gchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2015.04.020
Scopus ID: 2-s2.0-84943639292
Title:
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
Author: Li W; , Niu Z; , Liang X; , Li Z; , Huang N; , Gao S; , Wang C; , Muhammad S
Source Publication: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
Indexed By: SCI-E
Publishing Year: 2015
Volume: 41
pages begin: 88
pages end: 98
Language: 英语
Keyword: Above-ground biomass ; Canopy cover ; Geostatistical modeling ; LiDAR ; SPOT-6
Scopus Keyword: aboveground biomass ; forest canopy ; forest cover ; geostatistics ; kriging ; lidar ; mapping ; satellite data ; spatial distribution ; SPOT ; China
English Abstract: 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. © 2015 Elsevier B.V.
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被引频次[WOS]:17   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.177/globalchange/handle/2HF3EXSE/79491
Appears in Collections:气候变化事实与影响

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Affiliation: The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Masala, Finland; Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Wanshoushanhou, Beijing, China; Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China; Olympic Science and Technology Park of CAS, P.O. Box 9718, No. 20, Datun Road, Beijing, China

Recommended Citation:
Li W,, Niu Z,, Liang X,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,2015-01-01,41
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