文献类型：期刊文献

英文题名：Estimating Post-Logging Changes in Forest Biomass from Annual Satellite Imagery Based on an Efficient Forest Dynamic and Radiative Transfer Coupled Model

作者：Li, Xiaoyao[1,2] Sun, Xuexia[1,2] Liu, Yuxuan[1,2] Tan, Bingxiang[1,2] Lu, Jun[1] Du, Kai[3,4] Jia, Yunqian[1]

第一作者：Li, Xiaoyao

通信作者：Lu, J[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]State Key Lab Efficient Prod Forest Resources, Beijing 100091, Peoples R China;[3]Qinghai Normal Univ, Coll Geog Sci, Qinghai Prov Key Lab Phys Geog & Environm Proc, Xining 810008, Peoples R China;[4]Southern Qilian Mt Forest Ecosyst Observat & Res S, Huzhu 810500, Peoples R China

年份：2026

卷号：18

期号：2

外文期刊名：REMOTE SENSING

收录：;Scopus(收录号：2-s2.0-105029043680);WOS:【SCI-EXPANDED(收录号:WOS:001671493300001)】；

基金：This research was funded by Fundamental Research Funds of CAF (grant number CAFYBB2023MA013), the National Natural Science Foundation of China (grant number 42401475), and the Natural Science Foundation of Qinghai Province, China (grant number 2024-ZJ-960).

语种：英文

外文关键词：forest logging; stochastic radiative transfer (SRT); forest dynamic model; difference in above-ground biomass (dAGB)

摘要：Highlights What are the main findings? ZELIG and SRT can be feasibly coupled for efficiently simulations of forest logging dynamics. ZELIG-SRT coupled model provides a physical-based approach for post-logging biomass change estimation from annual satellite imagery. What are the implications of the main findings? The ZELIG-SRT model provides a framework for quantitative simulation of biomass changes and corresponding canopy spectral changes before and after forest logging, which can be used for analyzing the spectral response mechanism of forest logging. The post-logging biomass change can be estimated from annual optical satellite imagery by an efficient physical-based model, which is more interpretable than empirical methods and gives no need for complex scenario simulations.Highlights What are the main findings? ZELIG and SRT can be feasibly coupled for efficiently simulations of forest logging dynamics. ZELIG-SRT coupled model provides a physical-based approach for post-logging biomass change estimation from annual satellite imagery. What are the implications of the main findings? The ZELIG-SRT model provides a framework for quantitative simulation of biomass changes and corresponding canopy spectral changes before and after forest logging, which can be used for analyzing the spectral response mechanism of forest logging. The post-logging biomass change can be estimated from annual optical satellite imagery by an efficient physical-based model, which is more interpretable than empirical methods and gives no need for complex scenario simulations.Abstract The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R2 = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R2 = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms.