Abstract
Land provides a range of biophysical and socioeconomic goods and services that support the sustainability of ecosystem services, livelihoods, and human well-being. Maintaining ecosystem functions and services, while also supporting human well-being, are the primary goals of sustainable land management. Wide-scale multi-temporal land cover classifications created on the basis of satellite remote sensing data are recognized as necessary and important input to land degradation analysis. Evaluation of long-term land cover changes is of crucial importance for sustainable land management. In this research, we develop a Geo-intelligence framework providing quantitative assessment and mapping of such changes over large areas. To eliminate the inconsistency of land cover classes in overlapping classifications, a spatio-temporal merger of land cover classifications was carried out taking into account their probabilistic distribution. The proposed approach is applied in assessing long-term changes of the land cover in the Southern Ukraine, the region suffering the most from land degradation. Two land cover maps, for two distant years, were obtained with overall relevant accuracy. As a result of quantitative assessment of long-term changes of land cover by slightly modified T. Saaty’s analytic hierarchy procedure, a map of spatial distribution of land cover change of importance to the study area during the period of the study was developed.
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Popov, M. et al. (2021). Long-Term Satellite Data Time Series Analysis for Land Degradation Mapping to Support Sustainable Land Management in Ukraine. In: Singh, T.P., Singh, D., Singh, R.B. (eds) Geo-intelligence for Sustainable Development. Advances in Geographical and Environmental Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-16-4768-0_11
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