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Remote Sensing of Fluvial Systems
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Remote Sensing of Fluvial Systems

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (22 April 2022) | Viewed by 17744

Special Issue Editor

Dr. Mark S. Lorang

E-Mail Website
Guest Editor
President and Chief Science Officer for Freshwater Map, P.O. Box 166, Bigfork, MT 59911, USA
Interests: sediment transport; acoustic doppler profilers; aquatic habitat mapping; river restoration; remote sensing

Special Issue Information

Dear Colleagues,

The primary lens of ecohydrology is focused on understanding the distribution and abundance of biota in the context of how and why organisms are dependent on specific biophysical space (habitat) to complete one stage or another in their life cycles. Unfortunately, the spatial distribution and abundance of aquatic habitat is the least empirically quantified attribute of rivers and streams. This may be in part because little advancement has been made towards using remote sensing to assess flow velocity which is a requirement for defining aquatic habitat.

Development and application of remote sensing tools geared toward quantifying flow velocity could greatly enhance ecohydrological understanding of fluvial systems. This is especially true for regulated systems in need of environmental flow analysis aimed at minimizing harm to aquatic organisms from fish to rare plants through flow regulation by dam control or irrigation withdrawal.

New advances in hydro-acoustic river mapping, an emerging form of remote sensing of fluvial systems, allows direct empirical measurement of flow from the site scale to the river corridor scale covering 100’s of km. This approach should be linked to more traditional forms of aerial and satellite remote sensing of fluvial systems that have focused on assessment of channel bathymetry, widths, depths, slopes, plan form complexity, substrate composition, surface water temperatures and composition of riparian vegetation.

The focus of this special issue will be publishing studies that are striving to use and combine various forms remote sensing to measure flow and discharge in rivers to better enhance our understanding of rivers in light of climate change and environmental flow management.

Dr. Mark S. Lorang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Aquatic Habitat
  • Flow velocity
  • Discharge from Space
  • Ecohydrology
  • Climate Change
  • Hydro-acoustic Remote Sensing
  • Environmental Flow Analysis

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Published Papers (5 papers)

