Fluvial Remote Sensing for Science and Management

Dr Patrice E. Carbonneau, Lecturer in physical geography, Geography department, Durham University, UK>/p>

• Series Foreword, xv Foreword, xviiList of Contributors, xix1 Introduction: The Growing Use of Imagery in Fundamental and Applied River Sciences, 1Patrice E. Carbonneau and Herv´e Pi´egay1.1 Introduction, 11.2 Remote sensing, river sciences and management, 21.2.1 Key concepts in remote sensing, 21.2.2 A short introduction to ‘river friendly’ sensors and platforms, 41.2.3 Cost considerations, 71.3 Evolution of published work in Fluvial Remote Sensing, 81.3.1 Authorships and Journals, 91.3.2 Platforms and Sensors, 91.3.3 Topical Areas, 101.3.4 Spatial and Temporal Resolutions, 141.3.5 Summary, 161.4 Brief outline of the volume, 16References, 172 Management Applications of Optical Remote Sensing in the Active River Channel, 19W. Andrew Marcus, Mark A. Fonstad and Carl J. Legleiter2.1 Introduction, 192.2 What can be mapped with optical imagery?, 202.3 Flood extent and discharge, 212.4 Water depth, 222.5 Channel change, 242.6 Turbidity and suspended sediment, 252.7 Bed sediment, 272.8 Biotypes (in-stream habitat units), 292.9 Wood, 312.10 Submerged aquatic vegetation (SAV) and algae, 312.11 Evolving applications, 332.12 Management considerations common to river applications, 332.13 Accuracy, 352.14 Ethical considerations, 362.15 Why use optical remote sensing?, 36References, 383 An Introduction to the Physical Basis for Deriving River Information by Optical Remote Sensing, 43Carl J. Legleiter and Mark A. Fonstad3.1 Introduction, 433.2 An overview of radiative transfer in shallow stream channels, 453.2.1 Quantifying the light field, 453.2.2 Radiative transfer processes along the image chain, 493.3 Optical characteristics of river channels, 543.3.1 Reflectance from the water surface, 553.3.2 Optically significant constituents of the water column, 553.3.3 Reflectance properties of the streambed and banks, 583.4 Inferring river channel attributes from remotely sensed data, 603.4.1 Spectrally-based bathymetric mapping via band ratios, 603.4.2 Relative magnitudes of the components of the at-sensor radiance signal, 613.4.3 The role of sensor characteristics, 623.5 Conclusion, 663.6 Notation, 67References, 684 Hyperspectral Imagery in Fluvial Environments, 71Mark J. Fonstad4.1 Introduction, 714.2 The nature of hyperspectral data, 724.3 Advantages of hyperspectral imagery, 744.4 Logistical and optical limitations of hyperspectral imagery, 754.5 Image processing techniques, 784.6 Conclusions, 82Acknowledgments, 82References, 825 Thermal Infrared Remote Sensing of Water Temperature in Riverine Landscapes, 85Rebecca N. Handcock, Christian E. Torgersen, Keith A. Cherkauer, Alan R. Gillespie, Klement Tockner, Russel N. Faux and Jing Tan5.1 Introduction, 855.2 State of the art: TIR remote sensing of streams and rivers, 885.3 Technical background to the TIR remote sensing of water, 915.3.1 Remote sensing in the TIR spectrum, 915.3.2 The relationship between emissivity and kinetic and radiant temperature, 925.3.3 Using Planck’s Law to determine temperature from TIR observations, 935.3.4 Processing of TIR image data, 945.3.5 Atmospheric correction, 945.3.6 Key points, 955.4 Extracting useful information from TIR images, 965.4.1 Calculating a representative water temperature, 965.4.2 Accuracy, uncertainty, and scale, 965.4.3 The near-bank environment, 975.4.4 Key points, 985.5 TIR imaging sensors and data sources, 985.5.1 Ground imaging, 985.5.2 Airborne imaging, 985.5.3 Satellite imaging, 1015.5.4 Key points, 1015.6 Validating TIR measurements of rivers, 1025.6.1 Timeliness of data, 1025.6.2 Sampling site selection, 1035.6.3 Thermal stratification and mixing, 1045.6.4 Measuring representative temperature, 1045.6.5 Key points, 1055.7 Example 1: Illustrating the necessity of matching the spatial resolution of the TIR imaging device to river width using multi-scale observations of water temperature in the Pacific Northwest (USA), 1065.8 Example 