Estimating tree abundance from remotely sensed imagery in semi-arid and arid environments: bringing small trees to the light |
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Authors: | Aristides Moustakas Dionissios T Hristopulos |
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Institution: | (1) Geostatistics Research Unit, Mineral Resources Engineering, Technical University of Crete, University Campus, 73100 Chania, Crete, Greece;(2) Institute of Integrative and Comparative Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK |
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Abstract: | The analysis of remotely sensed images provides a powerful method for estimating tree abundance. However, a number of trees
have sizes that are below the spatial resolution of remote sensing images, and as a result they cannot be observed and classified.
We propose a method for estimating the number of such sub-resolution trees on forest stands. The method is based on a backwards
extrapolation of the size-class distribution of trees as observed from the remotely sensed images. We apply our method to
a tree database containing around 13,000 tree individuals to determine the number of sub-resolution trees. While the proposed
method is formulated for estimating tree abundance from remotely sensed images, it is generally applicable to any database
containing tree canopy surface area data with a minimum size cut-off. |
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Keywords: | Ecosystem assessment Enviroinformatics Forest management Negative exponential Size distribution Regression Abundance estimation Tree canopy Surface area Population change |
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