Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R²) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

中文翻译：

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基于地理加权回归的竹林地上生物量遥感估算

竹林广泛分布于亚热带地区，以生长迅速、固碳能力强而著称。为了认识竹林在区域生态系统中的潜在作用和功能，与森林生产力、森林碳循环，特别是森林生态系统中的碳汇密切相关的森林地上生物量 (AGB) 被计算和应用为一个指标。在考虑 AGB 估计的现有研究中，最常用的是线性或非线性回归模型；然而，这些方法没有考虑空间异质性的影响。地理加权回归（GWR）模型作为空间局部模型，可以在一定程度上解决这个问题。基于 Landsat 8 OLI 图像，我们使用随机森林 (RF) 方法筛选六个变量，包括 TM457、TM543、B7、NDWI、NDVI 和 W7B6VAR。然后，我们建立了 GWR 模型来估计竹林 AGB，并将结果与​​协同克里金法 (COK) 和正交最小二乘法 (OLS) 模型的结果进行比较。结果表明：（1）GWR模型精度高，预测能力强。预测精度（R² ) GWR 模型的误差分别比 COK 和 OLS 模型高 0.74、9% 和 16%，而误差 (RMSE) 分别比 COK 和 OLS 模型的误差低 7% 和 12%。(2) 浙江省GWR模型估算的竹林AGB在西北、西南和东北地区具有相对密集的空间分布。这与浙江省实际竹林AGB分布情况一致，表明了我们研究的潜在实用价值。(3) GWR 模型的最佳带宽为 156 m。通过计算带宽内不同位置的可变参数，在估计结果时密切关注局部变化规律，以减少模型误差。