INTEGRATION OF GEOSPATIAL INFORMATION IN A GIS SOFTWARE TO ESTIMATE THE FOREST FIRE RISK

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Germano, F (Author) Other; Lia Duarte (Author) FCUP; Ana Teodoro (Author) FCUP

Conference proceedings International

Title: EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS XII

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications XII / SPIE Remote Sensing Conference

ELECTR NETWORK, SEP 13-18, 2021

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ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00V-PDJ

DOI: 10.1117/12.2599747

Abstract (EN): The use of vegetation forest fires (FF) is known as one of the most important criteria in global change, especially because of their events on changes in land cover and atmospheric chemistry. Fire and climate are related with each other, meaning that the fire regime responds quickly to changes in climate. There are many environmental factors that affect the spread of fire, as ignition agents, topography, vegetation, landscape fragmentation, etc. Several of them can be obtained by Remote Sensing (RS) data/techniques. Fire risk (FR) assessment is one of the main components when determining the right approach to protect and prevent a region against FF. An analysis of the variables that influence FR was performed by applying a Geographical Information System (GIS) methodology to obtain a final FR map. The methodology used was based on the Chuvieco FR model. It was necessary to consider the fire danger (FD) and the fire vulnerability (FV). In this study, the main objective was to estimate the FR map, which is composed by two maps: ignition map, propagation map; and several variables, such as Fuel Moisture Content (FMC), Human Risk Index (HRI), vegetation indices (estimated through Sentinel-2 images), among others. The results were validated with the data relative to the fire occurrences in Valongo (Porto, Portugal) in 2019. It was demonstrated that the model is sufficiently accurate to apply in Portugal and in other areas with the respective local adjustments. In the future, this model will be implemented in a GIS plugin, under QGIS software.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15