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Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform
ISPRS International Journal of Geo-Information ( IF 3.4 ) Pub Date : 2020-09-29 , DOI: 10.3390/ijgi9100564
Elgar Barboza Castillo , Efrain Turpo Cayo , Cláudia de Almeida , Rolando Salas López , Nilton Rojas Briceño , Jhonsy Silva López , Miguel Barrena Gurbillón , Manuel Oliva , Raul Espinoza-Villar

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.

中文翻译:

使用GEE平台中的Landsat-8和Sentinel-2影像监视秘鲁东北部亚马逊地区的野火

在最近的几十年中,亚马逊森林遭受了植被的严重破坏,在许多情况下是野火的直接后果,野火在地方,国家和全球范围内成为一个问题,导致经济,社会和环境影响。因此,本研究致力于使用基于云的Google Earth Engine(GEE)平台,基于Landsat-8和Sentinel-2影像的最新多时相数据(2017-2019年),开发一种监测植被覆盖中火灾的例程。为了评估烧伤面积(BA),采用了光谱指数,例如归一化燃烧比(NBR),归一化燃烧比2(NBR2)和中红外燃烧指数(MIRBI)。所有这些指数均根据适当的阈值应用于BA评估。此外,为减少烧毁地区与其他土地覆盖类别之间的混淆,还使用了其他指标,例如考虑着火前后条件之间的时间差异的指标:差分中红外燃烧指数(dMIRBI),差分归一化燃烧比(dNBR),差分归一化燃烧比2(dNBR2)和差分近距离红外(dNIR)。在三年的调查跨度中,Sentinel-2计算出的BA较大(16.55、78.50和67.19 km2),并且比Landsat-8(16.39、6.24和32.93 km 2)提取的BA更详细(检测到的小区域)。本文中介绍的监视野火的例程基于一系列决策规则。这使得能够检测和监控烧毁的植被覆盖度,并且最初已应用于秘鲁东北部亚马逊地区的一项实验。比较两个卫星图像获得的结果的准确性指标和详细程度(BA补丁的大小)。2017、2018和2019年Landsat-8和Sentinel-2的准确度分别从82.7–91.4%到94.5–98.5%。
更新日期:2020-09-29
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