RT Journal Article
T1 Spatial analysis of tree species before forest fires
A1 Novo Gómez, Ana
A1 González Jorge, Higinio
A1 Martínez Sánchez, Joaquín
A1 Balado Frías, Jesús
K1 3311.02 Ingeniería de Control
K1 2505.02 Cartografía Geográfica
K1 3106 Ciencia Forestal
K1 3106.10 Incendios forestales
AB 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.
PB ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences
SN 21949034
YR 2022
FD 2022-05-30
LK http://hdl.handle.net/11093/4570
UL http://hdl.handle.net/11093/4570
LA eng
NO ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 959-966 (2022)
NO Universidade de Vigo
DS Investigo
RD 25-abr-2025