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        Chapter Absolute Density Measures Estimation Functions with Very High Resolution Satellite Images

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        Author(s)
        Cristina Gonçalves, Ana
        M. O. Sousa, Adélia
        Language
        English
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        Abstract
        Assessment and monitoring of forest structure is frequently done with absolute density measures with field forest inventory data and expansion methods. The development of basal area and the number of trees estimation functions with data derived from very high spatial resolution satellite images enable their short-term and cost-effective evaluation, allowing also the estimation for the area not requiring extrapolation methods. The functions of basal area and the number of trees per hectare are based on crown cover obtained with very high spatial resolution satellite images for two evergreen oaks and umbrella pine. The three tree species are especially important in the agroforestry systems of the Mediterranean region. The linear functions fitted for pure stands of the three species and mixed stands of cork and holm oak and of cork oak and umbrella pine showed a better performance for basal area than for the number of trees per hectare. The inclusion of dummy variables for species composition improved the accuracy of the functions.
        URI
        https://library.oapen.org/handle/20.500.12657/49266
        Keywords
        quickBird image, multi-resolution segmentation, crown horizontal projection, absolute density measures, regression
        DOI
        10.5772/intechopen.76817
        Publisher
        InTechOpen
        Publisher website
        https://www.intechopen.com/
        Publication date and place
        2018
        Classification
        Earth sciences
        Rights
        https://creativecommons.org/licenses/by/3.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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