Open data repositories and Geo Small Data for mapping the wildfire risk exposure in wildland urban interface (WUI) in Spain: A case study in the Valencian Region

Autor: Paul León-Cadena, Alfredo Ramon-Morte, José Tomás Navarro-Carrión
Přispěvatelé: Universidad de Alicante. Departamento de Análisis Geográfico Regional y Geografía Física, Universidad de Alicante. Instituto Interuniversitario de Geografía, Medio, Sociedad y Paisaje (MedSPai), Planificación y Gestión Sostenible del Turismo
Rok vydání: 2021
Předmět:
Zdroj: RUA. Repositorio Institucional de la Universidad de Alicante
Universidad de Alicante (UA)
ISSN: 2352-9385
DOI: 10.1016/j.rsase.2021.100500
Popis: The risk of forest fires in areas of wildland urban-interface (WUI.) is increasing due to the increase in urbanized areas and the progressive abandonment of traditional farming and forest uses. The global increase in catastrophic episodes in regions with a Mediterranean climate is worrying. Resilient towns and villages have given way to extensive and scattered residential estates that are in contact with forest fuel. These changes in the landscape when added to those of the climate, increase the danger of what some call ‘igneous storms’. The risk in many areas has increased greatly in the last few decades. It is necessary to limit the growing exposure to this type of risk – and geographical information resources are essential for determining the territorial magnitude of the problem. Official libraries offering remote sensing data or geographical databases on land use and land cover (LULC) may be the best option (although accessing this data is complex for many end users). This research proposes identifying areas exposed to fire risk in the wildland-urban interface (WUI) through the automated integration of massive data from official geographic information sources. The application of this study to the region of Valencia (Spain) shows that the integration of these official sources of geographical information can achieve the objective at a detailed scale with relatively short processing times and for large geographical areas (approximately 8 h required to process about 70 Gb of LIDAR data). Geo Small Data techniques for the process of large datasets and its application to the objective of the study have been the best way to automate the analysis of lidar point clouds, with more than 5 billion echoes, through the use of free and open tools, containerization technologies, parallel processing and specific python libraries for geospatial data management. The LIDAR data has provided the necessary geometric definition to complement and improve the WUI area map from the reclassification of hundreds of thousands of polygons from the official Spanish land use geodatabase (SIOSE), achieving a map scale of more than 1: 25,000, for its part, the quality of the SIOSE geodatabase has allowed us to reduce the total LIDAR data to process by 97.8%. This work was supported by grants from the Spanish Ministry of Economy and Competitiveness (Project SIOSE-INNOVA - CSO 2016-79420-R AEI/FEDER.UE) and the methodology has been developed by the project research team. The cartographic result has been collected in an unpublished work that has been awarded the XII Edition ‘Pare Tosca’ Cartography Prize of the Valencian Government (Valencian Cartographic Institute - ICV).
Databáze: OpenAIRE
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