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Below I present an annotated bibliography that compiles research on the current uses of GIS to complete assessments of riverine habitats. This includes literature on the current breadth of GIS techniques to integrate and analyze field collected vector data and remote sensed data to evaluate riparian vegetation, habitat composition and quantity, and sediment. This research primarily focuses on evaluating streams and rivers with regards to salmon habitat and distribution but includes research from multiple sources and systems.
Created by Shelby Burgess for GEOG 560, GIScience I: Introduction to Geographic Information Science
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Carbonneau, P., M. A. Fonstad, W. A. Marcus, and S. J. Dugdale. 2012. Making riverscapes real. Geomorphology 137:74-86.
The riverscape concept is a new framework of thought that emphasizes the importance of evaluating the spatial distribution and interactions of species and habitats throughout the river system, from micro-habitat to watershed scale. It states studies should focus on heterogeneity in the river landscapes. This concept has become popular in river analysis but must be tested with quantitative data. This article reviews recent advances in remote sensing data collection and analysis technologies and their effectiveness at quantifying an example riverscape, using the River Tromie, Scotland. The authors apply a newly developed tool, the Fluvial Information System (FIS), to 3-cm true-color aerial photos and 5-m resolution elevation data to derive channel widths, depths, sediment size, elevation and subsequently velocity, stream power, slope, Froude number, and shear stress. The authors determine that high resolution data can be efficiently collected for the entire riverscape, and that patchiness and variability were observed for variables at sub-reach and reach scales. The data collection and analysis techniques reported present an area of huge potential for future watershed scale research, however the methods used in this study are limited in their translatability to other systems, as they could not evaluate complex braided channel networks with the FIS. The authors also discuss potential disadvantages of these methods, including the difficulty of collecting biological data (e.g., fish distribution) on the same scale, the need to define accuracy and precision, as well as best practices, for data collection, and the ethics of using these methods and privacy issues that could arise.
Dietrich, J. T. 2017. Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry. Earth Surface Processes and Landforms 42(2):355-364. Available at: https://doi.org/10.1002/esp.4060
Stream bathymetry is important for a number of measurements and calculations used to characterize streams, including velocity, slope, and width. Measuring bathymetry of small streams with less than 2m is difficult given the resolution needed to capture small-scale phenomena. This study tests a new technique for remote sensing of bathymetry using multi-view stereo photogrammetry, specifically structure-from-motion (SfM). Light refraction presents a problem in many attempts to create accurate bathymetric maps using photogrammetry. In this study, Dietrich tests the use of multicamera refraction, or the use of refraction correction equations for every point/camera combination, in a SfM point cloud, to improve accuracies and precisions of bathymetric maps created from photogrammetry. Dietrich evaluated SfM collected with ground control points in two study areas, a controlled shallow water pool and a river reach in Vermont. Field surveys measured water depths and mapped the water’s edge and for the river in Vermont mapped the streambed topography for validation using an RTK (Real time kinematic) GPS. For each point in the SfM point cloud that was submerged, the software calculated refraction correction equations. Dietrich used existing literature values to determine the refractive index, which was used in determining his correction. Error was measured using the relative accuracy ratio, the relative precision ratio, and standard accuracy statistics. Error was lower in the controlled pool, however, the correction used produced high accuracy in both study locations and mean errors were less than 0.014 m. In the river system, error was patchy and visually correlated with noisier areas of the depth maps, which suggests that this study could be widely applied to larger systems, as error is not skewed to any one source. The use of photogrammetry is still limited to areas of clear water, and vegetation cover, shadows, waves, and turbidity would all add error and noise to this analysis. Photogrammetry is often cheaper and easier to collect than LiDAR or field collected bathymetry data (i.e. RTK GPS, topographic surveys); and thus, this is an important study for providing a cost effective and accurate method for future bathymetric data collection in rivers, estuaries, and coastal ecosystems.
Fausch, K. D., C. E. Torgersen, C. V. Baxter, and H. W. Li. 2002. Landscapes to riverscapes: bridging the gap between research and conservation of stream fishes. BioScience 52(6):483-498.