2: Thermal heterogeneity in river floodplains used to assess habitat diversity, 1085.9 Summary, 108Acknowledgements, 1095.10 Table of abbreviations, 110References, 1106 The Use of Radar Imagery in Riverine Flood Inundation Studies, 115Guy J-P. Schumann, Paul. D. Bates, Giuliano Di Baldassarre and David C. Mason6.1 Introduction, 1156.2 Microwave imaging of water and flooded land surfaces, 1166.2.1 Passive radiometry, 1176.2.2 Synthetic Aperture Radar, 1176.2.3 SAR interferometry, 1196.3 The use of SAR imagery to map and monitor river flooding, 1206.3.1 Mapping river flood inundation from space, 1206.3.2 Sources of flood and water detection errors, 1246.3.3 Integration with flood inundation modelling, 1296.4 Case study examples, 1296.4.1 Fuzziness in SAR flood detection to increase confidence in flood model simulations, 1296.4.2 Near real-time flood detection in urban and rural areas using high resolution space-borne SAR images, 1316.4.3 Multi-temporal SAR images to inform about floodplain dynamics, 1336.5 Summary and outlook, 135References, 1377 Airborne LiDAR Methods Applied to Riverine Environments, 141Jean-St´ephane Bailly, Paul J. Kinzel, Tristan Allouis, Denis Feurer and Yann Le Coarer7.1 Introduction: LiDAR definition and history, 1417.2 Ranging airborne LiDAR physics, 1427.2.1 LiDAR for emergent terrestrial surfaces, 1427.2.2 LiDAR for aquatic surfaces, 1447.3 System parameters and capabilities: examples, 1467.3.1 Large footprint system: HawkEye II, 1467.3.2 Narrow footprint system: EAARL, 1477.3.3 Airborne LiDAR capacities for fluvial monitoring: a synthesis, 1487.4 LiDAR survey design for rivers, 1487.4.1 Flight planning and optimising system design, 1487.4.2 Geodetic positioning, 1507.5 River characterisation from LiDAR signals, 1507.5.1 Altimetry and topography, 1507.5.2 Prospective estimations, 1527.6 LiDAR experiments on rivers: accuracies, limitations, 1537.6.1 LiDAR for river morphology description: the Gardon River case study, 1537.6.2 LiDAR and hydraulics: the Platte River experiment, 1547.7 Conclusion and perspectives: the future for airborne LiDAR on rivers, 158References, 1588 Hyperspatial Imagery in Riverine Environments, 163Patrice E. Carbonneau, Herv ´e Pi´egay, J ´ er ˆome Lejot, Robert Dunford and Kristell Michel8.1 Introduction: The Hyperspatial Perspective, 1638.2 Hyperspatial image acquisition, 1668.2.1 Platform considerations, 1668.2.2 Ground-tethered devices, 1668.2.3 Camera considerations, 1708.2.4 Logistics and costs, 1728.3 Issues, potential problems and plausible solutions, 1728.3.1 Georeferencing, 1738.3.2 Radiometric normalisation, 1768.3.3 Shadow correction, 1768.3.4 Image classification, 1798.3.5 Data mining and processing, 1808.4 From data acquisition to fluvial form and process understanding, 1828.4.1 Feature detection with hyperspatial imagery, 1828.4.2 Repeated surveys through time, 1838.5 Conclusion, 188Acknowledgements, 189References, 1899 Geosalar: Innovative Remote Sensing Methods for Spatially Continuous Mapping of Fluvial Habitat at Riverscape Scale, 193Normand Bergeron and Patrice E. Carbonneau9.1 Introduction, 1939.2 Study area and data collection, 1949.3 Grain size mapping, 1949.3.1 Superficial sand detection, 1969.3.2 Airborne grain size measurements, 1989.3.3 Riverscape scale grain size profile and fish distribution, 2009.3.4 Limitations of airborne grain size mapping, 2009.3.5 Example of application of grain size maps and long profiles to salmon habitat modelling, 2019.4 Bathymetry mapping, 2039.5 Further developments in the wake of the Geosalar project, 2059.5.1 Integrating fluvial remote sensing methods, 2059.5.2 Habitat data visualisation, 2079.5.3 Development of in-house airborne imaging capabilities, 2089.6 Flow velocity: mapping or modelling?, 2099.7 Future work: Integrating fish exploitation of the riverscape, 2119.8 Conclusion, 211Acknowledgements, 212References, 21210 Image Utilisation for the Study and Management of Riparian Vegetation: Overview and Applications, 215Simon Dufour, Etienne Muller, Menno Straatsma