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Article

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

1
Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea
2
MindForge, Seoul 08377, Korea
3
Korea Aerospace Research Institute, Daejeon 34133, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3372; https://doi.org/10.3390/rs12203372
Received: 23 September 2020 / Revised: 12 October 2020 / Accepted: 14 October 2020 / Published: 15 October 2020
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. View Full-Text
Keywords: deforestation; semantic segmentation; Kompsat-3; deep learning deforestation; semantic segmentation; Kompsat-3; deep learning
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MDPI and ACS Style

Lee, S.-H.; Han, K.-J.; Lee, K.; Lee, K.-J.; Oh, K.-Y.; Lee, M.-J. Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sens. 2020, 12, 3372. https://doi.org/10.3390/rs12203372

AMA Style

Lee S-H, Han K-J, Lee K, Lee K-J, Oh K-Y, Lee M-J. Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sensing. 2020; 12(20):3372. https://doi.org/10.3390/rs12203372

Chicago/Turabian Style

Lee, Seong-Hyeok, Kuk-Jin Han, Kwon Lee, Kwang-Jae Lee, Kwan-Young Oh, and Moung-Jin Lee. 2020. "Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques" Remote Sensing 12, no. 20: 3372. https://doi.org/10.3390/rs12203372

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