中南林业科技大学学报2026，Vol.46Issue(2)：1-12,12.DOI:10.14067/j.cnki.1673-923x.2026.02.001
森林储量遥感估测的不确定性研究进展
Research progress on uncertainty in remote sensing-based forest reserves estimation
摘要
Abstract
The three forest reserves,namely forest volume,biomass and carbon storage,are important indicators for monitoring forest resources at multiple levels,and also important parameters reflecting the quality and productivity of forest ecosystems.Remote sensing estimation of forest reserves is not a direct measurement process,but rather relies on the predictive model established between remote sensing variables and field-measured forest reserves.It inherently involves many sources of error and uncertainty,including sampling constraints,measurement inaccuracies,sensor noise,resolution constraints,atmospheric condition variations,model selection and parameter estimation uncertainty.Systematically identifying,quantifying and controlling these errors is crucial for enhancing the accuracy and reliability of remote sensing estimation of forest reserves.Therefore,this paper analyzes uncertainties in remote sensing-based estimation of forest reserves from three aspects:forest inventory samples,remote sensing data sources,and estimation models.Furthermore,it discusses and prospects methods for error control and uncertainty quantification in forest reserves estimation.This study is helpful to deeply understand the uncertainty sources in remote sensing estimation of forest reserves,and can provide a reference for improving the precision of forest reserves retrieval,as well as for optimizing strategies for forest resource monitoring and management.关键词
森林储量/遥感估测/不确定性来源/误差控制/度量方法Key words
forest reserves/remote sensing estimation/uncertainty sources/error control/measurement method分类
农业科技引用本文复制引用
孙华,龙依,王储,郑欢娜,高佳乐..森林储量遥感估测的不确定性研究进展[J].中南林业科技大学学报,2026,46(2):1-12,12.基金项目
岳麓山实验室重大新品种培育项目(YLS-2025-ZY02053) （YLS-2025-ZY02053）
国家自然科学基金面上项目(32471861,31971578) （32471861,31971578）
湖南省科技创新计划项目(2023RC1065) （2023RC1065）
湖南省林业科技攻关与创新项目(XLK202524). （XLK202524）
