opportunity to reemerge on the global agricultural market, if they succeed in increasing their productivity. The future of their agriculture, however, is highly sensitive to a combination of internal and external factors, such as institutional changes, land-use changes, climate variability and change, and global economic trends. The future of this region’s grain production is likely to have a significant impact on the global and regional food security over the next decades.

In India, land resources have reached a critical stage due to the rapidly growing population. This challenge requires an integrated approach toward harnessing land resources, while taking into account the vulnerable environmental conditions. Remote sensing and Geographical Information System (GIS) based technologies may be applied to an area in order to generate a sustainable development plan that is optimally suited to the terrain and to the productive potential of the local resources. The present study area is a part of the middle Ganga plain, known as Son-Karamnasa interfluve, in India. Alternative land use systems and the integration of livestock enterprises with the agricultural system have been suggested for land resources management. The objective of this paper is to prepare a land resource development plan in order to increase the productivity of land for sustainable development. The present study will contribute necessary input for policy makers to improve the socio-economic and environmental conditions of the region.

We present an approach to regional environmental monitoring in the Northern Eurasian grain belt combining time series analysis of MODIS normalized difference vegetation index (NDVI) data over the period 2001–2008 and land cover change (LCC) analysis of the 2001 and 2008 MODIS Global Land Cover product (MCD12Q1). NDVI trends were overwhelmingly negative across the grain belt with statistically significant (p≤0.05) positive trends covering only 1% of the land surface. LCC was dominated by transitions between three classes; cropland, grassland, and a mixed cropland/natural vegetation mosaic. Combining our analyses of NDVI trends and LCC, we found a pattern of agricultural abandonment (cropland to grassland) in the southern range of the grain belt coinciding with statistically significant (p≤0.05) negative NDVI trends and likely driven by regional drought. In the northern range of the grain belt we found an opposite tendency toward agricultural intensification; in this case, represented by LCC from cropland mosaic to pure cropland, and also associated with statistically significant (p≤0.05) negative NDVI trends. Relatively small clusters of statistically significant (p≤0.05) positive NDVI trends corresponding with both localized land abandonment and localized agricultural intensification show that land use decision making is not uniform across the region. Land surface change in the Northern Eurasian grain belt is part of a larger pattern of land cover land use change (LCLUC) in Eastern Europe, Russia, and former territories of the Soviet Union following realignment of socialist land tenure and agricultural markets. Here, we show that a combined analysis of LCC and NDVI trends provides a more complete picture of the complexities of LCLUC in the Northern Eurasian grain belt, involving both broader climatic forcing, and narrower anthropogenic impacts, than might be obtained from either analysis alone.

This paper focuses on the advantages of derivative vegetation indices over simple reflectance-based indices that are traditionally used for remote sensing of vegetation. The idea of using reflectance derivatives instead of simple reflectance spectra was proposed several decades ago. Despite this, it has not been widely used in monitoring systems because the derivatives lack reliable parameters. In addition, most satellite monitoring systems are not equipped with hyperspectral sensors, which are considered necessary for operating with the reflectance derivatives. Here, we present original data indicating that the chlorophyll-related derivative index D₇₂₅/D₇₀₂ we derived can be accurately estimated from a reflectance spectrum of 10 nm resolution that would be suitable for most satellite-based sensors. Furthermore, the index is not sensitive to soil reflectance and can therefore be used for testing of open crops. Presence of blanc reflectance is also unnecessary. Preliminary results of index testing are presented. Perspectives on using this and other derivative indices are discussed.

Central Asian dry lands are grass- and desert shrub-dominated ecosystems stretching across Northern Eurasia. This region supports a population of more than 100 million which continues to grow at an average rate of 1.5% annually. Dry steppes are the primary grain and cattle growing zone within Central Asia. Degradation of this ecosystem through burning and overgrazing directly impacts economic growth and food supply in the region. Fire is a recurrent disturbance agent in dry lands contributing to soil erosion and air pollution. Here we provide an overview of inter-annual and seasonal fire dynamics in Central Asia obtained from remotely sensed data. We evaluate the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) global fire products within Central Asian dry lands and use these products to characterize fire occurrence between 2001 and 2009. The results show that on average ~15 million ha of land burns annually across Central Asia with the majority of the area burned in August and September in grasslands. Fire is used as a common crop residue management practice across the region. Nearly 89% of all burning occurs in Kazakhstan, where 5% and 3% of croplands and grasslands, respectively, are burned annually.

Time-series of land surface phenology (LSP) data offer insights about vegetation growth patterns. They can be generated by exploiting the temporal and spectral reflectance properties of land surface components. Interannual and seasonal LSP data are important for understanding and predicting an ecosystem’s response to variations caused by natural and anthropogenic drivers. This research examines spatio-temporal change patterns and interactions between terrestrial phenology and 28 years of climate dynamics in Central Asia. Long-term (1981–2008) LSP records such as timing of the start, peak and length of the growing season and vegetation productivity were derived from remotely sensed vegetation greenness data. The patterns were analyzed to identify and characterize the impact of climate drivers at regional scales. We explored the relationships between phenological and precipitation and temperature variables for three generalized land use types that were exposed to decade-long regional drought events and intensified land and water resource use: rainfed agriculture, irrigated agriculture, and non-agriculture. To determine whether and how LSP dynamics are associated with climate patterns, a series of simple linear regression analyses between these two variables was executed. The three land use classes showed unique phenological responses to climate variation across Central Asia. Most of the phenological response variables were shown to be positively correlated to precipitation and negatively correlated to temperature. The most substantial climate variable affecting phenological responses of all three land use classes was a spring temperature regime. These results indicate that future higher temperatures would cause earlier and longer growing seasons.