This review proposes that rivers need to be viewed continuously as a spatially heterogenous scene, not just disjunct reaches. Fisheries research is largely conducted on small spatial and temporal scales, investigating reaches of 50- 500 meters stratified along a river or throughout a watershed over limited time periods, and it assumed that these are representative of larger spatial and temporal trends. However, fish population are still largely declining which suggests that these methods are not working. This review proposes a new approach for assessing rivers that focuses on evaluating the heterogeneity of river systems at different spatial scales and the role of fish movement in these systems. The authors set forth five principles to advance fisheries ecology with empirical evidence from the literature for each and discuss challenges to implement the new paradigm. The principals include: conducting research at appropriate spatial scales, evaluating physical and ecological processes and their interactions at the scales which they occur, understanding the impacts of unique events on stream fishes throughout a system, evaluating the consequences of anthropogenic disturbance upstream and downstream from the source, and matching surveys to the spatial and temporal scale at which the resources are managed. The authors emphasize the importance of spatial referencing stream and fish assessments to be able to evaluate the spatial and temporal arrangement of habitats and fish. Many concepts presented in this review have been discussed in the literature for the last 30 years, however advancements in technologies (e.g., use of GIS to track and analyze remote sensed data, ability to inexpensively collect temperature data, LiDAR, and aerial imagery) combined with intensive and large scale field efforts have now allowed their implementation on the spatial scales needed. These concepts will hopefully be applied to advance fisheries research to better understand what is driving declines and how to mediate it.
Fuller, I. C, R. G. Death, J. H. Garcia, N. Trenc, R. Pratt, C. Pitiot, B. Matoš, A. Ollero, A. Neverman, and A. Death. 2020. An index to assess the extent and success of river and floodplain restoration: recognizing dynamic response trajectories and applying a process‐based approach to managing river recovery. River Research and Applications, Early View, 2020-07-22. Web.
It is important to define a benchmark for restoration to be able to evaluate progress towards restoration goals and restoration effectiveness. The authors propose using the Natural Character Index (NCI), or the ratio of a given parameter at the time of survey to the measured value in the past (e.g., observed over expected), as that benchmark. The authors evaluate different parameters, including habitat mosaic areas, channel area, channel widths, island area, sinuosity, and floodplain widths using LiDAR-derived terrain maps, aerial imagery, and maps, published from 1869-2017 in reaches of varying lengths in three rivers in New Zealand, one in Spain, and one in Croatia. The authors discuss the results of the different case studies and highlight how channel dynamics influence responses to restoration and disturbance over time, and how those responses are detected in the NCI. This study demonstrates that historic data can effectively be digitized using GIS to reconstruct historic river conditions, which otherwise would be a data gap. While limitations exist digitizing the low resolution historic maps and aerial imagery, and this can be especially problematic when comparing the historic data to high resolution modern remote sensed data products, the method at least allows for some analysis of past river conditions and it allows for the comparison of the current status to that condition, which is important for setting restoration goals and evaluating the effort needed to reach those goals.
Harrison, L. R., C. J. Legleiter, B. T. Overstreet, T. W. Bell, and J. Hannon. 2020. Assessing the potential for spectrally based remote sensing of salmon spawning locations. River Research and Applications 36(8):1618-1632. Available at: https://doi.org/10.1002/rra.3690
In this study, Harrison et al. evaluate the effectiveness of using different remote sensed data and analysis techniques to predict salmon spawning (i.e., redd) locations in a large, gravel-bed river in California. The authors use unmanned aircraft systems (UAS) to collect true color (RGB) and hyperspectral imagery. Previous studies have documented that salmon redds can be visually identified from UAS imagery, but the method is time consuming and subjective. This study developed and tested different semi-automated workflow methods for identifying redds using the RGB and hyperspectral imagery. To build, train, and validate models, field data were collected including, redd locations, adjacent water depths, and ground-based reflectance data for a subset of salmon redds and adjacent undisturbed substrates. The authors determined that chlorophyll absorption could be used as a metric in identifying redds as spectral signatures varied in disturbed versus undisturbed sediments. Reflectance and measured water depths were used to estimate depths in the image data and then Optimal Band Ratio Analysis (OBRA) was used to create depth maps. Two binary classification techniques for determining redd probability were tested, logistic regression and support vector machines (SVMs), on