Advanced Geospatial and Ground Based Techniques in Forest Monitoring provides insights into advanced geospatial technology in the field of forestry. The book provides both traditional and special techniques for monitoring the forest, including biophysical and biochemical parameters, retrieval, species identification, mapping, and classification. In addition, it covers the latest technologies using SAR data, hyperspectral data, and the integration of datasets for the enhanced accuracy of the results and its outcome. This book will benefit the academic and research communities with the latest research ideas and problem-solving skills in forestry and land management.
Part 1. Introduction to Forest Monitoring
1. Traditional methods in forest management
2. An overview of remote sensing technology in forest management
3. A global vulnerability management of forest resources
4. Forest resource sustainable exploitation and management
Part 2. Forest Species Stand Classification: Definition and Perspectives
5. A general method for the classification of forest stands
6. Forest stand species mapping using the Sentinel-2
7. Multi-species stand classification: Definition and Perspectives
8. Classification of forest stand considering shapes and sizes of tree crown calculated
Part 3. Assessment of Biophysical and Biochemical Parameters
9. Establishing relationships between in situ measured between biophysical and biochemical parameters
10. Chlorophyll assessment and sensitivity analysis using NIR- 11. Carbon stock assessment using non-linear processes
12. Forest biodiversity and vegetation health assessment using narrow band hyperspectral data
Part 4. Methodological Considerations in the Study of Forest Ecosystems
13. Thermal hyperspectral applications in forest ecosystem classification
14. Invasive species identification and mapping using multi-source data
15. Social functional mapping of urban green space using remote sensing data
16. Bayesian data synthesis for forest fire estimation
Part 5. Artificial Intelligence, Machine Learning and Deep Learning Techniques
17. Developments of LiDAR for forest monitoring
18. Forest damage assessment using deep learning
19. Artificial intelligence and forest management
20. Application of machine-learning in forest monitoring: Recent progress and future challenges
Part 6. Challenges and Future Needs
21. Building capacity in remote sensing for conservation: present and future challenges
22. Developments of optical remote sensing: UAVs, hyperspectral and multispectral
23. Developments of Review of present perspective, challenges, and Future aspects
24. New satellite missions and sensors for forest monitoring
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