Machine learning approach for peatland delineation using multi-sensor remote sensing data in Ogan Komering Ilir Regency

Sencaki, Dionysius Bryan; Prayogi, Hari; Arfah, Siti; Pianto, Teguh Arif

doi:10.1088/1755-1315/500/1/012005

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Ref. [53] emphasized the critical role of peatlands in global climate regulation, utilizing Landsat 8 OLI and MODIS data alongside machine learning (ML) and deep learning (DL) methods for accurate delineation. The effectiveness of the Cat Boosting algorithm in achieving high accuracy delineation results suggests its potential for broader landcover mapping applications.…”

Section: Discussionmentioning

confidence: 99%

“…This aligns with our broader discussion on advancing remote sensing techniques and emphasizing the pivotal role of peatlands in diverse geographic contexts. Due to the limited global research on peatland classification, this study conducted a comparative analysis with several wetland classification studies in areas similar to peatlands [51][52][53][54][55]. Despite not utilizing SAR data with elevation information, our study achieved high spatial resolution and accuracy.…”

Section: Discussionmentioning

confidence: 99%

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

Cha,

Lee,

Choi

et al. 2024

Land

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Cha,

Lee,

Choi

et al. 2024

Land

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A comparative evaluation of state-of-the-art ensemble learning algorithms for land cover classification using WorldView-2, Sentinel-2 and ROSIS imagery

Çölkesen

Öztürk

2022

Arab J Geosci

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Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

Ingle

Habib

Connolly

et al. 2023

Sci Rep

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Wetlands are one of the major contributors of methane (CH4) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m−2 y−1) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m−2 y−1), degraded (0.15 and 0.27 gC m−2 y−1) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change.

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Machine learning approach for peatland delineation using multi-sensor remote sensing data in Ogan Komering Ilir Regency

Cited by 5 publications

References 4 publications

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

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

A comparative evaluation of state-of-the-art ensemble learning algorithms for land cover classification using WorldView-2, Sentinel-2 and ROSIS imagery

Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

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