Feasibility Study of Wood-Leaf Separation Based on Hyperspectral LiDAR Technology in Indoor Circumstances

Wood–leaf separation aiming at classifying LiDAR points into wood and leaf components is one of the most important genres for improving leaf area index estimation and forestry survey accuracy. The wood return signals could artificially increase the apparent foliage content, which needs to be screened out for deriving vital tree attributes accurately. Previous research works tended to use intensity, waveform, and geometric information extracted from a single wavelength LiDAR for wood–leaf separation. This article employs a revised hyperspectral LiDAR (HSL) to obtain spatial and ultrawide spectral data simultaneously. We also propose a simple three steps method to separate wood and leaf components based on HSL spatial and spectral measurements under the laboratory circumstances. First, the preprocessing is conducted to acquire 3-D spatial information and the multiband laser pulse reflectance for further separation. Second, preliminary separation (band division, key feature parameter calculation, and judgment) is implemented based on reflectance. Third, we employ K-Nearest Neighbor (KNN) method to enhance separation results based on preliminary separation results and spatial features and then update the results by recorrection. Then, 3-D reconstruction is accomplished by fusing wood–leaf separation results. The experimental results demonstrate that the proposed method can separate wood and leaf components with high accuracy and indicate tree attributes straightforwardly.