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Remote Sens. 2017, 9(3), 241; doi:10.3390/rs9030241

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

1,2,3
,
1,2,* , 3,4,5
and
3,4,5,*
1
National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, Changsha 410004, China
2
Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China
3
Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA
4
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
5
Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Erkki Tomppo, Huaiqing Zhang, Qi Chen, Lars T. Waser and Prasad S. Thenkabail
Received: 18 October 2016 / Revised: 22 February 2017 / Accepted: 1 March 2017 / Published: 5 March 2017
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
View Full-Text   |   Download PDF [6048 KB, uploaded 7 March 2017]   |  

Abstract

The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin. View Full-Text
Keywords: multivariate linear regression; logistic regression; improved kNN algorithm; leave-one-out cross-validation; spatial distribution; Xiangjiang River Basin multivariate linear regression; logistic regression; improved kNN algorithm; leave-one-out cross-validation; spatial distribution; Xiangjiang River Basin
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhu, J.; Huang, Z.; Sun, H.; Wang, G. Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods. Remote Sens. 2017, 9, 241.

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