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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/46DJB68
Repositorysid.inpe.br/mtc-m21d/2022/02.23.18.01
Last Update2022:02.23.18.01.14 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/02.23.18.01.14
Metadata Last Update2023:01.04.16.24.24 (UTC) simone
DOI10.3390/rs14030736
ISSN2072-4292
Citation KeyGanemXRFOCCRDS:2022:MaSoAm
TitleMapping South America's Drylands through Remote Sensing-A Review of the Methodological Trends and Current Challenges
Year2022
MonthFeb.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2821 KiB
2. Context
Author 1 Ganem, Khalil Ali
 2 Xue, Yongkang
 3 Rodrigues, Ariane de Almeida
 4 Franca Rocha, Washington
 5 Oliveira, Marceli Terra de
 6 Carvalho, Nathalia Silva de
 7 Cayo, Efrain Yury Turpo
 8 Rosa, Marcos Reis
 9 Dutra, Andeise Cerqueira
10 Shimabukuro, Yosio Edemir
Resume Identifier 1
 2
 3
 4
 5
 6
 7
 8
 9
10 8JMKD3MGP5W/3C9JJCQ
ORCID 1 0000-0001-9126-7138
 2 0000-0002-6169-9631
 3 0000-0003-2542-2288
 4 0000-0002-2175-2792
 5 0000-0001-6087-6344
 6 0000-0003-0651-6967
 7 0000-0002-6691-9867
 8 0000-0001-5367-8059
 9 0000-0002-4454-7732
10 0000-0002-1469-8433
Group 1
 2
 3
 4
 5 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
 6 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
 7
 8
 9 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
10 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation 1 University of California
 2 University of California
 3 Universidade de Brasília (UnB)
 4 Universidade Estadual de Feira de Santana (UEFS)
 5 Instituto Nacional de Pesquisas Espaciais (INPE)
 6 Instituto Nacional de Pesquisas Espaciais (INPE)
 7 Universidad Nacional Agraria La Molina
 8 Universidade Estadual de Feira de Santana (UEFS)
 9 Instituto Nacional de Pesquisas Espaciais (INPE)
10 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address 1 khalilganem@hotmail.com
 2 yxue@geog.ucla.edu
 3 arianerodrigues@gmail.com
 4 wrocha@uefs.br
 5 marceli.terra@inpe.br
 6 nathalia.carvalho@inpe.br
 7 eturpo@lamolina.edu.pe
 8 marcosrosa@alumni.usp.br
 9 andeise.dutra@inpe.br
10 yosio.shimabukuro@inpe.br
JournalRemote Sensing
Volume14
Number3
Pagese736
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2022-02-23 18:01:14 :: simone -> administrator ::
2022-02-23 18:01:14 :: administrator -> simone :: 2022
2022-02-23 18:02:30 :: simone -> administrator :: 2022
2023-01-03 16:46:01 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsland use and land cover
aridity
drought
Landsat
MODIS
savannas
shrublands
grasslands
woodlands
AbstractThe scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent's hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America's drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping South America's...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping South America's...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34T/46DJB68
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34T/46DJB68
Languageen
Target Fileremotesensing-14-00736-v2.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 5
sid.inpe.br/bibdigital/2022/04.03.22.23 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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