College of Behavioral & Social Sciences

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Subject: search.filters.subject.canopy height

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    Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar
    (MDPI, 2016-09-12) Tang, Hao; Swatantran, Anu; Barrett, Terence; DeCola, Phil; Dubayah, Ralph
    Airborne single-photon lidar (SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidar instruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidar instruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidar data, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filtering method that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidar heights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filtering method retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filtering algorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.
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    Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning
    (MDPI, 2021-09-07) Resop, Jonathan P.; Lehmann, Laura; Hession, W. Cully
    Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.
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    MEASURING AND MAPPING FOREST WILDLIFE HABITAT CHARACTERISTICS USING LIDAR REMOTE SENSING AND MULTI-SENSOR FUSION
    (2005-12-05) Hyde, Peter; Dubayah, Ralph O.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Managing forests for multiple, often competing uses is challenging; managing Sierra National Forest's fire regime and California spotted owl habitat is difficult and compounded by lack of information about habitat quality. Consistent and accurate measurements of forest structure will reduce uncertainties regarding the amount of habitat reduction or alteration that spotted owls can tolerate. Current methods of measuring spotted owl habitat are mostly field-based and emphasize the important of canopy cover. However, this is more because of convenience than because canopy cover is a definitive predictor of owl presence or fecundity. Canopy cover is consistently and accurately measured in the field using a moosehorn densitometer; comparable measurements can be made using airphoto interpretation or from examining satellite imagery, but the results are not consistent. LiDAR remote sensing can produce consistent and accurate measurements of canopy cover, as well as other aspects of forest structure (such as canopy height and biomass) that are known or thought to be at least as predictive as canopy cover. Moreover, LiDAR can be used to produce maps of forest structure rather than the point samples available from field measurements. However, LiDAR data sets are expensive and not available everywhere. Combining LiDAR with other, remote sensing data sets with less expensive, wall-to-wall coverage will result in broader scale maps of forest structure than have heretofore been possible; these maps can then be used to analyze spotted owl habitat. My work consists of three parts: comparison of LiDAR estimates of forest structure with field measurements, statistical fusion of LiDAR and other remote sensing data sets to produce broad scale maps of forest structure, and analysis of California spotted owl presence and fecundity as a function of LiDAR-derived canopy structure. I found that LiDAR was able to replicate field measurements accurately. Additionally, I was able to statistically combine LiDAR with passive optical and RaDAR (SAR backscatter and InSAR range) data to produce broad scale maps of forest structure that are consistent and accurate relative to field data and LiDAR data alone. Finally, I was able to demonstrate that these forest structural attributes predict spotted owl presence and absence as well as productivity.