Description
Advanced Geospatial and Ground Based Techniques in Forest Monitoring provides insight into advanced geospatial technology in the field of forestry. It provides both traditional and special techniques for monitoring the forest, including biophysical and biochemical parameters, retrieval, species identification, mapping, and classification. The book covers the latest technology to using SAR data, hyperspectral data, and the integration of data sets for the enhanced accuracy of the results and its outcome. Advanced Geospatial and Ground Based Techniques in Forest Monitoring will benefit the academic and the research community with latest research ideas and problem-solving skills in forestry and land management.- Includes the application of EO data for forest monitoring in both natural resources mapping and forest sustainable management- Presents advancements in geospatial technology using multispectral, hyperspectral, radar microwave, and LIDAR data in forest monitoring and its parameter retrieval- Covers upcoming satellite missions for forest monitoring
Table of Contents
Part 1. Introduction to Forest Monitoring1. Traditional methods in forest management2. An overview of remote sensing technology in forest management3. A global vulnerability management of forest resources4. Forest resource sustainable exploitation and managementPart 2. Forest Species Stand Classification: Definition and Perspectives5. A general method for the classification of forest stands6. Forest stand species mapping using the Sentinel-27. Multi-species stand classification: Definition and Perspectives8. Classification of forest stand considering shapes and sizes of tree crown calculatedPart 3. Assessment of Biophysical and Biochemical Parameters9. Establishing relationships between in situ measured between biophysical and biochemical parameters10. Chlorophyll assessment and sensitivity analysis using NIR- 11. Carbon stock assessment using non-linear processes12. Forest biodiversity and vegetation health assessment using narrow band hyperspectral dataPart 4. Methodological Considerations in the Study of Forest Ecosystems13. Thermal hyperspectral applications in forest ecosystem classification14. Invasive species identification and mapping using multi-source data15. Social functional mapping of urban green space using remote sensing data16. Bayesian data synthesis for forest fire estimationPart 5. Artificial Intelligence, Machine Learning and Deep Learning Techniques17. Developments of LiDAR for forest monitoring18. Forest damage assessment using deep learning19. Artificial intelligence and forest management20. Application of machine-learning in forest monitoring: Recent progress and future challengesPart 6. Challenges and Future Needs21. Building capacity in remote sensing for conservation: present and future challenges22. Developments of optical remote sensing: UAVs, hyperspectral and multispectral23. Developments of Review of present perspective, challenges, and Future aspects24. New satellite missions and sensors for forest monitoring