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Research

23 pages, 3776 KiB  
Article
Challenges with Regard to Unmanned Aerial Systems (UASs) Measurement of River Surface Velocity Using Doppler Radar
by Filippo Bandini, Monica Coppo Frías, Jun Liu, Kasparas Simkus, Sofia Karagkiolidou and Peter Bauer-Gottwein
Remote Sens. 2022, 14(5), 1277; https://doi.org/10.3390/rs14051277 - 5 Mar 2022
Cited by 8 | Viewed by 2532
Abstract
Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., [...] Read more.
Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remotely-accessible locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic–morphological conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g., foam, debris) when possible, or with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm s−1 and a coefficient of determination (R2) typically larger than 0.7 (in half of the cases larger than 0.85), when compared with acoustic Doppler current profiler (ADCP) or velocity probe, in all investigated rivers. However, UAS-borne Doppler radar measurements show low reliability because of UAS vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators suggesting that additional research is needed to measure surface velocity from UAS-borne Doppler radar. Full article
(This article belongs to the Special Issue Remote Sensing of Fluvial Systems)
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22 pages, 9466 KiB  
Article
Remote Sensing to Characterize River Floodplain Structure and Function
by F. Richard Hauer, Mark S. Lorang and Tom Gonser
Remote Sens. 2022, 14(5), 1132; https://doi.org/10.3390/rs14051132 - 25 Feb 2022
Cited by 1 | Viewed by 2778
Abstract
Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodplain in western Montana [...] Read more.
Advancing understanding of the complexities and expansive spatial scales of river ecology can be enhanced through the application of remote sensing. We obtained satellite (Quickbird) and airborne (LIDAR, hyperspectral, multispectral, and thermal) imagery data of an alluvial gravel-bed river floodplain in western Montana to quantify both riparian and aquatic habitats and processes. LIDAR data provided a detailed bare earth DEM and vegetation canopy DEM. We classified river hydraulics and aquatic habitats using a combination of the satellite multispectral, airborne hyperspectral, and LIDAR data coupled with spatially-explicit acoustic Doppler velocity profile data of water depth and velocity. Velocity, depth, and Froude classifications were aggregated into similar hydraulic zones of river habitat classes. Thermal imagery data were coupled with field measurements of temperature and radon gas tracer to identify patterns of water exchange between the alluvial aquifer and the surface. We found a high complexity of aquatic surface temperatures and radon tracer linked to groundwater discharge from the alluvial aquifer. Airborne hyperspectral data were used to identify “hot spots” of periphyton production, which coincided with the complex nature of groundwater–surface water exchange. Airborne hyperspectral data provided differentiation of vegetation patches by dominant species. When the hyperspectral data were coupled to LIDAR first return metrics, we were able to determine vegetation canopy height and relative vegetation patch age classes. The integration of these various remote sensing sources allowed us to characterize the distribution and abundance of floodplain aquatic and riparian species and model processes of change through space and time. Full article
(This article belongs to the Special Issue Remote Sensing of Fluvial Systems)
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28 pages, 9342 KiB  
Article
Mapping Benthic Algae and Cyanobacteria in River Channels from Aerial Photographs and Satellite Images: A Proof-of-Concept Investigation on the Buffalo National River, AR, USA
by Carl J. Legleiter and Shawn W. Hodges
Remote Sens. 2022, 14(4), 953; https://doi.org/10.3390/rs14040953 - 16 Feb 2022
Cited by 6 | Viewed by 4372
Abstract
Although rivers are of immense practical, aesthetic, and recreational value, these aquatic habitats are particularly sensitive to environmental changes. Increasingly, changes in streamflow and water quality are resulting in blooms of bottom-attached (benthic) algae, also known as periphyton, which have become widespread in [...] Read more.
Although rivers are of immense practical, aesthetic, and recreational value, these aquatic habitats are particularly sensitive to environmental changes. Increasingly, changes in streamflow and water quality are resulting in blooms of bottom-attached (benthic) algae, also known as periphyton, which have become widespread in many water bodies of US national parks. Because these blooms degrade visitor experiences and threaten human and ecosystem health, improved methods of characterizing benthic algae are needed. This study evaluated the potential utility of remote sensing techniques for mapping variations in algal density in shallow, clear-flowing rivers. As part of an initial proof-of-concept investigation, field measurements of water depth and percent cover of benthic algae were collected from two reaches of the Buffalo National River along with aerial photographs and multispectral satellite images. Applying a band ratio algorithm to these data yielded reliable depth estimates, although a shallow bias and moderate level of precision were observed. Spectral distinctions among algal percent cover values ranging from 0 to 100% were subtle and became only slightly more pronounced when the data were aggregated to four ordinal levels. A bagged trees machine learning model trained using the original spectral bands and image-derived depth estimates as predictor variables was used to produce classified maps of algal density. The spatial and temporal patterns depicted in these maps were reasonable but overall classification accuracies were modest, up to 64.6%, due to a lack of spectral detail. To further advance remote sensing of benthic algae and other periphyton, future studies could adopt hyperspectral approaches and more quantitative, continuous metrics such as biomass. Full article
(This article belongs to the Special Issue Remote Sensing of Fluvial Systems)
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18 pages, 9957 KiB  
Article
Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams
by Mary E. Gerlach, Kai C. Rains, Edgar J. Guerrón-Orejuela, William J. Kleindl, Joni Downs, Shawn M. Landry and Mark C. Rains
Remote Sens. 2022, 14(1), 63; https://doi.org/10.3390/rs14010063 - 24 Dec 2021
Cited by 6 | Viewed by 3710
Abstract
We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the [...] Read more.
We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance. Full article
(This article belongs to the Special Issue Remote Sensing of Fluvial Systems)
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15 pages, 3198 KiB  
Article
Matlab Software for Supervised Habitat Mapping of Freshwater Systems Using Image Processing
by Johnathan M. Bardsley, Marylesa Howard and Mark Lorang
Remote Sens. 2021, 13(23), 4906; https://doi.org/10.3390/rs13234906 - 3 Dec 2021
Cited by 5 | Viewed by 2941
Abstract
We present a software package for the supervised classification of images useful for cover-type mapping of freshwater habitat (e.g., water surface, gravel bars, vegetation). The software allows the user to select a representative subset of pixels within a specific area of interest in [...] Read more.
We present a software package for the supervised classification of images useful for cover-type mapping of freshwater habitat (e.g., water surface, gravel bars, vegetation). The software allows the user to select a representative subset of pixels within a specific area of interest in the image that the user has identified as a cover-type habitat of interest. We developed a graphical user interface (GUI) that allows the user to select single pixels using a dot, line, or group of pixels within a defined polygon that appears to the user to have a spectral similarity. Histogram plots for each band of the selected ground-truth subset aid the user in determining whether to accept or reject it as input data for the classification processes. A statistical model, or classifier, is then built using this pixel subset to assign every pixel in the image to a best-fit group based on reflectance or spectral similarity. Ideally, a classifier incorporates both spectral and spatial information. In our software, we implement quadratic discriminant analysis (QDA) for spectral classification and choose three spatial methods—mode filtering, probability label relaxation, and Markov random fields—to incorporate spatial context after computation of the spectral type. This multi-step interactive process makes the software quantitatively robust, broadly applicable, and easily usable for cover-type mapping of rivers, their floodplains, wetlands often components of these functionally linked freshwater systems. Indeed, this supervised classification approach is helpful for a wide range of cover-type mapping applications in freshwater systems but also estuarine and coastal systems as well. However, it can also aid many other applications, specifically for automatic and quantitative extraction of pixels that represent the water surface area of rivers and floodplains. Full article
(This article belongs to the Special Issue Remote Sensing of Fluvial Systems)
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