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2 November 2017 Evaluation of forest fires in Portugal Mainland during 2016 summer considering different satellite datasets
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Portugal is one of the most affected countries in Europe by forest fires. Every year in the summer, hundreds of hectares burn, destroying goods and forests at an alarming rate. The objective of this work was to analyze the forest areas burned in Portugal in 2016 (summer) using different satellite data with different spatial resolution (Sentinel-2A MSI and Landsat 8 OLI) in two affected areas. Data from spring from 2016 and 2017 were chosen (pre-fire event and post-fire event) in order to maximize the Normalized Difference Vegetation Index (NDVI) values. The QGIS software's plugin - Semi- Automatic Classification Plugin- which allowed to obtain NDVI values for the Landsat 8 OLI and Sentinel- 2A was used. The results showed that the NDVI decreased considerably in Arouca and Vila Nova de Cerveira after de fire event, meaning a marked drop in vegetation level. In Sintra municipality this change was not verified because non forest fire was registered in this area during the study period. The results from the Sentinel-2A and Landsat 8 OLI data analysis are in agreement, however the Sentinel-2A satellite gives results more accurate than Landsat-8 OLI since it has best spatial resolution. This study could help the experts to understand both the causes and consequences of spatial variability of post-fire effects. Other vegetation spectral indices related with fire and burnt areas could also be calculated in order to discriminate burnt areas. Added to the best spatial resolution of Sentinel-2A (10 m), the temporal resolution of Sentinel- 2A (10 days) was increased with the launch of the twin Sentinel–2B (very recently) and therefore the frequency of the combined constellation revisit will be 5 days. However, for historical studies, the Landsat program remains the best option.
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A. C. Teodoro and A. Amaral "Evaluation of forest fires in Portugal Mainland during 2016 summer considering different satellite datasets", Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104211R (2 November 2017);

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