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Full Description
This edited book is a collection of information on recent advancements in remote sensing for forestry application. The main focus of this book is to address the novel applications in remote sensing in terms of recent techniques, sensors, methods and data collection. Remote sensing has been used for many decades. Today, remote sensing is heavily utilized in forest management, which is acquired from airborne and space-borne platforms using satellite data. In recent years, there have been rapid advances in the new types of sensors. The new generations of satellite sensors are introduced not only to provide important information on forest ecosystems, but also to improve the techniques and accuracies obtained by the traditional approaches. Researchers have become increasingly aware of the potential of remote sensing to address important forestry issues and challenges. The number of forestry publications using remote sensing has grown very rapidly, and this is noticeable with many recent technologies and applications. This book provides valuable source of reference to foresters, researchers, ecologists, climate change scientists and scholars who use remote sensing in their work. Furthermore, it serves as an academic book for undergraduate and graduate students of forestry, agriculture, natural resources and environmental sciences. The book demonstrates and engages the students how the new advancements in remote sensing technology and theory relate to the many real-world forestry applications. It gives readers deeper understanding on how remote sensing has come a long way and evolved into a mature science in the past five decades.
Contents
1. Remote Sensing for Forest Inventory and Resources Assessment.- 2. Multiple Sensors and Platforms for Biophysical and Biochemical Characterizations of Various Ecosystem Types of Tropical Forests in Malaysia: Advance, Limitation and Opportunity.- 3. A Review on the Use of LiDAR Remote Sensing for Forest Landscape Restoration.- 4. Assessment and Modeling of Forest Biomass and Carbon Stock and Sequestration using Various Remote Sensing Sensor Systems.- 5. Spatial Modeling of Transport and Resources Accessibility for Protecting Forest Ecosystems Against Forest Fires.- 6. Assessment of Forest Aboveground Biomass Estimation from Superview-1 Satellite Image using Machine Learning Approaches.- 7. Floristic Distribution Modelling for Resource Partitioning of Four Native Tree Species in Azad Jammu and Kashmir (AJK), Pakistan using Maxent.- 8. Application of Remote Sensing Vegetation Indices for Forest Cover Assessments.- 9. Rainforest Assessment in Brunei Darussalam through Application of Remote Sensing.- 10. Rubber Trees and Biomass Estimation Using Remote Sensing Technology.- 11. The Use of Landsat TM Imagery for the Application of Rubber Tree Area and Stand Volume Predictive Models in Rubber Plantations in Selangor, Malaysia.- 12. Using Historical Disturbance Identified with LandTrendr in Google Earth Engine for Land Cover Mapping of Oil Palm Landscapes.- 13. Geospatial Technology - Unlocking the Management and Monitoring in Malaysian Mangrove Forests.- 14. Effect of Tidal Regime, Relative Sea Level and Wind Intensity on Changes of Mangrove Area using Remote Sensing Approach.- 15. Spatio-temporal Distribution of Mangrove at Kuala Sepetang Forest Reserve, Malaysia using Remotely-sensed Data.- 16. Determination of The Effect of Urban Forests and Other Green Areas on Surface Temperature in Antalya.- 17. Conceptualising the Citizen-Driven Urban Forest Framework to Improve Local Climate Condition: Geospatial Data Fusion and Numerical Simulation.- 18. State-of-the-Art on Airborne LiDAR Applications in the Field of Forest Engineering.- 19. Restoration of Damaged Forest and Roles of Remote Sensing.- 20. Recent Advances in UAV-based Structure-from-motion Photogrammetry for Aboveground Biomass and Carbon Storage Estimations in Forestry.- 21. Hyperspectral Identification of Selected Dipterocarp Montane at the Species Level.- 22. Tree Biophysical Parameters Retrieval from Multi-source Remote Sensing Data Fusion.
