Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data

doi:10.1016/j.ecolind.2018.07.050

	Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data
	Ma, Qin 1; Su, Yanjun 1,2; Luo, Laiping 1; Li, Le 3; Kelly, Maggi 4,5; Guo, Qinghua1,2
	2018
发表期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
卷号	95 页码:298-310
摘要	Forest ecosystems in the American west have long been influenced by timber harvests and fire suppression, and recently through treatments that reduce fuel for fire management. Precisely quantifying the structural changes to forests caused by fuel treatments is an essential step to evaluate their impacts. Satellite imagery-derived vegetation indices, such as the normalized difference vegetation index (NDVI), have been widely used to map forest dynamics. However, uncertainties in using these vegetation indices to quantify forest structural changes have not been thoroughly studied, mainly due to the lack of wall-to-wall validation data. In this study we generated forest structural changes in aboveground biomass (AGB) and canopy cover as a result of fuel treatments using bitemporal airborne light detection and ranging (LiDAR) data and field measurements in a mixed coniferous forest of northern Sierra Nevada, California, USA. These LiDAR-derived forest structural measures were used to evaluate the uncertainties of using Landsat-derived vegetation indices to quantify treatments. Our results confirmed that vegetation indices can accurately map the extents of forest disturbance and canopy cover changes caused by fuel treatments, but the accuracy in quantifying AGB changes varied by the pre-treatment forest densities and treatment intensity. Changes in vegetation indices had relatively weaker correlations (coefficient of determination < 0.45) to biomass changes in forests with sparse (AGB < 100 Mg/ha) or dense biomass (AGB > 700 Mg/ha), than in forests with moderate-density (AGB between 100 Mg/ha and 700 Mg/ha) before the disturbances. Moreover, understory treatments (canopy height < 10 m) were poorly indicated by changes in satellite-derived vegetation indices. Our results suggest that when relating vegetation indices to AGB changes, researchers and managers should be cautious about their uncertainties in extremely dense or sparse forests, particularly when treatments mainly removed small trees or understory fuels.
关键词	Forest fuel treatment Vegetation index Aboveground biomass LiDAR
学科领域	Biodiversity Conservation ; Environmental Sciences
DOI	10.1016/j.ecolind.2018.07.050
收录类别	SCI
语种	英语
WOS关键词	TIME-SERIES ; ABOVEGROUND BIOMASS ; FIRE SEVERITY ; CANOPY STRUCTURE ; SIERRA-NEVADA ; UNITED-STATES ; DISTURBANCE ; LANDSCAPE ; RECOVERY ; TRANSFORMATION
WOS记录号	WOS:000456907400029
出版者	ELSEVIER
文献子类	Article
出版地	AMSTERDAM
EISSN	1872-7034
资助机构	National Science FoundationNational Science Foundation (NSF) [DBI 1356077] ; USDA Forest Service Region 5 ; USDA Forest Service Pacific Southwest Research StationUnited States Department of Agriculture (USDA)United States Forest Service ; US Fish and Wildlife ServiceUS Fish & Wildlife Service ; California Department of Water Resources ; California Department of Fish and Wildlife ; California Department of Forestry and Fire Protection ; Sierra Nevada Conservancy
作者邮箱	qma@ucmerced.edu ; stiyanjun1987@gmail.com ; maggi@berkeley.edu
作品OA属性	Bronze, Green Published
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/20575
专题	植被与环境变化国家重点实验室
作者单位	1.Univ Calif, Sierra Nevada Res Inst, Merced, CA USA 2.Univ Calif, Sch Engn, Merced, CA USA 3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 4.South China Normal Univ, Sch Geog, Guangzhou 510631, Guangdong, Peoples R China 5.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA 6.Univ Calif, Div Agr & Nat Resources, Oakland, CA USA
推荐引用方式 GB/T 7714	Ma, Qin,Su, Yanjun,Luo, Laiping,et al. Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data[J]. ECOLOGICAL INDICATORS,2018,95:298-310.
APA	Ma, Qin,Su, Yanjun,Luo, Laiping,Li, Le,Kelly, Maggi,&Guo, Qinghua.(2018).Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data.ECOLOGICAL INDICATORS,95,298-310.
MLA	Ma, Qin,et al."Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data".ECOLOGICAL INDICATORS 95(2018):298-310.

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