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Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data

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  • Hans Martin Schulz
  • Ching-Feng Li
  • Boris Thies
  • Shih-Chieh Chang
  • Jörg Bendix

Abstract

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.

Suggested Citation

  • Hans Martin Schulz & Ching-Feng Li & Boris Thies & Shih-Chieh Chang & Jörg Bendix, 2017. "Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-17, February.
    • Handle: RePEc:plo:pone00:0172663
    DOI: 10.1371/journal.pone.0172663
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    References listed on IDEAS

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    1. Rocío Ponce-Reyes & Víctor-Hugo Reynoso-Rosales & James E. M. Watson & Jeremy VanDerWal & Richard A. Fuller & Robert L. Pressey & Hugh P. Possingham, 2012. "Vulnerability of cloud forest reserves in Mexico to climate change," Nature Climate Change, Nature, vol. 2(6), pages 448-452, June.
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