and S. Corgne10.1 Introduction, 21510.2 Image analysis in riparian vegetation studies: what can we know?, 21710.2.1 Mapping vegetation types and land cover, 21710.2.2 Mapping species and individuals, 22010.2.3 Mapping changes and historical trajectories, 22010.2.4 Mapping other floodplain characteristics, 22010.3 Season and scale constraints in riparian vegetation studies, 22110.3.1 Choosing an appropriate time window for detecting vegetation types, 22110.3.2 Minimum detectable object size in the riparian zone, 22110.3.3 Spatial/spectral equivalence for detecting changes, 22110.4 From scientists’ tools to managers’ choices: what do we want to know? And how do we get it?, 22310.4.1 Which managers? Which objectives? Which approach?, 22410.4.2 Limitations of image-based approaches, 22410.5 Examples of imagery applications and potentials for riparian vegetation study, 22610.5.1 A low-cost strategy for monitoring changes in a floodplain forest: aerial photographs, 22610.5.2 Flow resistance and vegetation roughness parametrisation: LiDAR and multispectral imagery, 22810.5.3 Potential radar data uses for riparian vegetation characterisation, 23010.6 Perspectives: from images to indicators, automatised and standardised processes, 233Acknowledgements, 234References, 23411 Biophysical Characterisation of Fluvial Corridors at Reach to Network Scales, 241Herv´e Pi´egay, Adrien Alber, J. Wesley Lauer, Anne-Julia Rollet and Elise Wiederkehr11.1 Introduction, 24111.2 What are the raw data available for a biophysical characterisation of fluvial corridors?, 24211.3 How can we treat the information?, 24311.3.1 What can we see?, 24311.3.2 Strategy for exploring spatial information for understanding river form and processes, 24511.3.3 Example of longitudinal generic parameters treatment using unorthorectified photos, 24811.3.4 The aggregation/disaggregation procedure applied at a regional network scale, 25011.4 Detailed examples to illustrate management issues, 25311.4.1 Retrospective approach on the Ain River: understanding channel changes and providing a sediment budget, 25411.4.2 The Droˆme network: example of up- and downscaling approach using homogeneous geomorphic reaches, 25611.4.3 Inter-reach comparisons at a network scale, 25911.5 Limitations and constraints when enlarging scales of interest, 26111.6 Conclusions, 265Acknowledgements, 265References, 26612 The Role of Remotely Sensed Data in Future Scenario Analyses at a Regional Scale, 271Stan Gregory, Dave Hulse, M´ elanie Bertrand and Doug Oetter12.1 Introduction, 27112.1.1 The purposes of scenario-based alternative future analyses, 27212.1.2 Processes of depicting alternative future scenarios, 27212.1.3 Methods of employing remotely sensed information in alternative futures, 27812.1.4 Alternative future scenarios for the Willamette River, Oregon as a case study, 27812.2 Methods, 27912.2.1 Ground truthing, 28112.2.2 Use of remotely sensed data in the larger alternative futures project, 28212.3 Land use/land cover changes since 1850, 28212.4 Plan trend 2050 scenario, 28312.5 Development 2050 scenario, 28712.6 Conservation 2050 scenario, 28712.7 Informing decision makers at subbasin extents, 28912.8 Discussion, 291Acknowledgements, 294References, 29413 The Use of Imagery in Laboratory Experiments, 299Michal Tal, Philippe Frey, Wonsuck Kim, Eric Lajeunesse, Angela Limare and Franc¸ois M´etivier13.1 Introduction, 29913.2 Bedload transport, 30013.2.1 Image-based technique to measure grainsize distribution and sediment discharge, 30213.2.2 Particle trajectories and velocities using PTV, 30413.3 Channel morphology and flow dynamics, 30613.3.1 Experimental deltas, 30813.3.2 Experimental river channels with riparian vegetation, 30913.4 Bed topography and flow depth, 31213.5 Conclusions, 317Acknowledgements, 318References, 31814 Ground based LiDAR and its Application to the Characterisation of Fluvial Forms, 323Andy Large and George Heritage14.1 Introduction, 32314.1.1 Terrestrial laser scanning in practice, 32414.2 Scales of application