Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

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.

Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

“…The sentinel-2 has high potential for burned area detection due to its higher temporal and spatial resolutions compared to other satellites which provide free data. For example, many research studies have been conducted to detect burned areas using Landsat imagery which has low temporal resolution (about 15 days) [3,11,12,[21][22][23]50,51]. The poor temporal resolution of Landsat negatively affects the timely mapping of burned areas.…”

“…They also analyzed the spatial distribution pattern of the global burned areas. Furthermore, Barboza Castillo et al [23] investigated forest burned areas using a combination of Sentinel-2 and Landsat-8 satellite images within the GEE cloud platform over Uttarakhand, Western Himalaya. They mapped forested burned area by differencing the spectral indices for post and pre fire events as well as combining unsupervised clustering and supervised classification methods.…”

Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

“…The methodology herein proposed was optimized through the use of the GEE cloud computing platform. The literature generally reports that, compared to other satellite data as Modis or TM, S2 offers higher accuracy in the discrimination and mapping of BA [ 112 , 123 ], that is relevant for the detection of small fire and for the monitoring of complex, heterogeneous, and fragmented ecosystems [ 125 ], typical of the Mediterranean area [ 135 ]. In addition, there are several techniques and methodologies for BA mapping.…”

“…Despite these facilities, related both to data availability and processing, up to now, a few studies have been conducted using the GEE platform or similar tools for Monitoring Wildfires for mapping burned areas in (i) the Northeastern Peruvian Amazon using Landsat-8 and Sentinel-2 Imagery [ 123 ], (ii) in Italy using S2 [ 124 ], and (iii) on the global scale using Landsat Images [ 125 , 126 , 127 , 128 ].…”

“…Medium spatial resolution satellite data available in the GEE platform were used for this paper, such as bottom of atmosphere (BOA) multispectral image collection as Sentinel-2 level-2A (ID: COPERNICUS/S2_SR) [ 123 , 124 ]. Sentinel-2 satellites acquire 13 spectral bands with spatial resolutions that range from 10 to 60 m. The spectral channels include four bands at 10 m spatial resolution, six bands at 20 m spatial resolution, and three bands at 60 m spatial resolution.…”