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1. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

   https://doi.org/10.3390/ijgi10030144  

   Gebrehiwot, Asmamaw A; Hashemi-Beni, Leila (March 2021, ISPRS International Journal of Geo-Information)

   null (Ed.)

   Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively. 

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2. A remote field course implementing high-resolution topography acquisition with geomorphic applications

   https://doi.org/10.5194/gc-5-101-2022  

   Bywater-Reyes, Sharon; Pratt-Sitaula, Beth (January 2022, Geoscience Communication)

   Abstract. Here we describe the curriculum and outcomes from a data-intensivegeomorphic analysis course, “Geoscience Field Issues Using High-ResolutionTopography to Understand Earth Surface Processes”, which pivoted to virtualin 2020 due to the COVID-19 pandemic. The curriculum covers technologies formanual and remotely sensed topographic data methods, including (1) GlobalPositioning Systems and Global Navigation Satellite System (GPS/GNSS)surveys, (2) Structure from Motion (SfM) photogrammetry, and (3) ground-based(terrestrial laser scanning, TLS) and airborne lidar. Course content focuseson Earth-surface process applications but could be adapted for othergeoscience disciplines. Many other field courses were canceled in summer2020, so this course served a broad range of undergraduate and graduatestudents in need of a field course as part of degree or researchrequirements. Resulting curricular materials are available freely within theNational Association of Geoscience Teachers' (NAGT's) “Teaching with Online Field Experiences” collection. Theauthors pre-collected GNSS data, uncrewed-aerial-system-derived (UAS-derived) photographs, and ground-based lidar, which students then used in courseassignments. The course was run over a 2-week period and had synchronousand asynchronous components. Students created SfM models that incorporatedpost-processed GNSS ground control points and created derivative SfM and TLSproducts, including classified point clouds and digital elevation models(DEMs). Students were successfully able to (1) evaluate the appropriatenessof a given survey/data approach given site conditions, (2) assess pros andcons of different data collection and post-processing methods in light offield and time constraints and limitations of each, (3) conduct error andgeomorphic change analysis, and (4) propose or implement a protocol to answera geomorphic question. Overall, our analysis indicates the course had asuccessful implementation that met student needs as well as course-specificand NAGT learning outcomes, with 91 % of students receiving an A, B, or Cgrade. Unexpected outcomes of the course included student self-reflectionand redirection and classmate support through a daily reflection anddiscussion post. Challenges included long hours in front of a computer,computing limitations, and burnout because of the condensed nature of thecourse. Recommended implementation improvements include spreading the courseout over a longer period of time or adopting only part of the course andproviding appropriate computers and technical assistance. This paperand published curricular materials should serve as an implementation andassessment guide for the geoscience community to use in virtual or in-personhigh-resolution topographic data courses that can be adapted for individuallabs or for an entire field or data course. 

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3. Comparison of UAS-Based Structure-from-Motion and LiDAR for Structural Characterization of Short Broadacre Crops

   https://doi.org/10.3390/rs13193975  

   Zhang, Fei; Hassanzadeh, Amirhossein; Kikkert, Julie; Pethybridge, Sarah Jane; van Aardt, Jan (October 2021, Remote Sensing)

   The use of small unmanned aerial system (UAS)-based structure-from-motion (SfM; photogrammetry) and LiDAR point clouds has been widely discussed in the remote sensing community. Here, we compared multiple aspects of the SfM and the LiDAR point clouds, collected concurrently in five UAS flights experimental fields of a short crop (snap bean), in order to explore how well the SfM approach performs compared with LiDAR for crop phenotyping. The main methods include calculating the cloud-to-mesh distance (C2M) maps between the preprocessed point clouds, as well as computing a multiscale model-to-model cloud comparison (M3C2) distance maps between the derived digital elevation models (DEMs) and crop height models (CHMs). We also evaluated the crop height and the row width from the CHMs and compared them with field measurements for one of the data sets. Both SfM and LiDAR point clouds achieved an average RMSE of ~0.02 m for crop height and an average RMSE of ~0.05 m for row width. The qualitative and quantitative analyses provided proof that the SfM approach is comparable to LiDAR under the same UAS flight settings. However, its altimetric accuracy largely relied on the number and distribution of the ground control points. 

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4. Deriving Land and Water Surface Elevations in the Northeastern Yucatán Peninsula Using PPK GPS and UAV-Based Structure from Motion

   https://doi.org/10.1080/23754931.2021.1871937  

   Pingel, Thomas J.; Saavedra, Andrea; Cobo, Lillian (February 2021, Papers in Applied Geography)

   null (Ed.)

   While UAV-based imaging methods such as drone lidar scanning (DLS) and Structure from Motion (SfM) are now widely used in geographic research, accurate water surface elevation (WSE) measurement remains a difficult problem, as water absorbs wavelengths commonly used for lidar and SfM feature matching fails on these dynamic surfaces. We present a methodology for measuring WSE in a particularly challenging environment, the Yucatán Peninsula, where cenotes – exposed, water-filled sinkholes – provide an observation point into the critically important regional groundwater supply. In the northeastern Yucatán, elevations are very close to sea level, the area is of low relief, and the near-vertical edges of the walls of the cenotes complicate the use of the so-called “water edge” technique for WSE measurement. We demonstrate how post-processing kinematic (PPK) correction of even a single Real Time Kinematic (RTK) Global Positioning System (GPS) unit can be used to finely register the SfM-derived point cloud, and present evidence from both simulations and an empirical study that quantify the effect of “dip” in SfM-based environmental reconstructions. Finally, we present a statistical analysis of the problem of “thick” or “fuzzy” point clouds derived from SfM, with particular emphasis on their interactions with WSE measurement. 

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5. Estimating Geometric Properties of Coral Reef Topography Using Obstacle‐ and Surface‐Based Approaches

   https://doi.org/10.1029/2019JC015870  

   Duvall, Melissa S.; Rosman, Johanna H.; Hench, James L. (June 2020, Journal of Geophysical Research: Oceans)

   Abstract In shallow water systems like coral reefs, bottom friction is an important term in the momentum balance. Parameterizations of bottom friction require a representation of canopy geometry, which can be conceptualized as an array of discrete obstacles or a continuous surface. Here, we assess the implications of using obstacle‐ and surface‐based representations to estimate geometric properties needed to parameterize drag. We collected high‐resolution reef topography data using a scanning multibeam sonar that resolved individual coral colonies within a set of 100‐m²reef patches primarily composed of moundingPoritescorals. The topography measurements yielded 1‐cm resolution continuous surfaces consisting of a single elevation value for each position in a regular horizontal grid. These surfaces were analyzed by (1) defining discrete obstacles and quantifying their properties (dimensions, shapes), and (2) computing properties of the elevation field (root mean square (rms) elevations, rms slopes, spectra). We then computed the roughness density (i.e., frontal area per unit plan area) using both analysis approaches. The obstacle and surface‐based estimates of roughness density did not agree, largely because small‐scale topographic variations contributed significantly to total frontal area. These results challenge the common conceptualization of shallow‐water canopies as obstacle arrays, which may not capture significant contributions of high‐wavenumber roughness to total frontal area. In contrast, the full range of roughness length scales present in natural reefs is captured by the continuous surface representation. Parameterizations of bottom friction over reef topography could potentially be improved by representing the contributions of all length scales to total frontal area and drag. 

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