Automation of Forest Fire Danger Index from the Near Real Time Satellite Datasets

Abstract

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also caus-es the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the near real time data sets that can be available for download through Earthdata website after one hour of the satellite overpass and also automated the entire procedure. Static Fire Danger Index (SFDI) is a constant over the study area, computed from the MODIS Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER GDEM datasets. In this study, Dynamic Fire Danger Index (DFDI) has been calculated from the Near Real Time (NRT) Level 2 MODIS Terra Land Surface Temperature datasets (MOD11_L2) and MODIS TERRA NRT surface reflectance dataset MOD09. DFDI has been developed from three parameters i.e., Potential surface temperature, Perpendicular Moisture Index and Modified Normalized Difference Fire Index (MNDFI). Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger i ndices by the additive model and the overall accuracy was ranging from 86% to 95% and Area under Curve (AUC) values ranging from 0.81 to 0.91 during the major fire episode of 2018. Thus, the FFDI has been useful to assess the fire danger accurately over the study area and can be useful anywhere, where the meteorological stations are un-available. The procedure of calculating the DFDI and FFDI has been automated in R studio environment in near real time and therefore, the fire danger maps can be disseminated to fire officials in near real time for the quick actions to suppress the fire activities.