期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 12, 期 9, 页码 2003-2007出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2451091
关键词
Accuracy improvement; forest carbon density; integration; Landsat Thematic Mapper (TM); regression; spectral unmixing
类别
资金
- Ministry of Science and Technology of China through the national 863 project Study on key technologies of digital forest resources monitoring [2012AA102001]
- Central South University of Forestry and Technology [0990]
- graduate student scientific innovation of Hunan province [CX2014B330]
Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%-39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据