TY - JOUR

T1 - Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal

AU - Parajuli, Ashok

AU - Gautam, Ambika Prasad

AU - Sharma, Sundar Prasad

AU - Bhujel, Krishna Bahadur

AU - Sharma, Gagan

AU - Thapa, Purna Bahadur

AU - Bist, Bhuwan Singh

AU - Poudel, Shrijana

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Forest fires have increased at an alarming rate in recent years, with multiple consequences in Nepal's forest ecosystem and landscapes. The research used remote sensing and GIS technology as well as statistical tools for developing forest fires risk models in two major landscapes of Nepal, i.e., Terai Arc Landscape (TAL) and Chitwan Annapurna Landscape (CHAL). A multi-parametric weighted index model was adopted to derive and demarcate the forest fire-risk map with risk variables such as vegetation, topographic factors, land surface temperature, and proximity to the road and settlements. To enhance the use of a fire risk map, collinearity between variables was checked (VIF <2) and validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots and Kernel Density Estimation (KDE) method. The MODIS hotspot data from 2001 to 2018 was also evaluated which indicates that the number of fire counts has a strong relation (R2 =0.82) with the burn area. Broadleaved forest in the pre-monsoon season is highly vulnerable to forest fire. More than half of the total forested area (65%) is in high fire risk, particularly in the TAL region. The study results could assist the decision-makers to implement preventive measures by minimizing the risk and impacts of forest fires.

AB - Forest fires have increased at an alarming rate in recent years, with multiple consequences in Nepal's forest ecosystem and landscapes. The research used remote sensing and GIS technology as well as statistical tools for developing forest fires risk models in two major landscapes of Nepal, i.e., Terai Arc Landscape (TAL) and Chitwan Annapurna Landscape (CHAL). A multi-parametric weighted index model was adopted to derive and demarcate the forest fire-risk map with risk variables such as vegetation, topographic factors, land surface temperature, and proximity to the road and settlements. To enhance the use of a fire risk map, collinearity between variables was checked (VIF <2) and validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots and Kernel Density Estimation (KDE) method. The MODIS hotspot data from 2001 to 2018 was also evaluated which indicates that the number of fire counts has a strong relation (R2 =0.82) with the burn area. Broadleaved forest in the pre-monsoon season is highly vulnerable to forest fire. More than half of the total forested area (65%) is in high fire risk, particularly in the TAL region. The study results could assist the decision-makers to implement preventive measures by minimizing the risk and impacts of forest fires.

KW - General Earth and Planetary Sciences

KW - General Environmental Science

U2 - 10.1080/19475705.2020.1853251

DO - 10.1080/19475705.2020.1853251

M3 - Article

VL - 11

SP - 2569

EP - 2586

JO - Geomatics, Natural Hazards and Risk

JF - Geomatics, Natural Hazards and Risk

SN - 1947-5705

IS - 1

ER -