Focused sunlight factor of forest fire danger assessment using Web-GIS and RS technologies

Nikolay V. Baranovskiy; Vladislav S. Sherstnyov; Elena P. Yankovich; Marina V. Engel; Vladimir V. Belov

doi:10.1117/12.2240378

12 August 2016 Focused sunlight factor of forest fire danger assessment using Web-GIS and RS technologies

Nikolay V. Baranovskiy, Vladislav S. Sherstnyov, Elena P. Yankovich, Marina V. Engel, Vladimir V. Belov

Proceedings Volume 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016); 968823 (2016) https://doi.org/10.1117/12.2240378
Event: Fourth International Conference on Remote Sensing and Geoinformation of the Environment, 2016, Paphos, Cyprus

Abstract

Timiryazevskiy forestry of Tomsk region (Siberia, Russia) is a study area elaborated in current research. Forest fire danger assessment is based on unique technology using probabilistic criterion, statistical data on forest fires, meteorological conditions, forest sites classification and remote sensing data. MODIS products are used for estimating some meteorological conditions and current forest fire situation. Geonformation technologies are used for geospatial analysis of forest fire danger situation on controlled forested territories. GIS-engine provides opportunities to construct electronic maps with different levels of forest fire probability and support raster layer for satellite remote sensing data on current forest fires. Web-interface is used for data loading on specific web-site and for forest fire danger data representation via World Wide Web. Special web-forms provide interface for choosing of relevant input data in order to process the forest fire danger data and assess the forest fire probability.

Citation Download Citation

Nikolay V. Baranovskiy, Vladislav S. Sherstnyov, Elena P. Yankovich, Marina V. Engel, and Vladimir V. Belov "Focused sunlight factor of forest fire danger assessment using Web-GIS and RS technologies", Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 968823 (12 August 2016); https://doi.org/10.1117/12.2240378

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