Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling

	Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling
	Li, Wang 1; Niu, Zheng 1; Liang, Xinlian 1; Li, Zengyuan 1; Huang, Ni 1; Gao, Shuai 1; Wang, Cheng 1; Muhammad, Shakir 1
刊名	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
	2015
卷号	41 页码:322-336
关键词	Geostatistical modeling LiDAR SPOT-6 Canopy cover Above-ground biomass
通讯作者	Li, W (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
英文摘要	Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics. (C) 2015 Elsevier B.V. All rights reserved.
研究领域[WOS]	Remote Sensing
收录类别	SCI
语种	英语
WOS记录号	WOS:000356996500008
内容类型	期刊论文
源URL	[http://ir.ceode.ac.cn/handle/183411/38133]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.[Li, Wang 2.Niu, Zheng 3.Huang, Ni 4.Gao, Shuai 5.Muhammad, Shakir] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 6.[Li, Wang 7.Muhammad, Shakir] Univ Chinese Acad Sci, Beijing 100049, Peoples R China 8.[Liang, Xinlian] Finnish Geodet Inst, Dept Remote Sensing & Photogrammetry, FI-02431 Masala, Finland 9.[Li, Zengyuan] Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China 10.[Wang, Cheng] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Lab Digital Earth Sci, Beijing 100094, Peoples R China
推荐引用方式 GB/T 7714	Li, Wang,Niu, Zheng,Liang, Xinlian,et al. Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2015,41:322-336.
APA	Li, Wang.,Niu, Zheng.,Liang, Xinlian.,Li, Zengyuan.,Huang, Ni.,...&Muhammad, Shakir.(2015).Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,41,322-336.
MLA	Li, Wang,et al."Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 41(2015):322-336.