Remote Sensing for Vegetation Monitoring: Technologies, Applications and Models provides insight on the pivotal role that remote sensing plays in vegetation monitoring. As traditional field assessments face challenges due to inaccessible study sites and lengthy data collection, this book offers a comprehensive view of remote sensing applications for monitoring various vegetation ecosystems, including forests, grasslands, mangroves, and agriculture. The book presents a coherent and consistent structure, across five sections that build upon prior knowledge and detail the quantitative and qualitative assessments made possible through remote sensing technologies. Remote Sensing for Vegetation Monitoring: Technologies, Applications and Models caters to a diverse audience, including researchers and practitioners seeking to navigate the evolving landscape of vegetation monitoring through case studies, new algorithms and state-of-the-art methods.
1. Introduction to Remote Sensing for Vegetation Monitoring
Section 1: Conventional and Advanced Forest Ecosystem Monitoring
2. Surveying Techniques and Sampling Techniques in Forest Ecosystems
3. Crop Canopy Stress/Chlorophyll Estimation Using Drone or Thermal Sensors
4. Biophysical And Biochemical Analysis and Monitoring of Forest Ecosystems
5. Species-Level Classification Using Pixel Based and OBIA Object Based Approaches Drones in Vegetation and Surroundings Assessment
6. Mangroves Forests - Blue Carbon Places to Help Mitigate Climate Change
7. Multi-Source and Multi-Sensor Approaches in Forest Monitoring
8. Forest Fire Analysis, Simulation and Modelling Using Advanced Techniques
9. Biophysical/Biochemical Parameter Retrieval from An Unmanned Autonomous Vehicle (UAV)
10. Forest Ecosystem Monitoring Summary
Section 2: Agriculture and Grassland Monitoring
11. Crop Stress and Water Deficit Relationship Using Remote Sensing and Field Inventory Methods
12. Crop Yield Estimation and Modelling
13. Crop Damage Assessment Using Multi-Sensors and Multi-Source Remote Sensing Data
14. Artificial Intelligence Techniques in Grassland Monitoring
15. Establishment Of Relationships Between in Situ Measured Biophysical/Biochemical Parameters and Ground-Measured Data
16. Agriculture and Grassland Monitoring Summary
Section 3: Monitoring Urban Green Space and Mangrove Forests
17. Urban Discomfort Analysis and Urban Green Space Assessment
18. Urban Heat Islands – Can This Be Mitigated by Increasing Green Spaces?
19. Monitoring Urban Green Space and Mangrove Forests Summary
Section 4: Advanced Modelling for Machine Learning / Artificial Intelligence
20. Hyperspectral Data for Quantification of Vegetation
21. Multi-Source and Machine Learning in Vegetation Classification
22. Data Fusion Technique and GUI Based Model in Vegetation Mapping and Monitoring
23. Deep Learning Techniques in Mangrove Forest Monitoring
24. ML and Modelling Summary
Section 5: Future Aspects and Challenges in Remote Sensing
25. Challenges And Emerging Applications in Vegetation Monitoring
26. Future Earth Observation Space Missions Devoted to Vegetation Monitoring for Sustainable Development Goals.
27. Summary of Future Challenges
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