气候变化领域集成服务门户(CCPortal): Spatio-temporal patterns of optimal Landsat data for burn severity index calculations: Implications for high northern latitudes wildfire research

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DOI	10.1016/j.rse.2021.112393
	Spatio-temporal patterns of optimal Landsat data for burn severity index calculations: Implications for high northern latitudes wildfire research
	Chen D.; Fu C.; Hall J.V.; Hoy E.E.; Loboda T.V.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differenced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of suboptimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as “optimal”. In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made. © 2021 Elsevier Inc.
英文关键词	Alaska; Arctic; Boreal forest; Burn severity; Canada; Disturbance; dNBR; Google Earth Engine; Landsat; Remote sensing; Tundra; Wildfire
语种	英语
scopus关键词	Fires; Alaska; Arctic; Boreal forests; Burn Severity; Canada; Differenced normalized burn ratio; Disturbance; Google earth engine; LANDSAT; Remote-sensing; Remote sensing; image analysis; Landsat; latitude; remote sensing; satellite data; spatiotemporal analysis; wildfire; Alaska; Arctic; United States
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/178883
作者单位	Department of Geographical Sciences, University of Maryland, College Park, MD, United States; Department of Geography, University of Zurich, Zurich, Switzerland; Global Science & Technology, Inc., NASA Goddard Space Flight Center, Greenbelt, MD, United States
推荐引用方式 GB/T 7714	Chen D.,Fu C.,Hall J.V.,et al. Spatio-temporal patterns of optimal Landsat data for burn severity index calculations: Implications for high northern latitudes wildfire research[J],2021,258.
APA	Chen D.,Fu C.,Hall J.V.,Hoy E.E.,&Loboda T.V..(2021).Spatio-temporal patterns of optimal Landsat data for burn severity index calculations: Implications for high northern latitudes wildfire research.Remote Sensing of Environment,258.
MLA	Chen D.,et al."Spatio-temporal patterns of optimal Landsat data for burn severity index calculations: Implications for high northern latitudes wildfire research".Remote Sensing of Environment 258(2021).