<p>Describing forest structure is fundamental to understanding forest ecology and calculating biomass estimations. To enable its characterization with large spatial coverage, we investigate data recorded by airborne LiDAR and three different radar frequencies over a deciduous broadleaf forest at the Hainich National Park in central Germany. This study aims at distilling the microwave frequencies and polarisations that most closely relate to structural metrics extracted from the LiDAR point clouds, and are therefore most promising for extending spatial or temporal coverage.</p> <p>The LiDAR point clouds, which are provided openly by the Thuringian State Office for Land Management and Geoinformation, were processed to five structural metrics at 25 m x 25 m pixel size. These metrics comprise an estimation of fractional cover based on vegetation return numbers, &#160;an intensity-based fractional cover approach (Hopkinson & Chasmer 2009), the skewness and standard deviation of the height distribution, as well as the the vertical complexity index as defined by van Ewijk (2011). These metrics were compared to terrain-corrected backscatter of phenologically matching scenes from three different sensor frequencies: an X Band scene from DLR TerraSAR-X, C Band from Copernicus Sentinel-1, and L Band from JAXA ALOS-2.&#160;</p> <p>The scenes represent leaf-off conditions. To reduce misleading factors, the analysis was limited to areas with moderate slope angles below 10 degrees. Subsequently, regression models between the lidar metrics and backscatter intensities were built.<br />First results from bivariate correlations indicate the best match between ALOS-2 HV and fractional cover (r&#178;=0.41) as well as standard deviation (r&#178;= 0.43). Among the metrics, fractional cover is associated most closely with backscatter in all frequencies: the highest correlation coefficients amount to 0.37 for X Band (VV), 0.22 for C Band (VH), and 0.41 for L Band (HV), respectively. In general, C Band exhibits the lowest pairwise correlations with most density metrics, compared to L- and X Band.&#160;<br />The poster will show the results of multivariate regression models and discuss which combination of frequencies and polarizations is best suited for the derivation of specific forest structure parameters at larger scales.</p> <p>---</p> <p>Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1), 275&#8211;288. DOI:10.1016/j.rse.2008.09.012</p> <p>van Ewijk, K. Y., Treitz, P. M., & Scott, N. A. (2011). Characterizing Forest Succession in Central Ontario using Lidar-derived Indices. Photogrammetric Engineering & Remote Sensing, 77(3), 261&#8211;269. DOI: 10.14358/PERS.77.3.261</p>
Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the “multiscale model to model cloud comparison using precision maps” plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.