TY - JOUR
T1 - Spatial mapping and analysis of forest fire risk areas in Sri Lanka – Understanding environmental significance
AU - Makumbura, Randika K.
AU - Dissanayake, Prasad
AU - Gunathilake, Miyuru B.
AU - Rathnayake, Namal
AU - Kantamaneni, Komali
AU - Rathnayake, Upaka
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/6
Y1 - 2024/6
N2 - This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country.
AB - This study presents the first attempt in Sri Lanka to generate a forest fire risk map covering the entire country using a GIS-based forest fire index (FFI) model. The model utilized seven parameters: land use, temperature, slope, proximity to roads and settlements, elevation, and aspect. All these parameters were derived using GIS techniques with ArcGIS10.4 and QGIS3.16. Data from Remote Sensing sources, particularly the MODIS hotspot real-world dataset, were employed to gather fire count information for the year 2020. Validation was conducted through the merging hotspot technique and kernel density estimation (KDE). The research findings highlight the districts in the Central and Uva provinces, such as NuwaraEliya (10.3 km2), Kandy (2.74 km2), and Badulla (10.41 km2), as having a “very low risk" of forest fire potential. Conversely, districts like Hambanthota (0.1 km2), Kaluthara (0.04 km2), and Kurunegala (0.2 km2) exhibit a “very high risk" of forest fire potential, although it is negligible compared country's total area. Overall, the study suggests that Sri Lanka is not currently facing a significant threat of forest fires and is a “medium risk" of forest fires as 49.49% of land falls under this category. These results are of immense value to relevant authorities, including the Ministry of Wildlife and Forest Resources Conservation, in formulating effective strategies to manage and mitigate forest fire risks in the country.
KW - Forest fire index (FFI)
KW - Geographic information system (GIS)
KW - Kernel density estimation (KDE)
KW - MODIS hotspot
KW - Remote sensing top of form
UR - http://www.scopus.com/inward/record.url?scp=85186992875&partnerID=8YFLogxK
U2 - 10.1016/j.cscee.2024.100680
DO - 10.1016/j.cscee.2024.100680
M3 - Article
AN - SCOPUS:85186992875
SN - 2666-0164
VL - 9
JO - Case Studies in Chemical and Environmental Engineering
JF - Case Studies in Chemical and Environmental Engineering
M1 - 100680
ER -