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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/3RAQTF2
Repositorysid.inpe.br/plutao/2018/06.19.04.48   (restricted access)
Last Update2018:06.19.15.42.42 (UTC) lattes
Metadata Repositorysid.inpe.br/plutao/2018/06.19.04.48.01
Metadata Last Update2021:03.06.19.29.45 (UTC) administrator
ISSN2073-445X
Labellattes: 3997386421385499 4 ReisDutSanVog:2018:CoReSe
Citation KeyReisEscDutSanVog:2018:CoReSe
TitleTowards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies
Year2018
Access Date2024, May 19
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size19085 KiB
2. Context
Author1 Reis, Mariane Souza
2 Escada, Maria Isabel Sobral
3 Dutra, Luciano Vieira
4 Sant'Anna, Sidnei João Siqueira
5 Vogt, Nathan David
Resume Identifier1
2 8JMKD3MGP5W/3C9JHRG
3 8JMKD3MGP5W/3C9JHMA
4 8JMKD3MGP5W/3C9JJ8N
Group1 CST-CST-SESPG-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade do Vale do Paraíba
Author e-Mail Address1 reis@dpi.inpe.br
2 isabel.escada@inpe.br
3 dutra@dpi.inpe.br
4 sidnei@dpi.inpe.br
5 reis@dpi.inpe.br
JournalLand
Volume7
Number2
Pages1-29
History (UTC)2018-06-19 04:48:01 :: lattes -> administrator ::
2018-06-19 11:34:31 :: administrator -> lattes :: 2018
2018-06-19 15:42:43 :: lattes -> administrator :: 2018
2018-06-20 17:45:18 :: administrator -> lattes :: 2018
2018-06-25 15:43:37 :: lattes -> administrator :: 2018
2021-03-06 19:29:45 :: administrator -> simone :: 2018
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(LULC)
Brazilian Amazon
remote sensing
class definition
field data collection
land cover meta language(LCML)
classification system
AbstractLand Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.
AreaCST
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Towards a Reproducible...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CST > Towards a Reproducible...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languageen
Target Filereis_towards.pdf
User Grouplattes
Reader Groupadministrator
lattes
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/449U4PL
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.15.00.20 4
sid.inpe.br/bibdigital/2013/09.09.15.05 2
sid.inpe.br/bibdigital/2021/03.06.05.18 2
DisseminationWEBSCI; PORTALCAPES.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel doi e-mailaddress format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Description control
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