森林地上生物量遥感估算研究综述

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森林地上生物量遥感估算研究综述

郝晴¹, 黄昌¹^,²^,^*(

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A review of forest aboveground biomass estimation based on remote sensing data

HAO Qing¹, HUANG Chang¹^,²^,^*(

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图2. 不同机器学习模型——多元逐步线性回归(MLSR)、K最近邻算法(KNN)、支持向量回归(SVR)和随机森林(RF)算法预测地上生物量的性能对比(据Zhang等(2019a)修改)。R², 决定系数; RMSE, 均方根误差; RMSE_r, 相对均方根误差。

Fig. 2. Performance of aboveground biomass estimation with different machine learning models: multiple stepwise linear regression (MLSR), K-nearest neighbor (KNN), support vector regression (SVR), and random forest (RF) (modified from Zhang et al. (2019a)). R², coefficient of determination; RMSE, root mean squared error; RMSE_r, relative root mean squared error.