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Studies examining the joint interactions and impacts of social-environmental system (SES) drivers on vegetation dynamics in Central Asia are scarce. We investigated seasonal trends and anomalies in drivers and their impacts on ecosystem structure and function (ESF). We explored the response of net primary production, evapotranspiration and normalized difference vegetation index (NDVI) to various SES drivers—climate, human influence, heat stress, water storage, and water content—and their latent relationships in Kazakhstan. We employed 13 predictor drivers from 2000 to 2016 to identify the interactions and impacts on ESF variables that reflect vegetation growth and productivity. We developed 12 models with different predictor–response variable combinations and separated them into two approaches. First, we considered the winter percent snow cover (SNOWc) and spring rainfall (P_MAM) as drivers and then as moderators in a structural equation model (SEM). SNOWc variability (SNOWcSD) as an SEM moderator exhibited superior model accuracy and explained the interactions between various predictor–response combinations. Winter SNOWcSD did not have a strong direct positive influence on summer vegetation growth and productivity; however, it was an important moderator between human influence and the ESF variables. Spring rainfall had a stronger impact on ESF variability than summer rainfall. We also found strong positive feedback between soil moisture (SM) and NDVI, as well as a strong positive influence of vegetation optical depth (VOD) and terrestrial water storage (TWS) on ESF. Livestock density (LSKD) exhibited a strong negative influence on ESF. Our results also showed a strong positive influence of socioeconomic drivers, including crop yield per hectare (CROPh), gross domestic product per capita (GDPca), and population density (POPD) on vegetation productivity. Finally, we found that vegetation dynamics were more sensitive to SM, VOD, LSKD and POPD than climatic drivers, suggesting that water content and human influence drivers were more critical in Kazakhstan.

As the two largest landlocked countries, Kazakhstan and Mongolia have similar biophysical conditions and socioeconomic roots in the former Soviet Union. Our objective is to investigate the direction, extent, and spatial variation of land cover change at three administrative levels over three decades (1990–2020). We selected three provinces from each country (Aktobe, Akmola, and Almaty province in Kazakhstan, and Arkhangai, Tov, and Dornod in Mongolia) to classify the land cover into forest, grassland, cropland, barren, and water. Altogether, 6964 Landsat images were used in pixel-based classification method with random forest model for image processing. Six thousand training data points (300 training points × 5 classes × 4 periods) for each province were collected for classification and change detection. Land cover changes at decadal and over the entire study period for five land cover classes were quantified at the country, provincial, and county level. High classification accuracy indicates localized land cover classification have an edge over the latest global land cover product and reveal fine differences in landscape composition. The vast steppe landscapes in these two countries are dominated by grasslands of 91.5% for Dornod in Mongolia and 74.7% for Aktobe in Kazakhstan during the 30-year study period. The most common land cover conversion was grassland to cropland. The cyclic land cover conversions between grassland and cropland reflect the impacts of the Soviet Union’s largest reclamation campaign of the 20th century in Kazakhstan and the Atar-3 agriculture re-development in Mongolia. Kazakhstan experienced a higher rate of land cover change over a larger extent of land area than Mongolia. The spatial distribution of land use intensity indicates that land use hotspots are largely influenced by policy and its shifts. Future research based on these large-scale land use and land cover changes should be focused the corresponding ecosystem and society functions.

Meeting the food and nutritional demands of ever growing human population will cause immense pressure on agricultural lands and natural resource bases across the world. This challenge can be met only by proper land and water management, which consists of crucial components like understanding cropping systems and crop fallow dynamics for sustainable intensification. In this work, a methodology was developed for crop and crop fallow land estimation using multi-temporal, high spatial resolution Sentinel-2A data in a test site of Odisha state, in India, comprising of two districts i.e., Bhadrak and Jajpur. Customized codes were written to find temporal variation pattern of NDVI values for each pixel in the study area. Observing the variation of NDVI over time, we have attempted to estimate crop life cycle duration and their type with rigorous field inputs. The cropland and fallow land intensification maps showed 10-different cropping pattern with classification accuracy of 83.33%, and kappa coefficient of 0.81. We observed that (1) kharif is the major crop in the study area, while rabi mainly grows in areas where external fresh water sources are available (2) a large portion of the area remains fallow for most part of the year as mapped from Sentinel 2A data. There is scope to utilise the fallow lands for multi-cropping with appropriate land and water management, through the government policy prescriptions. With Sentinel-2B sensor now on board, the temporal resolution of satellite-2 (2A and 2B combined) could improve leading to improved classification and upgradation of the algorithm followed here.