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Dataset of Mangroves in Southeast Asia


LV Tingting1ZHOU Xiang1LIU Chuang2TAO Zui1Yongyut Trisurat3
1 Institute of Remote Sensing and Digital Earth,CAS2 Institute of Geographical Sciences and Natural Resources Research CAS3 Kasetsart University Thailand

DOI:10.3974/geodb.2015.01.08.V1

Published:April 2015

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Data Downloaded:52533.43 MB      Citations:

Key Words:

Southeast Asia,mangroves,spatial distribution,LANDSAT-8

Abstract:

As an ecosystem with both terrestrial and marine characteristics, mangrove forests play important roles in buffering against wind-induced waves, stabilizing shorelines and protecting coastal inhabitants. Mangrove can be detected due to its unique spectral features in SWIR and its particular locations in intertidal zone. Based on 30m Landsat-8 from 2013 to 2015 and ASTER GDEM and training data acquired from GOOGLE EARTH, extent of mangroves is derived in Southeast Asia using multi-classifiers: support vector machine, artificial neural network, spectral angle, maximum likelihood, and minimum distance. Bayesian algorithm is applied to a parallel combination method to integrate the outputs of multi-classifiers. The mangrove dataset is evaluated using sample data acquired from GOOGLE EARTH and is delivered in both .shp and .kmz formats. Our findings indicate that mangrove totals 4.52�104 km2 in this region.

Foundation Item:

National Natural Science Fundation of China (41201442)

Data Citation:

LV Tingting, ZHOU Xiang, LIU Chuang, TAO Zui, Yongyut Trisurat.Dataset of Mangroves in Southeast Asia[J/DB/OL]. Digital Journal of Global Change Data Repository, 2015. https://doi.org/10.3974/geodb.2015.01.08.V1.

Data Product:

ID Data Name Data Size Operation
1 Mangrov_SEAsia_2015.kmz 30208.96KB
2 Mangrov_SEAsia_2015.rar 61930.77KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum