ABI Development: Forest3D - an open source platform for lidar applications in forestry

加州大学默塞德获得一笔赠款，用于开发森林研究中LiDAR数据分析的集成软件平台。光探测和测距（LiDAR）是一种主动遥感技术，它测量散射光的特性，以找到远处物体的范围。由于其能够生成具有高空间分辨率和准确性的三维（3D）数据，LiDAR技术越来越多地用于获取个体树木属性，测量森林生物物理参数和重建虚拟森林。尽管LiDAR数据的可用性越来越高，但是，很少有平台允许林业管理人员充分探索这些丰富的3D数据并无缝提取森林参数。该平台提供以下功能：数据管理和可视化、点云过滤、数字模型生成、森林度量计算、回归分析、单木分割、森林生物物理参数提取和森林可视化。该平台将使林业管理人员能够轻松操作LiDAR数据并提取一系列感兴趣的森林参数。首先，它将帮助森林管理者更好地管理我们宝贵的自然资源，并做出更明智的保护决策。其次，该平台将使生态系统科学家能够更广泛地从LiDAR数据中提取3D植被结构参数，从而促进对生态系统模式和过程的理解。第三，该项目将支持加州大学默塞德的教育和研究，这是一个西班牙裔服务机构。PI将积极培训和招募代表性不足的学生，并促进当地社区，州和联邦机构在决策过程中使用LiDAR技术和空间思维。

The University of California Merced is awarded a grant to develop an integrated software platform for LiDAR data analysis in forest research. Light Detection and Ranging (LiDAR) is an active remote sensing technology that measures the properties of scattered light to find the range of a distant object. Due to its ability to generate 3-dimensional (3D) data with high spatial resolution and accuracy, LiDAR technology is increasingly being used to obtain individual tree properties, measure forest biophysical parameters, and reconstruct virtual forests. Despite the increasing availability of LiDAR data, however, there are few platforms that allow forestry managers to fully explore this rich 3D data and seamlessly extract forest parameters. The platform provides the following functionality: data management and visualization, point cloud filtering, digital model generation, forest metrics calculation, regression analysis, individual tree segmentation, forest biophysical parameter extraction, and forest visualization. The platform will enable forestry managers to easily manipulate LiDAR data and extract a range of forest parameters of interest.The broader impacts of this research are threefold. First, it will help forest managers to better manage our precious natural resources and make more informed conservation decisions. Second, the platform will enable ecosystem scientists more broadly to extract 3D vegetation structure parameters from the LiDAR data, and thus advance the understanding of ecosystem patterns and processes. Third, this project will support education and research at the University of California, Merced which is a Hispanic Serving Institution. The PIs will actively train and recruit underrepresented students, and promote the use of LiDAR technology and spatial thinking in decision making processes for the local community, state, and federal agencies.