中南林业科技大学学报2025，Vol.45Issue(6)：9-21,13.DOI:10.14067/j.cnki.1673-923x.2025.06.002
基于Sentinel-2影像和冠层高度模型数据的森林蓄积量反演
Forest stock volume inversion based on Sentinel-2 images and canopy height model data
摘要
Abstract
[Objective]As an important indicator of the biomass storage capacity of forest ecosystems,forest stock is a key parameter for measuring forest health,ecological function and carbon stock.Accurate inversion of forest stock can provide scientific data support for forest resource management,climate change,ecological protection and sustainable management,which is one of the core tasks of forest resource investigation and monitoring.[Method]In this study,Liuyang City,Hunan Province was taken as the study area,and the remote sensing inversion of forest stock was carried out by combining the National Forest Inventory(NFI)data and Sentinel-2 remote sensing images.Firstly,key feature variables closely related to forest stock were extracted by Pearson correlation analysis and importance screening of feature variables.Subsequently,support vector machine(SVM),random forest(RF)and fully convolutional neural network(FCN)models were used for forest stock inversion.The model accuracy was calculated using the cross-validation method,and several assessment indices were applied to compare the model performance,and the model with the best fitting effect was finally selected for the accurate inversion of forest stock in the study area.[Result]The results showed that:1)each feature variable showed significant correlation with the forest stock;2)the introduction of the canopy height model(CHM)could significantly improve the accuracy of the forest stock inversion,in which the coefficient of determination(R2)of the support vector machine(SVM)model was increased from 0.34 to 0.52,representing an improvement of 52.95％.The root mean square error(RMSE)decreased from 30.52 m3·hm-2 to 25.59 m3·hm-2,representing a reduction of 16.15％.And the random forest(RF)model was increased from 0.39 to 0.55,representing an improvement of 41.03％.The RMSE decreased from 29.46 m3·hm-2 to 25.07 m3·hm-2,representing a reduction of 14.90％,which indicated that CHM could significantly improve the accuracy of the inversion of forest stock.This indicates that CHM has an important contribution to improving the accuracy of forest stock inversion;3)Compared with other models,the full convolutional neural network(FCN)model has the best performance in forest stock inversion,with an R2 value of 0.66 and the RMSE is 21.20 m3·hm-2.In addition,the CHM data based on the year 2022 significantly improved the prediction performance of the other models,which effectively overcomes the accuracy bottleneck of the traditional inversion methods due to the lack of measured tree height data and provides a good opportunity for the large-scale inversion.This effectively overcomes the bottleneck of accuracy caused by the lack of measured tree height data in traditional inversion methods and provides new ideas and methods for large-scale forest stock monitoring.[Conclusion]The FCN model combining measured data and Sentinel-2 imagery can realize accurate inversion and monitoring of forest stock volume,and the introduction of CHM data significantly improves the prediction accuracy of the inversion model,which provides an important reference for accurate monitoring and scientific management of forest resources in the future.关键词
森林蓄积量/Sentinel-2/冠层高度模型/特征变量/机器学习/深度学习Key words
forest stock volume/Sentinel-2/canopy height model/feature variables/machine learning/deep learning分类
农业科技引用本文复制引用
贺鹏,陈振雄,黄鑫..基于Sentinel-2影像和冠层高度模型数据的森林蓄积量反演[J].中南林业科技大学学报,2025,45(6):9-21,13.基金项目
国家自然科学基金项目(32271878). （32271878）
