文献类型：期刊文献

中文题名：机载高光谱与 LiDAR 单木树种分类精度的影响因素分析

英文题名：Analysis of factors influencing tree species classification accuracy using Airborne Hyperspectral and LiDAR data

作者：Jia, Wen[1] Pang, Yong[1] Li, Zengyuan[1] Kong, Dan[1] Liang, Xiaojun[1]

第一作者：荚文

机构：[1] 1. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China, 2. Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China

年份：2025

卷号：29

期号：10

起止页码：2916-2932

外文期刊名：National Remote Sensing Bulletin

收录：EI(收录号：20254419440213)

语种：中文

外文关键词：Biodiversity - Classification (of information) - Conservation - Data accuracy - Decision trees - Ecosystems - Forest ecology - Forestry - Image segmentation - Mixing - Natural resources - Optical radar - Random forests - Reflection - Remote sensing - Vegetation

摘要：This research aims to examine the key factors influencing the accuracy of tree species classification using airborne hyperspectral data combined with Light Detection And Ranging (LiDAR) data in forest environments. Accurate identification of individual tree species is essential for effective forest resource monitoring, management, ecosystem assessment, and biodiversity conservation. While many small-scale studies have explored tree species classification in forests with diverse species compositions and complex age structures, achieving this over larger areas remains a significant challenge. This study focuses on evaluating the effects of spectral consistency correction, canopy height information, and individual tree canopy segmentation on classification accuracy. Saihanba Mechanical Forest Farm, a large-scale artificial plantation, was selected as the study site to explore these factors. To assess the effect of different factors on tree species classification accuracy, this research utilized a random forest classification algorithm and developed four distinct classification strategies. The first strategy used vegetation indices derived from multiflightline images without applying Bidirectional Reflectance Distribution Function (BRDF) correction. The second strategy incorporated BRDF correction into the multiflightline images before deriving vegetation indices. The third strategy integrated canopy height information, specifically the Canopy Height Model (CHM), with the BRDF-corrected vegetation indices. The fourth and final strategy combined BRDF-corrected vegetation indices, CHM, and individual tree canopy segmentation data. The classification accuracy of each strategy was systematically compared to quantify the contribution of each factor toward improving tree species classification precision. Results indicated that individual tree canopy segmentation significantly reduced misclassification errors arising from the mixing of multiple species within a single canopy, leading to a notable 10.74% improvement in classification accuracy. Using the random forest model’s feature importance ranking, individual tree segmentation emerged as the most critical factor, followed by BRDF correction, then CHM. Although BRDF correction reduced spectral reflectance variability caused by differing Sun observation geometries across flight strips, it only led to a modest improvement in classification accuracy of 3.48%. The introduction of CHM yielded minimal gains in accuracy, contributing just 0.67%, particularly in areas with uniform vertical forest structures or species spanning multiple age cohorts. This study demonstrates that integrating airborne hyperspectral data with LiDAR data holds substantial promise for enhancing tree species classification in large-scale artificial plantations. Among the factors analyzed, individual tree segmentation proved to be the most impactful in improving accuracy. By contrast, the relatively minor influence of BRDF correction and canopy height features underscores the need for further refinement and optimization. Overall, the findings emphasize the importance of considering multiple factors in remote sensing workflows to enhance the efficiency and accuracy of forest resource monitoring, management, and other forestry-related applications, especially in expansive forest environments. These insights provide a valuable theoretical foundation and practical recommendations for future forest management and ecological monitoring efforts. ? 2025 Science Press. All rights reserved.