Abstract
Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.
Highlights
Forests perform many important roles that influence the lives of humans [1]
Forest cover was estimated in Costa Rica from 1986 to 1991 using Landsat-5 Thematic Mapper (TM) satellite images; the results showed 2250 km2 of forest loss in the study area, which represented about 50% of the Costa Rican territory, and the deforestation of about 450 km2 annually [17]
Most deep-learning studies of mountain deforestation have involved the analysis of high-resolution satellite and aerial images and models based on convolutional neural networks (CNNs) [30,31,32,33,34,35]
Summary
Forests perform many important roles that influence the lives of humans [1]. They store large amounts of carbon in vegetation and soil, and exchange it for oxygen (i.e., contribute oxygen to the atmosphere) [2]. A study of economic growth and mountain forest deforestation in southern Cameroon, in which land-use changes were examined using remote sensing images, demonstrated that deforestation had increased after the economic crisis of 1986 [18]. Most deep-learning studies of mountain deforestation have involved the analysis of high-resolution satellite and aerial images and models based on convolutional neural networks (CNNs) [30,31,32,33,34,35]. This study classified mountainous areas using remote sensing and deep-learning algorithms, to analyze land cover quantitatively in the context of landscape affected by deforestation in Korean forests. As existing deep-learning algorithms do not calculate location information, the processing of satellite image analysis results can be challenging Considering this technical problem, the best algorithms were selected to analyze the mountain forest area. Areas with standing water such as rivers/streams, lakes, reservoirs, seas, etc
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