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

文献类型：期刊论文

作者Ma, Qin3; Su, Yanjun1,3; Luo, Laiping3; Li, Le2; Kelly, Maggi4,5; Guo, Qinghua1,3
刊名ECOLOGICAL INDICATORS
出版日期2018
卷号95页码:298-310
关键词Forest fuel treatment Vegetation index Aboveground biomass LiDAR
ISSN号1470-160X
DOI10.1016/j.ecolind.2018.07.050
文献子类Article
英文摘要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.
学科主题Biodiversity Conservation ; Environmental Sciences
出版地AMSTERDAM
电子版国际标准刊号1872-7034
WOS关键词TIME-SERIES ; ABOVEGROUND BIOMASS ; FIRE SEVERITY ; CANOPY STRUCTURE ; SIERRA-NEVADA ; UNITED-STATES ; DISTURBANCE ; LANDSCAPE ; RECOVERY ; TRANSFORMATION
语种英语
WOS记录号WOS:000456907400029
出版者ELSEVIER
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
源URL[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, Sch Engn, Merced, CA USA
2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China
3.Univ Calif, Sierra Nevada Res Inst, Merced, CA USA
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.

入库方式： OAI收割

来源：植物研究所

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