Abstract
During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.
Highlights
Wildfires have been a common phenomenon throughout time and are caused by ignition sources, either human or natural, and their interaction with climate factors that foster combustion and propagation [1,2]
We focus on the Amazonas department, an administrative division that contains part of the Peruvian Amazon in northeastern Peru, where the wildfires occur very often during the dry season [41]
Sentinel-2 presented otherwise better values, with an accuracy between 94.5% and 98.5%, UA varying from 92.1% to 99.1%, PA ranging from 92.5% to 99.1%, and F1 attaining values oscillating between 94.4% and 98.5%, respectively (Table 4)
Summary
Wildfires have been a common phenomenon throughout time and are caused by ignition sources, either human or natural, and their interaction with climate factors that foster combustion and propagation [1,2]. Fires in vegetation cover generate negative impacts in most of the global biomes, like grasslands/savannahs (tropical and subtropical), forests (Mediterranean, temperate, boreal, and Amazonian), and agricultural fields [3,4]. Such fires may likewise negatively affect biodiversity, forest structure, and resilience to climate change [5,6]. The susceptibility to fires and their spatiotemporal distribution in the Amazon Forest are associated with extreme droughts caused by abnormal increases in sea surface temperature, like El Niño Southern Oscillation (ENSO), as well as the intensification of land use [7,8]. Satellite images offer a synoptic view, systematic coverage, besides a speed-up and cost-effective acquisition [4]
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