UID:
almahu_9949982622802882
Umfang:
1 online resource (1 volume) :
,
illustrations (colour).
ISBN:
9780323852845
,
032385284X
Serie:
Earth Observation
Inhalt:
Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments provides an overview of how unmanned aerial systems have revolutionized our capability to monitor river systems, soil characteristics, and related processes at unparalleled spatio-temporal resolutions. This capability has enabled enhancements in our capacity to describe water cycle and hydrological processes. The book includes guidelines, technical advice, and practical experience to support practitioners and scientists in increasing the efficiency of monitoring with the help of UAS. The book contains field survey datasets to use as practical exercises, allowing proposed techniques and methods to be applied to real world case studies.
Anmerkung:
Front Cover -- Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments -- Copyright Page -- Dedication -- Contents -- List of contributors -- List of abbreviations -- Introduction -- 1 Preface -- 2 Section 1 on general introduction on the use of unmanned aerial system for environmental monitoring -- 3 Section 2 on vegetation monitoring -- 4 Section 3 on soil mapping -- 5 Section 4 on river monitoring -- 6 Section 5 on tools and datasets -- References -- 1 General introduction on the use of UAS for environmental monitoring -- 1 Remote sensing of the environment using unmanned aerial systems -- 1.1 A brief history of unmanned aerial systems -- 1.2 Evolution of unmanned aerial systems for monitoring of natural and agricultural ecosystems -- 1.2.1 Precision agriculture -- 1.2.2 Monitoring of natural ecosystems -- 1.2.3 Water bodies -- 1.3 The social impact -- 1.4 Unmanned aerial system platforms -- 1.5 Unmanned aerial system sensors -- 1.6 Economic impact and regulations -- 1.7 Final remarks and challenges -- 1.8 Notes on the existing challenges and the purpose of this book -- 1.9 Epilogue -- References -- 2 Protocols for UAS-based observation -- 2.1 Introduction -- 2.2 Study design-guidance of survey preparation -- 2.2.1 Legislative and social impact of UAS -- 2.2.2 Platform and sensor option -- 2.2.2.1 Platforms -- 2.2.2.2 Sensors -- 2.2.3 Sensor settings and UAS control software -- 2.2.4 Georeferencing -- 2.3 Preflight fieldwork -- 2.3.1 Reconnaissance of the surveyed area -- 2.3.2 Ground control point distribution and radiometric calibration -- 2.3.3 Field data collection -- 2.4 Flight mission -- 2.5 Processing of aerial data -- 2.5.1 Geometric processing -- 2.5.2 Radiometric processing -- 2.6 Quality assurance -- 2.6.1 Quality assurance metrics for radiometric data.
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2.6.2 Quality assurance metrics application to thermal images -- 2.7 Summary and final remarks -- References -- 3 Using structure-from-motion workflows for 3D mapping and remote sensing -- 3.1 Introduction -- 3.2 Structure-from-motion workflow: from 2D images to 3D dense point cloud -- 3.2.1 Theoretical principles -- 3.2.1.1 Feature detection -- 3.2.1.2 Feature matching and validation -- 3.2.1.3 Structure from motion -- 3.2.1.4 Georeferencing -- 3.2.1.5 Refinement of bundle adjustment -- 3.2.1.6 Dense reconstruction -- 3.3 Generating geospatial products from structure-from-motion-based point clouds -- 3.3.1 Generating digital surface model and digital terrain models -- 3.3.2 Generating textured 3D models -- 3.3.3 Generating RGB orthomosaics -- 3.3.4 Generating multispectral orthomosaics -- 3.4 Using the Metashape processing workflow in 3D mapping and remote sensing -- 3.4.1 Generating dense point clouds -- 3.4.2 Generating digital surface models, textured models, and orthomosaics -- 3.5 Conclusions -- References -- 2 Vegetation monitoring -- 4 Vegetation mapping and monitoring by unmanned aerial systems (UAS)-current state and perspectives -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Two-dimensional mapping and monitoring -- 4.2.2 Three-dimensional mapping and monitoring -- 4.2.3 Novel approaches -- 4.2.3.1 Combining or fusing 2D and 3D approaches -- 4.2.3.2 Combining with other techniques -- 4.2.3.3 Machine and deep learning -- 4.2.3.4 Open-source software -- 4.3 Vegetation mapping and monitoring, examples of the best practices -- 4.3.1 State-biodiversity mapping -- 4.3.1.1 Pilot case study 1. Monitoring the plant invasion using consumer camera and OBIA -- 4.3.1.2 Pilot case study 2. Detection of sea grass on Dutch sea coast using deep learning -- 4.3.1.3 Pilot study case 3. Temperate forest species mapping using multitemporal imagery.
