气候变化领域集成服务门户(CCPortal): Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality

CCPortal

DOI	10.1016/j.rse.2019.111525
	Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality
	Kato A.; Thau D.; Hudak A.T.; Meigs G.W.; Moskal L.M.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Boreal forests are globally extensive and store large amounts of carbon, but recent climate change has led to drier conditions and increasing fire activity. The objective of this study is to quantify trends in fire size and frequency using data spanning multiple scales in space and time. We use multi-temporal Landsat image compositing on Google Earth Engine and validate results with reference fire maps from the Canadian Park Service. We also interpret general fire trends through the concept of Self-Organized Criticality (SOC). Our study site is Wood Buffalo National Park, which is a fire hot spot in Canada due to frequent lightning ignitions. The relativize differenced normalized burn ratio (RdNBR) was the most accurate Landsat-based burn severity metric we evaluated (52.2% producer's accuracy, 87.6% user's accuracy). The Landsat-based burn severity maps provided a better fit for a linear relationship on the log-log scale of fire size and frequency than a manually drawn fire map. Landsat-based fire trends since 1990 conformed to a power-law distribution with a slope of 1.9, which is related to fractal dimensions of the satellite-based fire perimeter shapes. The unburned and low-severity patches within the burn severity mosaic influenced the power-law slope and associated fractal dimensionality. This study demonstrates a multi-scale and multi-dataset technique to quantify general fire trends and changing fire cycles in remote locations and establishes a baseline database for assessing future fire activity. Testing criticality by power laws helps to quantify emergent trends of contemporary fire regimes, which could inform the strategic application of prescribed fire and other management activities. Natural resource managers can utilize information from this study to understand local ecosystem adaptability to large fire events and ecosystem stability in the context of recent increasing fire activity. © 2019 Elsevier Inc.
英文关键词	Boreal forest; Fire perimeter; Fire refugia; Forest fire; Fractal; Google earth engine; Image compositing; Landsat; Mosaic landscape; NBR; NDVI; Self-organized criticality; Time-series analysis
语种	英语
scopus关键词	Climate change; Criticality (nuclear fission); Deforestation; Ecosystems; Engines; Fire hazards; Fractal dimension; Fractals; Image processing; Time series analysis; Boreal forests; Forest fires; Google earths; Image compositing; LANDSAT; Mosaic landscapes; NDVI; Self-organized criticality; Fires; Boreal; boreal forest; forest fire; fractal analysis; Landsat; landscape; quantitative analysis; time series analysis; Canada; Wood Buffalo National Park
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/179563
作者单位	Graduate School of Horticulture, Chiba University, 648 Matsudo Matsudo, Chiba, 2710092, Japan; World Wildlife Fund, 1250 24th Street, N.W. Washington, DC 20037, United States; Rocky Mountain Research Station, USDA Forest Service, Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, United States; College of Forestry, Oregon State University, 321 Richardson Hall, Corvallis, OR 97333, United States; School of Environmental and Forest Sciences, University of Washington, Box 352100 Seattle, WA, 98195, United States
推荐引用方式 GB/T 7714	Kato A.,Thau D.,Hudak A.T.,et al. Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality[J],2020,237.
APA	Kato A.,Thau D.,Hudak A.T.,Meigs G.W.,&Moskal L.M..(2020).Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality.Remote Sensing of Environment,237.
MLA	Kato A.,et al."Quantifying fire trends in boreal forests with Landsat time series and self-organized criticality".Remote Sensing of Environment 237(2020).