Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-12
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
Series
Earth and Space Science, Volume 8, issue 12
Abstract
Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires.
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| openaire: EC/H2020/819202/EU//SOS.aquaterra Funding Information: We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) ( https://earthdata.nasa.gov/firms ), part of NASA's Earth Observing System Data and Information System (EOSDIS). We warmly thank the Center for International Cooperation in Sustainable Management of Tropical Peatland (CIMTROP), University of Palangka Raya, for collaboration during our field visit and seminar in 2019. The study was funded by the Academy of Finland funded project WATVUL (grant no. 317320), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 819202), Aalto University School of Engineering Doctoral Programme, and the Emil Aaltonen Foundation funded project “eat‐less‐water.” Funding Information: We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS). We warmly thank the Center for International Cooperation in Sustainable Management of Tropical Peatland (CIMTROP), University of Palangka Raya, for collaboration during our field visit and seminar in 2019. The study was funded by the Academy of Finland funded project WATVUL (grant no. 317320), European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 819202), Aalto University School of Engineering Doctoral Programme, and the Emil Aaltonen Foundation funded project ?eat-less-water.? Publisher Copyright: © 2021 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union.
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Citation
Horton, A J, Virkki, V, Lounela, A, Miettinen, J, Alibakhshi, S & Kummu, M 2021, ' Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning ', Earth and Space Science, vol. 8, no. 12, e2021EA001873 . https://doi.org/10.1029/2021EA001873