INTEGRATED GIS SYSTEM FOR POST-FIRE HAZARD ASSESSMENTS WITH REMOTE SENSING
Fires continue to devour hundreds of thousands of hectares of forest even in 2020, generating gigantic damage to the ecosystem, if we think that we are in the midst of a climate crisis caused precisely by CO<sub>2</sub> emissions into the atmosphere by man, due to burning of fossil fuels...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-11-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-3-W1-2020/13/2020/isprs-archives-XLIV-3-W1-2020-13-2020.pdf |
| Summary: | Fires continue to devour hundreds of thousands of hectares of forest even in 2020, generating gigantic damage to the ecosystem, if we think that we are in the midst of a climate crisis caused precisely by CO<sub>2</sub> emissions into the atmosphere by man, due to burning of fossil fuels. The action to safeguard the territory and the fight against its progressive environmental degradation focus a great attention towards forest fires, also considering the enormous environmental damage that these have caused to important and very large areas of the globe. The aim of the contribution that we here propose is the design and implementation of a software tool that performs predictive functions of triggering possible forest fires, thanks to the integration and manipulation of data from different sources and processed by predictive mathematical models, to support decisions; the comparison of techniques for the processing of high-resolution remote sensing data from optical satellites for the best automatic discrimination of the areas covered by fire plays a fundamental role in the analysis. This allows managing the burnt areas also considering subsequent fire risks, and the integration of the techniques developed in a GIS in order to obtain an accurate perimeter and a fire risk map prevision. |
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| ISSN: | 1682-1750 2194-9034 |