three different image based predictors for the RGB and hyperspectral imagery: raw images and spectral bands; spectral bands and water depths map; and spectral bands, water depths maps, and chlorophyll absorption values. The authors also used Object-based Image Analysis (OBIA) and the ENVI extraction tool on the imagery to cluster and segment the boundaries of individual redds as vector polygons. Models were evaluated for their accuracy, precision, and recall. While redds could be mapped with varying accuracies using both the RGB and hyperspectral imagery, analysis of hyperspectral imagery using both logistic regression and SVM found the highest accuracies (over 0.9), especially when spectral bands were combined with water depth and chlorophyll rasters. OBIA of the hyperspectral data resulted in high detection accuracies, however OBIA was not appropriate for RGB imagery. The authors conclude that using a combination of RGB and hyperspectral imagery in combination with the analysis techniques detailed in this study would be appropriate methods for future mapping of redd locations. This study is important because field mapping of redd locations is time consuming, can be dangerous, is limited by weather and water conditions, and can be destructive to redds. Having a standardized method of analysis using UAS data will allow for more consistent and efficient data collection, which will likely lead to increased spatial coverage of surveys. Additionally, this method can be combined with other remote sensed or field collected data to evaluate relationships between redd distributions and other physical characteristics (i.e., substrate size, hydrology, water temperatures) in rivers, which is important for understanding salmon life histories and achieving restoration goals in large river systems.
Helm, C., M. A. Hassan, and D. Reid. 2020. Characterization of morphological units in a small, forested stream using close-range remotely piloted aircraft imagery. Earth Surface Dynamics 8:913-929. Available at: https://doi.org/10.5194/esurf-8-913-2020
Analysis of remote sensing data (e.g., structure from motion photogrammetry, hyperspectral imagery) collected from Unmanned Aircraft Systems (UAS), helicopters or planes, or satellite imagery has proven effective for characterizing channel morphology and characteristics of large and open rivers, however most methods have produced low accuracies for small streams with dense forests, canopy cover, and shading. This study sought to determine an effective method for classifying morphological units in small (10-15 m bankfull width) forested salmon-bearing streams using close-range RPA imagery. Additionally, the authors sought to determine the reach length required to capture geomorphic channel variability in surveys. This study was conducted along 3 km of low gradient reaches with pool-riffle channel morphology in a small coastal stream on western Vancouver Island, BC. It was estimated 50% of the surveyed channel was shaded by deciduous and coniferous forest canopy. The channel was flown with a manually controlled DJI phantom below the canopy to have unobstructed views of the channel. Ground control points were placed on the dry exposed bars and checkpoints were placed on the exposed and submerged bed. Bed perpendicular and oblique images were taken to capture the streams and the banks where the UAS could not fly directly over, respectively. Eighty flight segments were completed with 30-1000 photos each to capture the 3 km survey extent. A digital elevation model (DEM) was generated from the imagery and channel elevation, channel slope, water depth, and grain size were all extracted. The authors used a principal component analysis (PCA) to determine which variables were important for characterizing channel morphology, and then applied a k-means clustering algorithm to identify clusters to which units were assigned as pool, riffle, coarse riffle, glide, run, or plane bed. Diversity of morphological units were also evaluated using the Shannon Diversity Index and was used to determine the spatial scale required to capture channel heterogeneity. Accuracy was assessed by comparing the algorithm classified morphological units to 100 locations where morphological units were visually assigned. On average, 85% of the sampled locations were correctly classified with plane-bed and glide units being most accurately classified (100% and 97% correct, respectively), and riffles being classified the least accurately (72% correct). The authors also noted that reach lengths of 13-15 times bankfull width capture geomorphic variability, which is less than most literature reported values. The results indicate that this approach could be used to classify channel morphology of small streams in lieu of using total stations or field habitat surveys, which are the common approaches for collecting bathymetry and channel units of small streams, respectively. The data acquisition rate in this study was three times higher than a total station survey performed in the same system, and the DEM and orthomosaic products created had higher resolutions. This method is promising as it demonstrates the ability to capture high resolution data efficiently in small, forested streams, and potentially will allow for surveys of difficult to access or wade streams. However, further research is needed to test these methods on streams with potentially different morphological units and constraints.
