Estimation of Tree Species Diversity in Warm Temperate Forests via GEDI and GF-1 Imagery

文献类型： 外文期刊

第一作者： Zhang, Lei

作者： Zhang, Lei;Yang, Liu;Sun, Jinhua;Zhu, Qimeng;Wang, Ting;Zhao, Hui;Zhao, Hui

作者机构：

关键词： forest; diversity index; spectral image; LiDAR; machine learning

期刊名称：FORESTS （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2025 年 16 卷 4 期

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收录情况： SCI

摘要： Estimates of tree species diversity via traditional optical remote sensing are based only on the spectral variation hypothesis (SVH); however, this approach does not account for the vertical structure of a forest. The relative height (RH) indices derived from GEDI spaceborne LiDAR provide vertical vegetation structure information through waveform decomposition. Although RH indices have been widely studied, the optimal RH index for tree species diversity estimation remains unclear. This study integrated GF-1 optical imagery and GEDI LiDAR data to estimate tree species diversity in a warm temperate forest. First, random forest plus residual kriging (RFRK) was employed to achieve wall-to-wall mapping of the GEDI-derived indices. Second, recursive feature elimination (RFE) was applied to select relevant spectral and LiDAR features. The random forest (RF), support vector machine (SVM), and k-nearest neighbor (kNN) methods were subsequently applied to estimate tree species diversity through remote sensing data. The results indicated that multisource data achieved greater accuracy in tree species diversity estimation (average R2 = 0.675, average RMSE = 0.750) than single-source data (average R2 = 0.636, average RMSE = 0.754). Among the three machine learning methods, the RF model (R2 = 0.760, RMSE = 2.090, MAE = 1.624) was significantly more accurate than the SVM (R2 = 0.571, RMSE = 2.556, MAE = 1.995) and kNN (R2 = 0.715, RMSE = 2.084, MAE = 1.555) models. Moreover, mean_mNDVI, mean_RDVI, and mean_Blue were identified as the most important spectral features, whereas RH30 and RH98 were crucial features derived from LiDAR for establishing models of tree species diversity. Spatially, tree species diversity was high in the west and low in the east in the study area. This study highlights the potential of integrating optical imagery and spaceborne LiDAR for tree species diversity modeling and emphasizes that low RH indices are most indicative of middle- to lower-canopy tree species diversity.

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