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doi:10.3808/jei.
Copyright © 2017 ISEIS. All rights reserved

Developing the Forest Fire Danger Index for the Country Kazakhstan by Using Geospatial Techniques

K. V. S. Babu1 *, G. Kabdulova1, and G. Kabzhanova1

  1. The Joint-Stock Company, National Company, Kazakhstan Gharysh Sapary, Astana 010000, Kazakhstan

*Corresponding author. Tel.: 789-520-8605; fax: +7 (7122) 24-88-61. E-mail address: sureshbabu.iiith@gmail.com (K. V. S. Babu).

Abstract


Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Kazakhstan forests are more prone to fires due to the presence of coniferous forests and loss was enormous. There is a need of forest fire danger indices to estimate the potential fire danger so that fire officials effectively controls the fires. Global forest fire danger indices require daily meteorological stations data as well as ground investigation data. But, there are less number of meteorological stations are available in Kazakhstan, hence, the satellite derived parameters were used to develop the fire danger index in this study. In this study, Static forest fire probability index was developed by using the SRTM DEM and MODIS TERRA and AQUA Land cover type product (MCD12Q1). Dynamic forest fire probability index was calculated by using the MODIS TERRA Land Surface Temperature (MOD11A1) and Surface reflectance (MOD09GA). Dynamic forest fire probability index has been developed from the parameters, i.e. LST, Normalized Multi-band Drought Index (NMDI), Visible Atmospheric Resistant Index (VARI) and Modified Normalized Difference Fire Index (MNDFI). Finally, Fire danger index was developed by adding both the static and dynamic probability indices and Fire hotspot data (MCD14) has been used for the validation of the index. Accuracy was ranging from 77.78% to 90.32% and the overall accuracy was 84.14%. Developed Fire danger index was in operational, calculating by using MODIS Near Real Time datasets and uploading and updating every day in the website.

Keywords: MODIS, forest fire, fire danger index, dynamic forest fire probability index, static forest fire probability index


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