Volume 26, Issue 3-1, september 2022
PIXEL AND OBJECT-BASED LAND COVER MAPPING AND CHANGE DETECTION FROM 1986 TO 2020 FOR HUNGARY USING HISTOGRAM-BASED GRADIENT BOOSTING CLASSIFICATION TREE CLASSIFIER
Authors: András Gudmann, László Mucsi
Abstract: The large-scale pixel-based land use/land cover classification is a challenging task, which depends on many circumstances. This study aims to create LULC maps with the nomenclature of Coordination of Information on the Environment (CORINE) Land Cover (CLC) for years when the CLC databases are not available. Furthermore, testing the predicted maps for land use changes in the last 30 years in Hungary. Histogram-based gradient boosting classification tree (HGBCT) classifier was tested at classification. According to the results, the classifier, with the use of texture variance and landscape metrics is capable to generate accurate predicted maps, and the comparison of the predicted maps provides a detailed image
of the land use changes.
Keywords: land use; land cover; image classification; change detection; gradient boosting
Received: May 06, 2022 | Revised: July 31, 2022 | Accepted: August 10, 2022