About: Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach

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article by Le Yu et al published 20 May 2013 in International Journal of Remote Sensing

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) наукова стаття, опублікована в травні 2013 (uk) im Mai 2013 veröffentlichter wissenschaftlicher Artikel (de) article by Le Yu et al published 20 May 2013 in International Journal of Remote Sensing (en)
publication date	wds:Q58068540-CCBFB64D-E1BE-4657-8A55-C9E1C4BCFCF7
publication date	2013-05-20 00:00:00Z (xsd:dateTime)
cites work	wds:Q58068540-033B47A8-152A-422A-B124-62C3C83D04A3 wds:Q58068540-1C82846A-1D18-43A9-AECE-D0A16A13D3CC wds:Q58068540-36521CCC-A827-4229-BEFB-960004CD126D wds:Q58068540-4AFBC786-ACA7-47E2-931B-3FAEDC6628F0 wds:Q58068540-76496CA7-17F3-4747-99D8-460719253F96 wds:Q58068540-93BAA107-CC2B-4DCB-BDE1-09B19298CEE8 wds:Q58068540-CBFDFC66-19A2-4D3D-8F81-C3973FC3826C wds:Q58068540-F967692B-CCA3-4D16-B9B0-90CFD8F621A0 wds:Q58068540-F9775440-299C-44A6-9433-6C567BA2A429
cites work	Terrestrial Ecoregions of the World: A New Map of Life on Earth Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation Very high resolution interpolated climate surfaces for global land areas Developing regional and species-level assessments of climate change impacts on biodiversity in the Cape Floristic Region Watersheds in digital spaces: an efficient algorithm based on immersion simulations Overview of the radiometric and biophysical performance of the MODIS vegetation indices Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
author name string	wds:Q58068540-6D0DBC90-2FB7-4367-AA91-554414B1F56E
author name string	Jie Wang
rdfs:label	Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (en) Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (nl)
skos:prefLabel	Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (en) Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (nl)
name	Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (en) Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (nl)
author	wds:Q58068540-6EF12116-D5A7-4C9F-9B71-12B91B953F16 wds:Q58068540-FA9BB6DC-F0AC-4E0A-BAEC-8F79E458C8D3
author	Peng Gong Le Yu
title	wds:Q58068540-D3F5C5B0-853B-4241-AE09-A82E3DA2ED15
title	Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach (en)
page(s)	wds:Q58068540-851FCA1D-160F-4C26-A537-5F362883B14F
page(s)	5851-5867
instance of	wds:Q58068540-15CFAADB-38BE-4C20-84EA-A3E68A191072
instance of	scholarly article
main subject	wds:Q58068540-486B702B-3D29-440F-BBF8-0E0B38AD3FF5 wds:Q58068540-C197DAD9-98A7-4546-B8FA-46A6C6AE5780
main subject	land cover remote sensing
published in	wds:Q58068540-53298B3D-0372-4EBE-AA68-7324904C5D8A
published in	International Journal of Remote Sensing
issue	wds:Q58068540-BA4CF5BF-9C31-48CC-9177-1A97C9BFE452
volume	wds:Q58068540-28928A29-510D-41CB-9E60-09F3123CA9D7
issue	16
volume	34
DOI	wds:Q58068540-B28D8131-8CB9-46E5-94D4-4F7AD3E6A6B1
DOI	http://dx.doi.org/10.1080/01431161.2013.798055
DOI	10.1080/01431161.2013.798055
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q58068540
is cites work of	Meta-discoveries from a synthesis of satellite-based land-cover mapping research Exploring the correlations between ten monthly climatic variables and the vegetation index of four different crop types at the global scale Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations Exploring the potential role of feature selection in global land-cover mapping A multi-resolution global land cover dataset through multisource data aggregation A new research paradigm for global land cover mapping FROM-GC: 30 m global cropland extent derived through multisource data integration Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map Mapping finer-resolution land surface emissivity using Landsat images in China Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China An all-season sample database for improving land-cover mapping of Africa with two classification schemes Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images Environmental Drivers and Predicted Risk of Bacillary Dysentery in Southwest China. Monitoring cropland changes along the Nile River in Egypt over past three decades (1984–2015) using remote sensing Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data Rapid corn and soybean mapping in US Corn Belt and neighboring areas A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST A Circa 2010 Thirty Meter Resolution Forest Map for China
is cites work of	wds:Q58068489-F67879BD-995B-470A-B9CF-B74170C9FF63 wds:Q58068502-CD1A1F07-9D67-4DD6-9C59-8ED079E1A814 wds:Q58281496-33286296-CB5F-4EC4-8E0C-A5100C63F45B wds:Q58397373-8B0A0CFC-40B9-49BB-B0E0-06D0D7759413 wds:Q58068483-8C3D1024-BAB4-4FFB-83CB-15D1AC2279B1 wds:Q58068488-94902E2E-4BF0-465F-91EE-3B9696C26541 wds:Q58068511-C89A648E-A03A-44F5-BA5C-227BF480918E wds:Q58068529-FF34C964-F0E4-4AFD-BC87-A413BF9AFE67 wds:Q57410714-8A2B7D4B-FFA9-4787-9996-B2D4AA546EBB wds:Q58068476-6FDE48D2-CC8C-4879-9D02-46ECBF5D11FE wds:Q58068500-AABD1672-BF36-4D20-9067-943914F89B4E wds:Q37394273-BF01B68C-7179-4451-8FE7-07A81517DB3E wds:Q58068496-BB4ECC03-7332-4E8B-9C30-E5272D2F8076 wds:Q58068505-88BD8238-57D6-4F41-A9CA-1B97E2B09C8D wds:Q58068526-C32C1E98-1D2F-42C4-876B-250AB3350A42 wds:Q58068538-3878C074-7F99-4FBA-BFFE-66EDEC567F26 wds:Q38679244-CD84290B-EC56-4886-85BF-F61DC5B43AC3 wds:Q58068522-E4ED92D7-EBEA-43CB-9EDA-87462C0183B0 wds:Q58068525-63007B5B-5438-457D-9152-2197A1B2130B

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