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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Acknowledgments
We thank Arsenia Chorti for useful comments and help with the statistical analysis. The data used in the application were collected by Matthias Guenther and Dr. Karl-Heinz Mueller (University of Marburg, Germany) in the framework of the BIOTA-Africa project (http://www.biota-africa.org). Dr. Kerstin Wiegand (Friedrich-Schiller University, Jena, Germany) and Dr. David Ward (University of KwaZulu Natal, South Africa) had a contribution during field work verification. The comments of two anonymous reviewers considerably improved an earlier manuscript version.
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Moustakas, A., Hristopulos, D.T. Estimating tree abundance from remotely sensed imagery in semi-arid and arid environments: bringing small trees to the light. Stoch Environ Res Risk Assess 23, 111–118 (2009). https://doi.org/10.1007/s00477-007-0199-x
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DOI: https://doi.org/10.1007/s00477-007-0199-x