SYSSIFOSS - Synthetic structural remote sensing data for improved forest inventory models

SYSSIFOSS - 用于改进森林清查模型的合成结构遥感数据

• 批准号: 411263134
• 负责人: Professor Dr. Fabian Fassnacht
• 金额: --
• 依托单位: Professur für Geoinformatik / GIScience (GIS)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/411263134?language=en
• 关键词: SYSSIFOSS; Synthetic; structural; remote; sensing

Airborne light detection and ranging (LiDAR) data provides reliable information on forest structure. Related forest inventory approaches recently evolved into operational tools. Today, further optimization of existing approaches is pursued to ensure high data quality of the inventory information and cost-efficiency over varied environmental and silvicultural conditions. Synthetic LiDAR data has been suggested as useful tool to better understand the interactions between forest canopies and LiDAR acquisitions and hence as a key instrument for identifying further optimization potential. However, so far synthetic LiDAR data has either been simulated with a very high level of detail and for small areas or with simplistic approaches for larger areas. In SYSSIFOSS we suggest a new approach to create synthetic LiDAR data by combining the outputs of an established forest growth simulator with a to-be-created database of species-specific model trees extracted from real LiDAR point clouds. This approach will result in inventory information at the single tree level and a matching 3D forest structure which can be obtained for large areas. The 3D forest structure will serve as input to the “Heidelberg LiDAR Operations Simulator” (HELIOS), a LiDAR ray-tracing tool with which accurate LiDAR acquisitions can be simulated. Based on the HELIOS simulations, we will on the one hand conduct a sensitivity analysis (considering e.g., field inventory design, field plot size, statistical model, LiDAR acquisition settings, etc.) to identify the most important factors influencing LiDAR based forest inventories and thereby identify optimization potentials. On the other hand, we will examine the potential of the created synthetic data to minimize the amount of field-collected reference data. The latter will be realized by developing a look-up table like approach where synthetic data matching the local conditions of the area for which real LiDAR data is available are used to calibrate models which can directly be applied to the real LiDAR dataset. The project will focus on central European forests, but the concepts developed in the project are applicable to forests worldwide.

机载光探测和测距(LiDAR)数据提供了有关森林结构的可靠信息。相关的森林清查方法最近演变为业务工具。今天，正在寻求进一步优化现有方法，以确保高质量的清单信息数据和各种环境和营林条件下的成本效益。合成激光雷达数据被认为是更好地了解森林树冠和激光雷达采集之间相互作用的有用工具，因此是确定进一步优化潜力的关键工具。然而，到目前为止，合成激光雷达数据要么针对小区域以非常高的细节水平进行模拟，要么针对较大区域使用简单的方法进行模拟。在SYSSIFOSS中，我们建议了一种新的方法来创建合成LiDAR数据，方法是将已建立的森林生长模拟器的输出与从真实LiDAR点云中提取的特定物种模型树数据库相结合。这种方法将产生单一树木一级的清查信息和匹配的三维森林结构，可以在大范围内获得。三维森林结构将作为“海德堡激光雷达操作模拟器”(HELIOS)的输入，这是一种激光雷达射线跟踪工具，可以用来模拟准确的激光雷达采集。基于HELIOS模拟，我们一方面将进行敏感性分析(例如，考虑现场库存设计、现场地块大小、统计模型、激光雷达采集设置等)。确定影响基于LiDAR的森林资源清查的最重要因素，从而确定优化潜力。另一方面，我们将检查创建的合成数据的潜力，以最大限度地减少现场收集的参考数据的数量。后者将通过开发一种类似查找表的方法来实现，其中使用与可获得真实激光雷达数据的区域的当地条件匹配的合成数据来校准可直接应用于真实激光雷达数据集的模型。该项目将侧重于中欧森林，但项目中提出的概念也适用于世界各地的森林。