Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/239401
Title: Developing Forest Fire Danger index using geo spatial techniques
Researcher: K V Suresh Babu
Guide(s): P. Ramachandra Prasad and Arijit Roy
Keywords: ASTER GDEM
Engineering and Technology,Computer Science,Remote Sensing
Forest fire
Forest fire danger index
MCD12Q1
MODIS
University: International Institute of Information Technology, Hyderabad
Completed Date: 2019
Abstract: Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue warnings to the public on the basis of fire danger index classes. Geospatial techniques such as satellite remote sensing based approaches can be useful to develop the fire danger indices in those countries that lack sufficient meteorological stations. In this study, Static Fire danger Index has been developed using MODIS Land cover type product (MCD12Q1) and ASTER GDEM datasets. Fuel type danger index, Terrain ruggedness danger index, Slope danger index, Aspect danger index and Elevation danger index were computed from the ASTER GDEM and MCD12Q1 datasets. Uttarakhand state has very few meteorological stations so geospatial techniques can be useful to derive the fire danger indices. So, Dynamic Fire Danger Index (DFDI) has been developed from three parameters i.e. potential surface temperature, Perpendicular Moisture Index (PMI) and Modified Normalized Multiband Fire Index (MNDFI) using the MODIS Terra satellite datasets. 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). Finally, Forest Fire Danger Index (FFDI) has been developed by integrating both the Static and Dynamic fire danger indices and also used the near real time data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. The overall fire danger prediction accuracy was around 84% for the year 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 entire procedure of calculating FFDI from NRT datasets was semi-automated so that the fire danger maps will be disseminated to the fire officials for taking timely action in controlling the forest fires. newline
Pagination: 
URI: http://hdl.handle.net/10603/239401
Appears in Departments:Spatial Informatics

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