VALIDATION OF LAND DEGRADATION CARDS ON THE BASIS OF GEOSPATIAL DATA

Abstract

Today there is a lot of satellite data and products based on it in the public domain. By integrating them with heterogeneous socio-economic information and soil maps, model biophysical data using modern machine learning methods and modern approaches to geospatial data processing, it becomes possible to create maps of land degradation. Considering that classification maps, productivity maps and deforestation maps are the main intellectual components to create a degradation map, it is these products that affect the overall reliability of the results. For their validation, the necessary quality metrics are determined in the work, and the corresponding calculations are made. To evaluate the land cover map, independent test data were used to build a confusion matrix, and the obtained areas of the main crops were compared with statistical data. Agricultural land productivity was estimated using time series land cover classification maps and Crop Growth Modeling System (CGMS) biophysical plant development, as well as biophysical plant growth parameters using satellite data and biophysical plant development models. The LAI Map Accuracy Assessment (based on CGMS) is based on the comparison of Leaf Area Index (LAI) values modeled using the CGMS software framework with LAI ground measurement data collected through ground surveys. Numerous experiments were carried out to assess the quality of models and the results of deforestation maps on an independent test sample, which was not used at the neural network training stage. Degradation maps for several years were also analyzed and their validation was carried out with respect to productivity, in particular for the region that has undergone significant changes for the territory of Ukraine.