Details
Originalsprache | Englisch |
---|---|
Aufsatznummer | 121729 |
Seitenumfang | 21 |
Fachzeitschrift | Forest ecology and management |
Jahrgang | 555 |
Frühes Online-Datum | 31 Jan. 2024 |
Publikationsstatus | Veröffentlicht - 1 März 2024 |
Abstract
Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic and topographical factors, while this research expands the scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), and machine learning (ML) methodologies for identifying and assessing forest fire-prone areas in the STR and their vulnerability to climate change. To achieve this, the study employed a comprehensive dataset of forty-four influencing factors, including topographic, climate-hydrologic, forest health, vegetation indices, radar features, and anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Gradient Boosting Machine (GBM), Random Forest (RF), and its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm Optimization (RF-PSO), and genetic algorithm (RF-GA). The study revealed high FFS in both the northern and southern portions of the study area, with the nnet and RF-PSO models demonstrating susceptibility percentages of 12.44% and 12.89%, respectively. Conversely, very low FFS zones consistently displayed susceptibility scores of approximately 23.41% and 18.57% for the nnet and RF-PSO models. The robust mapping methodology was validated by impressive AUROC (>0.88) and kappa coefficient (>0.62) scores across all ML validation metrics. Future climate models (ssp245 and ssp585, 2022–2100) indicated high FFS zones along the northern and southern edges of the STR, with the central zone categorized from low to very low susceptibility. Boruta analysis identified actual evapotranspiration (AET) and relative humidity as key factors influencing forest fire ignition. SHAP evaluation reinforced the influence of these factors on FFS, while also highlighting the significant role of distance to road, distance to settlement, dNBR, slope, and humidity in prediction accuracy. These results emphasize the critical importance of the proposed approach for forest fire mapping and provide invaluable insights for firefighting teams, forest management, planning, and qualification strategies to address future fire sustainability.
ASJC Scopus Sachgebiete
- Agrar- und Biowissenschaften (insg.)
- Forstwissenschaften
- Umweltwissenschaften (insg.)
- Natur- und Landschaftsschutz
- Umweltwissenschaften (insg.)
- Management, Monitoring, Politik und Recht
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in: Forest ecology and management, Jahrgang 555, 121729, 01.03.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India
T2 - English
AU - Singha, Chiranjit
AU - Swain, Kishore Chandra
AU - Moghimi, Armin
AU - Foroughnia, Fatemeh
AU - Swain, Sanjay Kumar
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic and topographical factors, while this research expands the scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), and machine learning (ML) methodologies for identifying and assessing forest fire-prone areas in the STR and their vulnerability to climate change. To achieve this, the study employed a comprehensive dataset of forty-four influencing factors, including topographic, climate-hydrologic, forest health, vegetation indices, radar features, and anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Gradient Boosting Machine (GBM), Random Forest (RF), and its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm Optimization (RF-PSO), and genetic algorithm (RF-GA). The study revealed high FFS in both the northern and southern portions of the study area, with the nnet and RF-PSO models demonstrating susceptibility percentages of 12.44% and 12.89%, respectively. Conversely, very low FFS zones consistently displayed susceptibility scores of approximately 23.41% and 18.57% for the nnet and RF-PSO models. The robust mapping methodology was validated by impressive AUROC (>0.88) and kappa coefficient (>0.62) scores across all ML validation metrics. Future climate models (ssp245 and ssp585, 2022–2100) indicated high FFS zones along the northern and southern edges of the STR, with the central zone categorized from low to very low susceptibility. Boruta analysis identified actual evapotranspiration (AET) and relative humidity as key factors influencing forest fire ignition. SHAP evaluation reinforced the influence of these factors on FFS, while also highlighting the significant role of distance to road, distance to settlement, dNBR, slope, and humidity in prediction accuracy. These results emphasize the critical importance of the proposed approach for forest fire mapping and provide invaluable insights for firefighting teams, forest management, planning, and qualification strategies to address future fire sustainability.
AB - Accurately assessing forest fire susceptibility (FFS) in the Similipal Tiger Reserve (STR) is essential for biodiversity conservation, climate change mitigation, and community safety. Most existing studies have primarily focused on climatic and topographical factors, while this research expands the scope by employing a synergistic approach that integrates geographical information systems (GIS), remote sensing (RS), and machine learning (ML) methodologies for identifying and assessing forest fire-prone areas in the STR and their vulnerability to climate change. To achieve this, the study employed a comprehensive dataset of forty-four influencing factors, including topographic, climate-hydrologic, forest health, vegetation indices, radar features, and anthropogenic interference, into ten ML models: neural net (nnet), AdaBag, Extreme Gradient Boosting (XGBTree), Gradient Boosting Machine (GBM), Random Forest (RF), and its hybrid variants with differential evolution algorithm (RF-DEA), Gravitational Based Search (RF-GBS), Grey Wolf Optimization (RF-GWO), Particle Swarm Optimization (RF-PSO), and genetic algorithm (RF-GA). The study revealed high FFS in both the northern and southern portions of the study area, with the nnet and RF-PSO models demonstrating susceptibility percentages of 12.44% and 12.89%, respectively. Conversely, very low FFS zones consistently displayed susceptibility scores of approximately 23.41% and 18.57% for the nnet and RF-PSO models. The robust mapping methodology was validated by impressive AUROC (>0.88) and kappa coefficient (>0.62) scores across all ML validation metrics. Future climate models (ssp245 and ssp585, 2022–2100) indicated high FFS zones along the northern and southern edges of the STR, with the central zone categorized from low to very low susceptibility. Boruta analysis identified actual evapotranspiration (AET) and relative humidity as key factors influencing forest fire ignition. SHAP evaluation reinforced the influence of these factors on FFS, while also highlighting the significant role of distance to road, distance to settlement, dNBR, slope, and humidity in prediction accuracy. These results emphasize the critical importance of the proposed approach for forest fire mapping and provide invaluable insights for firefighting teams, forest management, planning, and qualification strategies to address future fire sustainability.
KW - Boruta-SHAP
KW - Forest fire
KW - Machine learning
KW - Risk map
KW - Susceptibility map
UR - http://www.scopus.com/inward/record.url?scp=85183983684&partnerID=8YFLogxK
U2 - 10.1016/j.foreco.2024.121729
DO - 10.1016/j.foreco.2024.121729
M3 - Article
AN - SCOPUS:85183983684
VL - 555
JO - Forest ecology and management
JF - Forest ecology and management
SN - 0378-1127
M1 - 121729
ER -