Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (>850 m) and steeper slopes (>26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field.

热带雨林在有限的全球分布范围内为地球上的大部分动植物物种提供了支持，并在调节地球的气候中发挥了重要作用。但是，关于森林类型或栖息地的现有知识相对较差，这些森林中碳储量的量化存在很大的不确定性。使用激光雷达的机载激光扫描在描述森林的三维结构方面具有优于其他遥感技术的优势。关于不同物种的栖息地需求，森林结构可以通过冠层高度，冠层覆盖和生物量的垂直排列来定义。在这项研究中，根据印度尼西亚苏门答腊热带雨林中基于LiDAR的冠层高度模型的分类和层次合并，确定了森林斑块。所识别斑块的属性被用作k-medoids聚类的输入。然后通过将其与田间已识别的森林类型进行比较来分析这些集群。集群与田间确定的森林类型之间存在显着的关联，其中林木和混合农林贡献最大。分析了群集的地形属性，以确定结构类别和潜在的森林类型是否与地形相关。最高的星团出现在比其他星团明显更高的高度（> 850 m）和更陡的坡度（> 26°）上。这些很可能是未受干扰的原始森林的残余物，对于保护和生境研究以及碳储量的估算很重要。