Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass

doi:10.1016/j.isprsjprs.2015.02.007

	Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass
	Li, Le 1; Guo, Qinghua2 ; Tao, Shengli 3; Kelly, Maggi 4; Xu, Guangcai
	2015
发表期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN	0924-2716
卷号	102 页码:198-208
摘要	Forests play a key role in the global carbon cycle, and forest above ground biomass (AGB) is an important indictor to the carbon storage capacity and the potential carbon pool size of a forest ecosystem. Accurate estimation of forest AGB has become increasingly important for a wide range of end-users. Although satellite remote sensing provides abundant observations to monitor forest coverage, validation of coarse-resolution AGB derived from satellite observations is difficult because of the scale mismatch between the footprints of satellite observations and field measurements. In this study, we use airborne Lidar to bridge the scale gaps between satellite-based and field-based studies, and evaluate satellite-derived indices to estimate regional forest AGB. We found that: (1) Lidar data can be used to accurately estimate forest AGB using tree height and tree quadratic height, (2) linear regression, among four tested models, achieve the best performance (R-2 = 0.74; RMSE = 183.57 Mg/ha); (3) for MODIS-derived vegetation indices at varied spatial resolution (250-1000 m), accumulated NDVI, accumulated LAI, and accumulated FPAR could explain 53-74% variances of forest AGB, whereas accumulated NDVI derived from 1 km MODIS products gives higher R-2 (74%) and lower RMSE (13.4 Mg/ha) than others. We conclude that Lidar data can be used to bridge the scale gap between satellite and field studies. Our results indicate that combining MODIS and Lidar data has the potential to estimate regional forest AGB. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
关键词	Lidar Forest biomass MODIS Terrestrial Scale
学科领域	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.1016/j.isprsjprs.2015.02.007
收录类别	SCI
语种	英语
WOS关键词	LANDSAT TM DATA ; CANOPY-HEIGHT ; IMPROVED STRATEGY ; AIRBORNE LIDAR ; CLIMATE-CHANGE ; INVENTORY DATA ; STEM VOLUME ; BASAL AREA ; CARBON ; VEGETATION
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000352665500017
出版者	ELSEVIER
文献子类	Article
出版地	AMSTERDAM
EISSN	1872-8235
资助机构	USDA Forest Service Region 5 ; USDA Forest Service Pacific Southwest Research Station ; US Fish and Wildlife Service ; California Department of Water Resources ; California Department of Fish and Game ; California Department of Forestry and Fire Protection ; Sierra Nevada Conservancy ; State Key Laboratory of Earth Processes and Resource Ecology ; National Natural Science Foundation Project of China [41471363]
作者邮箱	qguo@ucmerced.edu
引用统计	被引频次：47[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/2S10CLM1/25687
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.Beijing Normal Univ, State Key Lab Earth Proc & Resource Ecol, Beijing 100875, Peoples R China 3.Univ Calif Merced, Sch Engn, Sierra Nevada Res Inst, Merced, CA 95344 USA 4.Peking Univ, Dept Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China 5.Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA
推荐引用方式 GB/T 7714	Li, Le,Guo, Qinghua,Tao, Shengli,et al. Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2015,102:198-208.
APA	Li, Le,Guo, Qinghua,Tao, Shengli,Kelly, Maggi,&Xu, Guangcai.(2015).Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,102,198-208.
MLA	Li, Le,et al."Lidar with multi-temporal MODIS provide a means to upscale predictions of forest biomass".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 102(2015):198-208.

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