NATIONAL LAND COVER MAPPING USING VARIOUS REMOTE SENSING DATASETS IN GEE

Abstract

National land-cover maps are essential for the development of the countries as land-use patterns have shifted dramatically throughout the world in the previous decades. With the latest development in the remote sensing community, the power and ease of web-mapping and web-based map and GIS services have increased. This work investigates several datasets for land cover categorization on a national scale across North Macedonia (25,713 km2) using Sentinel images within Google Earth Engine (GEE), a cloud computing platform designed to store and process huge data sets for analysis and ultimate decision making. Both single and synergetic use of Sentinel-1 and Sentinel-2 satellite images have been investigated. The basic land-cover components are generated, upon which the more detailed final land-cover/land-use data were derived and defined. Comparing the results of the datasets indicate the influence of the radar data over the optical data in land cover classification. Also, the influence of the investigated data over every class is calculated. The results showed that the various datasets lead to different overall accuracy. Also, the different datasets performed differently over single classes, even though their overall accuracy was the same. As a result, a high accuracy national-level land cover classification has been created. The results have been compared to the latest Corine data. The results can be critical to making informed policy, development, planning, and resource management decisions. This provided the standardized references from which landscape changes could be determined and quantified.