Access to highly detailed models of heterogeneous forests from the near surface to above the tree canopy at varying scales is of increasing demand as it enables more advanced computational tools for analysis, planning, and ecosystem management. LiDAR sensors available through different scanning platforms including terrestrial, mobile and aerial have become established as one of the primary technologies for forest mapping due to their inherited capability to collect direct, precise and rapid 3D information of a scene. However, their scalability to large forest areas is highly dependent upon use of effective and efficient methods of co-registration of multiple scan sources. Surprisingly, work in forestry in GPS denied areas has mostly resorted to methods of co-registration that use reference based targets (e.g., reflective, marked trees), a process far from scalable in practice. In this work, we propose an effective, targetless and fully automatic method based on an incremental co-registration strategy matching and grouping points according to levels of structural complexity. Empirical evidence shows the method's effectiveness in aligning both TLS-to-TLS and TLS-to-ALS scans under a variety of ecosystem conditions including pre/post fire treatment effects, of interest to forest inventory surveyors.

翻译：对异质性森林从近地表至树冠上方不同尺度的精细建模需求日益增长，这为分析、规划与生态系统管理提供了更先进的计算工具。通过地面、移动和空中等不同扫描平台获取的LiDAR传感器，因其具备直接、精准且快速采集场景三维信息的固有特性，已成为森林测绘的主要技术之一。然而，其在大范围林区的可扩展性高度依赖于多扫描源数据协同配准的高效方法。令人意外的是，在GPS受限林区的林业研究中，多数方法仍依赖基于参考目标的配准技术（如反光标记树、标示树木），这类方法在实践中难以规模化推广。本研究提出一种无需标靶的全自动高效方法，采用增量式协同配准策略，根据结构复杂度层级对点云进行匹配与分类。实验证据表明，该方法在多种生态系统条件下（包括火前/火后处理效应监测）均能有效配准TLS-TLS及TLS-ALS扫描数据，这对森林资源清查具有重要应用价值。