Wells Lucas A, Chung Woodam
Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, Corvallis, OR 97331, USA.
Sensors (Basel). 2023 Aug 9;23(16):7043. doi: 10.3390/s23167043.
Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics. Additionally, associating individual tree positions with dendrometric variables like diameter, taper, and species can provide data for highly optimized, site-specific silvicultural prescriptions designed to achieve diverse management objectives. Aerial photogrammetry has proven effective for mapping individual trees; however, its utility is limited due to the inability to directly estimate many dendrometric variables. In contrast, terrestrial mapping methods can directly observe essential individual tree characteristics, such as diameter, but their mapping accuracy is governed by the accuracy of the global satellite navigation system (GNSS) receiver and the density of the canopy obstructions between the receiver and the satellite constellation. In this paper, we introduce an integrated approach that combines a camera-based motion and tree detection system with GNSS positioning, yielding a stem map with twice the accuracy of using a consumer-grade GNSS receiver alone. We demonstrate that large-scale stem maps can be generated in real time, achieving a root mean squared position error of 2.16 m. We offer an in-depth explanation of a visual egomotion estimation algorithm designed to enhance the local consistency of GNSS-based positioning. Additionally, we present a least squares minimization technique for concurrently optimizing the pose track and the positions of individual tree stem[s].
传统上，森林是通过林分水平描述符来表征的，如断面积、平均直径和立木密度。近年来，人们越来越关注提高森林清查的分辨率，以研究景观中树木的空间结构和格局。单株树木的空间排列与森林的各种非货币方面密切相关，包括水质、野生动物栖息地和美学。此外，将单株树木的位置与直径、尖削度和物种等测树变量相关联，可以为旨在实现多样化管理目标的高度优化的特定地点造林处方提供数据。航空摄影测量已被证明对绘制单株树木有效；然而，由于无法直接估计许多测树变量，其效用受到限制。相比之下，地面测绘方法可以直接观测单株树木的基本特征，如直径，但其测绘精度受全球卫星导航系统（GNSS）接收器的精度以及接收器与卫星星座之间树冠障碍物密度的影响。在本文中，我们介绍了一种综合方法，该方法将基于相机的运动和树木检测系统与GNSS定位相结合，生成的树干图精度是单独使用消费级GNSS接收器的两倍。我们证明可以实时生成大规模树干图，实现均方根位置误差为2.16米。我们深入解释了一种旨在增强基于GNSS定位的局部一致性的视觉自我运动估计算法。此外，我们提出了一种最小二乘最小化技术，用于同时优化姿态轨迹和单株树干的位置。