Marcus, W. A., C. J. Legleiter, R. J. Aspinall, J. W. Boardman, and R. L. Crabtree. 2003. High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams. Geomorphology 55(1-4):363–380. Available at: https://doi.org/10.1016/S0169-555X(03)00150-8
The goal of this study was to use high resolution hyperspectral imagery to classify instream habitat types, depths, and wood cover in three reaches of varying size in the mountainous Lamar River and Soda Butte Creek in Yellowstone, Montana. Past attempts to use hyperspectral imagery for instream characterizations have observed low accuracies, however the use of higher resolution imagery and consistent geotagging may improve outcomes. The selected reaches displayed pool/riffle morphology and were similar in surface turbulence, substrate, and water column characteristics. Hyperspectral imagery (128-band) was collected via helicopter followed by field surveys within 10 days of data collection. Crews mapped habitat units and wood directly to print out copies of the compiled true color composites from the hyperspectral imagery. Depths were also measured, and locations of depths were documented on the print maps. The software package, Environment for Visualizing Images (ENVI), was used for all remote sensing analyses. Hyperspectral imagery was classified using image bands; image data was transformed using a non-standardized, variance-based principal component algorithm. For mapping of in-stream habitat types, multiple techniques for classification were attempted, but the authors report the methods and results of classification with a maximum likelihood supervised classification based on training data within that reach, as that was the most successful. Depth analyses were performed using a stepwise multiple regression that related depth to spectral reflectance. Wood was evaluated in the principal component image using the matched filter approach in ENVI. The authors found in-stream habitat classification accuracies of 67.6% for the third-order reach, 72.3% for the fourth-order reach, and 85.5% for the fifth order reach. The authors attempted to remove several edge buffers widths from the difficult to classify transitional boundaries and found that using 2 m buffer increased accuracies by up to 20% in fifth order streams. Depth classification accuracy also varied across reaches, with smaller streams having lower accuracies. The results of wood mapping varied depending on the threshold used, but at best accuracies were 83%. The results of this study suggest that the use of high-resolution hyperspectral imagery is limited in streams with vegetation cover and turbid water, conditions which are especially prevalent in low order streams. However, this tool could be applied to map large rivers and large spatial scales where field surveys may not be possible due to access or dangerous wading conditions.
Piégay, H., F. Arnaud, B. Belletti, M. Bertrand, S. Bizzi, P. Carbonneau, S. Dufour, F. Liébault, V. Ruiz-Villanueva, and L. Slater. 2020. Remotely sensed rivers in the Anthropocene: state of the art and prospects. Earth Surface Processes and Landforms 45(1):157–188. Available at: https://doi.org/10.1002/esp.4787
The Anthropocene has inflicted large scale changes on natural systems, and thus rivers are undergoing significant shifts. The results of human disturbance and effects of human attempts to mitigate or reverse disturbances have led to reach and watershed changes in river systems. Evaluation of remote sensed data (e.g., aerial imagery) has been used to map and characterize river systems as far back as the 1930s, however major advances have been made in remote sensing data collection and analysis. This paper is a review of the historic and current techniques and their applicability for mapping riverscapes over many spatial and temporal scales. This paper covers the sources, applications, limitations, and future potential of remote sensing data for river analyses. Aerial, satellite, and ground imagery, as well as LiDAR and hyperspectral imagery, are discussed as well as their use in mapping channel dynamics, landforms (e.g., sediment, erosion, deposition), large wood, riparian cover, grain size, and water depths. This paper also discusses the use of these inputs in building catchment-scale models and how these advances can be used to by managers and practitioners to improve river management in the Anthropocene.
