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Integrating remote sensing and image processing to test for disturbance effects in a post-hurricane mangrove ecosystem

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Abstract

We use drone images of a post-hurricane mangrove in south-west Mexico to apply landscape segmentation and evaluate direct disturbance effects on forest cover and indirect effects on two closely related bird species. Hurricane Carlotta made landfall in June 2012. We obtained images at ten sites comprising two mangrove types in August 2015 and made standardised counts of Rufous-naped Wren and Banded Wren at these sites in 2011 and 2015. Superpixels were extracted from drone images using an energy-driven sampling algorithm. The attributes of a sub-sample of representative superpixels were encoded as classifier inputs for feature extraction, and supervised classification was then attained with a random decision forest. The scale of classified superpixels was congruent with micropatches in wren habitats. We used percentages derived from superpixel classes of live trees and dead wood to calculate post-hurricane fractional vegetation cover. We applied generalised linear mixed models to relate live tree cover to pre- and post-hurricane frequency indices for both wren species. White mangrove sites produced markedly higher fractional cover values than red mangrove sites. Whereas the Rufous-naped Wren was not associated with live tree cover, Banded Wren occupancy was higher in areas with greater cover. Segmentation methods combined with bird monitoring constitute a tool to analyse how faunal populations respond to or are impacted by hurricane-induced changes to wetland vegetation.

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Acknowledgements

The first author would like to thank the Science and Technology Council of Mexico, CONACyT, for its financial support throughout the project CB-2015/256126. Sincere thanks are extended to Luis Oscar Rios for providing all photography equipment and piloting the drone. We are grateful to two anonymous reviewers for their helpful comments

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Authors and Affiliations

  1. Information Technologies Laboratory, Tecnológico Nacional de México/ITS de Irapuato, Irapuato, Guanajuato, Mexico

    Juan Pablo Serrano-Rubio & Ulises Vidal-Espitia

  2. The Barn Owl Trust, Ashburton, Devon, UK

    Mateo D. M. Ruiz

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  1. Juan Pablo Serrano-Rubio
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  2. Mateo D. M. Ruiz
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  3. Ulises Vidal-Espitia
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Correspondence to Mateo D. M. Ruiz.

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Serrano-Rubio, J.P., Ruiz, M.D.M. & Vidal-Espitia, U. Integrating remote sensing and image processing to test for disturbance effects in a post-hurricane mangrove ecosystem. SIViP 15, 351–359 (2021). https://doi.org/10.1007/s11760-020-01754-9

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