Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya

Bar, Somnath; Parida, Bikash Ranjan; Pandey, Arvind Chandra

doi:10.1016/j.rsase.2020.100324

Cited by 97 publications

(63 citation statements)

References 44 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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 ].…”

Section: Methodsmentioning

confidence: 99%

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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

Fattore

Abate

Faridani

et al. 2021

Sensors

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In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Fattore

Abate

Faridani

et al. 2021

Sensors

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“…The comparison between the Landsat-8 and Sentinel-2 products proved to be of value for a sound detection of BA in the Peruvian Amazon. The literature generally reports that Sentinel-2 offers higher accuracies [12,83], while some authors consider that Landsat-8 is superior to Sentinel-2 when compared with MCD64A1 products [60,84]. According to such authors, Landsat-8 owns better spectral bands than Sentinel-2.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, however, the comparison with further products, such as the series of "MODIS Combined Data" (MCD45, MCD64) and the "Fire Climate Change Initiative" (FIRE CCI), has not been made in the face of their unfeasible application in the Amazon region [4], due to large differences in spatial resolution [60]. Some studies have used the product MCD64A1 (coarse resolution) to detect wildfires at a global scale [88][89][90][91][92][93], but it is unable to detect small and spatially fragmented BA, with which the Landsat-8 and Sentinel-2 products can easily cope [60,94]. On the other hand, several techniques and methodologies have been proposed for mapping BA.…”

Section: Discussionmentioning

confidence: 99%

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

Castillo

Cayo

Almeida

et al. 2020

IJGI

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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.

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Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms

Shahfahad¹,

Talukdar²,

Das³

et al. 2022

Advances in Remote Sensing for Forest Monitoring

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Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya

Cited by 97 publications

References 44 publications

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

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

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

Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms

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