Rusnák, M. J. Sládek, J. Pacina, and A. Kidová. 2018. Monitoring of avulsion channel evolution and river morphology changes using UAV photogrammetry: case study of the gravel bed Ondava river in outer western Carpathians. Area 51(3):549-560. Available at: https://doi.org/10.1111/area.12508
This study aims to evaluate the mechanisms and process of chute cut off and assess post-cut off channel morphology and vegetation response utilizing a combination of UAV mobile mapping and field surveys. To do this, the authors use a case study from a chute cut off in Ondava river located in eastern Slovakia that occurred during a large flood event in 2010. Close range photogrammetry was collected during the 2010 chute cut off events and subsequent nadir images were collected using a drone in June 2012, and April and July 2014. Orthophoto mosaics and digital terrain models were developed from the drone collected imagery. Field surveys were also performed in 2012 with total stations to ground truth the imagery and to calculate vertical errors as the root mean square error (RMSE). Additionally, sediment was measured at the entrance, the middle, and backwater connection of the avulsion channel and used to calculate medium grain size (D50) and an electrical resistivity profile (ERT) was used to determine floodplain architecture near the avulsion channel cut bank. Errors were primarily associated with vegetation cover; in bare ground areas the average vertical RMSE was only 0.209 m, but in areas with vegetation, the vertical RMSE was 0.673 m. Errors were also impacted by the ground control point distribution, the number of photographs, and the sole use of nadir photography for imaging. Other studies have noted that vegetation limits the usability of photogrammetry for evaluating channel morphology, and that errors can be reduced by increasing the resolution, diversifying image angles, and utilizing well placed accurately surveyed ground control points. Using sediment and ERT results, the authors conclude that sediment structure, land use, and bank vegetation facilitated the floodplain headcutting that led to the chute cut off. The authors also document increased bank erosion in the time periods after the chute cut off, indicating the avulsion channel is likely to remain and the meander will become completely cut off. This study demonstrates the ability to utilize UAV collected data with field surveys to improve data resolution and efficiency. This method could be applied in streams with low vegetation cover to create high resolution DEMs to reduce field data collection needs to facilitate evaluation of floodplain processes after restoration, or floods or other disturbances.
Singh H. V., B. R. Faulkner, A. A. Keeley, J. Freudenthal, and K. J. Forshay. 2018. Floodplain restoration increases hyporheic flow in the Yakima River Watershed, Washington. Ecological Engineering 116:110-120. Available at: https://doi.org/10.1016/j.ecoleng.2018.02.001
Levee setbacks are a commonly implemented floodplain restoration measure to allow channel migration, increase off channel habitat, and to increase hyporheic exchange. In this study, Singh et al. developed a groundwater model to quantify hyporheic flow emerging from the Yakima river pre- and post-restoration. The authors aimed to parametrize a groundwater model for the 458 km2 area surrounding the restored area of the Yakima River, WA, model groundwater flow using LiDAR, and compare the generated hyporheic flow pathlines from the Yakima river pre and post-restoration. After restoration, hyporheic flow path lengths increased, steadily and abruptly, and the directions changed, which expanded the area of surface and groundwater hyporheic exchange, indicating an improvement. This study demonstrates the use of LiDAR in evaluating floodplain restoration parameters, beyond just evaluating changes in bathymetry using digital elevation models.
Spreitzer, G., J. Tunnicliffe, and H. Friedrich. 2019. Using Structure from Motion photogrammetry to assess large wood (LW) accumulations in the field. Geomorphology 346:106851. Available at: https://doi.org/10.1016/j.geomorph.2019.106851
Quantifying large wood (LW) in rivers is a common aspect of habitat assessments as LW impacts channel morphology, sediment movement and loading, and flow hydraulics, and is important habitat for aquatic species. Previous studies have attempted to quantify wood using LiDAR and conventional photogrammetry but found that generated point clouds and pixels, respectively, were too low resolution for accurate mapping of small LW pieces. This study assesses the efficacy of a novel workflow for assessing the volume of LW accumulations using Structure from Motion (SfM) photogrammetry and several meshing algorithms compared to traditional methods of LW assessment. The authors observed that volumes calculated from interpolated volumes from point clouds and volumes calculated from meshes varied less than 19%, and calculated volumes from the novel approach were a slight overestimation from those using simple geometric methods. Field approaches for calculating wood volumes are generally based on single log dimensions or visual estimation and can be subjective and difficult to capture. This method is replicable and consistent and represents a significant improvement for modelling wood volume over existing approaches. Additionally, the ability to capture 3D models of LW in GIS will allow for further analyses of wood budgeting and distribution, as well as facilitate analyses of the impacts of LW on channel morphology, sediment, and hydrology.
Tamminga, A., C. Hugenholtz, B. Eaton, and M. Lapointe. 2015. Hyperspatial remote sensing of channel reach morphology and hydraulic fish habitat using an unmanned aerial vehicle (UAV): a first assessment in the context of river research and management. River Research and Applications 31(3):379-391.
