文献类型：期刊文献

英文题名：Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images

作者：Jiang, Fugen[1,2,3] Sun, Hua[1] Chen, Erxue[2,3] Wang, Tianhong[1] Cao, Yaling[1] Liu, Qingwang[2,3]

第一作者：Jiang, Fugen

通信作者：Liu, QW[1];Liu, QW[2]

机构：[1]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China

年份：2022

卷号：14

期号：22

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20224813189964);Scopus(收录号：2-s2.0-85142751499);WOS:【SCI-EXPANDED(收录号:WOS:000887820000001)】；

基金：This research was funded by National Science and Technology Major Project of China's High Resolution Earth Observation System, grant number 21-Y20B01-9001-19/22; the National Key R&D Program of China, grant number 2017YFD0600902; and Research of Key Technologies for Monitoring Forest Plantation Resources, grant number 2017YFD0600900.

语种：英文

外文关键词：above-ground biomass; coniferous plantations; Landsat 9; machine learning; regression kriging

摘要：Accurate estimation of forest above-ground biomass (AGB) is critical for assessing forest quality and carbon stocks, which can improve understanding of the vegetation growth processes and the global carbon cycle. Landsat 9, the latest launched Landsat satellite, is the successor and continuation of Landsat 8, providing a highly promising data resource for land cover change, forest surveys, and terrestrial ecosystem monitoring. Regression kriging was developed in the study to improve the AGB estimation and mapping using the Landsat 9 image in Wangyedian forest farm, northern China. Multiple linear regression (MLR), support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF) were used as the original models to predict the AGB trends, and the optimal model was used to overlay the results of kriging interpolation based on the residuals to obtain the new AGB predictions. In addition, Landsat 8 images in Wangyedian were used for comparison and verification with Landsat 9. The results showed that all bands of Landsat 8 and Landsat 9 maintained a high degree of uniformity, with positive correlation coefficients ranging from 0.77 to 0.89 (p < 0.01). RF achieved the highest estimation accuracy among all the original models based on the two data sources. However, kriging regression can significantly reduce the estimation error, with the root mean square error (RMSE) decreasing by 55.4% and 51.1%, for Landsat 8 and Landsat 9, respectively, compared to the original RF. Further, the R-2 and the lowest RMSE for Landsat 8 were 0.88 and 16.83 t/ha, while, for Landsat 9, they were 0.87 and 17.91 t/ha. The use of regression kriging combined with Landsat 9 imagery has great potential for achieving efficient and highly accurate forest AGB estimates, providing a new reference for long-term monitoring of forest resource dynamics.