Fluvial Remote Sensing for Science and Management -- Contents -- Series Foreword -- Foreword -- List of Contributors -- Chapter 1 Introduction: The Growing Use of Imagery in Fundamental and Applied River Sciences -- 1.1 Introduction -- 1.2 Remote sensing, river sciences and management -- 1.2.1 Key concepts in remote sensing -- 1.2.2 A short introduction to `river friendly' sensors and platforms -- 1.2.3 Cost considerations -- 1.3 Evolution of published work in Fluvial Remote Sensing -- 1.3.1 Authorships and Journals -- 1.3.2 Platforms and Sensors -- 1.3.3 Topical Areas -- 1.3.4 Spatial and Temporal Resolutions -- 1.3.5 Summary -- 1.4 Brief outline of the volume -- References -- Chapter 2 Management Applications of Optical Remote Sensing in the Active River Channel -- 2.1 Introduction -- 2.2 What can be mapped with optical imagery? -- 2.3 Flood extent and discharge -- 2.4 Water depth -- 2.5 Channel change -- 2.6 Turbidity and suspended sediment -- 2.7 Bed sediment -- 2.8 Biotypes (in-stream habitat units) -- 2.9 Wood -- 2.10 Submerged aquatic vegetation (SAV) and algae -- 2.11 Evolving applications -- 2.12 Management considerations common to river applications -- 2.13 Accuracy -- 2.14 Ethical considerations -- 2.15 Why use optical remote sensing? -- References -- Chapter 3 An Introduction to the Physical Basis for Deriving River Information by Optical Remote Sensing -- 3.1 Introduction -- 3.2 An overview of radiative transfer in shallow stream channels -- 3.2.1 Quantifying the light field -- 3.2.2 Radiative transfer processes along the image chain -- 3.3 Optical characteristics of river channels -- 3.3.1 Reflectance from the water surface -- 3.3.2 Optically significant constituents of the water column -- 3.3.3 Reflectance properties of the streambed and banks.

3.4 Inferring river channel attributes from remotely sensed data -- 3.4.1 Spectrally-based bathymetric mapping via band ratios -- 3.4.2 Relative magnitudes of the components of the at-sensor radiance signal -- 3.4.3 The role of sensor characteristics -- 3.5 Conclusion -- 3.6 Notation -- References -- Chapter 4 Hyperspectral Imagery in Fluvial Environments -- 4.1 Introduction -- 4.2 The nature of hyperspectral data -- 4.3 Advantages of hyperspectral imagery -- 4.4 Logistical and optical limitations of hyperspectral imagery -- 4.5 Image processing techniques -- 4.6 Conclusions -- Acknowledgments -- References -- Chapter 5 Thermal Infrared Remote Sensing of Water Temperature in Riverine Landscapes -- 5.1 Introduction -- 5.2 State of the art: TIR remote sensing of streams and rivers -- 5.3 Technical background to the TIR remote sensing of water -- 5.3.1 Remote sensing in the TIR spectrum -- 5.3.2 The relationship between emissivity and kinetic and radiant temperature -- 5.3.3 Using Planck's Law to determine temperature from TIR observations -- 5.3.4 Processing of TIR image data -- 5.3.5 Atmospheric correction -- 5.3.6 Key points -- 5.4 Extracting useful information from TIR images -- 5.4.1 Calculating a representative water temperature -- 5.4.2 Accuracy, uncertainty, and scale -- 5.4.3 The near-bank environment -- 5.4.4 Key points -- 5.5 TIR imaging sensors and data sources -- 5.5.1 Ground imaging -- 5.5.2 Airborne imaging -- 5.5.3 Satellite imaging -- 5.5.4 Key points -- 5.6 Validating TIR measurements of rivers -- 5.6.1 Timeliness of data -- 5.6.2 Sampling site selection -- 5.6.3 Thermal stratification and mixing -- 5.6.4 Measuring representative temperature -- 5.6.5 Key points.

