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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3R8F2ME
Repositorysid.inpe.br/mtc-m21c/2018/06.04.15.20   (restricted access)
Last Update2018:06.04.15.20.55 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2018/06.04.15.20.55
Metadata Last Update2020:12.07.21.11.46 (UTC) administrator
DOI10.1080/22797254.2018.1451782
ISSN2279-7254
Citation KeySidhuPebeCama:2018:SiUsCa
TitleUsing Google Earth engine to detect land cover change: Singapore as a use case
Year2018
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size3584 KiB
2. Context
Author1 Sidhu, Nanki
2 Pebesma, Edzer
3 Camara, Gilberto
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHB8
ORCID1 0000-0002-2422-8769
2 0000-0001-8049-7069
Group1
2
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Westfaelische-Wilhelms Universitaet
2 Westfaelische-Wilhelms Universitaet
3 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalEuropean Journal of Remote Sensing
Volume51
Number1
Pages486-500
History (UTC)2018-06-04 15:29:33 :: simone -> administrator :: 2018
2019-01-04 16:57:05 :: administrator -> simone :: 2018
2019-01-07 10:41:30 :: simone -> administrator :: 2018
2020-12-07 21:11:46 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsGoogle Earth Engine
big-data architecture
land cover
urban areas
time series analysis
AbstractThis paper investigates the web-based remote sensing platform, Google Earth Engine (GEE) and evaluates the platform's utility for performing raster and vector manipulations on Landsat, Moderate Resolution Imaging Spectroradiometer and GlobCover (2009) imagery. We assess its capacity to conduct space-time analysis over two subregions of Singapore, namely, Tuas and the Central Catchment Reserve (CCR), for Urban and Wetlands land classes. In its current state, GEE has proven to be a powerful tool by providing access to a wide variety of imagery in one consolidated system. Furthermore, it possesses the ability to perform spatial aggregations over global-scale data at a high computational speed though; supporting both spatial and temporal analysis is not an obvious task for the platform. We examine the challenges that GEE faces, also common to most parallel-processing, big-data architectures. The ongoing refinement of this system makes it promising for big-data analysts from diverse user groups. As a use case for exploring GEE, we analyze Singapore's land use and cover. We observe the change in Singapore's landmass through land reclamation. Also, within the region of the CCR, a large protected area, we find forest cover is not affected by anthropogenic factors, but instead is driven by the monsoon cycles affecting Southeast Asia.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Using Google Earth...
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4. Conditions of access and use
Languageen
Target Filesidhu_using.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.49.18 4
sid.inpe.br/bibdigital/2013/09.09.15.05 2
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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