30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

Tengfei Long,Ranyu Yin,Xiaomei Zhang,Zhaoming Zhang,Bingfang Wu,Guizhou Wang,Chao Tang,Weili Jiao,Guojin He

doi:10.3390/rs11050489

Abstract

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

Highlights

Accurate and complete data of fire locations and Burned areas (BA) are important for a variety of applications including quantifying trends and patterns of fire occurrence and assessing the impacts of fires on a range of natural and social systems, e.g., simulating carbon emissions from biomass burning [1]
We focused on an automated approach to generate global-scale high resolution BA maps with dense time-series of Landsat images on Google Earth Engine (GEE), in which all the available Landsat-8 images and various spectral indices were utilized to calculate the burned probability of each pixel using a machine learning model, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters
An automated pipeline for generating 30-m resolution global-scale annual burned area maps utilizing Google Earth Engine was proposed in this study

Summary

Introduction

Accurate and complete data of fire locations and Burned areas (BA) are important for a variety of applications including quantifying trends and patterns of fire occurrence and assessing the impacts of fires on a range of natural and social systems, e.g., simulating carbon emissions from biomass burning [1]. Australia released its Fire Scars (AFS) products derived from all available Landsat 5, 7, and 8 images using the time-series change detection technique [15]. The Monitoring Trends in Burn Severity (MTBS) project, sponsored by the Wildland Fire Leadership Council (WFLC), provides consistent, 30-m resolution burn severity data and fire perimeters across all lands of the United States from 1984–2015 (only fires larger than 200 ha in the eastern U.S and 400 ha in the western U.S are mapped) [16]. The Burned Area Essential Climate Variable (BAECV), developed by the U.S Geological Survey (USGS), produces Landsat-derived BA products across the conterminous United States (CONUS) from 1984–2015, and its products were released in April 2017 [14]. The main differences between MTBS and BAECV is that the BAECV products are automatically generated based on all available Landsat images

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Discussion

Conclusion