The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 959–966, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-959-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 959–966, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-959-2022
 
30 May 2022
30 May 2022

SPATIAL ANALYSIS OF TREE SPECIES BEFORE FOREST FIRES

A. Novo1, H. González-Jorge2, J. Martínez-Sánchez1, and J. Balado1 A. Novo et al.
  • 1CINTECX, Universidade de Vigo, GeoTECH group, Campus Universitario de Vigo, As Lagoas, Marcosende 36310, Vigo, Spain
  • 2Universidade de Vigo, Engineering Physics Group, Campus Universitario de Ourense, 32004, Ourense, Spain

Keywords: Mapping, Forest fires, Remote Sensing, Satellite images, Sentinel-2, GIS analysis, Tree species

Abstract. Spain is included in the top five European countries with the highest number of wildfires. The occurrence and magnitude of forest fires involves aspects of a very diverse nature, from those of a socio-economic, climatic, or physiographic nature, to those concerning fuel or the availability and quantity of resources and means of extinction. The distribution of wildfires in Galicia is not random and that fire occurrence may depend on ownership conflicts also a spatial dependence between productive or non-productive area exists. Satellite data play a major role in providing knowledge about fires by delivering rapid information to map fire-damaged areas precisely and promptly. In addition, the availability of large-scale data and the high temporal resolution offered by the Sentinel-2 satellite enables to classify and determine the land cover changes with high accuracy. This study describes a methodology to detect burned areas and analyse the Land Cover and Land Use (LCLU) classes present in these areas during the period of 5 years (2016–2021) by Sentinel-2 images. The training areas were obtained by photointerpretation and the image classification was performed using the Random Forest algorithm which shows an overall accuracy range between 80–85%. The methodology concluded that Lobios and Muiños were the most affected municipalities by wildfires. Additionally, the spatial analysis determined that the Deciduous Forest mainly composed by Quercus sp. were the most affected in 2017 followed by Coniferous Forest mainly composed by Pinus sp.in 2016. Although, Scrub and Rock are the classes more affected for wildfire during 2016–2020 period.