准确、高效地估测森林蓄积量有利于衡量森林健康状况和评价森林的固碳能力。红边波段对植被叶绿素变化敏感,但其在森林蓄积量估测上的有效性需要进一步验证。为了探讨红边波段估测森林蓄积量的可行性,以南宁市兴宁区作为研究区,基于Sentinel-2A影像提取普通波段反射率、红边波段反射率,以及普通植被指数和红边植被指数,构建不同的建模变量集,通过多元线性回归和随机森林算法对森林蓄积量进行估测,利用2019年森林资源二类调查数据库作为实测值对模型估测结果进行精度评价。通过对比不同模型和变量集的建模效果,分析红边波段对森林蓄积量估测精度的影响。结果表明:红边植被指数均与森林蓄积量显著相关(P<0.01);在不同变量集中,红边植被指数变量集森林蓄积量估测精度显著优于其他变量集;在两种蓄积量估测模型中,随机森林模型的估测效果更佳;所有变量集构建的随机森林模型精度均优于多元线性回归;在包含普通波段反射率、红边波段反射率、普通植被指数和红边植被指数变量集4中,随机森林模型实现了最高的估测精度,其R²,RMSE,RRMSE分别为0.66,28.63,23.54%。通过研究可知:Sentinel-2A的红边波段信息可有效应用于森林蓄积量遥感估测,能为森林资源遥感高效监测和管理提供信息数据支持。

Accurate and efficient estimation of forest stock volume is useful for measuring forest health and evaluating the carbon sequestration capacity of forests. The red-edge band is sensitive to vegetation chlorophyll changes,but its validity in forest stock volume estimation needs further verification. To explore the feasibility of red-edge band in estimating forest stock volume,the Xingning district of Nanning City was used as the study area,and different sets of modeling variables were constructed based on Sentinel-2A images to extract common band reflectance,red-edge band reflectance,common vegetation index and red-edge vegetation index,and the forest stock volume was estimated by multiple linear regression and random forest algorithm. The forest resources planning and design survey results data was used as the actual measurements for model accuracy evaluation. By comparing the modeling effects of the models and variable sets,the influence of the red-edge band on the estimation accuracy of the forest stock volume was analyzed. The results showed that the red-edge vegetation index was significantly correlated with the forest stock volume (P<0.01),and the forest stock volume estimation accuracy of the red-edge vegetation index variable set was significantly better than the other variable sets in different variable sets; among the two estimation models,the random forest model was more effective,and the accuracy of the random forest model was better than that of the multiple linear regression model in all variable sets. The random forest model using the variable set 4 achieved the highest estimation accuracy with R²,RMSE,and RRMSE of 0.66,28.63,and 23.54%,respectively. It can be concluded from the study that the red-edge band information of Sentinel-2A can be effectively used for remote sensing estimation of forest stock volume,which can provide a reference for efficient monitoring and management of forest resources by remote sensing.