Advanced Geospatial and Ground Based Techniques in Forest Monitoring
Herausgeber: Kumar, Pavan; Kumar, Kireet; Roy, Parth Sarathi; Arunachalam, Ayyanadar; Khan, Mohamed Latif; Srivastava, Prashant Kumar
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Advanced Geospatial and Ground Based Techniques in Forest Monitoring
Herausgeber: Kumar, Pavan; Kumar, Kireet; Roy, Parth Sarathi; Arunachalam, Ayyanadar; Khan, Mohamed Latif; Srivastava, Prashant Kumar
- Broschiertes Buch
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…mehr
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- Produktdetails
- Verlag: Elsevier Health Sciences
- Seitenzahl: 368
- Erscheinungstermin: 1. Juli 2025
- Englisch
- Abmessung: 235mm x 191mm
- Gewicht: 450g
- ISBN-13: 9780443189494
- ISBN-10: 0443189498
- Artikelnr.: 65507028
- Verlag: Elsevier Health Sciences
- Seitenzahl: 368
- Erscheinungstermin: 1. Juli 2025
- Englisch
- Abmessung: 235mm x 191mm
- Gewicht: 450g
- ISBN-13: 9780443189494
- ISBN-10: 0443189498
- Artikelnr.: 65507028
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
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