Modeling, GIS, and Remote Sensing for Discussion Support in Irrigated Land Management in the inner Aral Sea Basin

The interdisciplinary research project “Assessing Land Value Changes and Developing a Discussion-Support-Tool for Improved Land Use Planning in the Irrigated Lowlands of Central Asia” (LaVaCCA) aimed at identifying factors that explain changes or trends of land-use production in the inner Aral Sea Basin (ASB). Since this region is severely affected by immense land degradation and consequent losses of land productivity, methods were developed for closing existing data gaps that hamper spatially explicit analyses. These are urgently required for land use planners to improve future land management in the ASB. As a result of three years project implementation, and while tapping free Landsat Archives, effective mapping routines have been developed for monitoring the dynamics of cropland use on study sites in Kazakhstan and Uzbekistan. The resulting maps document a strong decline of rice cropping intensity between 1984 and 2004 in the Kazalinsk region, Kazakhstan, as well as the impact of rehabilitation activities implemented in 2005-2016. This was, according to farmers, due to changes in farm management from the previous state-order to the currently dominating private production systems. Remote sensing (RS) based routines for mapping crop biophysical parameters were analysed for their accuracy through standardized field campaigns. For instance, regression trees allowed for accurate estimations of the leaf area index, a basic parameter of Uzbekistan’s national monitoring program, permitting rapid and wide-spread crop yield estimations. The combination of the analyses with optical RS and hydrological modelling resulted in more detailed assessments of salt dynamics than any of the methods alone. Overall, due to an effective collaboration between the partners from Central Asia and Germany, LaVaCCA findings could not only support the knowledge gain about drivers of land use production changes, but also on land abandonment dynamics as identified through statistical modelling. Further salient results and achievements of the on-going project will conclude this presentation