Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R² increase from 20 % to 30 %) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

中文翻译：

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基于航空影像和离散激光扫描数据，分层森林过度伐木以提高有效的LAI估计

在景观水平上准确绘制森林有效叶面积指数（LAIe）是更好地模拟各种生态和生理过程（例如光合作用，呼吸作用，蒸腾作用和降水截留）的关键步骤。从二维（2-D）遥感光学图像获得的LAIe产品通常由于无法识别垂直森林结构和消除森林背景（例如灌木，草，雪和裸土）的影响而存在偏差。 。在这项研究中，我们首先对森林的上层和背景层进行分层，然后基于隐含在空中激光扫描（ALS）数据中的结构信息生成森林背景遮罩层。我们通过结合基于光检测和测距（激光雷达）的三维（3-D）结构和二维光谱信息来提高LAIe的检索精度。然后，我们通过掩盖来自光学遥感图像的森林背景像素，获得了改进的最终LAIe估计结果。我们的结果表明：（1）删除森林背景信息可以有效（R²从20％增加到30％）取决于森林结构特征，提高了基于光学的森林LAIe的估算精度。（2）与高冠层的林分相比，低冠层的林分的森林背景对LAIe估计的影响更为明显。（3）结合ALS和光学遥感数据，可以通过去除森林背景信息有效地产生最佳的LAIe检索结果。