Phenological classification using deep learning and the sentinel-2 satellite to identify priority afforestation sites in north korea

Joon Kim, Chul Hee Lim, Hyun Woo Jo, Woo Kyun Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Article number2946
JournalRemote Sensing
Volume13
Issue number15
DOIs
Publication statusPublished - 2021 Aug 1

Keywords

  • Afforestation
  • Deep learning
  • Deforestation
  • Landcover classification
  • North Korea
  • Vegetation phenology

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Fingerprint

Dive into the research topics of 'Phenological classification using deep learning and the sentinel-2 satellite to identify priority afforestation sites in north korea'. Together they form a unique fingerprint.

Cite this