气候变化领域集成服务门户(CCPortal): Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm

CCPortal

DOI	10.1016/j.rse.2020.111815
	Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm
	Quintano C.; Fernández-Manso A.; Roberts D.A.
发表日期	2020
ISSN	00344257
卷号	244
英文摘要	Successful post-fire management depends on accurate burn severity maps that are increasingly derived from satellite data, replacing field-based estimates. Post-fire vegetation and soil changes, besides modifying the reflected and emitted radiation recorded by sensors onboard satellites, strongly alters water balance in the fire affected area. While fire-induced spectral changes can be well represented by fraction images from Multiple Endmember Spectral Mixture Analysis (MESMA), changes in water balance are mainly registered by evapotranspiration (ET). As both types of variables have a clear physical meaning, they can be easily understood in terms of burn severity, providing a clear advantage compared to widely-used spectral indices. In this research work, we evaluate the potential of Landsat-derived ET to estimate burn severity, together with MESMA derived Sentinel-2 fraction images and important environment variables (pre-fire vegetation, climate, topography). In this study, we use the random forest (RF) classifier, which provides information on variable importance allowing us to identify the combination of input variables that provided the most accurate estimate. Our study area is located in Central Portugal, where a mega-fire burned >450 km2 from 17 to 24 June 2017. We used the official burn severity map as ground reference. The RF algorithm identified ET as the most important variable in the burn severity model, followed by MESMA char fractions. When both ET and MESMA char fraction image were used as RF inputs, burn severity estimates reached higher accuracy than if only one of them was used, which suggests their potential synergetic interaction. In particular, when environmental variables were used in addition to ET and char fraction, the highest accuracy for burn severity was reached (κ = 0.79). Our main conclusion is that post-fire fine resolution ET is a useful and easily understandable indicator of burn severity in Mediterranean ecosystems, in particular when used in combination with a MESMA char fraction image. This novel approach to estimate burn severity may help to develop successful post-fire management strategies not only in Mediterranean ecosystems but also in other ecosystems, due to ease of generalization. © 2020 Elsevier Inc.
英文关键词	Burn severity; Energy balance; Evapotranspiration; MESMA; Random forest
语种	英语
scopus关键词	Classification (of information); Decision trees; Ecosystems; Fires; Learning algorithms; Machine learning; Topography; Vegetation; Emitted radiation; Environmental variables; Fire-affected areas; Mediterranean ecosystem; Multiple endmember spectral mixture analysis; Physical meanings; Synergetic interactions; Variable importances; Image enhancement; abundance estimation; evapotranspiration; image analysis; machine learning; satellite sensor; soil-vegetation interaction; strategic approach; Mediterranean Region; Portugal
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179320
作者单位	Electronic Technology Department, University of Valladolid, C/Francisco Mendizábal, s/n, Valladolid, 47014, Spain; Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agricultural and Food Research and Technology, Spain; Department of Geography, University of California, Santa Barbara, CA 93106, United States; Agrarian Science and Engineering Department, University of León, Av. Astorga s/n, Ponferrada, 24400, Spain
推荐引用方式 GB/T 7714	Quintano C.,Fernández-Manso A.,Roberts D.A.. Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm[J],2020,244.
APA	Quintano C.,Fernández-Manso A.,&Roberts D.A..(2020).Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm.Remote Sensing of Environment,244.
MLA	Quintano C.,et al."Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm".Remote Sensing of Environment 244(2020).