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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/43T9NNB
Repositorysid.inpe.br/mtc-m21c/2021/01.05.16.27
Last Update2021:01.12.13.53.56 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2021/01.05.16.27.07
Metadata Last Update2024:01.10.18.58.09 (UTC) administrator
DOI10.3390/rs12233922
ISSN2072-4292
Citation KeyDoblasPrietoShSaCaArAl:2020:OpNeRe
TitleOptimizing near real-time detection of deforestation on tropical rainforests using sentinel-1 data
ProjectMonitoramento dos Biomas Brasileiros por Satélite – Construção de Novas Capacidades (2019 - 2023)
Year2020
MonthDec
Access Date2024, May 22
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size12046 KiB
2. Context
Author1 Doblas Prieto, Juan
2 Shimabukuro, Yosio Edemir
3 Sant'Anna, Sidnei João Siqueira
4 Carneiro, Arian Ferreira
5 Aragão, Luiz Eduardo Oliveira e Cruz de
6 Almeida, Claudio Aparecido de
Resume Identifier1
2 8JMKD3MGP5W/3C9JJCQ
3 8JMKD3MGP5W/3C9JJ8N
ORCID1 0000-0002-2573-3783
2
3
4
5 0000-0002-4134-6708
6 0000-0002-1032-6966
Group1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
6 DIDSR-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 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 juan.doblas@inpe.br
2 yosio.shimabukuro@inpe.br
3 sidnei.santanna@inpe.br
4 arian.carneiro@inpe.br
5 luiz.aragao@inpe.br
6 claudio.almeida@inpe.br
JournalRemote Sensing
Volume12
Number23
Pagese3922
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2021-01-05 16:27:07 :: simone -> administrator ::
2021-01-05 16:27:09 :: administrator -> simone :: 2020
2021-01-05 16:27:57 :: simone -> administrator :: 2020
2021-01-07 12:38:18 :: administrator -> simone :: 2020
2023-12-18 22:31:02 :: simone -> administrator :: 2020
2024-01-10 18:58:09 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsearly warning systems
synthetic aperture radar
brazilian amazon
time series analysis
AbstractEarly Warning Systems (EWS) for near real-time detection of deforestation are a fundamental component of public policies focusing on the reduction in forest biomass loss and associated CO2 emissions. Most of the operational EWS are based on optical data, which are severely limited by the cloud cover in tropical environments. Synthetic Aperture Radar (SAR) data can help to overcome this observational gap. SAR measurements, however, can be altered by atmospheric effects on and variations in surface moisture. Different techniques of time series (TS) stabilization have been used to mitigate the instability of C-band SAR measurements. Here, we evaluate the performance of two different approaches to SAR TS stabilization, harmonic deseasonalization and spatial stabilization, as well as two deforestation detection techniques, Adaptive Linear Thresholding (ALT) and maximum likelihood classification (MLC). We set up a rigorous, Amazon-wide validation experiment using the Google Earth Engine platform to sample and process Sentinel-1A data of nearly 6000 locations in the whole Brazilian Amazonian basin, generating more than 8M processed samples. Half of those locations correspond to non-degraded forest areas, while the other half pertained to 2019 deforested areas. The detection results showed that the spatial stabilization algorithm improved the results of the MLC approach, reaching 94.36% global accuracy. The ALT detection algorithm performed better, reaching 95.91% global accuracy, regardless of the use of any stabilization method. The results of this experiment are being used to develop an operational EWS in the Brazilian Amazon.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Optimizing near real-time...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/43T9NNB
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/43T9NNB
Languageen
Target Fileremotesensing-12-03922-v2.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2017/11.22.19.04.03
Next Higher Units8JMKD3MGP6W34M/4AH5NEL
8JMKD3MGPCW/3ER446E
Citing Item Listsid.inpe.br/marte2/2024/01.10.18.57 7
sid.inpe.br/mtc-m21/2012/07.13.15.00.20 3
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress readpermission rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Description control
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