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Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea

Authors
Kim, JoonLim, Chul-HeeJo, Hyun-WooLee, Woo-Kyun
Issue Date
8월-2021
Publisher
MDPI
Keywords
North Korea; afforestation; deep learning; deforestation; landcover classification; vegetation phenology
Citation
REMOTE SENSING, v.13, no.15
Indexed
SCIE
SCOPUS
Journal Title
REMOTE SENSING
Volume
13
Number
15
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137050
DOI
10.3390/rs13152946
ISSN
2072-4292
Abstract
The role of forests to sequester carbon is considered an important strategy for mitigating climate change and achieving net zero emissions. However, forests in North Korea have continued to be cleared since the 1990s due to the lack of food and energy resources. Deforestation in this country has not been accurately classified nor consistently reported because of the characteristics of small patches. This study precisely determined the area of deforested land in North Korea through the vegetation phenological classification using high-resolution satellite imagery and deep learning algorithms. Effective afforestation target sites in North Korea were identified with priority grade. The U-Net deep learning algorithm and time-series Sentinel-2 satellite images were applied to phenological classification; the results reflected the small patch-like characteristics of deforestation in North Korea. Based on the phenological classification, the land cover of the country was classified with an accuracy of 84.6%; this included 2.6 million ha of unstocked forest and reclaimed forest. Sites for afforestation were prioritized into five grades based on deforested characteristics, altitude and slope. Forest area is expanded and the forest ecosystem is restored through successful afforestation, this may improve the overall ecosystem services in North Korea. In the long term, it will be possible to contribute to carbon neutrality and greenhouse gas reduction on the Korean Peninsula level through optimal afforestation by using these outcomes.
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LEE, Woo Kyun
생명과학대학 (환경생태공학부)
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