气候变化领域集成服务门户(CCPortal): Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty

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DOI	10.1016/j.rse.2019.111497
	Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty
	Furniss T.J.; Kane V.R.; Larson A.J.; Lutz J.A.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Satellite-derived fire severity metrics are a foundational tool used to estimate fire effects at the landscape scale. Changes in surface characteristics permit reasonably accurate delineation between burned and unburned areas, but variability in severity within burned areas is much more challenging to detect. Previous studies have relied primarily on categorical data to calibrate severity indices in terms of classification accuracy, but this approach does not readily translate into an expected amount of error in terms of actual tree mortality. We addressed this issue by examining a dataset of 40,370 geolocated trees that burned in the 2013 California Rim Fire using 36 Landsat-derived burn severity indices. The differenced Normalized Burn Ratio (dNBR) performed reliably well, but the differenced SWIR:NIR ratio most accurately predicted percent basal area mortality and the differenced normalized vegetation index (dNDVI) most accurately predicted percent mortality of stems ≥10 cm diameter at breast height. Relativized versions of dNBR did not consistently improve accuracy; the relativized burn ratio (RBR) was generally equivalent to dNBR while RdNBR had consistently lower accuracy. There was a high degree of variability in observed tree mortality, especially at intermediate spectral index values. This translated into a considerable amount of uncertainty at the landscape scale, with an expected range in estimated percent basal area mortality greater than 37% for half of the area burned (>50,000 ha). In other words, a 37% range in predicted mortality rate was insufficient to capture the observed mortality rate for half of the area burned. Uncertainty was even greater for percent stem mortality, with half of the area burned exceeding a 46% range in predicted mortality rate. The high degree of uncertainty in tree mortality that we observed challenges the confidence with which Landsat-derived spectral indices have been used to measure fire effects, and this has broad implications for research and management related to post-fire landscape complexity, distribution of seed sources, or persistence of fire refugia. We suggest ways to account for uncertainty that will facilitate a more nuanced and ecologically-accurate interpretation of fire effects. This study makes three key contributions to the field of remote sensing of fire effects: 1) we conducted the most comprehensive comparison to date of all previously published severity indices using the largest contiguous set of georeferenced tree mortality field data and revealed that the accuracy of both absolute and relative spectral indices depends on the tree mortality metric of interest;2) we conducted this study in a single, large fire that enabled us to isolate variability due to intrinsic, within-landscape factors without the additional variance due to extrinsic factors associated with different biogeographies or climatic conditions; and 3) we identified the range in tree mortality that may be indistinguishable based on spectral indices derived from Landsat satellites, and we demonstrated how this variability translates into a considerable amount of uncertainty in fire effects at the landscape scale. © 2019 Elsevier Inc.
英文关键词	Differenced Normalized Burn Ratio; Fire severity; Landsat 8; Monitoring trends in burn severity; Smithsonian ForestGEO; Yosemite Forest Dynamics Plot
语种	英语
scopus关键词	Fires; Population statistics; Remote sensing; Uncertainty analysis; Burn Severity; Differenced Normalized Burn Ratio; Fire severity; Forest dynamics; LANDSAT; Smithsonian ForestGEO; Forestry; accuracy assessment; basal area; climate conditions; detection method; error analysis; forest fire; Landsat; landscape; monitoring system; mortality; NDVI; satellite data; uncertainty analysis; California; 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/179521
作者单位	Wildland Resources Department and Ecology Center, Utah State University, Logan, UT 84322, United States; School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, United States; Wilderness Institute and Department of Forest Management, University of Montana, Missoula, MT 59812, United States
推荐引用方式 GB/T 7714	Furniss T.J.,Kane V.R.,Larson A.J.,et al. Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty[J],2020,237.
APA	Furniss T.J.,Kane V.R.,Larson A.J.,&Lutz J.A..(2020).Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty.Remote Sensing of Environment,237.
MLA	Furniss T.J.,et al."Detecting tree mortality with Landsat-derived spectral indices: Improving ecological accuracy by examining uncertainty".Remote Sensing of Environment 237(2020).