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4.3.2 Structure-assessing stand complexity and biomass -- 4.3.2.1 Pilot case study 4. Fusion between terrestrial laser scanning and photogrammetric-UAS-derived point clouds -- 4.3.3 Status-assessing phenology and stress -- 4.3.4 Dynamics-monitoring the development -- 4.3.4.1 Pilot case study 5. Windthrow detection-comparison of LiDAR and photogrammetry -- 4.4 Challenges and perspectives -- References -- 5 Monitoring agricultural ecosystems -- 5.1 Introduction -- 5.1.1 Unmanned aerial system applications in precision agriculture -- 5.1.2 Plant function and performance: phenotypic crop traits -- 5.1.3 Mapping tree crop structure and condition -- 5.1.4 Biomass and yield mapping -- 5.1.5 Irrigation and evaporation mapping -- 5.1.6 Unmanned aerial system-based plant disease detection -- 5.1.7 Summary -- 5.2 Case study 1: multispectral unmanned aerial system-based mapping of tree crop structure and condition -- 5.2.1 Introduction -- 5.2.2 Materials and methods -- 5.2.3 Results and discussion -- 5.2.4 Summary -- 5.3 Case study 2: multispectral and thermal unmanned aerial system-based mapping of vegetation stress -- 5.3.1 Introduction -- 5.3.2 Material and methods -- 5.3.3 Results and discussion -- 5.3.4 Summary -- 5.4 General discussion -- 5.5 Summary -- References -- 3 Soil mapping -- 6 Mapping soil properties for unmanned aerial system-based environmental monitoring -- 6.1 Overview -- 6.2 Sampling and determining soil characteristics -- 6.2.1 Soil sampling and exploratory data analysis techniques -- 6.2.2 Key soil properties for environmental modeling -- 6.3 Soil transfer models-from pedotransfer to spectral transfer functions -- 6.3.1 Pedotransfer functions -- 6.3.2 Spectral transfer functions to estimate soil hydraulic properties -- 6.3.2.1 Adopting the spectral resolution from field and laboratory to a unmanned aerial system sensor.
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6.3.2.2 Development of a spectral transfer function -- 6.3.3 Deriving spatial information on soil hydraulic properties -- 6.4 Variability of soil parameters and spatial analysis -- 6.4.1 Scale and scaling in environmental sciences -- 6.4.1.1 The issue of scale -- 6.4.1.2 Spatial models of scale -- 6.4.2 Spatial statistics -- 6.4.3 Stochastic and geostatistical techniques for soil mapping including uncertainties -- 6.4.3.1 Using available tools -- 6.5 Summary and future perspectives -- References -- Further reading -- 7 Soil moisture monitoring using unmanned aerial system -- 7.1 Introduction -- 7.2 Theoretical background of soil moisture retrieval and downscaling methods -- 7.2.1 Thermal inertia model -- 7.2.2 Kubelka-Munk model and multilayer radiative transfer model of soil reflectance -- 7.2.3 Simplified temperature-vegetation triangle model -- 7.2.4 Random forest regression model -- 7.3 Data acquisition and preprocessing -- 7.3.1 Unmanned aerial system flights and data acquisition -- 7.3.2 Data preprocessing -- 7.4 Soil moisture data retrieval and downscaling -- 7.4.1 Surface soil moisture retrieval using thermal inertia method -- 7.4.2 Surface soil moisture retrieval using the Kubelka-Munk model -- 7.4.3 Soil moisture retrieval using the simplified temperature-vegetation triangle model -- 7.5 Soil moisture downscaling using random forest regression model -- 7.6 Discussion and conclusions -- References -- 4 River monitoring -- 8 Geometric correction and stabilization of images collected by UASs in river monitoring -- 8.1 Geometric distortion of images -- 8.1.1 Camera model -- 8.1.2 Radial lens distortions and their removal -- 8.1.3 Perspective distortions and displacements -- 8.2 Orthorectification -- 8.2.1 Approaches to orthorectification -- 8.2.1.1 Parametric and nonparametric orthorectification (resampling) -- 8.2.1.2 Polynomial rectification.
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8.2.1.3 Projective rectification -- 8.2.1.4 Differential rectification -- 8.2.1.5 Orthomosaics -- 8.2.2 Orthorectification for image velocimetry in river flow observations -- 8.3 Image stabilization -- 8.3.1 UAS motion -- 8.3.2 Impact on image velocimetry -- 8.3.3 Image stabilization workflow -- 8.4 Advices and good practices -- 8.4.1 Orthorectification -- 8.4.2 Stabilization -- 8.4.3 Software -- References -- 9 River flow monitoring with unmanned aerial system -- 9.1 Introduction -- 9.2 General workflow of river flow monitoring with UAS -- 9.3 Best practices of data acquisition -- 9.3.1 General considerations concerning data acquisition -- 9.3.2 Traceable features -- 9.3.3 Reasons to use ground control points -- 9.3.4 Flight height -- 9.3.5 Duration of the video for image velocimetry analysis -- 9.3.6 Metadata -- 9.4 Data preprocessing -- 9.4.1 Sampling image frequency and resolution -- 9.4.2 Image stabilization, geo-referencing, and correction of geometric distortions -- 9.4.3 Image enhancement -- 9.4.4 Quantification of seeding characteristics -- 9.4.5 Selection of the optimal footage portion to analyze -- 9.4.6 Practical recommendations for data acquisition -- 9.4.7 Practical recommendations with respect to image preprocessing -- 9.5 Data processing -- 9.5.1 Methods for image velocimetry -- 9.5.2 Tools for image velocimetry -- 9.5.3 Practical recommendations for method and tool selection -- 9.6 Data postprocessing -- 9.6.1 Outlier detection and filtering -- 9.6.2 Interpolation, smoothing, and aggregation -- 9.6.3 Recommendations with regards to data postprocessing -- 9.6.3.1 Visualization of velocimetry results and data exchange -- 9.7 Validation of image velocimetry results -- 9.7.1 Depth-averaged velocities -- 9.7.2 Discharge -- 9.7.3 Flow patterns -- 9.8 Limitations of image velocimetry methods and future perspectives -- 9.9 Hands-on experience.
Weitere Ausg.:
Print version: Manfreda, Salvatore Unmanned Aerial Systems for Monitoring Soil, Vegetation, and Riverine Environments San Diego : Elsevier,c2023 ISBN 9780323852838
Sprache:
Englisch
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