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:从近表面到树冠上方的高度详细森林模型不同规模的获取需求不断增加,因为这可以提供更先进的分析、规划和生态系统管理的计算工具。通过陆地、移动和航空等不同扫描平台提供的激光雷达传感器已经确立为森林测绘的主要技术之一,因为它们继承了收集直接、精确和快速的场景三维信息的能力。然而,这些传感器在大面积森林地区的可扩缩性在很大程度上取决于使用多种扫描源共同登记的有效和高效方法。奇怪的是,在未获得全球定位系统的地区,其林业工作大多采用采用使用基于参照目标的共同登记方法(例如,反射、标志树),这一过程在实际中远非可扩缩。在这项工作中,我们提出一种有效、无目标、完全自动的方法,其依据是渐进式的共同登记战略,根据结构复杂程度对各点进行匹配和分组。实证证据表明,在各种生态系统条件下,包括火灾前/火灾后处理,在森林清点勘测中,对TLS-TLS和TLS-ALS-ALS扫描方法的有效性。</s>