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

Author:

Cha Sungeun1ORCID,Lee Junghee2,Choi Eunho3,Lim Joongbin2ORCID

Affiliation:

1. Forest Fire Division, National Institute of Forest Science, Seoul 02455, Republic of Korea

2. Forest ICT Research Center, National Institute of Forest Science, Seoul 02455, Republic of Korea

3. Global Forestry Division, 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 km2 in 1999 to 5156 km2 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.

Funder

National Institute of Forest Science

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unveiling Hidden Patterns: Harnessing the Matrix Profile Algorithm for Enhanced Peatland Monitoring and Analysis;2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT);2024-04-29

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