The aim of this study is to evaluate the effectiveness of using unmanned aerial vehicles (UAVs) to characterize channel morphology and aquatic habitat of a 1 km reach in Elbow River, in Alberta Canada, and to discuss the advantages and disadvantages with regards to fisheries and river research and management. The authors collected true-color images with embedded geotags and metadata using an UAV. Forty-five ground control points were surveyed in the reach with a real time kinematic (RTK) GPS to assist in the processing. An additional 297 geolocated checkpoints were surveyed using the RTK to test the accuracy of the generated digital elevation model (DEM). To account for errors associated with water refraction for instream bathymetry, the authors tested two methods: a simple first-order correction for the refractive index of water and an empirically calibrated depth estimate using the orthomosaic pixel color values. The authors also generated water depth maps using the DEM, calculated the median sediment grain size using close ranges geolocated vertical photos of bar surface patches, and manually digitized large wood (LW) jams and other fish cover (e.g., undercut banks, overhead vegetation, pools, and water surface turbulence) using the orthomosaic. Lastly, the authors used a 2D hydrodynamic model to map depth and velocity distributions. The authors generated a highly accurate 5-cm orthomosaic and DEM, with a vertical root mean square error (RMSE) of 8.8 cm in dry areas and 11.9 cm in submerged areas, which is an improvement over many other methods, including some LiDAR generated DEMs. The authors were able to run the hydrodynamic model and identify sediment, large wood, and cover, however no field surveys were performed for in-channel features to validate characterizations. Errors associated with vegetation cover and turbidity can limit the effectiveness of the use of photogrammetry to characterize riverine systems, however, the cost, efficiency, and relative ease of data collection along with the high vertical accuracy of data products makes it a viable and useful tool for fisheries and river research, especially when alternatives such as LiDAR are cost prohibitive. Future studies should include evaluating the effectiveness of photogrammetry at mapping sediment, LW, and cover relative to field assessments.
Torgersen, C. E., D. M. Price, H. W. Li, and B. A. McIntosh. 1999. Multiscale thermal refugia and stream habitat associates of Chinook salmon in northeastern Oregon. Ecological Applications 9(1):301-319.
Understanding the relationship between temperatures and aquatic organisms is important to restoration and conservation of aquatic species. Riparian degradation, and associated channel widening and decreased shade and cover, is commonly cited as a cause of increased stream temperatures. Pacific salmon are known to respond to thermal stress by moving to cool water refuges, such as seeps and confluences. This study evaluated adult Chinook salmon distribution and thermal refugia at habitat unit (pool/riffle), reach, and entire stream section spatial scales, with the objectives of testing the effectiveness of thermal remote sensing, identifying habitat and thermal characteristics in habitats (pool, reach, river section) used by Chinook, and comparing Chinook response in two different rivers in the John Day River watershed in Oregon with different geomorphology and levels of degradation. Snorkel surveys were performed to enumerate adult Chinook salmon throughout the study area and the habitat use (e.g., pool/riffle), number of fish, instream cover, water temperature, depth, and substrate, as well as geographic locations using a global positioning system (GPS) were recorded for each encounter. Stream habitat survey data were also collected in the study reaches in both streams. Temperature was assessed using low-altitude forward-looking infrared (FLIR) via a helicopter and by instream temperature recorders placed throughout the study area that recorded temperature every 30 minutes to provide daily and seasonal water temperature fluctuations and ground truthing for the flights. All temperature data were processed in GIS to create a mosaic map of continuous temperatures. Additionally, stream survey data were georeferenced and fish observation data were entered into GIS using their GPS coordinates. Electivity analysis was used to test the correlation of salmon and cool water area distributions and logistic regression was used to test the association between salmon distribution and the longitudinal patterns of stream temperatures and habitats. The authors observed that Chinook distribution was non-uniform in reaches and throughout each river. There was a strong relationship between salmon distribution and cool water refugia at the reach level in the warmer and more disturbed stream surveyed, and a weaker relationship at the reach level in the cooler and less disturbed stream. In both streams, pools were the preferred habitat of Chinook, but riffles were used more in the cooler stream. This suggests that assessing patterns of thermal refugia should be used when evaluating connectivity and can be used to prioritize restoration. This study not only highlights an emerging technology used to evaluate temperatures on a large scale, but it also underscores the importance of incorporating larger spatial scales when evaluating fish distributions and patterns of habitat use to able to understand drivers.
