全球变化知识资源中心

Knowledge Resources Center for Global Change


Email:
Passwd
验证:
	换一张
Have you forgotten your password? Stay signed in Log In

globalchange > 过去全球变化的重建

DOI:	10.1371/journal.pone.0172663
论文题名:	Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data
作者:	Hans Martin Schulz; Ching-Feng Li; Boris Thies; Shih-Chieh Chang; Jörg Bendix
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2017
发表日期:	2017-2-28
卷:	12, 期:2
语种:	英语
英文关键词:	Forests ; Clouds ; Taiwan ; Monsoons ; Machine learning ; Fog ; Forecasting ; Rain
英文摘要:	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.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0172663&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/25626
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

Files in This Item:	Download All

File Name/ File Size	Content Type	Version	Access	License
journal.pone.0172663.pdf（3624KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

Laboratory for Climatology and Remote Sensing, Philipps-Universität Marburg, Marburg, Germany;School of Forestry and Resource Conservation, National Taiwan University, Taipei, Taiwan;Laboratory for Climatology and Remote Sensing, Philipps-Universität Marburg, Marburg, Germany;Department of Natural Resources and Environmental Studies, National Dong Hwa University, Hualien, Taiwan;Laboratory for Climatology and Remote Sensing, Philipps-Universität Marburg, Marburg, Germany

Recommended Citation:

Hans Martin Schulz,Ching-Feng Li,Boris Thies,et al. Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data[J]. PLOS ONE,2017-01-01,12(2)

Service
	Recommend this item
	Sava as my favorate item
	Show this item's statistics
	Export Endnote File
Google Scholar
	Similar articles in Google Scholar
	[Hans Martin Schulz]'s Articles
	[Ching-Feng Li]'s Articles
	[Boris Thies]'s Articles
百度学术
	Similar articles in Baidu Scholar
	[Hans Martin Schulz]'s Articles
	[Ching-Feng Li]'s Articles
	[Boris Thies]'s Articles
CSDL cross search
	Similar articles in CSDL Cross Search
	[Hans Martin Schulz]‘s Articles
	[Ching-Feng Li]‘s Articles
	[Boris Thies]‘s Articles
Related Copyright Policies
Null
收藏/分享