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  • 1
    Online Resource
    Online Resource
    Saint Louis :Elsevier,
    Keywords: Earth sciences--Remote sensing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (448 pages)
    Edition: 1st ed.
    ISBN: 9780128030318
    DDC: 550.285
    Language: English
    Note: Front Cover -- Sensitivity Analysis in Earth Observation Modelling -- Sensitivity Analysis in Earth Observation Modelling -- Copyright -- Dedication -- Contents -- List of Contributors -- Preface -- ABOUT THE COVER -- 1 - INTRODUCTION TO SA IN EARTH OBSERVATION (EO) -- 1 - OVERVIEW OF SENSITIVITY ANALYSIS METHODS IN EARTH OBSERVATION MODELING -- 1. INTRODUCTION -- 1.1 DEFINING THE MODEL OUTPUTS AND INPUTS FOR SENSITIVITY ANALYSIS -- 1.1.1 Defining Factor (or Parametric) Uncertainty -- 2. LOCAL SENSITIVITY ANALYSIS -- 2.1 CORRELATION ANALYSIS -- 2.2 REGRESSION ANALYSIS -- 3. GLOBAL SENSITIVITY ANALYSIS -- 3.1 ONE-AT-A-TIME SENSITIVITY ANALYSIS METHODS -- 3.2 THE MORRIS METHOD FOR FACTOR SCREENING -- 3.3 VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.4 SAMPLING METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.4.1 Random Sampling -- 3.4.2 Stratified Sampling and the Latin Hypercube -- 3.4.3 Sampling for Sensitivity Indices -- 3.5 SURROGATE MODELS FOR GLOBAL SENSITIVITY ANALYSIS -- 3.5.1 Generalized Linear Modeling -- 3.5.2 Neural Networks -- 3.5.3 Direct Sensitivity Analysis of Surrogate Models -- 3.6 POLYNOMIAL CHAOS -- 3.7 GAUSSIAN PROCESS AND BAYES LINEAR EMULATION -- 4. GRAPHICAL METHODS FOR GLOBAL SENSITIVITY ANALYSIS -- 4.1 SCATTER PLOTS -- 4.2 PLOTTING THE RESPONSE SURFACE -- 4.3 PLOTTING THE SENSITIVITY INDICES -- 5. CONCLUSIONS -- REFERENCES -- 2 - MODEL INPUT DATA UNCERTAINTY AND ITS POTENTIAL IMPACT ON SOIL PROPERTIES -- 1. INTRODUCTION -- 2. A WORLD OF MODELS - HOW CAN THEY BE CLASSIFIED? -- 3. CAN WE TRUST MODELS? - MODEL ACCURACY AND THEIR SENSITIVITY TO INPUT DATA UNCERTAINTY -- 4. SELECTING THE MOST APPROPRIATE MODEL -- 5. WHY AND HOW TO ACCOUNT FOR MODELING UNCERTAINTIES CAUSED BY DIFFERENT INPUT DATA SOURCES -- 6. ASSESSING SENSITIVITY OF ENVIRONMENTAL MODELS. , 7. HOW SOIL TEXTURE MEASURED WITH VISIBLE-NEAR-INFRARED SPECTROSCOPY AFFECTS HYDROLOGICAL MODELING: A CASE STUDY -- 7.1 STUDY SITES AND INSTRUMENTS -- 7.2 SOIL SAMPLES -- 7.3 CHEMOMETRICS -- 7.4 IMPACT OF CHEMOMETRIC METHOD ON SOIL PREDICTION -- 7.5 DIFFERENT INSTRUMENTS, DIFFERENT SOIL PREDICTIONS? WHAT WAS FINALLY THE BEST SOIL PREDICTION ACCURACY? -- 7.6 WHAT DOES THIS FINALLY MEAN FOR OUR ENVIRONMENTAL MODELING? -- 8. WHAT DID WE LEARN? -- REFERENCES -- 2 - LOCAL SA METHODS: CASE STUDIES -- 3 - LOCAL SENSITIVITY ANALYSIS OF THE LANDSOIL EROSION MODEL APPLIED TO A VIRTUAL CATCHMENT -- 1. INTRODUCTION -- 2. MATERIALS AND METHODS -- 2.1 MODEL DESCRIPTION -- 2.2 SENSITIVITY ANALYSIS -- 2.2.1 Parameters -- 2.2.2 Virtual Catchment -- 2.2.3 Sensitivity Calculation -- 3. RESULTS AND DISCUSSION -- 3.1 LINEAR HILLSLOPE -- 3.1.1 Aggregated Parameters -- 3.2 COMPLEX HILLSLOPES -- 4. CONCLUDING REMARKS -- Acknowledgments -- REFERENCES -- 4 - SENSITIVITY OF VEGETATION PHENOLOGICAL PARAMETERS: FROM SATELLITE SENSORS TO SPATIAL RESOLUTION AND TEMPORAL CO ... -- 1. INTRODUCTION -- 2. MONITORING VEGETATION PHENOLOGY -- 3. SENSITIVITY ANALYSIS -- 4. SENSITIVITY OF REMOTELY SENSED PHENOLOGICAL PARAMETERS -- 4.1 SATELLITE SENSOR -- 4.2 VEGETATION INDEX -- 4.3 SPATIAL RESOLUTION -- 4.4 COMPOSITE PERIOD, SMOOTHING, AND FILTERING -- 4.4.1 Composite Period -- 4.4.2 Smoothing Techniques -- 5. CASE STUDY -- 5.1 STUDY AREA -- 5.2 DATA AND METHODOLOGY -- 5.3 RESULTS AND DISCUSSION -- 6. CONCLUSION -- REFERENCES -- 5 - RADAR RAINFALL SENSITIVITY ANALYSIS USING MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR∗ -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 STUDY AREA AND DATA SOURCES -- 2.2 THE MULTIVARIATE DISTRIBUTED ENSEMBLE GENERATOR -- 2.3 THE XINANJIANG MODEL -- 3. METHODOLOGY -- 3.1 EXPERIMENTAL DESIGN -- 3.2 VERIFICATION METHOD -- 4. RESULTS AND DISCUSSION. , 4.1 IMPLEMENTATION OF ENSEMBLE FLOW GENERATION -- 4.2 IMPACT OF ERROR DISTRIBUTION ON MODEL OUTPUT -- 4.3 IMPACT OF SPATIOTEMPORAL DEPENDENCE ON MODEL OUTPUT -- 5. CONCLUSIONS -- REFERENCES -- 6 - FIELD-SCALE SENSITIVITY OF VEGETATION DISCRIMINATION TO HYPERSPECTRAL REFLECTANCE AND COUPLED STATISTICS -- 1. INTRODUCTION -- 2. BACKGROUND ON SPECTRAL DISCRIMINATION OF VEGETATION -- 2.1 PARAMETRIC VERSUS NONPARAMETRIC STATISTICAL TESTS -- 2.1.1 Other Discrimination Methods -- 2.2 UNALTERED VERSUS PROCESSED HYPERSPECTRAL REFLECTANCE -- 2.3 CASE STUDIES FOR EFFECTS OF TYPE OF REFLECTANCE AND STATISTICAL TEST ON THE VEGETATION DISCRIMINATION RESULTS -- 3. SENSITIVITY OF SPECTRAL DISCRIMINATION OF VEGETATION TO THE TYPE OF REFLECTANCE AND STATISTICAL TEST -- 3.1 HYPERSPECTRAL DATA AND METHOD DESCRIPTION -- 3.2 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO REFLECTANCE TYPE AND STATISTICAL METHOD -- 3.3 SENSITIVITY OF VEGETATION SPECTRAL DISCRIMINATION TO THE NUMBER OF OBSERVATIONS -- 4. FINAL REMARKS -- REFERENCES -- 3 - GLOBAL (OR VARIANCE)-BASED SA METHODS: CASE STUDIES -- 7 - A MULTIMETHOD GLOBAL SENSITIVITY ANALYSIS APPROACH TO SUPPORT THE CALIBRATION AND EVALUATION OF LAND SURFACE MODELS -- 1. INTRODUCTION -- 2. MODEL AND METHODS -- 2.1 REGIONAL SENSITIVITY ANALYSIS -- 2.2 VARIANCE-BASED SENSITIVITY ANALYSIS -- 2.3 THE PAWN DENSITY-BASED METHOD -- 2.4 THE JULES MODEL -- 2.5 THE SANTA RITA CREOSOTE STUDY SITE -- 2.6 EXPERIMENTAL SETUP: DEFINITION OF INPUT FACTORS AND OUTPUTS -- 2.7 DEFINITION OF THE RANGE OF VARIATION OF THE INPUT FACTORS -- 3. RESULTS -- 3.1 RESULTS OF REGIONAL SENSITIVITY ANALYSIS -- 3.2 RESULTS OF VARIANCE-BASED SENSITIVITY ANALYSIS -- 3.3 RESULTS OF PAWN -- 3.4 OVERALL SENSITIVITY ASSESSMENT FROM THE MULTIMETHOD APPROACH -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES. , 8 - GLOBAL SENSITIVITY ANALYSIS FOR SUPPORTING HISTORY MATCHING OF GEOMECHANICAL RESERVOIR MODELS USING SATELLITE I ... -- 1. INTRODUCTION -- 2. CASE STUDY -- 2.1 SURFACE DEFORMATION AT THE KB-501 WELL OF IN SALAH SITE -- 2.2 THREE-DIMENSIONAL HYDROMECHANICAL MODEL OF KB-501 -- 3. METHODS -- 3.1 VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS -- 3.2 PRINCIPLES OF METAMODELING -- 3.3 INTRODUCTION TO KRIGING METAMODEL -- 3.4 DESCRIPTION OF THE WORKFLOW -- 4. APPLICATION -- 4.1 REDUCING THE NUMBER OF UNCERTAINTY INPUT PARAMETERS -- 4.2 CALIBRATION OF THE RESERVOIR YOUNG'S MODULUS -- SUMMARY AND FUTURE WORK -- Acknowledgments -- REFERENCES -- 9 - ARTIFICIAL NEURAL NETWORKS FOR SPECTRAL SENSITIVITY ANALYSIS TO OPTIMIZE INVERSION ALGORITHMS FOR SATELLITE-BAS ... -- 1. INTRODUCTION -- 2. DATA AND METHODS -- 2.1 ARTIFICIAL NEURAL NETWORKS: OVERVIEW -- 2.1.1 Artificial Neural Networks for the Inversion of Satellite Measurements of Spectral Radiation for the Observation of the Ear ... -- 2.2 NEURAL NETWORK-BASED TECHNIQUES TO REDUCE THE INPUT VECTOR DIMENSIONALITY -- 2.2.1 Extended Pruning -- 2.2.2 Autoassociative Neural Networks -- 2.3 SAMPLE DATA SET -- 2.3.1 Sulfate Aerosols and Their Extinction Coefficient -- 2.3.2 Thermal Infrared Satellite Pseudo-Observations -- 3. RESULTS -- 3.1 TRAINING AND TESTING THE MAXIMUM DIMENSIONALITY NEURAL NETWORK -- 3.2 SELECTION OF THE INPUT WAVELENGTHS AND SPECTRAL SENSITIVITY ANALYSIS -- 3.3 COMPARING THE PERFORMANCES OF REDUCED DIMENSIONALITY NEURAL NETWORK -- 4. CONCLUSIONS -- Acknowledgments -- REFERENCES -- 10 - GLOBAL SENSITIVITY ANALYSIS FOR UNCERTAIN PARAMETERS, MODELS, AND SCENARIOS -- 1. INTRODUCTION -- 2. MORRIS METHOD -- 3. SOBOL' METHOD -- 3.1 FIRST-ORDER AND TOTAL-EFFECT SENSITIVITY INDICES -- 3.2 MONTE CARLO IMPLEMENTATION AND TWO APPROXIMATION METHODS. , 3.2.1 Sparse-Grid Collocation for Evaluating Mean and Variance -- 3.2.2 Distributed Evaluation of Local Sensitivity Analysis -- 4. SOBOL' METHOD FOR MULTIPLE MODELS AND SCENARIOS -- 4.1 HIERARCHICAL FRAMEWORK FOR UNCERTAINTY QUANTIFICATION -- 4.2 GLOBAL SENSITIVITY INDICES FOR SINGLE MODEL AND SINGLE SCENARIO -- 4.3 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS BUT SINGLE SCENARIO -- 4.4 GLOBAL SENSITIVITY INDICES FOR MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5. SYNTHETIC STUDY WITH MULTIPLE SCENARIOS AND MODELS -- 5.1 SYNTHETIC CASE OF GROUNDWATER REACTIVE TRANSPORT MODELING -- 5.2 UNCERTAIN SCENARIOS, MODELS, AND PARAMETERS -- 5.3 TOTAL SENSITIVITY INDEX FOR HEAD UNDER INDIVIDUAL MODELS AND SCENARIOS -- 5.4 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS BUT INDIVIDUAL SCENARIOS -- 5.5 TOTAL SENSITIVITY INDEX FOR HEAD UNDER MULTIPLE MODELS AND MULTIPLE SCENARIOS -- 5.6 TOTAL SENSITIVITY INDEX FOR ETHENE CONCENTRATION -- 6. USING GLOBAL SENSITIVITY ANALYSIS FOR SATELLITE DATA AND MODELS -- 7. CONCLUSIONS AND PERSPECTIVES -- Acknowledgments -- REFERENCES -- 4 - OTHER SA METHODS: CASE STUDIES -- 11 - SENSITIVITY AND UNCERTAINTY ANALYSES FOR STOCHASTIC FLOOD HAZARD SIMULATION -- 1. INTRODUCTION -- 2. BASIC PRINCIPLES OF STOCHASTIC APPROACH TO FLOOD HAZARD -- 2.1 STOCHASTIC SIMULATION OF RESERVOIR INFLOWS -- 2.1.1 Storm Seasonality -- 2.1.2 Precipitation Magnitude-Frequency Relationship -- 2.1.3 Temporal and Spatial Distribution of Storms -- 2.1.4 Air Temperature and Freezing Level Temporal Patterns -- 2.1.5 The 1000-mb Air Temperature Simulation -- 2.1.6 Air Temperature Lapse Rates -- 2.1.7 Freezing Level -- 2.1.8 Watershed Model Antecedent Conditions Sampling -- 2.1.9 Initial Reservoir Level -- 2.2 SIMULATION OF RESERVOIR OPERATION-FLOOD ROUTING -- 2.3 SIMULATION PROCEDURE. , 3. UNCERTAINTY ASSOCIATED WITH STOCHASTICALLY DERIVED FLOOD QUANTILES.
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  • 2
    Online Resource
    Online Resource
    San Diego :Elsevier,
    Keywords: Soil moisture. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (441 pages)
    Edition: 1st ed.
    ISBN: 9780128033890
    Language: English
    Note: Front Cover -- Satellite Soil Moisture Retrieval: Techniques and Applications -- Copyright -- Dedication -- Contents -- List of Contributors -- Author Biographies -- Preface -- Acknowledgments -- About the Cover -- Section I: Introduction -- Chapter 1: Soil Moisture from Space: Techniques and Limitations -- 1. Introduction -- 2. Means of Measuring Soil Moisture -- 2.1. Remotely Sensed Soil Moisture, The Main Approaches -- 2.2. Microwave as a Tool for Soil Moisture Monitoring: Current Status -- 3. Satellite Missions -- 4. Soil Moisture Retrieval From Space Using Passive Microwaves -- 4.1. Surface Soil Moisture -- 4.2. Root-Zone Soil Moisture -- 5. Way Forward -- 6. Caveats -- 7. Conclusions and Perspectives -- References -- Chapter 2: Available Data Sets and Satellites for Terrestrial Soil Moisture Estimation -- 1. Introduction -- 2. In Situ Data Sets for Soil Moisture -- 2.1. International Soil Moisture Network -- 2.2. Field Campaigns -- 2.2.1. Soil Moisture Experiments Series -- 2.2.1.1. Soil Moisture Experiment 2002 -- 2.2.1.2. Soil Moisture Experiment 2003 -- 2.2.1.3. Soil Moisture Experiment 2004 -- 2.2.1.4. Soil Moisture Experiment 2005 -- 2.2.2. Canadian Experiment for Soil Moisture in 2010 -- 2.2.3. Soil Moisture Active Passive Validation Experiment -- 2.2.3.1. SMAPVEX08 -- 2.2.3.2. SMAPVEX12 -- 2.2.4. Soil Moisture Active Passive Experiments -- 2.3. FLUXNET sites -- 3. Satellite Data Sets for Soil Moisture -- 3.1. The Scanning Multichannel Microwave Radiometer (SMMR) -- 3.2. Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E/2) -- 3.3. Advanced Scatterometer (ASCAT) -- 3.4. Soil Moisture and Ocean Salinity (SMOS) -- 3.5. Soil Moisture Active and Passive (SMAP) Mission -- 4. Conclusion -- References -- Section II: Optical and Infrared Techniques & -- Synergies Between them. , Chapter 3: Soil Moisture Retrievals Using Optical/TIR Methods -- 1. Introduction -- 2. Optical/TIR Model History and Concept -- 2.1. History -- 2.2. The Ts/VI Concept -- 3. Optical/TIR Models Used for SM Estimation -- 3.1. Direct Estimation of SM From Ts/VI Space -- 3.2. Models Based on Ts/VI and Empirical Equations -- 4. Case Study: Estimation of SM Using Optical/TIR RS in the Canadian Prairies -- 4.1. Introduction -- 4.2. Materials and Methods -- 4.2.1. Study Area and Data -- 4.2.2. Methodology -- 4.3. Results -- 4.3.1. Comparing EF Estimations Retrieved From Three Different Approaches -- 4.3.2. SM Estimation From Evaporative Fraction -- 4.3.3. Correlations Between Estimated SM and Field Data -- 5. Summary and Future Outlook -- References -- Chapter 4: Optical/Thermal-Based Techniques for Subsurface Soil Moisture Estimation -- 1. Introduction -- 2. Methodology -- 2.1. Study Area -- 2.2. Satellite Data, Estimation of TVDI, and Measurements -- 3. Results and Discussion -- 3.1. TVDI Parameters -- 3.2. Comparison of TVDI and Subsurface Soil Moisture Measurements -- 4. Conclusions -- References -- Chapter 5: Spatiotemporal Estimates of Surface Soil Moisture from Space Using the Ts/VI Feature Space -- 1. Introduction -- 2. The Ts/VI Domain -- 3. Experimental Set Up and Data Sets -- 3.1. CarboEurope In Situ Measurements -- 3.2. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Imagery -- 3.3. The Advanced Along Track Scanning Radiometer (AATSR) Imagery -- 3.4. The SimSphere Land Biosphere Model -- 4. Methodology -- 4.1. Preprocessing -- 4.2. ``Triangle´´ Implementation -- 4.3. Coupling EO With the SVAT Model to Retrieve SMC -- 5. Results -- 6. Discussion -- 7. Conclusions -- Acknowledgments -- References -- Chapter 6: Spatial Downscaling of Passive Microwave Data With Visible-to-Infrared Information for High-Resolution Soil Mo. , 1. Introduction -- 2. A Semiempirical Model to Capture the Synergy of Passive Microwaves With Optical Data at Different Spatial Scales -- 3. High-Resolution Soil Moisture Mapping From Space -- 3.1. Long-Term Validation Over the Central Part of the Duero Basin -- 3.2. Exploring the Use of SWIR-Based Vegetation Indices to Disaggregate SMOS Observations to 500m -- 4. Airborne Field Experiments -- 4.1. Airborne Platform for Simultaneous Thermal, VNIR Hyperspectral and Microwave L-Band Acquisions: Proof-of-Concept and... -- 4.2. Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation -- 5. Future Lines and Recommendations -- Acknowledgments -- References -- Section III: Microwave Soil Moisture Retrieval Techniques -- Chapter 7: Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach: Theory and Applications -- 1. Introduction -- 2. Radiative Transfer Equation -- 2.1. Tau-Omega Model -- 2.2. Effective Temperature, Single Dispersion and Vegetation Optical Depth -- 2.3. A Combined Optical-Passive Microwave Approach -- 2.4. Case Study-Optical Passive Microwave at in-situ level -- 2.5. Case Study-Optical Passive Microwave at Regional Scale -- 2.5.1. Study Area -- 2.5.2. Data -- 2.5.3. Results -- 2.5.4. Discussion -- 2.6. Toward a Thermal Infrared Contribution in the Optical-Passive Microwave Approach -- 3. Conclusions -- Acknowledgments -- References -- Chapter 8: Nonparametric Model for the Retrieval of Soil Moisture by Microwave Remote Sensing -- 1. Introduction -- 2. Material and Methods -- 2.1. Instrumentation Setup and Observations -- 2.2. Radial Basis Function Artificial Neural Network -- 2.3. Performance Indices -- 3. Results and Discussion -- 3.1. Assessment of Data Sets -- 3.2. Estimation of Soil Moisture Using the RBFANN -- 4. Conclusions -- References. , Chapter 9: Temperature-Dependent Spectroscopic Dielectric Model at 0.05-16 GHz for a Thawed and Frozen Alaskan Organic Soil -- 1. Introduction -- 2. Soil Samples and Measurement Procedures -- 3. Concept of a Multirelaxation Spectroscopic Dielectric Model -- 4. Retrieving the Parameters of the Multirelaxation Spectroscopic Dielectric Model -- 5. The Temperature-Dependent Multirelaxation Spectroscopic Dielectric Model -- 6. Evaluation of the TD MRSDM -- 7. Conclusions -- References -- Chapter 10: Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation -- 1. Introduction -- 1.1. Measurement Spatial Resolution -- 1.2. Soil Moisture Sensitivity and Estimation Accuracy -- 2. SR CAP Soil Moisture Retrieval -- 3. MR CAP Soil Moisture Retrieval -- 3.1. Multi-Temporal and Multi-Scale Method -- 3.2. Probabilistic and Machine Learning Techniques -- 4. Forward Electromagnetic Scattering and Emission Model Considerations -- 5. Further Discussions -- References -- Chapter 11: Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European ... -- 1. Introduction -- 2. Materials and Methods -- 2.1. In Situ Soil Moisture Observations -- 2.2. ECV Soil Moisture Observations -- 2.3. ASAR Soil Moisture Observations -- 2.4. Characterization of Errors -- 3. Results and Discussion -- 3.1. Time Series Temporal Analysis -- 3.2. Seasonal Analysis -- 4. Conclusions -- Acknowledgments -- References -- Section IV: Advanced Applications of Soil Moisture -- Chapter 12: Use of Satellite Soil Moisture Products for the Operational Mitigation of Landslides Risk in Central Italy -- 1. Introduction -- 2. PRESSCA Early Warning System -- 3. Case Study and Data Sets -- 3.1. Study Area and Ground Meteorological Observations -- 3.2. Satellite Soil Moisture Observations -- 4. Results and Discussion. , 4.1. Comparison Between Satellite, In Situ, and Modeled Soil Moisture Data -- 4.2. Impact of Soil Moisture Condition on Landslide Hazard -- 5. Conclusions and Future Perspectives -- Acknowledgments -- References -- Chapter 13: Remotely Sensed Soil Moisture as a Key Variable in Wildfires Prevention Services: Towards New Prediction Tool... -- 1. Introduction -- 2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 2.1. Remote Sensing of the Earth's Soil Moisture -- 2.2. Remotely Sensed Soil Moisture, Climate Change, and Fire Risk -- 3. The Role of Soil Moisture in Forest Fires -- 3.1. Droughts and High Temperatures Lead to Extreme Forest Fires Events -- 3.2. Dead Fuels Moisture Is a Key Variable in Forest Fire Risk Indices -- 4. Linking Remotely Sensed Soil Moisture With Forest Fires Ignition and Propagation -- 5. Fire Risk Assessment in the Iberian Peninsula Using SMOS Data -- 5.1. New Fire Risk Maps Over the Iberian Peninsula Based on SMOS Data -- 5.2. Fire Risk Maps Availability and Operational Applications -- 6. Conclusions -- Acknowledgments -- References -- Chapter 14: Integrative Use of Near-Surface Satellite Soil Moisture and Precipitation for Estimation of Improved Irrigati... -- 1. Introduction -- 2. Material and Methods -- 2.1. Study Area -- 2.2. Ground Validation Data -- 2.3. Satellite Data -- 2.4. Numerical Modeling -- 2.4.1. Initial and Boundary Conditions -- 2.4.2. Soil Parameters -- 2.5. Evaluation Criteria -- 3. Results and Discussion -- 3.1. Comparison of TRMM Rainfall With Ground-Based Rainfall Measurements -- 3.2. Soil Hydraulic Parameters -- 3.3. Soil Moisture Calibration and Validation -- 4. Conclusions -- Acknowledgments -- References -- Chapter 15: A Comparative Study on SMOS and NLDAS-2 Soil Moistures Over a Hydrological Basin-With Continental Climate -- 1. Introduction -- 2. Data and Methodology. , 2.1. Study Area and Data Sets.
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  • 3
    Online Resource
    Online Resource
    San Diego :Elsevier,
    Keywords: Earth sciences-Remote sensing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (468 pages)
    Edition: 1st ed.
    ISBN: 9780128196939
    DDC: 550.28
    Language: English
    Note: Front Cover -- GPS and GNSS Technology in Geosciences -- GPS and GNSS Technology in Geosciences -- Contents -- Contributors -- Foreword -- I - General introduction to GPS/GNSS technology -- 1 - Introduction to GPS/GNSS technology -- 1. Background -- 2. Major segments of GPS -- 3. Functioning of GPS -- 3.1 Pseudorange -- 3.2 Carrier-phase measurement -- 3.3 GPS broadcast message, ephemeris, and almanac -- 4. GPS errors -- 4.1 Satellite and receiver clock errors -- 4.2 Multipath error -- 4.3 Ionospheric delay -- 4.4 Tropospheric delay -- 4.5 GPS ephemeris errors -- 4.6 Other limitations -- 5. GPS technologies -- 6. Global Navigation Satellite System -- 6.1 NAVSTAR -- 6.2 GLONASS -- 6.3 Galileo -- 6.4 Compass/BeiDou -- 6.5 Quasi-Zenith Satellite System -- 6.6 IRNSS/NavIC -- 7. Applications of GPS/GNSS -- 7.1 Navigation -- 7.2 Military services -- 7.3 Geodetic control surveys -- 7.4 Cadastral survey -- 7.5 Photogrammetry, remote sensing, and GIS -- 7.6 Ground truthing and validation -- 7.7 Disaster, response, and mitigation -- 7.8 Integration of GPS with mobile and google maps and GPS -- 8. Conclusions -- References -- Further reading -- 2 - Fundamentals of structural and functional organization of GNSS -- 1. GNSS structural organization -- 1.1 Introduction -- 1.2 Some notation and definitions -- 1.3 GNSS global coverage -- 1.4 GNSS regional coverage -- 1.5 Three main GNSS segments -- 1.6 Navigation using one satellite -- 1.7 2D navigation using two satellites -- 1.8 2D navigation using three satellite -- 1.8.1 The main idea of an iterative algorithm to compensate for the systematic error Δρ -- 1.8.2 Inaccurate vehicle clock synchronization -- 1.9 3D GNSS using N satellites -- 1.10 Summary and conclusions on the topic structural organization of GNSS -- 2. GNSS functional organization -- 2.1 GNSS functional principle -- 2.1.1 Systems of coordinates. , 2.1.2 Time systems -- 2.1.3 Factors affecting accuracy -- 2.1.4 GNSS accuracy improvement -- 2.2 GNSS signal structure, encoding, and frequency -- 2.3 Pseudoranges -- 2.4 GNSS positioning -- 2.5 Differential GNSS architecture -- 2.5.1 Local Area Differential GNSS positioning -- 2.5.2 Regional Area Differential GNSS positioning -- 2.5.3 Wide Area Differential GNSS positioning -- 2.6 Summary and conclusions on the topic functional organization of GNSS -- References -- References additional -- 3 - Security of GNSS -- 1. Introduction -- 2. GNSS interference -- 3. GNSS jamming -- 4. GNSS self-jamming -- 5. GNSS meaconing -- 6. GNSS spoofing -- 6.1 The cloud-based GNSS positioning -- 7. The cloud-based GNSS spoofing detection -- 8. Some notation and definitions for detection of spoofing -- 8.1 Dual-antenna spoofing detector -- 8.2 Measuring the distance between antennas in normal navigation mode -- 8.3 Measurement the distance between antennas in spoofing mode -- 8.3.1 The decisive rule 1 -- 8.4 Spoofing detection by the dispersion of the pseudorange difference of two antennas -- 8.4.1 The decisive rule 2 -- 8.4.2 Discussion of the decisive rules -- 8.5 Single-antenna spoofing detector -- 8.6 Measuring the distance between two positions of single antenna in normal mode -- 8.7 Measurement of spacing between two positions of single antenna in spoofing mode -- 8.8 The decisive rule -- 9. GNSS spoofer DIY (Do It Yourself) -- 10. GNSS self-spoofing -- 11. Briefly about antispoofing -- 12. Summary and conclusions -- 13. Postscript -- References -- II - GPS/GNSS concept and algorithms -- 4 - GNSS multipath errors and mitigation techniques -- 1. Introduction -- 2. Multipath errors and their characteristics -- 2.1 Code multipath error -- 2.2 Phase multipath error, SNR/CNR, and their relationship -- 2.2.1 Case 1: no (constructive) carrier-phase multipath effect. , 2.2.2 Case 2: maximum carrier-phase multipath effect -- 2.2.3 Case 3: no (destructive) carrier-phase multipath effect -- 2.3 Characteristics of multipath errors -- 3. Multipath mitigation techniques -- 3.1 Hardware-based multipath mitigation techniques -- 3.2 Software-based multipath mitigation techniques -- 3.2.1 Stochastic modeling -- 3.2.1.1 Elevation angle of satellite -- 3.2.1.2 Signal-to-noise ratio or carrier-to-noise ratio -- 3.2.2 Carrier-phase multipath reconstruction using the correlations of carrier-phase multipath with SNR -- 3.2.3 Sidereal day-to-day repeatability analysis -- 3.2.4 Antenna array -- 3.2.5 Ray tracing method -- 3.2.6 Comparison and discussion on software-based multipath mitigation techniques -- 4. Summary -- References -- 5 - Antenna technology for GNSS -- 1. Introduction -- 1.1 Line-of-Sight and reflected signals -- 1.2 Circular polarization-mitigating the multipath -- 2. Key antenna parameters for GNSS receivers -- 2.1 Radiation pattern and antenna gain -- 2.2 Axial ratio -- 2.3 Performance versus cost -- 3. Antennas for GNSS -- 3.1 Microstrip patch antennas -- 3.2 Sequentially rotated arrays -- 3.3 3D antenna structures: quadrifilar helix and electromagnetic dipole -- 3.4 Omnidirectional GNSS antennas -- 3.5 Choke rings, EBG, and low-angle signals -- 4. Final remarks -- References -- 6 - Probing the tropospheric water vapor using GPS -- 1. Introduction -- 1.1 Motivation for water vapor study -- 1.2 Navigation satellite system and GPS -- 2. GPS error sources -- 2.1 Atmospheric errors -- 3. Water vapor retrieval using GPS -- 3.1 Network GPS data processing -- 3.2 PPP GPS data processing -- 3.3 GPS datasets used for perceptible water vapor estimation -- 3.4 Computation of PWV from ZTD -- 3.5 Future scope and challenges -- 4. Conclusions -- Acknowledgment -- References -- 7 - Probing the upper atmosphere using GPS. , 1. Introduction -- 1.1 Quiescent ionosphere -- 1.2 Geomagnetic storms -- 1.3 Equatorial spread-F -- 1.4 Solar eclipse -- 1.5 Earthquake -- 2. Conclusions -- 3. Recommendations -- Acknowledgment -- References -- 8 - Video-based navigation using convolutional neural networks -- 1. Introduction -- 2. Proposed Super Navigation method -- 2.1 Navigation problem as an image classification problem -- 2.2 Collecting the data -- 2.3 Generating the Super Navigation image -- 2.3.1 Super Navigation image design option: number of frames -- 2.3.2 Super Navigation image design option: frame selection -- 2.4 Selecting the CNN model -- 2.5 Training the CNN model -- 2.6 Inferencing to predict the navigation direction -- 3. Implementation on low-power CNN accelerators -- 3.1 GnetFC model -- 4. Experimental results -- 4.1 Indoor navigation -- 4.1.1 Data collection and labeling -- 4.1.2 Generating Super Navigation images -- 4.1.3 Model training and accuracy comparison -- 4.2 Outdoor navigation -- 4.2.1 Data collection and labeling -- 4.2.2 Generating Super Navigation images -- 4.2.3 Model training and accuracy comparison -- 5. Conclusion and future work -- References -- III - Applications of GPS/GNSS -- 9 - GNSS monitoring natural and anthropogenic phenomena -- 1. Introduction -- 2. Earthquakes -- 3. Landslides monitoring -- 4. Crustal deformations -- 5. Challenges -- 6. Summary -- References -- 10 - Environmental sensing: a review of approaches using GPS/GNSS -- 1. Introduction -- 2. Data collection -- 2.1 Smartphones as sensors -- 2.2 Specialized devices -- 3. Data organization/analysis -- 4. Data visualization -- 5. Applications -- 5.1 Water and soil monitoring/pollution -- 5.2 Air monitoring/pollution -- 5.3 Noise monitoring/pollution -- 6. Discussion and concluding remarks -- References -- 11 - GNSS-derived data for the study of the ionosphere -- 1. The ionosphere. , 2. Ionosphere monitoring -- 3. Ionosphere modeling -- 4. TEC from GNSS -- 5. GNSS TEC for ionosphere studies -- 6. Final remarks -- Acknowledgement -- References -- 12 - Automatic pattern recognition and GPS/GNSS technology in marine digital terrain model -- 1. Introduction -- 2. Datasets description -- 3. Methodology implementation -- 4. The application of pattern recognition in marine pollution and structural studies -- 5. Conclusions -- Acknowledgment -- References -- 13 - Monitoring ionospheric scintillations with GNSS in South America: scope, results, and challenges -- 1. Introduction -- 1.1 Monitoring networks -- 2. Aspects of the climatology of ionospheric scintillations and their effects on GNSS-based applications in South America -- 2.1 Aspects of the climatology of scintillations in South America -- 2.1.1 Summary remarks of the climatology of scintillations in South America -- 2.2 Experimental setup to demonstrate effects of scintillations on field applications -- 3. Statistical modeling of amplitude scintillation -- 3.1 Discussions on application of statistical modeling of amplitude scintillation to mitigate effects of scintillations on GNSS ... -- 4. Low-cost instrumentation for ionospheric plasma bubbles monitoring -- 4.1 Experimental validation -- 4.2 Other initiatives for low-cost receiver design -- 5. Discussion -- 6. Final remarks & -- future outlook -- Acknowledgments -- References -- 14 - The versatility of GNSS observations in hydrological studies -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area -- 2.2 Datasets -- 2.2.1 GNSS datasets -- 2.2.2 Global Land Data Assimilation System -- 2.2.3 GRACE mascon solution -- 2.2.4 Global Precipitation Climatology Centre -- 2.3 Methodology -- 2.3.1 Hydrologic loading -- 2.3.2 GNSS-derived integrated water vapor -- 2.3.3 GNSS-based drought indicator -- 3. Results. , 3.1 Land water storage prediction using observed radial displacements.
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  • 4
    Online Resource
    Online Resource
    San Diego :Elsevier Science & Technology,
    Keywords: Water-supply, Agricultural-Management. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (492 pages)
    Edition: 1st ed.
    ISBN: 9780128123638
    DDC: 333.91317
    Language: English
    Note: Front Cover -- Agricultural Water Management -- Agricultural Water Management -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- I - Introduction -- 1 - Concepts and methodologies for agricultural water management -- 1. Introduction -- 2. Irrigation water management techniques -- 2.1 HYDRA -- 2.2 Decision support system for agrotechnology transfer (DSSAT) -- 2.3 Hydrus -- 2.4 MOPECO model -- 2.5 Linear optimisation model for efficient use of irrigation water -- 2.6 A network flow model for irrigation water management -- 2.6.1 Minimum cost network flow problem -- 2.7 Bayesian data-driven models for irrigation water management -- 2.8 CropSyst model -- 2.9 Water balance method -- 2.10 Irrigation practices -- 3. Conclusion -- References -- 2 - Traditional water management in India -- 1. Introduction -- 2. The role of traditional knowledge -- 3. Revisiting some traditional practices -- 3.1 Bamboo drip irrigation system -- 3.2 Zabo system -- 3.3 Katta -- 3.4 Madakas/Johads/Pemghara -- 3.5 Eri -- 3.6 Kuhl -- 3.7 Surangam -- 3.8 Jackwells -- 3.9 Ramtek model -- 3.10 Pat system -- 3.11 Zings -- 3.12 Khadins -- 3.13 Swing basket -- 3.14 Moat (rope-and-bucket lift) -- 3.15 Dhekli -- 3.16 Persian wheel -- 3.17 Don -- 3.18 Archimedean screw -- 3.19 Paddle wheel -- 4. Conclusion -- References -- 3 - Application of geospatial technology in agricultural water management -- 1. Introduction -- 1.1 Conventional geospatial techniques for water resource management -- 1.2 Advanced techniques -- 2. Way forward -- References -- II - Convetional technqiues -- 4 - Treated waste water as an alternative to fresh water irrigation with improved crop production -- 1. Introduction -- 2. Materials and methods -- 2.1 Experimental sites -- 2.2 Materials -- 2.3 Experimental design -- 2.4 Methods -- 2.4.1 Physicochemical analysis of soil and effluent. , 2.4.2 Heavy metal and biochemical analysis -- 2.4.3 Germination and vegetative growth study -- 3. Results and discussion -- 3.1 Physicochemical properties of soil -- 3.2 Physicochemical properties of water samples -- 3.3 Heavy metal analysis -- 3.4 Germination and vegetative growth of seedlings -- 3.5 Biochemical/nutritional studies -- 3.5.1 Protein and Ascorbic acid -- 3.5.2 Phosphorus (P) and Potassium (K) -- 3.5.3 Sodium (Na), Magnesium (Mg) and Calcium (Ca) -- 4. Conclusion -- Acknowledgements -- References -- 5 - Assessing the suitability of Ghaghra River water for irrigation purpose in India -- 1. Introduction -- 2. Study area -- 3. Material and methods -- 4. Results and discussion -- 4.1 Physicochemical characteristics (pH, EC, TDS and TH) -- 4.2 Irrigation indices -- 4.2.1 Sodium percentage (Na%) -- 4.2.2 United States Salinity Laboratory's (USSL's) diagram -- 4.2.3 Residual sodium carbonate (RSC) -- 4.2.4 Permeability index (PI) -- 4.2.5 Magnesium hazard (MH) -- 4.2.6 Kelly's index (KI) -- 5. Management plan -- 6. Conclusions -- Acknowledgements -- References -- 6 - Desiccation-tolerant rhizobacteria: a possible approach for managing agricultural water stress -- 1. Introduction -- 1.1 Drought -- 1.2 Effect of drought stress on plant growth and development -- 1.3 Concept behind drought adaptations -- 1.4 Bacterial response against water stress in soil -- 1.5 Bacterial-mediated drought tolerance in crop plants -- 2. Rhizobacteria -- 3. Plant-microbe interaction -- 4. Screening and characterization of rhizobacteria for water-stress management in agriculture system -- 5. Rhizobacteria as a tool for water-stress management in agriculture system -- 5.1 Improve root system architecture for water uptake -- 5.2 Improve shoot growth -- 5.3 Relative water content -- 5.4 Osmotic adjustment -- 5.5 Plant growth regulators -- 6. Conclusion. , Acknowledgement -- References -- 7 - Irrigation water quality appraisal using statistical methods and WATEQ4F geochemical model -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area -- 2.2 Collection of sample and analysis -- 2.3 Irrigation indices -- 2.3.1 Sodicity -- 2.3.2 Sodium adsorption ratio (SAR) -- 2.3.3 Residual sodium carbonate (RSC)/Residual alkalinity (RA) -- 2.3.4 Percent sodium (%Na) or sodium hazard -- 2.3.5 Kelly's ratio (KR) or Kelly's index (KI) -- 2.3.6 Permeability index (PI) -- 2.3.7 Magnesium adsorption ratio (MAR) -- 2.3.8 Potential salinity (PS) -- 2.3.9 Chloroalkaline indices (CAI1 and CAI2) -- 2.3.10 Corrosivity ratio (CR) -- 2.3.11 Total dissolved salts (TDS) -- 2.3.12 Total hardness (TH) -- 2.3.13 Classification of groundwater -- 2.4 Statistical analysis -- 2.5 WATEQ4F model -- 3. Result and discussions -- 3.1 Hydrogeochemistry of water -- 3.2 Correlation matrix -- 3.3 Principal component analysis -- 3.4 Cluster analysis -- 3.5 Irrigation indices -- 3.6 Saturation index -- 4. Conclusion -- References -- III - Earth observation techniques -- 8 - Estimation of potential evapotranspiration using INSAT-3D satellite data over an agriculture area -- 1. Introduction -- 2. Materials and methodology -- 2.1 Study area -- 2.2 INSAT-3D -- 2.2.1 Instrument detail -- 2.2.1.1 Imager -- 2.2.1.2 Sounder -- 2.2.1.3 DRT (Data Relay Transponder) -- 2.2.1.4 SAS& -- R (Satellite Aided Search & -- Rescue) -- 2.3 Hamon's method -- 3. Performance analysis -- 4. Results and discussions -- 4.1 Evaluation of temperature data from INSAT-3D and ground base -- 4.2 Comparative assessment of evapotranspiration products -- 5. Conclusions -- References -- 9 - Estimation of evapotranspiration using surface energy balance system and satellite datasets -- 1. Introduction -- 2. Materials and methods -- 2.1 Study area. , 3. Data used and methodology -- 4. Theory of SEBS -- 4.1 Surface radiation balance equation -- 4.1.1 Surface albedo (α) -- 4.1.2 Incoming shortwave radiation (RS↓) -- 4.1.3 Outgoing long wave radiation (RL↑) -- 4.1.4 Incoming long wave radiation (RL↓) -- 4.2 Soil heat flux (G) -- 4.3 Sensible heat flux (H) -- 4.4 Latent heat flux (λ·ET), instantaneous ET (ETinst) -- 5. Results and discussion -- 5.1 Land-use/Land-cover classification (LULC) -- 5.2 Albedo -- 5.3 Normalized difference vegetation index (NDVI) -- 5.4 Land surface temperature (LST) -- 5.5 Net radiation flux (Rn) -- 5.6 Soil heat flux (G) -- 5.7 Instantaneous evapotranspiration (ET) -- 6. Conclusion -- References -- 10 - Large-scale soil moisture mapping using Earth observation data and its validation at selected agricultural sit ... -- 1. Introduction -- 2. Study area -- 3. Data and methodology -- 3.1 SMAP-derived soil moisture data products (SAC-ISRO) -- 3.2 In-situ soil moisture data -- 4. Validation of soil moisture (SAC-ISRO) products using in-situ data -- 4.1 Accuracy assessment based on metrics computation -- 4.2 Validation approach for coarse-scale satellite-derived data products based on scaling method -- 4.2.1 Cumulative Density Function (CDF) matching approach -- 5. Results and discussion -- 6. Conclusion -- Acknowledgements -- References -- 11 - A preliminary evaluation of the 'simplified triangle' with Sentinel-3 images for mapping surface soil moisture ... -- 1. Introduction -- 2. Materials -- 2.1 Study sites and in-situ data -- 2.2 Sentinel data: acquisition and pre-processing -- 3. Methods -- 3.1 Statistical analysis -- 4. Preliminary results -- 5. Discussion -- 6. Conclusions -- Acknowledgements -- References -- IV - Computational intelligence techniques -- 12 - Artificial neural network for the estimation of soil moisture using earth observation datasets -- 1. Introduction. , 2. Materials and methods -- 2.1 Data used -- 2.2 Performance indices -- 2.3 Artificial neural network architecture and modelling -- 3. Results and discussions -- 3.1 Optimization of ANN models -- 3.2 Performance analysis of ANN models -- 4. Conclusion -- Acknowledgement -- References -- 13 - Soil moisture retrieval from the AMSR-E -- 1. Introduction -- 2. Microwave theory -- 3. Surface roughness and vegetation effects -- 4. Atmospheric effects -- 5. Soil moisture retrieval for the Brue catchment using AMSR-E -- 5.1 Modelling approach -- 5.2 LPRM calibration based on event-based water balance equation -- 5.3 LPRM calibration based on continuous PDM storage predictions -- 6. Conclusions -- References -- 14 - Bistatic scatterometer for the retrieval of soil moisture -- 1. Introduction -- 2. Materials and methods -- 2.1 Bistatic scatterometer measurement and computation of bistatic scattering coefficients -- 2.2 Soil moisture measurement -- 2.2.1 Materials -- 2.2.2 Procedure -- 2.2.3 Computation of soil moisture -- 2.3 Soil surface roughness measurement -- 2.3.1 RMS surface height -- 2.3.2 Autocorrelation function -- 2.3.3 Correlation length -- 2.4 Statistical performance indices -- 3. Result and discussions -- 3.1 Soil surface roughness analysis -- 3.2 Angular response of bistatic scattering coefficient -- 3.3 Estimation of soil moisture using ANN -- 4. Conclusion -- References -- 15 - Soil water content influence on pesticide persistence and mobility -- 1. Introduction -- 2. Methods -- 3. Results and discussion -- 4. Conclusion -- Acknowledgements -- References -- V - Geospatial techniques -- 16 - Irrigation water demand estimation in Bundelkhand region using the variable infiltration capacity model -- 1. Introduction -- 2. Study site -- 3. Materials and methods -- 3.1 Datasets -- 3.1.1 VIC input datasets. , 3.1.2 ESA Climate Change Initiative soil moisture (CCI-SM).
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  • 5
    Online Resource
    Online Resource
    Cham :Springer International Publishing AG,
    Keywords: Environmental sciences -- Remote sensing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (223 pages)
    Edition: 1st ed.
    ISBN: 9783319059068
    Series Statement: Society of Earth Scientists Series
    Language: English
    Note: Intro -- Foreword -- Contents -- About the Editors -- Introduction -- Part IClassical Remote Sensing Applications -- 1 Remote Sensing-Based Determination of Conifer Needle Flushing Phenology over Boreal-Dominant Regions -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Description of the Study Area and Data Requirements -- 2.2 Generation of AGDD Maps -- 2.3 Determination of AGDD and NDWI Thresholds for CNG Occurrence -- 2.4 Integration of both AGDD and NDWI Threshold for CNG Occurrence -- 2.5 Mapping of CNF Using the Best Prediction Criteria -- 3…Results and Discussion -- 3.1 Determination of AGDD Threshold for CNF Occurrence -- 3.2 Determination of NDWI Thresholds for CNF Occurrence -- 3.3 Integration of both AGDD and NDWI Thresholds -- 3.4 Spatial Dynamics of CNF Across the Landscape -- 4…Concluding Remarks -- Acknowledgements -- References -- 2 Information System for Integrated Watershed Management Using Remote Sensing and GIS -- Abstract -- 1…Introduction -- 1.1 Why Management of Natural Resources on Watershed Basis? -- 1.2 Role of Geographic Information System (GIS) and Remote Sensing (RS) in Watershed Management -- 1.3 Decision Support System in Watershed Management -- 1.4 Need for Advanced and Augmented Techniques for Watershed Management -- 2…Study Area -- 3…Conceptual Design -- 3.1 Data Used -- 3.2 Tools and Technologies Used -- 3.3 System Architecture -- 4…Online Generation and Implementation of WATMIS -- 5…Conclusion -- References -- 3 Sensitivity Exploration of SimSphere Land Surface Model Towards Its Use for Operational Products Development from Earth Observation Data -- Abstract -- 1…Introduction -- 2…Sensitivity Analysis: An Overview -- 3…Materials and Methods -- 3.1 SimSphere Model -- 3.2 The Bayesian GSA Method -- 3.3 BACCO Implementation -- 4…Results -- 4.1 Emulator Validation -- 4.2 SA Results -- 5…Discussion. , 6…Conclusions -- Acknowledgments -- References -- 4 Remote Estimation of Land Surface Temperature for Different LULC Features of a Moist Deciduous Tropical Forest Region -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Study Area and Datasets -- 2.2 Image Interpretation for LULC -- 2.3 Surface Temperature Estimation -- 3…Results and Discussions -- Acknowledgments -- References -- 5 Geospatial Strategy for Estimation of Soil Organic Carbon in Tropical Wildlife Reserve -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Study Area -- 2.2 Data Used -- 2.3 Image Interpretation -- 2.4 Estimation of Soil Organic Carbon -- 3…Results and Discussion -- 3.1 Land Use Land Cover Classification -- 3.2 Bare Soil Index -- 3.3 Soil Type Map -- 3.4 Soil Organic Carbon and Regression Analysis -- 4…Conclusion -- Acknowledgment -- References -- Part IIAdvanced Remote Sensing Applications -- 6 A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam -- Abstract -- 1…Introduction -- 2…Study Area and Spatial Database -- 2.1 Study Area Characteristics -- 2.2 Spatial Database -- 2.2.1 Landslide Inventory -- 2.2.2 Digital Elevation Model and Derivatives -- 2.3 Lithology and Distance to Faults -- 2.4 Land Use and Soil Type -- 3…Methodology -- 3.1 Training and Validation Dataset -- 3.2 Fuzzy Unordered Rules Induction Algorithm -- 3.3 Decision Tree -- 3.4 Bagging -- 3.5 Generation of Landslide Susceptibility Maps -- 4…Validation and Comparison of Landslide Susceptibility Models -- 4.1 Model Performance and Evaluation -- 4.2 Model Validation -- 4.3 Relative Contribution of the Conditioning Factors -- 5…Conclusion -- Acknowledgement -- References. , 7 Application of Geo-Spatial Technique for Flood Inundation Mapping of Low Lying Areas -- Abstract -- 1…Introduction -- 2…Study Area and Datasets -- 2.1 Tapi Basin -- 2.2 Surat City -- 2.2.1 Geology and Soil Conditions -- 2.2.2 Ground Water Table -- 2.2.3 Climate -- 2.2.4 Temperature and Rainfall -- 2.2.5 Demography/Population in the Study Area -- 2.3 Hydraulic Structures at LTB -- 2.3.1 Ukai Dam -- 2.3.2 Kakrapar Weir -- 2.3.3 Singanpur Weir -- 2.4 Flood Event 2006 -- 2.5 Data Collection -- 3…Methodology -- 4…Results and Discussion -- 5…Validation -- 6…Conclusion -- Acknowledgments -- References -- 8 Spatial Variations in Vegetation Fires and Carbon Monoxide Concentrations in South Asia -- Abstract -- 1…Introduction -- 2…Data Sets and Methodology -- 2.1 Vegetation Fires -- 2.2 MOPITT CO Retrievals -- 2.3 Aerosol Optical Depth (AOD) and Aerosol Small Mode Fraction (SMAF) -- 2.4 Spatial Gridding, Ordinary Linear Regression (OLR) and Locally Weighted regression -- 3…Results and Discussion -- Acknowledgements -- References -- 9 Land Use Fragmentation Analysis Using Remote Sensing and Fragstats -- Abstract -- 1…Introduction -- 2…Study Area -- 3…Materials and Methods -- 3.1 Classification of Satellite Data -- 3.2 Fragmentation Analysis -- 4…Result and Discussion -- 4.1 Land Use and Land Cover Distribution -- 4.2 Landscape Level Metrics -- 4.3 Class Level Metrics -- 4.4 Class Level Metric Analysis -- 4.5 Land Fragmented Class Analysis -- 4.6 Estimating Effects on Water Quality -- 5…Conclusion -- Acknowledgments -- References -- 10 Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms -- Abstract -- 1…Introduction -- 2…Materials and Methods -- 2.1 Field Experiment and Canopy Spectral Measurements -- 2.2 Data Analysis -- 2.3 Chlorophyll Estimation -- 2.4 Statistical Analysis. , 2.4.1 Descriptive Statistics -- 2.4.2 Regression Analysis -- 2.4.3 Paired t Test -- 3…Results and Discussion -- 3.1 Descriptive Statistics of the Data -- 3.2 Continuum Removal and REIP Evaluation -- 3.3 Calculation of Vegetation Indices -- 3.4 Relationship of Chlorophyll with Vegetation Indices -- 3.5 Sensitivity Analysis of the Empirical Relationships Developed -- 4…Conclusion -- Acknowledgment -- References -- 11 Remote Sensing Based Identification of Painted Rock Shelter Sites: Appraisal Using Advanced Wide Field Sensor, Neural Network and Field Observations -- Abstract -- 1…Introduction -- 2…Material and Methodology -- 2.1 Study Area -- 2.2 Satellite and GIS Datasets -- 2.3 Classifiers/Algorithms Implemented in this Study -- 2.3.1 Artificial Neural Network (ANN) -- 2.3.2 Maximum Likelihood Classification (MLC) -- 3…Accuracy Assessment of the Classified Images -- 4…Results and Discussion -- 4.1 Land Covers Distribution and Accuracy Assessment -- 4.2 Interpretation of the Results and Archaeological Relevance -- 5…Conclusion -- Acknowledgment -- References.
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  • 6
    Online Resource
    Online Resource
    Dordrecht :Springer Netherlands,
    Keywords: Earth sciences -- Data processing. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (273 pages)
    Edition: 1st ed.
    ISBN: 9789401786423
    DDC: 363.70028563
    Language: English
    Note: Intro -- Preface -- Contents -- Contributors -- About the Editors -- Part I: General -- Chapter 1: Computational Intelligence Techniques and Applications -- 1.1 Introduction -- 1.2 Neural Networks -- 1.2.1 Basic Principles -- 1.2.2 Applications -- 1.3 Evolutionary Computation -- 1.3.1 Basic Principles -- 1.3.2 Applications -- 1.4 Swarm Intelligence -- 1.4.1 Basic Principles -- 1.4.2 Applications -- 1.5 Artificial Immune Systems -- 1.5.1 Basic Principles -- 1.5.2 Applications -- 1.6 Fuzzy Systems -- 1.6.1 Basic Principles -- 1.6.2 Applications -- 1.7 Conclusions -- References -- Part II: Classical Intelligence Techniques in Earth and Environmental Sciences -- Chapter 2: Vector Autoregression (VAR) Modeling and Forecasting of Temperature, Humidity, and Cloud Coverage -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Data -- 2.2.2 Test of Stationarity -- 2.2.2.1 Augmented Dickey-Fuller Test -- 2.2.2.2 Phillips-Perron Test -- 2.2.2.3 Kwiatkowski-Phillips-Schmidt-Shin Test -- 2.2.3 Vector Autoregression Model -- 2.2.3.1 Selection of Variables Under Study -- 2.2.3.2 Making a Model of Order p (Arbitrary) -- 2.2.3.3 Determining the Value of Order p -- 2.2.3.4 Estimating Parameters -- 2.2.3.5 Diagnostic Checking -- 2.2.3.6 Cross Validity of the Fitted VAR Models -- 2.2.3.7 Forecasting -- 2.2.3.8 Forecast Error Variance Decomposition -- 2.2.3.9 Impulse Response Function -- 2.3 Results and Discussion -- 2.3.1 Descriptive Statistics -- 2.3.2 Tests for Stationarity -- 2.3.3 Selection of Variables Under Study -- 2.3.4 Selection of Order (p) -- 2.3.5 Estimation of Parameters -- 2.3.6 Diagnostic Checking -- 2.3.7 Cross Validity of the Fitted VAR Models -- 2.3.8 Forecast Error Variance Decomposition -- 2.3.9 Impulse Response Function -- 2.3.10 Forecasting Using VAR(8) Model -- 2.4 Conclusion -- References. , Chapter 3: Exploring the Behavior and Changing Trends of Rainfall and Temperature Using Statistical Computing Techniques -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 K-Means Clustering -- 3.2.2 Mann-Kendall Trend Test -- 3.2.3 Seasonal Mann-Kendall Trend Test -- 3.2.4 Sen´s Slope Estimator -- 3.2.5 Naive Model -- 3.2.6 Autoregressive Integrated Moving Average Model -- 3.2.7 Random Walk with Drift -- 3.2.8 Theta Model -- 3.2.9 Wilcoxon Test -- 3.2.10 RMS Error -- 3.2.11 SMAPE -- 3.2.12 Materials Used During the Study -- 3.2.13 Data Used in the Study -- 3.3 Results and Discussion -- 3.3.1 Change in Temperature -- 3.3.2 Changes in Monthly Total Rainfall -- 3.3.3 Clustering of Rainfall -- 3.3.4 Comparison of Different Time Series Forecasting Models -- 3.4 Concluding Remarks -- References -- Chapter 4: Time Series Model Building and Forecasting on Maximum Temperature Data -- 4.1 Introduction -- 4.2 Materials and Methods -- 4.2.1 Time Series Data on Temperature -- 4.2.2 Testing Stationarity of the Time Series -- 4.2.3 Box-Jenkins Modeling Strategy -- 4.2.3.1 Identification of Order for the SARIMA Structure -- 4.2.3.2 Parameter Estimation of the SARIMA Model -- 4.2.3.3 Diagnostic Checking of the Fitted Model -- 4.2.3.4 Forecasting of the Study Variable -- 4.3 Results and Discussion -- 4.3.1 Testing Stationarity Status of Temperature -- 4.3.2 Model Building and Forecasting -- 4.3.2.1 Identification of Parameters Value of the SARIMA Structure -- 4.3.2.2 Estimation of Parameters for Selected SARMA Models -- 4.3.2.3 Diagnostic Checking for Estimated SARMA Models -- 4.3.2.4 Forecasting of Temperature Using Selected SARMA Models -- 4.4 Conclusion -- References -- Chapter 5: GIS Visualization of Climate Change and Prediction of Human Responses -- 5.1 Introduction -- 5.2 Method and Materials. , 5.3 Wet Bulb (Twb) and Globe (Tg) Temperature as Physical Model to Predict WBGT -- 5.4 WBGT and Tolerance Time a Tool for Climate Change Assessment -- 5.4.1 Tolerance Time -- 5.5 Discussion -- 5.6 Conclusion -- References -- Part III: Probabilistic and Transforms Intelligence Techniques in Earth and Environmental Sciences -- Chapter 6: Markov Chain Analysis of Weekly Rainfall Data for Predicting Agricultural Drought -- 6.1 Introduction -- 6.2 Study Area and Method -- 6.2.1 Climatic Condition of the Barind Region -- 6.2.2 Markov Chain Agricultural Drought Index -- 6.2.3 Test of Null Hypothesis -- 6.2.4 Mapping the Spatial Extent of Agricultural Drought -- 6.3 Results and Discussion -- 6.3.1 Temporal and Spatial Characteristics of Agricultural Drought -- 6.3.2 Probability of Wet Spell -- 6.3.3 Relative Frequency of Occurrence of Agricultural Drought -- 6.3.4 Result of Hypothesis Testing -- 6.4 Conclusions -- References -- Chapter 7: Forecasting Tropical Cyclones in Bangladesh: A Markov Renewal Approach -- 7.1 Introduction -- 7.2 Data Sources and Description -- 7.3 Methods -- 7.3.1 Markov Renewal Process and Its Properties -- 7.3.2 Likelihood Construction and Parameter Estimation -- 7.3.3 Cross-State Prediction -- 7.3.4 Asymptotic Behavior: Mean Recurrence Time -- 7.4 Results and Discussion -- 7.5 Conclusion -- References -- Chapter 8: Performance of Wavelet Transform on Models in Forecasting Climatic Variables -- 8.1 Introduction -- 8.2 Wavelet Transformation -- 8.3 Data Processing and Forecasting Framework -- 8.3.1 Approach-1 -- 8.3.2 Approach-2 -- 8.4 Comparison of Forecasting Performance -- 8.5 Empirical Results -- 8.5.1 Forecasting Based on Original Series -- 8.5.2 Forecasting Based on Decomposed Series Using Wavelet Transformation -- 8.5.2.1 Approach-1 -- 8.5.2.2 Approach-2 -- 8.5.3 Comparison -- 8.6 Conclusions -- References. , Chapter 9: Analysis of Inter-Annual Climate Variability Using Discrete Wavelet Transform -- 9.1 Introduction -- 9.2 Multiband Decomposition of Climate Signals -- 9.2.1 Fourier-Based Filter Bank and Its Limitations -- 9.2.2 Wavelet-Based Filter Bank -- 9.3 Annual Cycle Extraction -- 9.4 Results and Discussion -- 9.5 Conclusions -- References -- Part IV: Hybrid Intelligence Techniques in Earth and Environmental Sciences -- Chapter 10: Modeling of Suspended Sediment Concentration Carried in Natural Streams Using Fuzzy Genetic Approach -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Fuzzy Logic -- 10.2.2 Genetic Algorithm -- 10.2.3 Fuzzy Genetic Approach -- 10.2.4 Adaptive Network-Based Fuzzy Inference System -- 10.2.5 Neural Networks -- 10.2.6 Sediment Rating Curve -- 10.3 Applications and Results -- 10.4 Conclusion -- References -- Chapter 11: Prediction of Local Scour Depth Downstream of Bed Sills Using Soft Computing Models -- 11.1 Introduction -- 11.2 Material and Methods -- 11.2.1 Physical Definition of Scouring -- 11.2.2 Scouring Prediction at Bed Sills -- 11.2.2.1 Empirical Equations -- 11.2.2.2 Genetic Algorithm -- 11.2.2.3 Gene Expression Programming -- 11.2.2.4 M5 Tree Model -- 11.2.2.5 Data Set -- 11.2.2.6 Selection of Input and Output Parameters -- 11.2.3 Experimental Setup -- 11.3 Results -- 11.3.1 GA Model -- 11.3.2 GEP Model -- 11.3.3 M5 Tree Equations -- 11.4 Performance Analysis of Results -- 11.5 Conclusions -- References -- Chapter 12: Evaluation of Wavelet-Based De-noising Approach in Hydrological Models Linked to Artificial Neural Networks -- 12.1 Introduction -- 12.2 Study Area and Data -- 12.3 Materials and Methods -- 12.3.1 Wavelet De-noising Procedure -- 12.3.2 Artificial Neural Network and Efficiency Criteria -- 12.4 Results and Discussion -- 12.4.1 Ad Hoc ANN -- 12.4.1.1 Stream-Flow Forecasting -- 12.4.1.2 SSL Forecasting. , 12.4.2 Hybrid ANN-Wavelet -- 12.4.2.1 Wavelet-Based De-noising Approach -- 12.4.2.2 Stream-Flow Forecasting -- 12.4.2.3 SSL Forecasting -- 12.4.3 Comparison of Models -- 12.5 Concluding Remarks -- References -- Chapter 13: Evaluation of Mathematical Models with Utility Index: A Case Study from Hydrology -- 13.1 Introduction -- 13.2 Study Area and Data Used -- 13.3 Models -- 13.3.1 LLR Model -- 13.3.2 ANN and ANFIS Models -- 13.3.3 Support Vector Machines -- 13.3.4 Wavelet Hybrid Models -- 13.3.5 Index of Model Utility (U) -- 13.4 Results and Discussions -- 13.4.1 Comparison of Data Models Using Utility Index -- 13.4.2 Comparison of Data Models Using Statistical Indices -- 13.5 Conclusions -- References -- Index.
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    Online Resource
    Online Resource
    Milton :Taylor & Francis Group,
    Keywords: Water-supply--Remote sensing. ; Electronic books.
    Description / Table of Contents: This book advances the scientific understanding, development, and application of geospatial technologies related to water resource management. It presents recent developments and applications specifically by utilizing new earth observation datasets such as TRMM/GPM, AMSR E/2, SMOS, SMAP and GCOM in combination with GIS, artificial intelligence, and hybrid techniques. By linking geospatial techniques with new satellite missions for earth and environmental science, the book promotes the synergistic and multidisciplinary activities of scientists and users working in the field of hydrological sciences.
    Type of Medium: Online Resource
    Pages: 1 online resource (326 pages)
    Edition: 1st ed.
    ISBN: 9781498719698
    DDC: 333.91
    Language: English
    Note: Cover -- Half Title -- Title Page -- Copyright Page -- Preface -- Table of Contents -- List of Contributors -- Section I: General -- 1: Introduction to Geospatial Technology for Water Resources -- Section II: Geographical Information System Based Approaches -- 2: GIS Supported Water Use Master Plan: A Planning Tool for Integrated Water Resources Management in Nepal -- 3: Spatial Integration of Rice-based Cropping Systems for Soil and Water Quality Assessment Using Geospatial Tools and Techniques -- 4: A Geographic Information System (GIS) Based Assessment of Hydropower Potential within the Upper Indus Basin Pakistan -- 5: Flood Risk Assessment for Kota Tinggi, Johor, Malaysia -- 6: Delineation and Zonation of Flood Prone Area Using Geo-hydrological Parameters: A Case Study of Lower Ghaghara River Valley -- 7: Geospatial Technology for Water Resource Development in WGKKC2 Watershed -- Section III: Satellite Based Approaches -- 8: Predicting Flood-vulnerability of Areas Using Satellite Remote-sensing Images in Kumamoto City, Japan -- 9: Validation of Hourly GSMaP and Ground Base Estimates of Precipitation for Flood Monitoring in Kumamoto, Japan -- 10: Appraisal of Surface and Groundwater of the Subarnarekha River Basin, Jharkhand, India: Using Remote Sensing, Irrigation Indices and Statistical Technique -- 11: Spatial and Temporal Variability of Sea Surface Height Anomaly and its Relationship with Satellite Derived Chlorophyll a Pigment Concentration in the Bay of Bengal -- 12: Monitoring Soil Moisture Deficit Using SMOS Satellite Soil Moisture: Correspondence through Rainfall-runoff Model -- Section IV: Artificial Intelligence and Hybrid Approaches -- 13: A Deterministic Model to Predict Frost Hazard in Agricultural Land Utilizing Remotely Sensed Imagery and GIS. , 14: A Statistical Approach for Catchment Calibration Data Selection in Flood Regionalisation -- 15: Prediction of Caspian Sea Level Fluctuations Using Artificial Intelligence -- 16: Spatio-temporal Uncertainty Model for Radar Rainfall -- 17: Soil Moisture Retrieval from Bistatic Scatterometer Measurements using Fuzzy Logic System -- Section V: Challenges in Geospatial Technology For Water Resources Development -- 18: Challenges in Geospatial Technology for Water Resources Development -- Index -- About the Editors.
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  • 8
    Online Resource
    Online Resource
    Newark :John Wiley & Sons, Incorporated,
    Keywords: Emergency management. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (345 pages)
    Edition: 1st ed.
    ISBN: 9781119359173
    Language: English
    Note: Cover -- Title Page -- Copyright Page -- CONTENTS -- CONTRIBUTORS -- PREFACE -- Section I Introduction -- Chapter 1 Concepts and Methodologies of Environmental Hazards and Disasters -- 1.1. INTRODUCTION -- 1.2. HYDROMETEOROLOGICAL HAZARDS IN AGRICULTURE -- 1.3. BIOPHYSICAL HAZARDS IN AGRICULTURE -- 1.4. SUMMARY -- REFERENCES -- Chapter 2 Indigenous Knowledge for Disaster Solutions in the Hilly State of Mizoram, Northeast India -- 2.1. INTRODUCTION -- 2.2. TRADITIONAL PRACTICES FOR LANDSLIDE PREVENTION -- 2.3. BAMBOO‐BASED HOUSING IN EARTHQUAKE PRONE ZONE -- 2.4. COMMUNITY-BASED EFFORTS TO COMBAT FOREST FIRES -- 2.5. LOCAL WATER HARVESTING SYSTEMS IN MIZORAM -- 2.6. CONCLUSIONS -- REFERENCES -- Chapter 3 Urban Risk and Resilience to Climate Change and Natural Hazards: A Perspective from Million‐Plus Cities on the Indian Subcontinent -- 3.1. BACKGROUND -- 3.2. URBAN SYSTEM IN CHANGING CLIMATIC CONDITIONS -- 3.3. URBAN HAZARDS AND RISK -- 3.4. URBAN RESILIENCE AND ADAPTATION -- 3.5. CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 4 The Contribution of Earth Observation in Disaster Prediction, Management, and Mitigation: A Holistic View -- 4.1. INTRODUCTION -- 4.2. EARTH OBSERVATION (EO) FOR NATURAL DISASTER PREDICTION -- 4.3. EARTH OBSERVATION (EO) FOR NATURAL DISASTER MANAGEMENT AND MITIGATION -- 4.4. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Section II Atmospheric Hazards and Disasters -- Chapter 5 Tropical Cyclones Over the North Indian Ocean in Changing Climate -- 5.1. INTRODUCTION -- 5.2. DATA AND METHODOLOGY -- 5.3. RESULT AND ANALYSIS -- 5.4. CONCLUSIONS -- ACKNOWLEDGMENTs -- REFERENCES -- Chapter 6 Simulation of Intensity and Track of Tropical Cyclones Over the Arabian Sea Using the Weather Research and Forecast (WRF) Modeling System: with Different Initial Conditions (ICs) -- 6.1. INTRODUCTION. , 6.2. SYNOPTIC CONDITIONS ASSOCIATED WITH THE TROPICAL CYCLONES -- 6.3. DESCRIPTION OF THE WEATHER RESEARCH AND FORECAST MODEL AND NUMERICAL EXPERIMENTS -- 6.4. RESULTS AND DISCUSSION -- 6.5. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 7 Development of a Soft Computing Model from the Reanalyzed Atmospheric Data to Detect Severe Weather Conditions -- 7.1. INTRODUCTION -- 7.2. METHODOLOGY -- 7.3. RESULTS AND DISCUSSION -- 7.4. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 8 Lightning, the Global Electric Circuit, and Climate -- 8.1. INTRODUCTION -- 8.2. LIGHTNING DISCHARGES -- 8.3. LIGHTNING DISCHARGES AND THE GLOBAL ELECTRIC CIRCUIT -- 8.4. ATMOSPHERIC DISCHARGES AND CLIMATE -- 8.5. CONCLUSIONS AND RECOMMENDATION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 9 An Exploration of the Panther Mountain Crater Impact Using Spatial Data and GIS Spatial Correlation Analysis Techniques -- 9.1. INTRODUCTION -- 9.2. MATERIALS -- 9.3. METHODOLOGY -- 9.4. RESULTS -- 9.5. DISCUSSION -- 9.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Section III Land Hazards and Disasters -- Chapter 10 Satellite Radar Interferometry Processing and Elevation Change Analysis for Geoenvironmental Hazard Assessment -- 10.1. INTRODUCTION -- 10.2. STUDY AREA -- 10.3. GEOENVIRONMENTAL MONITORING -- 10.4. RADAR INTERFEROMETRY -- 10.5. MULTITEMPORAL ELEVATIONS CHANGE ANALYSIS -- 10.6 CONCLUSIONS -- REFERENCES -- Chapter 11 Assessing the Use of Sentinel-2 in Burnt Area Cartography: Findings from a Case Study in Spain -- 11.1. INTRODUCTION -- 11.2. MATERIALS AND METHODS -- 11.3. RESULTS -- 11.4. DISCUSSION -- 11.5. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 12 Assimilating SEVIRI Satellite Observation into the Name-III Dispersion Model to Improve Volcanic Ash Forecast -- 12.1. INTRODUCTION -- 12.2. NAME DISPERSION MODEL -- 12.3. DATA AND METHODOLOGY. , 12.4. RESULTS AND DISCUSSIONS -- 12.5. CONCLUSIONS -- REFERENCES -- Chapter 13 Geoinformation Technology for Drought Assessment -- 13.1. INTRODUCTION -- 13.2. STUDY AREA -- 13.3. MATERIALS AND METHODS -- 13.4. METHODOLOGY -- 13.5. RESULTS AND DISCUSSIONS -- 13.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 14 Introduction to Landslides -- 14.1. INTRODUCTION -- 14.2. MORPHOLOGY OF LANDSLIDES -- 14.3. SLOPE FAILURE -- 14.4. CAUSES OF LANDSLIDES -- 14.5. TYPES OF LANDSLIDES -- 14.6. CLASSIFICATION OF LANDSLIDES -- 14.7. IDENTIFICATION OF LANDSLIDES -- 14.8. GROUND OBSERVATION -- 14.9. MEASUREMENT OF LANDSLIDES -- 14.10. LANDSLIDE HAZARD ZONATION MAPPING -- 14.11. LANDSLIDE HAZARD MITIGATION -- 14.12. SUMMARY -- REFERENCES -- Chapter 15 Probabilistic Landslide Hazard Assessment using Statistical Information Value (SIV) and GIS Techniques: A Case Study of Himachal Pradesh, India -- 15.1. INTRODUCTION -- 15.2. STATISTICAL INFORMATION VALUE (SIV) MODEL -- 15.3. LANDSLIDE CONDITIONING FACTORS -- 15.4. METHODOLOGY -- 15.5. RESULTS AND DISCUSSION -- 15.6. CONCLUSION -- REFERENCES -- Chapter 16 One-Dimensional Hydrodynamic Modeling of the River Tapi: The 2006 Flood, Surat, India -- 16.1. INTRODUCTION -- 16.2. STUDY AREA AND DATA USED -- 16.3. METHODOLOGIES -- 16.4. RESULTS -- 16.5. VALIDATION OF SIMULATED FLOW -- 16.6. SENSITIVITY OF STAGE RELATION WITH TIDAL CONDITION -- 16.7. RECOMMENDATIONS AND SUGGESTION FOR FLOOD PREVENTION -- 16.8. DISCUSSIONS -- 16.9. CONCLUSIONS -- ACKNOWLEDGMENTs -- REFERENCES -- Section IV Ocean Hazards and Disasters -- Chapter 17 Tropical Cyclone-Induced Storm Surges and Wind Waves in the Bay of Bengal -- 17.1. INTRODUCTION -- 17.2. METHODOLOGY -- 17.3. TROPICAL CYCLONE ACTIVITY OVER THE NORTH INDIAN OCEAN -- 17.4. STUDIES ON TROPICAL CYCLONE-INDUCED STORM SURGES FOR THE BAY OF BENGAL. , 17.5. CHARACTERISTICS OF OCEAN WIND WAVES AND THEIR ROLE DURING EXTREME WEATHER EVENTS -- 17.6. COUPLED WAVE‐HYDRODYNAMIC MODELS -- 17.7 STORM SURGE AND INUNDATION MODELING FOR CYCLONE THANE -- 17.8. STORM SURGE AND INUNDATION MODELING FOR CYCLONE AILA -- 17.9. COUPLED MODELING SYSTEM FOR CYCLONE PHAILIN -- 17.10. COUPLED MODELING SYSTEM FOR CYCLONE HUDHUD -- 17.11. SUMMARY AND CONCLUSIONS -- REFERENCES -- Chapter 18 Space-Based Measurement of Rainfall Over India and Nearby Oceans Using Remote Sensing Application -- 18.1. INTRODUCTION -- 18.2. RAINFALL ESTIMATION USING INFRARED OBSERVATIONS -- 18.3. RAINFALL ESTIMATION USING MICROWAVE OBSERVATIONS -- 18.4. RAINFALL ESTIMATION USING MERGED IR AND MICROWAVE OBSERVATIONS -- 18.5. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 19 Modeling Tsunami Attenuation and Impacts on Coastal Communities -- 19.1. INTRODUCTION -- 19.2. MODELING TSUNAMIS -- 19.3. TSUNAMI DEFENSE STRUCTURES -- 19.4. TSUNAMI-BORNE DEBRIS -- 19.5. INFRASTRUCTURE -- 19.6. SUMMARY -- REFERENCES -- INDEX -- EULA.
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  • 9
    Online Resource
    Online Resource
    Cham : Springer International Publishing | Cham : Imprint: Springer
    Keywords: Natural disasters. ; Geographic information systems. ; Earth sciences. ; Geomorphology. ; Environmental geography.
    Description / Table of Contents: Introduction -- Landslide/ Slope failures/ Flood and glacial lake outburst floods (GLOFS) /Drought/ Desertification status mapping/ Tsunami/ Lightening/ Forest Fire/ Earthquake / Volcanic eruptions -- Statistical, multi-criteria decision making (MCDM), machine learning models (spatial prediction models) -- Challenges and future needs.
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource(XXXVII, 517 p. 203 illus., 192 illus. in color.)
    Edition: 1st ed. 2024.
    ISBN: 9783031510533
    Language: English
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