Open AccessMapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data
Author(s) -
Hans Martin Schulz,
ChingFeng Li,
Boris Thies,
ShihChieh Chang,
Jörg Bendix
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0172663
Subject(s) - cloud forest , remote sensing , moderate resolution imaging spectroradiometer , digital elevation model , vegetation (pathology) , environmental science , altitude (triangle) , elevation (ballistics) , random forest , raster data , montane ecology , cloud computing , enhanced vegetation index , data set , physical geography , normalized difference vegetation index , geography , raster graphics , climate change , geology , satellite , ecology , vegetation index , computer science , mathematics , artificial intelligence , aerospace engineering , oceanography , pathology , engineering , biology , operating system , geometry , machine learning , medicine
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.