Multi-sensor System Applications in the Everglades Ecosystem
This book explores the applicability of multiple remote sensors to acquire information relevant to restoration and conservation efforts in wetlands using data collected from airborne and space multispectral/hyperspectral sensors, light detection and ranging (LiDAR), Unmanned Aircraft Systems (UAS), and a hand-held spectroradiometer. This book also examines digital data processing techniques such as object-based image analysis, machine learning, texture analysis, and data fusion. After an introduction to the Everglades and to remote sensing, the book is divided into four parts based on the sensor systems used. There are chapters on vegetation mapping, biomass and water quality modeling, applications of hyperspectral data for plant stress analysis and coral reef mapping, studies of airborne LiDAR data for coastal vulnerability analysis and DEM improvement, as well as chapters that explore a fusion of multiple sensors for different datasets.
- Introduces concepts, theories, and advanced processing techniques
- A complete introduction of machine learning, object-based image analysis, data fusion, and ensemble analysis techniques in processing data from multiple remote sensors
- Explains how multiple remote sensing systems are applied in the wetland ecosystems of Florida
- The author had been teaching and using both systems and her research is widely recognized
Multi-sensor System Applications in the Everglades Ecosystems provides a comprehensive application of remote sensing techniques in the Florida Everglades and its coastal ecosystems. It will prove an invaluable resource for the restoration and conservation of the Florida Everglades and beyond, for global wetlands in general. Any professional, scientist, engineer, or student working with remote sensing and wetland ecosystems will reap enormous benefits from this book.
Part I. Florida Everglades and Remote Sensing. 1. Florida Everglades and Restoration. 2. Introduction to Remote Sensing. 3. Vegetation Classification System in the Everglades. Part II. 4. Applying Aerial Photography to Map Marsh Species in the Wetland of Lake Okeechobee. 5. Unmanned Aircraft System (UAS) for Wetland Species Mapping. 6. Spaceborne Multispectral Sensors for Vegetation Mapping and Change Analysis. 7. Water Quality Modeling and Mapping using Landsat Data. 8. Mapping Sawgrass Aboveground Biomass using Landsat Data. 9. Applying Landsat Products to Assess the Damage and Resilience of Mangroves from Hurricanes. Part III. Hyperspectral Remote Sensing Applications 10. Applying Point Spectroscopy Data to Assess the Effects of Salinity and Sea Level Rise on Canopy Water Content of Juncus roemaerianus. 11. Applying Point Spectroscopy Data to Characterize Sand Properties. 12. Land Cover-level Vegetation Mapping using AVIRIS. 13. Species-level Vegetation Mapping in the Kissimmee River Floodplain using HyMap Data. 14. Benthic Habitat Mapping in the Florida Keys using EO-1/Hyperion. Part IV. Lidar Remote Sensing Applications. 15. Vulnerability Analysis of Coastal Everglades to Sea Level Rise using SLAMM. 16. Enhancing Lidar Data Integrity in the Coastal Everglades. 17. Assessing the Effects of Hurricane Irma on Mangrove Structures in the Coastal Everglades using Airborne Lidar Data. Part V. Fusing Multiple Sensors for Everglades Applications. 18. Integrating Aerial Photography, EO-1/Hyperion, and Lidar Data to Map Vegetation in the Coastal Everglades. 19. Assessing a Multi-sensor Fusion Approach to Map Detailed Reef Benthic Habitats in the Florida Reef Tract. Index.