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Unveiling the Past: Deep-Learning-Based Estimation of Historical Peatland Distribution

Author

Listed:
  • Sungeun Cha

    (Forest Fire Division, National Institute of Forest Science, Seoul 02455, Republic of Korea)

  • Junghee Lee

    (Forest ICT Research Center, National Institute of Forest Science, Seoul 02455, Republic of Korea)

  • Eunho Choi

    (Global Forestry Division, National Institute of Forest Science, Seoul 02455, Republic of Korea)

  • Joongbin Lim

    (Forest ICT Research Center, National Institute of Forest Science, Seoul 02455, Republic of Korea)

Abstract

Acknowledging the critical role of accurate peatland distribution estimation, this paper underscores the significance of understanding and mapping these ecosystems for effective environmental management. Highlighting the importance of precision in estimating peatland distribution, the research aims to contribute valuable insights into ecological monitoring and conservation efforts. Prior studies lack robust validation, and while recent advancements propose machine learning for peatland estimation, challenges persist. This paper focuses on the integration of deep learning into peatland detection, underscoring the urgency of safeguarding these global carbon reservoirs. Results from convolutional neural networks (CNNs) reveal a decrease in the classified peatland area from 8226 km 2 in 1999 to 5156 km 2 in 2019, signifying a 37.32% transition. Shifts in land cover types are evident, with an increase in estate plantation and a decrease in swamp shrub. Human activities, climate, and wildfires significantly influenced these changes over two decades. Fire incidents, totaling 47,860 from 2000 to 2019, demonstrate a substantial peatland loss rate, indicating a correlation between fires and peatland loss. In 2020, wildfire hotspots were predominantly associated with agricultural activities, highlighting subsequent land cover changes post-fire. The CNNs consistently achieve validation accuracy exceeding 93% for the years 1999, 2009, and 2019. Extending beyond academic realms, these discoveries establish the foundation for enhanced land-use planning, intensified conservation initiatives, and effective ecosystem management—a necessity for ensuring sustainable environmental practices in Indonesian peatlands.

Suggested Citation

  • Sungeun Cha & Junghee Lee & Eunho Choi & Joongbin Lim, 2024. "Unveiling the Past: Deep-Learning-Based Estimation of Historical Peatland Distribution," Land, MDPI, vol. 13(3), pages 1-18, March.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:3:p:328-:d:1350881
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    References listed on IDEAS

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    1. Evan Ross DeLancey & Jahan Kariyeva & Jason T Bried & Jennifer N Hird, 2019. "Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-23, June.
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