1 research outputs found
Analysis of spectral separability for detecting burned areas using Landsat-8 OLI/TIRS images under different biomes in Brazil and Portugal
Data supporting the findings of this study are available in the public domain. Landsat-8 data (https://earthexplorer.usgs.gov/, accessed on 20 April 2020). BDQueimadas vector data (https://queimadas.dgi.inpe.br/queimadas/aq30m/, accessed on 20 April 2020). ICNF burned areas vector data (https://www.icnf.pt/florestas/gfr/gfrgestaoinformacao/dfciinformacaocartgrafica, accessed on 20 April 2020).Fire is one of the natural agents with the greatest impact on the terrestrial ecosystem and
plays an important ecological role in a large part of the terrestrial surface. Remote sensing is an
important technique applied in mapping and monitoring changes in forest landscapes affected by fires.
This study presents a spectral separability analysis for the detection of burned areas using Landsat-8
OLI/TIRS images in the context of fires that occurred in different biomes of Brazil (dry ecosystem)
and Portugal (temperate forest). The research is based on a fusion of spectral indices and automatic
classification algorithms scientifically proven to be effective with as little human interaction as possible.
The separability index (M) and the Reed–Xiaoli automatic anomaly detection classifier (RXD) allowed
the evaluation of the spectral separability and the thematic accuracy of the burned areas for the different
spectral indices tested (Burn Area Index (BAI), Normalized Burn Ratio (NBR), Mid-Infrared Burn Index
(MIRBI), Normalized Burn Ratio 2 (NBR2), Normalized Burned Index (NBI), and Normalized Burn
Ratio Thermal (NBRT)). The analysis parameters were based on spatial dispersion with validation
data, commission error (CE), omission error (OE), and the Sørensen–Dice coefficient (DC). The results
indicated that the indices based exclusively on the SWIR1 and SWIR2 bands showed a high degree
of separability and were more suitable for detecting burned areas, although it was observed that the
characteristics of the soil affected the performance of the indices. The classification method based
on bitemporal anomalous changes using the RXD anomaly proved to be effective in increasing the
burned area in terms of temporal alteration and performing unsupervised detection without relying
on the ground truth. On the other hand, the main limitations of RXD were observed in non-abrupt
changes, which is very common in fires with low spectral signal, especially in the context of using
Landsat-8 images with a 16-day revisit period. The results obtained in this work were able to provide
critical information for fire mapping algorithms and for an accurate post-fire spatial estimation in dry
ecosystems and temperate forests. The study presents a new comparative approach to classify burned
areas in dry ecosystems and temperate forests with the least possible human interference, thus helping
investigations when there is little available data on fires in addition to favoring a reduction in fieldwork
and gross errors in the classification of burned areas.The article processing charge (APC) was funded by the University of JaĂ©n through the Center for Advanced Studies on Earth Sciences, Energy and Environment CEACTEMA and the University of Minho.Research was supported by the project “Applied Remote Sensing in the Study of Hot Spots in Forests in Brazil and the Iberian Peninsula” from the Department of Cartographic Engineering and Surveying (DECart) of the Federal University of Pernambuco (UFPE/Brazil), by POIUJA-2023/2024 and CEACTEMA from University of JaĂ©n (Spain), and RNM-282 research group from the Junta de AndalucĂa (Spain). This work was also supported by national funding awarded by FCT—Foundation for Science and Technology, I.P., projects UIDB/04683/2020 and UIDP/04683/2020