• Contact

  • Newsletter

  • About us

  • Delivery options

  • Prospero Book Market Podcast

  • Remote Sensing for Vegetation Monitoring: Technologies, Applications and Models

    Remote Sensing for Vegetation Monitoring by Pandey, Prem Chandra; Behera, Mukunda; Kantamaneni, Komali; Kumar, Navneet;

    Technologies, Applications and Models

      • GET 10% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 153.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        60 148 Ft (57 284 Ft + 5% VAT)
      • Discount 10% (cc. 6 015 Ft off)
      • Discounted price 54 133 Ft (51 556 Ft + 5% VAT)

    60 148 Ft

    db

    Availability

    Not yet published.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Publisher Elsevier Science
    • Date of Publication 15 October 2026

    • ISBN 9780443330766
    • Binding Paperback
    • No. of pages450 pages
    • Size 235x191 mm
    • Language English
    • 700

    Categories

    Long description:

    Remote Sensing for Vegetation Monitoring: Technologies, Applications and Models provides insight on the pivotal role that remote sensing plays in vegetation monitoring. As traditional field assessments face challenges due to inaccessible study sites and lengthy data collection, this book offers a comprehensive view of remote sensing applications for monitoring various vegetation ecosystems, including forests, grasslands, mangroves, and agriculture. The book presents a coherent and consistent structure, across five sections that build upon prior knowledge and detail the quantitative and qualitative assessments made possible through remote sensing technologies. Remote Sensing for Vegetation Monitoring: Technologies, Applications and Models caters to a diverse audience, including researchers and practitioners seeking to navigate the evolving landscape of vegetation monitoring through case studies, new algorithms and state-of-the-art methods.

    More

    Table of Contents:

    1. Introduction to Remote Sensing for Vegetation Monitoring

    Section 1: Conventional and Advanced Forest Ecosystem Monitoring
    2. Surveying Techniques and Sampling Techniques in Forest Ecosystems
    3. Crop Canopy Stress/Chlorophyll Estimation Using Drone or Thermal Sensors
    4. Biophysical And Biochemical Analysis and Monitoring of Forest Ecosystems
    5. Species-Level Classification Using Pixel Based and OBIA Object Based Approaches Drones in Vegetation and Surroundings Assessment
    6. Mangroves Forests - Blue Carbon Places to Help Mitigate Climate Change
    7. Multi-Source and Multi-Sensor Approaches in Forest Monitoring
    8. Forest Fire Analysis, Simulation and Modelling Using Advanced Techniques
    9. Biophysical/Biochemical Parameter Retrieval from An Unmanned Autonomous Vehicle (UAV)
    10. Forest Ecosystem Monitoring Summary

    Section 2: Agriculture and Grassland Monitoring
    11. Crop Stress and Water Deficit Relationship Using Remote Sensing and Field Inventory Methods
    12. Crop Yield Estimation and Modelling
    13. Crop Damage Assessment Using Multi-Sensors and Multi-Source Remote Sensing Data
    14. Artificial Intelligence Techniques in Grassland Monitoring
    15. Establishment Of Relationships Between in Situ Measured Biophysical/Biochemical Parameters and Ground-Measured Data
    16. Agriculture and Grassland Monitoring Summary

    Section 3: Monitoring Urban Green Space and Mangrove Forests
    17. Urban Discomfort Analysis and Urban Green Space Assessment
    18. Urban Heat Islands - Can This Be Mitigated by Increasing Green Spaces?
    19. Monitoring Urban Green Space and Mangrove Forests Summary

    Section 4: Advanced Modelling for Machine Learning / Artificial Intelligence
    20. Hyperspectral Data for Quantification of Vegetation
    21. Multi-Source and Machine Learning in Vegetation Classification
    22. Data Fusion Technique and GUI Based Model in Vegetation Mapping and Monitoring
    23. Deep Learning Techniques in Mangrove Forest Monitoring
    24. ML and Modelling Summary

    Section 5: Future Aspects and Challenges in Remote Sensing
    25. Challenges And Emerging Applications in Vegetation Monitoring
    26. Future Earth Observation Space Missions Devoted to Vegetation Monitoring for Sustainable Development Goals.
    27. Summary of Future Challenges

    More
    0