in studies of river systems, 32514.2.1 The sub-grain scale, 32514.2.2 The grain scale, 32514.2.3 The sub-bar unit scale, 32714.2.4 In-channel hydraulic unit scale, 32914.2.5 Micro-topographic roughness units, 33014.2.6 The bar unit scale, 33014.2.7 Reach-scale morphological analyses, 33214.2.8 Terrestrial laser scanning at the landscape scale, 33414.2.9 Towards a protocol for TLS surveying of fluvial systems, 336References, 33815 Applications of Close-range Imagery in River Research, 341Walter Bertoldi, Herv´e Pi´egay, Thomas Buffin-B´ elanger, David Graham and Stephen Rice15.1 Introduction, 34115.2 Technologies and practices, 34215.2.1 Technology, 34215.2.2 Overview of possible applications, 34415.3 Post-processing, 34715.3.1 Analysis of vertical images for particle size, 34715.3.2 Analysis of vertical images for particle shape, 34915.3.3 Analysis of oblique ground images, 34915.4 Application of vertical and oblique close-range imagery to monitor bed features and fluvial processes at different spatial and temporal scales, 35015.4.1 Vertical ground imagery for characterising grain size, clast morphometry and petrography of particles, 35015.4.2 Monitoring fluvial processes, 35215.4.3 Survey of subaerial bank processes, 35315.4.4 Inundation dynamics of braided rivers, 35515.4.5 River ice dynamics, 35615.4.6 Riparian structure and dead wood distributions along river corridors, 35915.5 Summary of benefits and limitations, 36115.6 Forthcoming issues for river management, 362Acknowledgements, 363References, 36316 River Monitoring with Ground-based Videography, 367Bruce J. MacVicar, Alexandre Hauet, Normand Bergeron, Laure Tougne and Imtiaz Ali16.1 Introduction, 36716.2 General considerations, 36816.2.1 Flow visualisation and illumination, 36816.2.2 Recording, 36816.2.3 Image ortho-rectification, 36916.3 Case 1 – Stream gauging, 36916.3.1 Introduction, 36916.3.2 Field site and apparatus, 37016.3.3 Image processing, 37016.3.4 Stream gauging, 37116.3.5 Results, 37116.4 Case 2 – Filtering bed and flare effects from LSPIV measurements, 37216.4.1 Introduction, 37216.4.2 Field site and apparatus, 37316.4.3 Data filtering, 37316.4.4 Results, 37316.5 Case 3 – At-a-point survey of wood transport, 37616.5.1 Introduction, 37616.5.2 Field site and apparatus, 37616.5.3 Manual detection and measurement, 37616.5.4 Image segmentation and analysis, 37716.5.5 Results, 37916.6 Discussion and conclusion, 380References, 38117 Imagery at the Organismic Level: From Body Shape Descriptions to Micro-scale Analyses, 385Pierre Sagnes17.1 Introduction, 38517.2 Morphological and anatomical description, 38617.2.1 Identification, 38617.2.2 Characterisation of life-history traits and ontogenetic stages, 39017.2.3 Ecomorphological studies, 39317.3 Abundance and biomass, 39417.4 Detection of stress and diseases, 39617.4.1 Direct visualisation of stress (or its effects), 39617.4.2 Activity of organisms as stress indicator, 39817.4.3 Fluctuating asymmetry as stress indicator, 39817.5 Conclusion, 399References, 39918 Ground Imagery and Environmental Perception: Using Photo-questionnaires to Evaluate River Management Strategies, 405Yves-Francois Le Lay, Marylise Cottet, Herv´e Pi´egay and Anne Rivi `ere-Honegger18.1 Introduction, 40518.2 Conceptual framework, 40618.3 The design of photo-questionnaires, 40918.3.1 The questionnaire and selection of photographs, 40918.3.2 The attitude scales, 41018.3.3 The selection of participant groups, 41218.4 Applications with photo-questionnaires, 41218.4.1 From judgment assessment to judgment prediction, 41218.4.2 Comparing reactions between scenes and between observers, 41518.4.3 Linking judgments to environmental factors, 41718.4.4 Modelling and predicting water landscape judgments, 42018.4.5 Photographs and landscape perception, a long history of knowledge production, 42018.5 Conclusions and perspectives, 425Acknowledgements, 426References, 42619 Future Prospects and Challenges for River Scientists and Managers, 431Patrice E. Carbonneau and Herv´e Pi´egayReferences, 433Index, 435