한국환경복원기술학회지 (Journal of the Korean Society of Environmental Restoration Technology)

  • 제19권1호
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  • Pages.73-83
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  • 2016
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  • 1229-3032(pISSN)
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  • 2733-5011(eISSN)
한국환경복원기술학회 (The Korea Society of Environmental Restoration Technology)
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MODIS 다중시기 영상을 이용한 북한 다락밭 분류

Terrace Fields Classification in North Korea Using MODIS Multi-temporal Image Data

  • 정승규 (서울대학교 농업생명과학연구원) ;
  • 박종훈 (서울대학교 농업생명과학연구원) ;
  • 박종화 (서울대학교 환경대학원 환경조경학과) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Jeong, Seung Gyu (Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Park, Jonghoon (Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Park, Chong Hwa (Graduate School of Environmental Studies, Seoul National University) ;
  • Lee, Dong Kun (Department of Landscape Architecture and Rural System Engineering, CALS, Seoul National University)
  • 투고 : 2015.12.22
  • 심사 : 2016.02.17
  • 발행 : 2016.02.29
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초록

Forest degradation reduces ecosystem services provided by forest and could lead to change in composition of species. In North Korea, there has been significant forest degradation due to conversion of forest into terrace fields for food production and cut-down of forest for fuel woods. This study analyzed the phenological changes in North Korea, in terms of vegetation and moisture in soil and vegetation, from March to Octorber 2013, using MODIS (MODerate resolution Imaging Spectroradiometer) images and indexes including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index), and NDWI (Normalized Difference Water Index). In addition, marginal farmland was derived using elevation data. Lastly, degraded terrace fields of 16 degree was analyzed using NDVI, NDSI, and NDWI indexes, and marginal farmland characteristics with slope variable. The accuracy value of land cover classification, which shows the difference between the observation and analyzed value, was 84.9% and Kappa value was 0.82. The highest accuracy value was from agricultural (paddy, field) and forest area. Terrace fields were easily identified using slope data form agricultural field. Use of NDVI, NDSI, and NDWI is more effective in distinguishing deforested terrace field from agricultural area. NDVI only shows vegetation difference whereas NDSI classifies soil moisture values and NDWI classifies abandoned agricultural fields based on moisture values. The method used in this study allowed more effective identification of deforested terrace fields, which visually illustrates forest degradation problem in North Korea.

키워드

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피인용 문헌

  1. Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest vol.8, pp.12, 2016, https://doi.org/10.3390/rs8120997
  2. U-Net 기반 딥러닝 모델을 이용한 다중시기 계절학적 토지피복 분류 정확도 분석 - 서울지역을 중심으로 - vol.37, pp.3, 2016, https://doi.org/10.7780/kjrs.2021.37.3.4
  3. Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea vol.13, pp.15, 2021, https://doi.org/10.3390/rs13152946
  4. Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea vol.13, pp.17, 2016, https://doi.org/10.3390/rs13173501