Dr. Christian Mata Profile

Dr. Christian Mata

at Univ Politecnica de Catalunya

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Proceedings Article | 9 October 2018 Paper

GIS-based integration of spatial and remote sensing data for wildfire monitoring

Mario Valero, Oriol Rios, Christian Mata, Elsa Pastor, Eulália Planas

Proceedings Volume 10790, 107900R (2018) https://doi.org/10.1117/12.2325649

KEYWORDS: Remote sensing, Geographic information systems, Vegetation, Thermography, Ecosystems, Data modeling, Infrared imaging

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The impact of wildfires on society is increasing. Annually burned area, fire season length and fire severity have increased during the last decade in several regions of the world, and a number of tragic events have occurred recently in fire-prone areas. Whereas forest fires are natural phenomena, emergency management must be improved in order to minimise human and economic losses as well as undesired effects to ecosystems not resilient enough to fire. However, wildland fire behaviour is not completely understood. Fire rate of spread depends on terrain, weather and vegetation, but the exact relationship between all involved variables is unknown. Remote sensing can help to solve this issue because it has a great potential to measure real-scale fire behaviour with a high spatio-temporal resolution. In this paper, we propose the use of a Geographic Information System (GIS) to combine wildfire remote sensing data with spatial information about vegetation, weather and terrain. This integrated framework facilitates the systematic analysis of multisource data and the study of observed relationships between variables. We describe which operations may be applied to remote sensing and geospatial data in order to display the observed fire evolution and extract relevant statistical relationships. Among others, fire spread is displayed over a topographic map; burned area and fire perimeter evolution are measured; spatially-explicit rates of spread (ROS) are computed; surface ROS is derived from horizontal ROS using a Digital Elevation Model (DEM); and relationships between ROS, intensity, slope and vegetation type are studied.

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