Management Applications of Optical Remote Sensing in the Active River Channel

Marcus, W. Andrew; Fonstad, Mark A.; Legleiter, Carl J.

doi:10.1002/9781119940791.ch2

Cited by 18 publications

(22 citation statements)

References 76 publications

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“…The AC is defined in literature as the low-flow channel plus adjacent exposed sediment bar surfaces between established edges of perennial, terrestrial vegetation, which are generally subjected to erosion or deposition [10][11][12][13]. Exposed bars are characterized by vegetation encroachment, which plays a key role in the evolution of alluvial islands [14], i.e., patches of mature vegetation surrounded by water and bare sediment bars.…”

Section: Introductionmentioning

confidence: 99%

“…It also provides a useful indicator of changes in potential ecosystem productivity [10,17,18]. To date, the concept behind the use of RS for the delineation of the riverscape units has been rather simple: features visible on air photos can be manually traced or digitized and transferred to map coordinates [11,19,20]. Since the 1990s, the use of this technique has expanded rapidly, mainly because of the widespread availability of Geographical Information Systems (GIS), which made it much easier to digitize river features from aerial or satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

2016

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Abstract:In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc.) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0.91 and K = 0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

2016

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“…Aerial surveys were completed roughly every two weeks over the active channel [2], or more frequently as river conditions necessitated, with a total of 18 different flights over the summer of 2013 (20 May 2013-22 November 2013). Because passive radiation (sunlight) must pass through the intervening water column, reflect off the bottom, and then back through the water column to the UAV sensor for optical imaging (i.e., radiation in the visible region), the reflectance reaching the image sensor at a given wavelength depends on the volume attenuation characteristics of the water.…”

Section: Data Acquisition Missionsmentioning

confidence: 99%

“…Ambient lighting and meteorological conditions result in inconsistent spectral signatures across different UAV flights, hence the signature presented in Figure 7 is unique to that flight. Procedures for radiometric adjustment of the digital image for atmospheric correction could potentially be applied [25], however, changes in turbidity, water depth, and plant physiological condition further obfuscate the spectral response [66] making remote sensing of rivers a challenging endeavor [2].…”

Section: Uav Use In Freshwater Benthic Ecologymentioning

confidence: 99%

“…Prior work (non-SAV related) has been completed using anything from handheld poles to commercial satellites [28], unmanned aerial vehicles (UAVs) [26], even kites and balloons [29,30] to map habitat, channel geomorphology, the water surface, or bathymetric features [2] at centimeter to meter scales. The use of conventional RGB aerial photography [25,26] or multispectral cameras [31] in these applications obviously requires the river water column to be clear and not obstructed by trees or overhanging banks [2,24].…”

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confidence: 99%

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Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle

2014

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Abstract:A passive method for remote sensing of the nuisance green algae Cladophora glomerata in rivers is presented using an unmanned aerial vehicle (UAV). Included are methods for UAV operation, lens distortion correction, image georeferencing, and spectral analysis to support algal cover mapping. Eighteen aerial photography missions were conducted over the summer of 2013 using an off-the-shelf UAV and three-band, wide-angle, red, green, and blue (RGB) digital camera sensor. Images were post-processed, mosaicked, and georeferenced so automated classification and mapping could be completed. An adaptive cosine estimator (ACE) and spectral angle mapper (SAM) algorithm were used to complete the algal identification. Digital analysis of optical imagery correctly identified filamentous algae and background coverage 90% and 92% of the time, and tau coefficients were 0.82 and 0.84 for ACE and SAM, respectively. Thereafter, algal cover was characterized for a one-kilometer channel segment during each of the 18 UAV flights. Percent cover ranged from <5% to >50%, and increased immediately after vernal freshet, peaked in midsummer, and declined in the fall. Results indicate that optical remote sensing with UAV holds promise for completing spatially precise, and multi-temporal measurements of algae or submerged aquatic vegetation in shallow rivers with low turbidity and good optical transmission.

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Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

Woodget

Carbonneau

Visser

et al. 2014

Earth Surf Processes Landf

369

401

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Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process‐oriented investigations of flow hydraulics, sediment dynamics and in‐stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through‐water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. While the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM‐photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary‐winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50–100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM‐photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). Copyright © 2014 John Wiley & Sons, Ltd.

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Management Applications of Optical Remote Sensing in the Active River Channel

Cited by 18 publications

References 76 publications

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle

Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry

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