Structure from motion will revolutionize analyses of tidal wetland landscapes

Kalácska, Margaret; Chmura, Gail L.; Lucanus, Oliver; Bérubé, Dominique; Arroyo‐Mora, J. Pablo

doi:10.1016/j.rse.2017.06.023

Cited by 98 publications

(82 citation statements)

References 69 publications

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“…Of particular interest is the use of Structure from Motion (SfM), a relatively recent photogrammetry approach [1][2][3]. Studies have focused on the accuracy of SfM models by measuring and validating relative and absolute accuracy using ground control points collected using a rover and base station RTK surveying system [4], others have looked at various SfM applications including agriculture [5], mining [6] forestry [7], and wetland hydrology [8]. Yet we argue that a UAV-generated very high-resolution digital elevation model (DEM) can be used for examining the validity of classical stream and floodplain classification theory, thereby not only looking at applications as is typically done but also augmenting our scientific understanding of hydrological processes at the (very) small scale, in particular of flood processes, as in the case presented here.…”

Section: Introductionmentioning

confidence: 99%

Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

Schumann

Muhlhausen²,

Andreadis

2019

Remote Sensing

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Unmanned Aerial Vehicle (UAV) platforms have rapidly developed as tools for remote mapping at very high spatial resolutions. They have recently gained in popularity in many application fields owing to the versatility of platforms and sensors, ease of deployment, and a steady increase in computational power. Obtaining highly detailed topography data over very small scales is one of the more typical application domains. Here, we demonstrate this application using Structure from Motion (SfM) processing over a small river floodplain in Howard County (Maryland, USA). Evaluation of the derived bare-earth terrain model with state-of-the art LiDAR shows a trivial bias of 1.6 cm and a root mean square deviation (RMSD) of 39 cm. We then applied this terrain model to extract floodplain and river cross-section geometries of a small stream, important during high-magnitude urban flash flood events, with the aim to assess its value for floodplain inundation mapping and first order characterization of in-channel hydraulics. Initial findings agree with traditional stream and floodplain classification theory and thus show very promising results for this type of UAV usage. We expect this type of application to gain more momentum in the near future with the ever-growing importance of more detailed data in order to increase resilience to flood risk, especially in urban areas.

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Section: Introductionmentioning

confidence: 99%

Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

Schumann

Muhlhausen²,

Andreadis

2019

Remote Sensing

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“…In the wet season these areas serve as critical habitat for several fish species. As described in [1], the SfM-MVS dense 3D point clouds were generated from UAV photographs with Pix4D Mapper Pro [8,20,21], producing ground sampling distances (GSD) ranging from 1.20-2.38 cm (Table 2 and Figure 2). Pix4D Mapper utilizes a modification of the SIFT algorithm [22,23], where local gradients rather than sample intensities are used to create descriptors of each key point [24].…”

Section: Methodsmentioning

confidence: 99%

UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil

Kalácska

Lucanus

Sousa

et al. 2019

Data

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Dense 3D point clouds were generated from Structure-from-Motion Multiview Stereo (SFM-MVS) photogrammetry for five representative freshwater fish habitats in the Xingu river basin, Brazil. The models were constructed from Unmanned Aerial Vehicle (UAV) photographs collected in 2016 and 2017. The Xingu River is one of the primary tributaries of the Amazon River. It is known for its exceptionally high aquatic biodiversity. The dense 3D point clouds were generated in the dry season when large areas of aquatic substrate are exposed due to the low water level. The point clouds were generated at ground sampling distances of 1.20–2.38 cm. These data are useful for studying the habitat characteristics and complexity of several fish species in a spatially explicit manner, such as calculation of metrics including rugosity and the Minkowski–Bouligand fractal dimension (3D complexity). From these dense 3D point clouds, substrate complexity can be determined more comprehensively than from conventional arbitrary cross sections.

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“…Both coastal and lotic environments have seen increased use of UAS technology to identify and map critical habitat, including tidal wetlands (Kalacska et al. ), fish nursery grounds (Ventura et al. ), inlet topography (Long et al.…”

mentioning

confidence: 99%

“…Currently, UASs in fisheries research are used for two primary purposes: (1) to delineate habitats and provide high-quality evaluations and (2) to catalog the occurrence of species or individuals (Kopaska 2014). Both coastal and lotic environments have seen increased use of UAS technology to identify and map critical habitat, including tidal wetlands (Kalacska et al 2017), fish nursery grounds (Ventura et al 2016), inlet topography (Long et al 2016), coastline mapping (Mancini et al 2013;Darwin et al 2014;Turner et al 2016), channel morphology (Casado et al 2015;Tamminga et al 2015), river bathymetry (Zinke and Flener 2013), restoration monitoring (Cress et al 2015), and physical habitat assessments (Hentz et al 2018). These data streams can then be used for any number of important management tasks from identification of critical nursery habitat to determining sites for remediation and removal of invasive species.…”

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confidence: 99%

Use of Drones in Fishery Science

et al. 2019

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The use of unmanned aircraft systems (UASs), commonly referred to as drones, has rapidly expanded across many scientific disciplines. Like other fields, fisheries research would benefit significantly from broader use of this emerging technology but has lagged behind other disciplines. Like the implementation of satellite and aircraft‐based remote sensing technology in previous decades brought a greater understanding of large‐scale spatial patterns and processes, UAS technology has the potential to put those tools in the hands of individual researchers, allowing implementation at finer spatial and temporal scales for a fraction of the cost. Our goal is to provide a “how‐to” for fisheries researchers interested in using UAS technology. We outline the necessary steps for any UAS project from choosing the appropriate platform and sensors to data acquisition and analysis. We also present the current ways in which UASs are being used in a fisheries research context as well as potential future research directions.

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Structure from motion will revolutionize analyses of tidal wetland landscapes

Cited by 98 publications

References 69 publications

Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

UAV-Based 3D Point Clouds of Freshwater Fish Habitats, Xingu River Basin, Brazil

Use of Drones in Fishery Science

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