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  • Hyperspectral Remote Sensing: Theory and Applications

    Hyperspectral Remote Sensing by Pandey, Prem Chandra; Srivastava, Prashant K.; Balzter, Heiko;

    Theory and Applications

    Series: Earth Observation;

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      • Publisher's listprice EUR 140.00
      • 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.

        58 065 Ft (55 300 Ft + 5% VAT)
      • Discount 10% (cc. 5 807 Ft off)
      • Discounted price 52 259 Ft (49 770 Ft + 5% VAT)

    58 065 Ft

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    Product details:

    • Publisher Elsevier Science
    • Date of Publication 7 August 2020

    • ISBN 9780081028940
    • Binding Paperback
    • No. of pages506 pages
    • Size 234x190 mm
    • Weight 1050 g
    • Language English
    • 44

    Categories

    Long description:

    Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology.

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    Table of Contents:

    Section 1 Introduction to Hyperspectral Remote Sensing and Principles of Theory and Data Processing
    1. Revisiting hyperspectral remote sensing: origin, processing, applications and way forward
    2. Spectral smile correction for airborne imaging spectrometers
    3. Anomaly detection in hyperspectral remote sensing images
    4. Atmospheric parameter retrieval and correction using hyperspectral data
    5. Hyperspectral image classifications and feature selection
    Section 2 Hyperspectral Remote Sensing Application in Vegetation
    6. Identification of functionally distinct plants using linear spectral mixture analysis
    7. Estimation of chengal trees relative abundance using coarse spatial resolution hyperspectral systems
    8. Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends
    9. Discriminating tropical grasses grown under different nitrogen fertilizer regimes in KwaZulu-Natal, South Africa
    Section 3 Hyperspectral Remote Sensing Application in Water, Snow, Urban Research
    10. Effect of contamination and adjacency factors on snow using spectroradiometer and hyperspectral images
    11. Remote sensing of inland water quality: a hyperspectral perspective
    12. Efficacy of hyperspectral data for monitoring and assessment of wetland ecosystem
    Section 4 Hyperspectral Remote Sensing Application in Soil and Mineral Exploration
    13. Spectroradiometry as a tool for monitoring soil contamination by heavy metals in a floodplain site
    14. Hyperspectral remote sensing applications in soil: a review
    15. Mineral exploration using hyperspectral data
    16. Metrological hyperspectral image analysis through spectral differences
    Section 5 Hyperspectral Remote Sensing: Multi-sensor, Fusion and Indices applications for Pollution
    Detection and Other Applications
    17. Improving the detection of cocoa bean fermentation-related changes using image fusion
    18. Noninvasive detection of plant parasitic nematodes using hyperspectral and other remote sensing systems
    19. Evaluating the performance of vegetation indices for detecting oil pollution effects on vegetation using hyperspectral (Hyperion EO-1) and multispectral (Sentinel-2A) data in the Niger Delta
    20. Hyperspectral vegetation indices to detect hydrocarbon pollution
    Section 6 Hyperspectral Remote Sensing: Challenges, Future Pathway for Research & Emerging Applications
    21. Future perspectives and challenges in hyperspectral remote sensing

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