Whited, D. C., J. S. Kimball, M. S. Lorang, and J. A. Stanford. 2013. Estimation of juvenile salmon habitat in pacific rim rivers using multiscalar remote sensing and geospatial analysis. River Research and Applications 29(2):135-148. Available at: https://doi.org/10.1002/rra.1585.
The use of remote sensed data has allowed for increased scales of analyses across river corridors, however, given cost and computational limitations, many studies still focus on scales less than 100 km2, This study aimed to characterize river habitat at coarse and fine scales, over 3,400,000 km2 in the North Pacific Rim (NPR) and 31 representative floodplains, respectively, to compare metrics derived from each in their ability to estimate abundance and distribution of juvenile salmon habitats. The authors derived physical habitat metrics including channel sinuosity, nodes, and floodplain width from fine, 2.4 m resolution satellite and airborne multispectral imagery and coarse, 30 m resolution Landsat imagery and coarse scale 90 m resolution global terrain data, resolution data. For fine scale data, water bodies and shallow shoreline (SS) were determined using software packages with classifications, and floodplain boundaries and parafluvial (PF) and orthofluvial (OF) spring brooks were manually delineated using ArcGIS software in 31 floodplains. Depths and velocities were assigned to appropriate clusters using water depth and flow measurements collected at similar flows. Sinuosity, percent vegetation, nodes, floodplain widths and channel lengths were all derived from the imagery. The Riverscape Analysis Project was used to generate metrics from the coarse scale data in the salmon bearing streams of the NPR. The authors compared congruence between the datasets to link metrics between the fine and coarse scale habitat classifications and observed significant metric correspondence, which allowed that critical habitats mapped using the fine scale data to be extrapolated across the NPR. The distribution of floodplain habitats varied throughout the NPR but reflected regional patterns of topography and latitudinal climate. The authors determined that the combination of using coarse scale open source data, fine scale data, and field surveys could successfully characterize floodplain features to evaluate salmon habitat on substantial spatial scales. This assessment technique not only is important for assessing salmon habitat distribution and availability, but it also presents an opportunity for identifying and prioritizing salmon restoration on a regional scale.
Wirth, L., A. Rosenberger, A. Prakash, R. Gens, F. J. Margraf, and T. Hamazaki. 2012. A remote-sensing, GIS-based approach to identify, characterize, and model spawning habitat for fall-run Chum salmon in a sub-arctic, glacially fed river. Transactions of the American Fisheries Society 141(5):349-1363. Available at: https://doi.org/10.1080/00028487.2012.692348.
In this study, Wirth et al. seek to model habitat selection of fall-run chum salmon and identify spawning habitat in a river in the Tanana river, Alaska using a combination of ground surveys and remote sensing. Northern Alaska represents the northern limit of the Chum distribution and thermal refugia and overwintering habitat can restrict distribution. Adult female chum were radiotagged and their distribution was tracked throughout the study to determine spawning locations. Satellite imagery was collected from multiple datasets and both the total river surface area prior to freezing and the areas that remained ice free during winter were digitized. Sinuosity and a river braiding index were calculated. Additionally, temperature loggers were placed instream throughout the reach in ice free and frozen areas and forward-looking infrared imagery (FLIR) was used to investigate temperatures in ice free zones. Wirth et al. observed a strong preference by spawning fall-run chum salmon for ice free areas. The authors discuss the importance of groundwater mixing environments to fall chum salmon and suggest that future monitoring should evaluate water chemistry of these areas to investigate if spawning fall chum salmon are using chemical cues to home to spawning grounds. Microhabitat data, such as substrate, depth, or velocity, could not be field collected during this survey, but would be important to analyze in future spawning distribution studies. This study highlights how remote sensing can be used in remote and difficult to access areas to investigate distribution and habitat preferences. Utilizing remote sensed methods to incorporate substrate, depths, or velocities, and incorporating field-based water quality methods could bolster analyses in future studies of this nature.
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This annotated bibliography follows the AFS style guide recommended citation format
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