Advances in Remote Sensing for Forest Monitoring
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Advances in Remote Sensing for Forest Monitoring
Arellano, Paul; Pandey, Prem C.
John Wiley & Sons Inc
11/2022
400
Dura
Inglês
9781119788126
15 a 20 dias
Descrição não disponível.
1. Introduction to Forest Monitoring Using Advanced Remote Sensing Technology
2. Geospatial Perspectives of Sustainable Forests Management to Enhance Ecosystem Services and Livelihood Security
3. Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data
4. Modelling of Abiotic Stress of Conifers with Remote Sensing Data
5. Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar
6. Photosynthetic Variables Estimation in a Mangrove Forest
7. Quantifying Carbon Stock Variability of Species within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery
8. Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia
9. Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms
10. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: a Case Study of Victoria, Australia.
11. Recent Advancement and Role of Drones in Forest monitoring: Research and Practices
12. Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping
13. Challenges and Monitoring Methods of Forest Management through Geospatial Application
14. Challenges and Future Possibilities Towards Himalayan Forest Monitoring
2. Geospatial Perspectives of Sustainable Forests Management to Enhance Ecosystem Services and Livelihood Security
3. Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data
4. Modelling of Abiotic Stress of Conifers with Remote Sensing Data
5. Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar
6. Photosynthetic Variables Estimation in a Mangrove Forest
7. Quantifying Carbon Stock Variability of Species within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery
8. Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia
9. Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms
10. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: a Case Study of Victoria, Australia.
11. Recent Advancement and Role of Drones in Forest monitoring: Research and Practices
12. Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping
13. Challenges and Monitoring Methods of Forest Management through Geospatial Application
14. Challenges and Future Possibilities Towards Himalayan Forest Monitoring
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Remote sensing in forests; forestry; advanced remote sensing; remote sensing technologies; sustainable forests; sustainable forestry; sustainable forest management; forest management; geospatial approaches in forestry; forest monitoring
1. Introduction to Forest Monitoring Using Advanced Remote Sensing Technology
2. Geospatial Perspectives of Sustainable Forests Management to Enhance Ecosystem Services and Livelihood Security
3. Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data
4. Modelling of Abiotic Stress of Conifers with Remote Sensing Data
5. Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar
6. Photosynthetic Variables Estimation in a Mangrove Forest
7. Quantifying Carbon Stock Variability of Species within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery
8. Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia
9. Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms
10. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: a Case Study of Victoria, Australia.
11. Recent Advancement and Role of Drones in Forest monitoring: Research and Practices
12. Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping
13. Challenges and Monitoring Methods of Forest Management through Geospatial Application
14. Challenges and Future Possibilities Towards Himalayan Forest Monitoring
2. Geospatial Perspectives of Sustainable Forests Management to Enhance Ecosystem Services and Livelihood Security
3. Distinguishing Carotene and Xanthophyll Contents in the Leaves of Riparian Forest Species by Applying Machine Learning Algorithms to Field Reflectance Data
4. Modelling of Abiotic Stress of Conifers with Remote Sensing Data
5. Retrieval of Mangrove Forest Properties Using Synthetic Aperture Radar
6. Photosynthetic Variables Estimation in a Mangrove Forest
7. Quantifying Carbon Stock Variability of Species within a Reforested Urban Landscape Using Texture Measures Derived from Remotely Sensed Imagery
8. Mapping Oil Palm Plantations in the Fringe of Sebangau National Park, Central Kalimantan, Indonesia
9. Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms
10. Leveraging Google Earth Engine (GEE) and Landsat Images to Assess Bushfire Severity and Postfire Short-Term Vegetation Recovery: a Case Study of Victoria, Australia.
11. Recent Advancement and Role of Drones in Forest monitoring: Research and Practices
12. Applications of Multi-Source and Multi-Sensor Data Fusion of Remote Sensing for Forest Species Mapping
13. Challenges and Monitoring Methods of Forest Management through Geospatial Application
14. Challenges and Future Possibilities Towards Himalayan Forest Monitoring
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.