RT info:eu-repo/semantics/article
T1 A satellite-based burned area dataset for the northern boreal region from 1982 to 2020
A1 Arbelo Pérez, Manuel Imeldo
A1 Moreno Ruiz, José Andrés
A1 García Lázaro, José Rafael
A1 Hernández Leal, Pedro A.
K1 Bayesian network algorithm
K1 Boreal forest
K1 Remote sensing
AB Background. Fires in the boreal forest occur with natural frequencies and patterns. Burned area (BA) is an essential variable in assessing the impact of climate change in boreal regions. Aims. Spatial wildfire occurrence data since the 1950s are available for North America. However, there are no reliable data for Eurasia, mainly for Siberia, during the 1980s and 1990s. Methods. A Bayesian- network algorithm was applied to the Long-Term Data Record (LTDR) Version 5 to generate a BA DataSet (BA-LTDR-DS) for the Boreal region from 1982 to 2020, validated using official reference data and compared with the MODIS MCD64A1 product. Key results. A high correlation (>93%) with all the reference BA datasets was found. BA-LTDR-DS data grouped by decades estimated a linear increase in BA of 4.47 million ha/decade. This trend provides evidence of how global warming affects fire activity in these boreal forests. Conclusions. BA-LTDR-DS constitutes a unique data source for the pre-MODIS era, and becomes a reliable source when other products with higher spatial/spectral resolution are not available. Implications. The BA-LTDR-DS dataset constitutes the longest time series developed for the boreal region at this spatial resolution. BA-LTDR-DS could be used as input in global climate models, helping improve wildfire prediction capabilities and understand the interactions between fire, climate and vegetation dynamics.
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/35897
UL http://riull.ull.es/xmlui/handle/915/35897
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 08-jul-2024