SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory

<p>The benchmarking project of image-based point cloud for forest inventory (SFM-Forest-Benchmark) was initiated in 2019 and supported by ISPRS Scientific Initiative 2019. The main goal of the project was the evaluation of the applicability of terrestrial image-based point clouds for forest inventories, the clarification of the potential and limitations of the state-of-the-art techniques, and the exploration of the best practices in practical field inventories. In the project, related tree parameter (i.e. tree position diameter at breast height - DBH) were derived from 14 algorithms and evaluated using field inventory data as a reference. In order to clarify the potential of terrestrial image-based point clouds, the results from the image-based point clouds were also compared to results derived from the best available point clouds obtained by terrestrial laser scanning (TLS).</p><p>The project is consisted of two phases. In the first phase, we established two research plots in each country (Austria, China, Czech, Finland and Slovakia), ten plots in total. The stem density ranged from 272 to 875 stems/ha and plot size ranged approximately from 700 to 2500 m<sup>2</sup>. Dominant tree species across research plots were Norway spruce, European beech, bald cypress, Chinese tulip poplar, Scots pine, European silver fir and sessile oak. TLS, images and reference data acquisition were performed on each study site, where TLS data were acquired through multi-scan approach, images were taken in the stop-and-go mode, and tree positions and the DBHs were measured with a tachymeter and a calliper as field references. Images were processed with structure from motion algorithm within Agisoft Metashape software to final point clouds. The TLS data was pre-processed with RiProcess software. And, the co-registration of all three data sources (TLS, SFM, and reference data) was done with OPALS software.</p><p>In the benchmarking phase, we distributed point clouds to participants of the benchmark. Altogether 14 different research groups processed the data with own algorithms. The individual results are evaluated through the reference to clarify the applicability of the image-point clouds in deriving tree parameters, were compared to each other to reveal the state-of-the-art of technologies, and were benchmarked to the up-to-data the most accurate data from TLS to explore the strength and weakness of the image-based point cloud. In this presentation the first benchmark results will be presented and discussed.</p><p>All images and point clouds collected for this project will be available as open access data for non-commercial uses.</p>