[논문]Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques

Lee, Seong-Hyeok; Han, Kuk-Jin; Lee, Kwon; Lee, Kwang-Jae; Oh, Kwan-Young; Lee, Moung-Jin

doi:10.3390/rs12203372

[해외논문] Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques 원문보기

Remote sensing, v.12 no.20, 2020년, pp.3372 -

Lee, Seong-Hyeok (Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea) , Han, Kuk-Jin (Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea) , Lee, Kwon (MindForge, Seoul 08377, Korea) , Lee, Kwang-Jae (Korea Aerospace Research Institute, Daejeon 34133, Korea) , Oh, Kwan-Young (Korea Aerospace Research Institute, Daejeon 34133, Korea) , Lee, Moung-Jin (Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea)

Abstract ▼ AI-Helper

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.

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