摘要
【目的】通过构建多源遥感数据集并考虑不同坡度分级的地形校正对于估测的影响,以提升森林蓄积量反演精度,为地形复杂区域的森林蓄积量遥感估算提供参考。【方法】基于Sentinel-2和GF-6遥感影像,结合旺业甸林场野外实测数据,构建多个传统非参数模型和集成学习模型对内蒙古赤峰旺业甸林场森林蓄积量进行反演。为降低地形波动对反演的影响,分别利用Teillet、VECA和SCS+C方法通过坡度分级对影像进行地形校正以提高森林蓄积量反演精度。利用Boruta法和SRF法对不同数据源特征变量进行筛选。【结果】集成学习算法的估测效果整体优于传统非参数模型,其中随机森林模型在所有模型中表现最佳。与单一数据源构建的随机森林模型相比,联合Sentinel-2和GF-6数据源后森林蓄积量反演效果有所提升,基于Boruta法的联合数据源较单一Sentinet和GF-6数据源模型均方根误差(RMSE)分别降低了7.41%和9.61%。经过基于坡度分级的地形校正后,模型RMSE降低了18.48%,森林蓄积量空间分布与旺业甸林场实际情况一致性较高。【结论】以Sentinel-2和GF-6为数据源,构建的集成学习算法可更有效地对森林蓄积量进行估测,基于坡度分级的地形校正有效提高了森林蓄积量估测精度。
【Objective】To improve the accuracy of forest stock volume inversion and provide a reference for remote sensing estimation of forest stock volume in areas with complex terrain,this study aims to construct a multisource remote sensing dataset and examine the impact of terrain correction at different slope classifications on the estimation results.【Method】Using Sentinel-2 and GF-6 remote sensing images,combined with field measurement data from Wangyedian Forest Farm in Chifeng,Inner Mongolia,this study constructed multiple traditional nonparametric models and ensemble learning models to invert the forest stock volume of Wangyedian Forest Farm.To reduce the influence of terrain fluctuations on inversion results,terrain corrections were performed on the images using the Teillet,VECA,and SCS+C methods at different slope classifications to improve the accuracy of forest stock volume inversion.【Result】The estimation performance of ensemble learning algorithms was generally superior to that of traditional nonparametric models,with the random forest model demonstrating the best performance among all models.Compared with the random forest model constructed using a single data source,combining Sentinel-2 and GF-6 data improved the inversion results of forest stock volume inversion performance,reducing the root mean square error(RMSE)of the models using Boruta by 7.41%and 9.61%,respectively.After terrain correction based on slope classification,the RMSE of the model decreased by 18.48%,and the spatial distribution of forest stock volume showed a high degree of consistency with the actual situation in Wangyedian Forest Farm.【Conclusion】Using Sentinel-2 and GF-6 as data sources,the constructed ensemble learning algorithms can more effectively estimate forest stock volume.Terrain correction based on slope classification significantly improves the estimation accuracy of forest stock volume.
作者
高佳乐
蒋馥根
龙依
陈帅
孙华
GAO Jiale;JIANG Fugen;LONG Yi;CHEN Shuai;SUN Hua(Research Center of Forestry Remote Sensing and Information Engineering,Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security,Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China,Central South University of Forestry and Technology,Changsha 410004,China)
出处
《南京林业大学学报(自然科学版)》
北大核心
2025年第6期13-25,共13页
Journal of Nanjing Forestry University:Natural Sciences Edition
基金
国家重点研发计划(2023YFD2201703)
国家自然科学基金项目(32471861,31971578)
湖南省科技创新计划(2023RC1065)
湖南省自然科学基金项目(2022JJ30078)。
关键词
森林蓄积量
遥感反演
联合数据源
地形校正
坡度分级
forest stock volume
remote sensing inversion
federated data source
−topographic correction
slope classification
