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

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。有关的森林清查办法最近演变为业务工具。今天，正在进一步优化现有办法，以确保在各种环境和造林条件下的库存资料的高数据质量和成本效益。合成激光雷达数据被认为是更好地理解森林冠层与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是用非常高的细节水平和小区域来模拟，要么是用简单的方法来模拟大区域。在SYSSIFOSS中，我们提出了一种创建合成激光雷达数据的新方法，该方法将已建立的森林生长模拟器的输出与从真实激光雷达点云中提取的物种特定模型树的待创建数据库相结合。这种方法将产生单树级别的库存信息和匹配的3D森林结构，可以获得大面积的森林结构。3D森林结构将作为“海德堡激光雷达操作模拟器”（HELIOS）的输入，这是一种激光雷达光线追踪工具，可以模拟精确的激光雷达采集。基于HELIOS模拟，我们将一方面进行敏感性分析（考虑野外盘存设计、野外地块大小、统计模型、LiDAR采集设置等），以确定影响基于LiDAR的森林盘存的最重要因素，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，以尽量减少字段收集的参考数据的数量。后者将通过开发一种类似查表的方法来实现，其中使用与真实LiDAR数据可用区域的当地条件相匹配的合成数据来校准可直接应用于真实LiDAR数据集的模型。该项目将以中欧森林为重点，但项目中发展的概念适用于世界各地的森林。

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.