Remote Sensing of Geomorphology, Volume 23

Description

Remote Sensing of Geomorphology, Volume 23, discusses the new range of remote-sensing techniques (lidar, structure from motion photogrammetry, advanced satellite platforms) that has led to a dramatic increase in terrain information, and as such provided new opportunities for a better understanding of surface morphology and related Earth surface processes. As several papers have been published (including paper reviews and special issues) on this topic, this book summarizes the major advances in remote sensing techniques for the analysis of Earth surface morphology and processes, also highlighting future challenges. Useful for MSc and PhD students, this book is also ideal for any scientists that want to have a single volume guideline to help them develop new ideas. In addition, technicians and private and public sectors working on remote sensing will find the information useful to their initiatives. Front Cover Remote Sensing of Geomorphology Copyright Contents Contributors Foreword Reference Introduction to remote sensing of geomorphology Chapter 1: Structure from motion photogrammetric technique 1. Introduction 1.1. Brief historical summary and state of the art 1.2. Reasons for success in geomorphological surveys 2. Method 2.1. Choosing suitable settings to comply with the application at hand 2.1.1. Image quality 2.1.2. Ground sampling distance 2.1.3. Image network geometry 2.1.4. Camera parameter choice during bundle adjustment 2.1.5. Referencing: GCP weights and distribution 2.1.6. Exterior influences 2.2. Accuracy considerations in geomorphological applications 2.3. Direct geo-referencing (DG) for flexible UAV applications 2.3.1. Achievable accuracies 2.3.2. Guidelines for DG applications 3. Reconstructing processes across space 4. Reconstructing processes in time 4.1. Past and real-time reconstruction 4.2. Time-lapse imagery for 4D change detection 4.2.1. Guidelines for time-lapse SfM photogrammetry 5. Final remarks References Further reading Chapter 2: Topo-bathymetric airborne LiDAR for fluvial-geomorphology analysis 1. High-resolution topography: Where is the bathymetry? 2. Synoptic fluvial bathymetry survey techniques 2.1. Topo-bathymetric lidar vs existing approaches 2.2. Topo-bathymetric airborne lidar sensors 2.3. Survey examples and typical data characteristics 3. Controls on depth penetration and surveyable rivers 3.1. Theoretical controls on the bathymetric waveform and bottom echo intensity 3.2. Results on maximum measurable depth and sensor comparison 3.3. Depth uncertainty and detail resolving capability 3.4. Surveyable rivers and survey strategy 4. Data processing 4.1. Water-surface detection, bathymetric classification, and refraction correction 4.2. FWF analysis 5. Applications in fluvial geomorphology 5.1. Multi-scale high-resolution fluvial geomorphology 5.2. Coupling with 2D-3D hydraulic modeling 5.3. Synoptic channel morphodynamics and sediment budget 6. Conclusions and remaining challenges 6.1. A priori prediction of depth penetration and river bathymetric cover 6.2. Automatic classification on massive lidar datasets 6.3. FWF analysis in the context of fluvial environments 6.4. Large-scale hydraulic modeling on topo-bathymetric data Acknowledgments References Chapter 3: Ground-based remote sensing of the shallow subsurface: Geophysical methods for environmental applications 1. Introduction 2. Methods 2.1. Geo-electrical (DC resistivity) methods 2.2. EMI methods and GPR 2.3. Seismics 3. Application examples 3.1. System structure 3.1.1. The Settolo site 3.1.2. The Trecate site 3.1.3. The Aviano site 3.1.4. The Fondo Paviani site 3.1.5. The Turriaco site 3.2. Fluid dynamics monitoring 3.2.1. The Decimomannu site 3.2.2. The Trento Nord site 3.2.3. The Grugliasco site 3.2.4. The Bregonze site 3.2.5. The Bari IRSA-CNR site 4. Future challenges and conclusions Acknowledgments References Further reading Chapter 4: Topographic data from satellites 1. The importance of topography 2. Collection of topographic data from satellites 2.1. Satellite lidar 2.2. Radar 2.3. Stereo imaging 3. Global and large regional datasets 3.1. GTOPO30 3.2. SRTM 3.3. ASTER 3.4. ALOS PRISM 3.5. TanDEM-X 3.6. ArcticDEM and REMA 3.7. High Himalaya DEM 3.8. MERIT DEM 3.9. Other instruments and summary 4. Accuracy of global datasets 4.1. Common sources of error 4.2. Methods of comparison between datasets 4.3. Error estimates for specific datasets 4.3.1. SRTM accuracy 4.3.2. ASTER accuracy 4.3.3. ALOS world 3D accuracy 4.3.4. TanDEM-X DEM accuracy 4.3.5. MERIT DEM accuracy 4.3.6. ArcticDEM, REMA, and High Mountain Asia DEM accuracy 4.4. Dataset intercomparison 4.5. Summary of vertical accuracy 5. Implications of increasing resolution on geomorphic studies 5.1. Geomorphic metrics and data processing 5.2. Simple preprocessing 5.2.1. Grid resolution: Implications for curvature and slope measurements 5.3. Accuracy of channel profiles 6. Future developments 7. Conclusions References Chapter 5: Linking life and landscape with remote sensing 1. Introduction 2. Linking remote sensed data to life and landscapes 2.1. Erosive, depositional, and constructive processes modulated by biota 2.2. Life and landscape patterns 2.3. Measureable vegetation properties 2.4. Soils and belowground organic carbon 3. Passive remote sensing methods 3.1. Vegetation indicators