机器学习算法在森林地上生物量估算中的应用

森林地上生物量是森林生产力的重要评价指标,对其进行高效监测对维持全球碳平衡和保护生态系统具有重要意义。本文首先基于冠层高度模型数据,通过分水岭分割算法得到单木冠幅边界;然后在单木冠幅范围内提取23个LiDAR变量,结合佩诺布斯科特试验森林的87组实测数据,利用随机森林和支持向量机建立森林地上生物量估算模型;最后对样地模型估算的结果进行了比较,讨论了预测结果及其精度。结果表明:本文选用的随机森林模型和支持向量机模型在估算森林地上生物量的应用中获得了较高的精度;并且,随机森林模型在基于机载雷达数据估测森林地上生物量中的估算精度更高,模型泛化能力更强,制图精度也更好,具有更好的适用性。

Application of machine learning algorithms in estimation of above-ground biomass of forest

Above-ground biomass of forests is an important evaluation index of forest productivity, and efficient monitoring of it is of great significance to maintaining global carbon balance and protecting ecosystems. The research is based on the canopy height model data, obtained the single tree canopy width boundary through the watershed segmentation algorithm, and then extracted a total of 23 LiDAR variables within the single tree canopy range,combined with 87 sets of measured data from the Penobscot experimental forest, random forest (RF) and support vector machine (SVM) are used to establish forest above-ground biomass estimation model, and the estimation results of the sample plot models are compared, and the prediction results and their accuracy are discussed. The results show that the random forest model and support vector machine model selected in this study have achieved high accuracy in estimating the above-ground biomass of forests. The random forest model has higher estimation accuracy in estimating forest above-ground biomass based on airborne radar data, stronger model generalization ability, better mapping accuracy, and better suitability.