5.7 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) -- 5.8 Example 2: Thermal heterogeneity in river floodplains used to assess habitat diversity -- 5.9 Summary -- Acknowledgements -- 5.10 Table of abbreviations -- References -- Chapter 6 The Use of Radar Imagery in Riverine Flood Inundation Studies -- 6.1 Introduction -- 6.2 Microwave imaging of water and flooded land surfaces -- 6.2.1 Passive radiometry -- 6.2.2 Synthetic Aperture Radar -- 6.2.3 SAR interferometry -- 6.3 The use of SAR imagery to map and monitor river flooding -- 6.3.1 Mapping river flood inundation from space -- 6.3.2 Sources of flood and water detection errors -- 6.3.3 Integration with flood inundation modelling -- 6.4 Case study examples -- 6.4.1 Fuzziness in SAR flood detection to increase confidence in flood model simulations -- 6.4.2 Near real-time flood detection in urban and rural areas using high resolution space-borne SAR images -- 6.4.3 Multi-temporal SAR images to inform about floodplain dynamics -- 6.5 Summary and outlook -- References -- Chapter 7 Airborne LiDAR Methods Applied to Riverine Environments -- 7.1 Introduction: LiDAR definition and history -- 7.2 Ranging airborne LiDAR physics -- 7.2.1 LiDAR for emergent terrestrial surfaces -- 7.2.2 LiDAR for aquatic surfaces -- 7.3 System parameters and capabilities: examples -- 7.3.1 Large footprint system: HawkEye II -- 7.3.2 Narrow footprint system: EAARL -- 7.3.3 Airborne LiDAR capacities for fluvial monitoring: a synthesis -- 7.4 LiDAR survey design for rivers -- 7.4.1 Flight planning and optimising system design -- 7.4.2 Geodetic positioning -- 7.5 River characterisation from LiDAR signals -- 7.5.1 Altimetry and topography.

7.5.2 Prospective estimations -- 7.6 LiDAR experiments on rivers: accuracies, limitations -- 7.6.1 LiDAR for river morphology description: the Gardon River case study -- 7.6.2 LiDAR and hydraulics: the Platte River experiment -- 7.7 Conclusion and perspectives: the future for airborne LiDAR on rivers -- References -- Chapter 8 Hyperspatial Imagery in Riverine Environments -- 8.1 Introduction: The Hyperspatial Perspective -- 8.2 Hyperspatial image acquisition -- 8.2.1 Platform considerations -- 8.2.2 Ground-tethered devices -- 8.2.3 Camera considerations -- 8.2.4 Logistics and costs -- 8.3 Issues, potential problems and plausible solutions -- 8.3.1 Georeferencing -- 8.3.2 Radiometric normalisation -- 8.3.3 Shadow correction -- 8.3.4 Image classification -- 8.3.5 Data mining and processing -- 8.4 From data acquisition to fluvial form and process understanding -- 8.4.1 Feature detection with hyperspatial imagery -- 8.4.2 Repeated surveys through time -- 8.5 Conclusion -- Acknowledgements -- References -- Chapter 9 Geosalar: Innovative Remote Sensing Methods for Spatially Continuous Mapping of Fluvial Habitat at Riverscape Scale -- 9.1 Introduction -- 9.2 Study area and data collection -- 9.3 Grain size mapping -- 9.3.1 Superficial sand detection -- 9.3.2 Airborne grain size measurements -- 9.3.3 Riverscape scale grain size profile and fish distribution -- 9.3.4 Limitations of airborne grain size mapping -- 9.3.5 Example of application of grain size maps and long profiles to salmon habitat modelling -- 9.4 Bathymetry mapping -- 9.5 Further developments in the wake of the Geosalar project -- 9.5.1 Integrating fluvial remote sensing methods -- 9.5.2 Habitat data visualisation -- 9.5.3 Development of in-house airborne imaging capabilities -- 9.6 Flow velocity: mapping or modelling?.

9.7 Future work: Integrating fish exploitation of the riverscape -- 9.8 Conclusion -- Acknowledgements -- References -- Chapter 10 Image Utilisation for the Study and Management of Riparian Vegetation: Overview and Applications -- 10.1 Introduction -- 10.2 Image analysis in riparian vegetation studies: what can we know? -- 10.2.1 Mapping vegetation types and land cover -- 10.2.2 Mapping species and individuals -- 10.2.3 Mapping changes and historical trajectories -- 10.2.4 Mapping other floodplain characteristics -- 10.3 Season and scale constraints in riparian vegetation studies -- 10.3.1 Choosing an appropriate time window for detecting vegetation types -- 10.3.2 Minimum detectable object size in the riparian zone -- 10.3.3 Spatial/spectral equivalence for detecting changes -- 10.4 From scientists' tools to managers' choices: what do we want to know? And how do we get it? -- 10.4.1 Which managers? Which objectives? Which approach? -- 10.4.2 Limitations of image-based approaches -- 10.5 Examples of imagery applications and potentials for riparian vegetation study -- 10.5.1 A low-cost strategy for monitoring changes in a floodplain forest: aerial photographs -- 10.5.2 Flow resistance and vegetation roughness parametrisation: LiDAR and multispectral imagery -- 10.5.3 Potential radar data uses for riparian vegetation characterisation -- 10.6 Perspectives: from images to indicators, automatised and standardised processes -- Acknowledgements -- References -- Chapter 11 Biophysical Characterisation of Fluvial Corridors at Reach to Network Scales -- 11.1 Introduction -- 11.2 What are the raw data available for a biophysical characterisation of fluvial corridors? -- 11.3 How can we treat the information? -- 11.3.1 What can we see?.

11.3.2 Strategy for exploring spatial information for understanding river form and processes.