from passive instruments 3.2. Coarse resolution passive sensors 3.3. Medium and fine resolution passive sensors 4. Radar 4.1. Satellite-based radar systems 5. Lidar 5.1. A primer on lidar remote sensing 5.2. Quantifying canopy structure with airborne lidar 5.2.1. Canopy height models and canopy gaps 5.2.2. Identifying individual trees 5.2.3. Mapping AGB and ACD 5.2.3.1. Area-based approaches 5.2.3.2. Individual-based approaches 5.2.3.3. Calibration and uncertainty 5.2.4. Quantifying PAI and vertical distributions of plant area density 5.3. Spaceborne lidar 5.3.1. ICESat/GLAS 5.3.2. GEDI 5.3.3. ICESat-2/ATLAS 5.4. Data fusion 6. Airborne electromagnetics 7. Conclusions 7.1. Finding the right sensor 7.2. The importance of scale 7.3. Trade-offs between resolution and spatial coverage 7.4. Future outlook Acknowledgments References Chapter 6: SfM photogrammetry for GeoArchaeology 1. Remote sensing 2. SfM photogrammetry 3. SfM in geoarchaeology: Agricultural terraces in Europe 3.1. Case study: Ingram Valley (UK) 3.2. SfM workflow 3.2.1. Fieldwork 3.2.2. SfM processing 3.2.3. SfM postprocessing 3.2.4. DTM generation 3.3. Result and discussion 4. Final remarks Acknowledgments References Chapter 7: Landslide analysis using laser scanners 1. Introduction 2. A short history 3. Basics of laser scanners 3.1. Lasers and safety 3.2. LiDAR devices 3.3. TOF LiDAR 3.3.1. LiDAR using phase measurements 3.3.2. LASER scanning based on triangulation 3.4. Beam characteristics 4. LiDAR uses 4.1. Issues 4.2. Different LiDAR configurations 4.3. Filtering 4.4. Georeferencing and coregistration 5. Characterization of landslides 5.1. Mapping 5.2. Rock structure characterization and rockfall sources 5.3. Volume estimation 6. Monitoring 6.1. Surface changes 6.2. Potential methods for real-time monitoring 7. Modeling based on LDTM 8. Discussion and perspectives Acknowledgments References Further reading Chapter 8: Terrestrial laser scanner applied to fluvial geomorphology 1. Challenges in using terrestrial laser scanner to understand river dynamics 2. Data acquisition 2.1. Equipment consideration 2.2. Data registration, georeferencing, and survey strategy 2.3. Boundary conditions monitoring and long-term monitoring 3. 3D point cloud postprocessing operations 3.1. Point cloud registration and preprocessing 3.2. Vegetation classification 3.3. Point-based vs raster-based analysis 3.4. Core points as a way to cope with data volume and spatial variations in point density 3.5. Metrics calculation and segmentation 3.6. 3D spatial analysis 4. Topographic change measurement and volume calculation 4.1. Source of uncertainties 4.2. Vertical change detection 4.3. 3D distance and bank erosion measurement 5. Science from point clouds in fluvial geomorphology 5.1. Grain size distribution 5.2. Sediment transport and bank erosion 5.3. Bedrock erosion 5.4. Vegetation, hydraulics, and sedimentation 6. Conclusion and outlook Acknowledgments References Chapter 9: Remote sensing for the analysis of anthropogenic geomorphology: Potential responses to sediment dynamics in th ... 1. Introduction 2. Materials and methods 2.1. Relative path impact index 2.2. Connectivity index 3. Study area 3.1. Spain 3.2. Italy 4. Results 4.1. Spain 4.2. Italy 5. A holistic view of land planning 6. Conclusions Acknowledgments References Further reading Chapter 10: Using UAV and LiDAR data for gully geomorphic changes monitoring 1. Introduction 1.1. LiDAR in geosciences 1.2. Digital photogrammetry and SfM in geosciences 2. Study area: The reservoir bottom gullies from Jijia Hills (Romania) 3. Materials and methods 3.1. LiDAR data 3.2. UAV images 3.3. Structure from motion 3.3.1. SfM approach 3.3.2. Point cloud postprocessing 3.4. DEM generation 3.5. Geomorphic change detection 3.6. Geomorphological mapping 4. Results 5. Discussions 6. Conclusions Acknowledgments References Further reading Chapter 11: Zero to a trillion: Advancing Earth surface process studies with open access to high-resolution topography 1. Introduction 2. Scientific motivations for open access to topographic data 3. Broad impacts from openly available topographic data 4. OpenTopography overview and impact 5. OpenTopography partnerships 6. Lessons learned and challenges for supporting open access to topographic data 7. Outlook Theme 1: A cloud and HPC platform for scalability and sustainability Theme 2: Enabling community innovation 8. Conclusions Acknowledgments References Chapter 12: Reproducible topographic analysis 1. Topographic analysis and (reproducible) geomorphology 2. Scientific reproducibility 2.1. Reproducibility or replicability? 2.2. Benefits of reproducible research 3. Reproducibility in the context of topographic analysis for geomorphology 3.1. Initial observations of landscape form 3.2. Paper contour map analysis 3.3. The beginning of computational topographic analysis 3.3.1. Vector representations of elevation data 3.3.2. Gridded representations of elevation data 3.4. The reproducibility of early computational topographic analysis 3.5. Modern topographic analysis 3.6. The reproducibility of modern topographic analysis 4. Barriers to reproducible topographic analysis 4.1. Topographic analysis workflows 4.2. Data 4.3. Paywalls 5. Making topographic analysis reproducible 5.1. Workflows 5.2. Data 5.3. Paywalls 5.4. Our recommendations 6. Conclusions References Index Back Cover