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

Sungeun Cha, Junghee Lee, Eunho Choi and Joongbin Lim ()
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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

Land, 2024, vol. 13, issue 3, 1-18

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.

Keywords: peatland detection; peatland loss; deep learning; convolutional neural networks (CNNs); multi-temporally integrated satellite imageries; land cover change (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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