文献类型： 外文期刊

作者： Mao, Zhihui ¹ ; Deng, Lei ¹ ; Liu, Xinyi ¹ ; Wang, Yueyang ¹ ;

作者机构： 1.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China

2.Chinese Acad Fishery Sci, Resource & Environm Res Ctr, Beijing 100141, Peoples R China

3.Minist Educ, Engn Res Ctr Spatial Informat Technol, Beijing 100048, Peoples R China

关键词： forest structure parameters; SAR; optical; sparse forest; radiative transfer model

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 8 期

页码：

收录情况： SCI

摘要： Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = -6.73%), H (CV = -52.68%), and LAI (CV = -63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential.