Geospatial Information Handbook for Water Resources and Watershed Management, Volume 2: Methods and Modelling

Description

Volume II of Geospatial Information Handbook for Water Resources and Watershed Management discusses Geospatial Technology (GT) approaches using integrated modeling as applied to advanced water resource assessments. Features include multiple date land cover analyses as change in land cover influences water quality, model sensitivity analyses of DEM resolution and influences on modeling water characteristics like Manning’s n, development of seabed cover classification and sensitivity, and forecasting urban growth over time with climate vulnerability impacts on water. A detailed case study presents a range of water quality issues, all effectively demonstrating GT inputs to water quality studies from headwaters to receiving estuarine waters. Also analyzed are the comparison of evapotranspiration simulation performance by APEX model in dryland and irrigated cropping systems and perspectives on the future of transient storage modeling. Captures advanced technologies and applications for implementation with models to address a broad spectrum of water issues Provides real-world applications and case studies using advanced spectral and spatial sensors combined with geospatially facilitated water process models Features a Neuse River Basin case study integrating hydrologic methods and modeling along with remote sensing and GIS technologies for nonpoint source water quality evaluations Global coverage with applications demonstrated by more than 170 experts from around the world This handbook is a wide-ranging and contemporary reference of advanced geospatial techniques used in numerous practical applications at the local and regional scale and is an in-depth resource for professionals and the water research community worldwide. Cover Half Title Title Page Copyright Page Table of Contents Editors Contributors 1. Introduction to Volume II and Neuse River Watershed and Water Quality Case Study History Traditional Approaches Augmented Building Spatial Datasets Spatial Positioning Spectral Resolution Scaling and Modeling Deterministic Modeling Verification Applications 2. Neuse Case Study and Water Quality–Related Methods Neuse River Watershed and Water Quality Case Study Volume II, Chapter 3, Ground Truthing Land Cover (LULC) Volume II, Chapter 4, Land Cover Dynamics and Accuracy of Indices Volume II, Chapter 5, Multiple Sensor Land Cover Changes Volume II, Chapter 6 Land Cover Change via High-Frequency Monitoring Volume II, Chapter 7 Land Cover and Nitrogen Compound Watershed Modeling Volume II, Chapter 8, Downstream Transport and Fate Volume II, Chapter 9, DEM Sensitivity Analyses Volume II, Chapter 10 Forecasting Land Cover Change Volume II, Chapter 11, Sensitivity Analysis of Seabed Covers 3. Virtual Field Reference Database for Assessment of Land Cover Data and Variability Introduction and Background Study Area Methods Sampling Frame Design Field Sampling Protocol Location and Time Physical Measurements Biophysical Measurements Digital Photographic Documentation Quality Assurance and Quality Control Database Design and Functionality Data Entry Quality Control Data Display and Distribution Results Conclusions Acknowledgments References 4. Vegetation Dynamics and Identification of Land Cover Change in a Complex Land Use Community Introduction Background Study Site Study Objectives Methods Data Preprocessing Calibration Reference Data Set Development NDVI Change Analysis MID Change Analysis Accuracy Assessment of Change Detection Images Results Discussion Conclusions Acknowledgments References 5. Land-Cover Change Detection Using Multi-Temporal MODIS NDVI Data Introduction Background Study Objectives Study Area Methods Accuracy Assessment Results NDVI Profiles Change Detection Discussion Conclusions Acknowledgments References 6. Spectral and GIS Rule-Based Land-Cover Classification in the Neuse River Introduction Study Area Methods Initial Classification Sub-Level Classifications Agriculture and Herbaceous Wetland and Water Urban (Impervious) Spectrally Inseparable Areas Final Data Processing Accuracy Assessment Sampling Frame Design Results Reference Data Variability Accuracy Assessment Discussion LC Classification Accuracy Assessment Conclusions Acknowledgments References 7. Modeling the Distribution of Diffuse Nitrogen Sources and Sinks in the Neuse River Basin Introduction Study Overview Study Area Methods Land-Cover Classification Mass Balance Modeling Hydrologic Modeling Surface Runoff Percolation and Subsurface Runoff Evapotranspiration Transported Nitrogen Denitrification Results Land-Cover Products Model Performance Modeling Products Model Comparisons Discussion Conclusions Acknowledgments References 8. Monitoring of Water Colorants Using AVIRIS Hyperspectral Sensing Introduction Study Area Study Objectives Methods Imagery Collection Field Data Atmospheric Corrections Sun Glint Water Vapor and Aerosol Corrections Chl a SeaWiFS Algorithms CDOM and TSS Results Chl a CDOM, TSS, and FSS Spatial Patterns and Distributions Discussion Conclusions Acknowledgments References 9. DEM Resolution and Roughness Effect in Relation to Model Performance Introduction and Background Study Areas and Data Used Methods DEM Data Processing Hydraulic Simulations and Ensemble Modeling Model Performance Quantification Volume Estimation Results Overall Performance Results Variation in Sizes of Overlap, Over- and Underestimation, and Disparities as Separate Effect of DEM and Manning's n Used Comparison of Cross-Sectional Profiles and Water Surface Elevations Maximum Depth and Total Volume Variations as Separate Effects of DEM and Manning's n Conclusions Acknowledgments References 10. Application of Densely Stacked Satellite Image Classification and Multinomial Logistic Regression Analysis in Predicting Urban Sprawl Introduction GIS Based CA-MARKOV Chain Model Identification of Causative Factors Design Methodology to Identify the Relationship between Causative Factors and Built-up Pixels Data Acquisition Identification of Built-up Pixels Accuracy Assessment Selection of the Causative Factors Statistical Analysis of Causative Factors vs. Built-up Pixels CA-Markov Chain Model Run for Decadal Prediction Variation of Built-up Area Till the Year 2020 Using Supervised Classification Algorithm Predicted Urban Sprawl from 2020 to 2050 Resultant Changes in the Range of Urban Sprawl Significant Findings Acknowledgment References 11. A Feasibility Study of Seabed Cover Classification Standards Introduction Evaluation of SNI 7987–2014 Evaluation of Prototype Feasibility How to Determine the Seabed Cover Map Custodian? Conclusions and Recommendations Acknowledgment References Index