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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3AFKS9M
Repositorydpi.inpe.br/plutao/2011/09.22.16.13.13
Last Update2011:10.10.13.18.11 (UTC) marciana
Metadata Repositorydpi.inpe.br/plutao/2011/09.22.16.13.14
Metadata Last Update2018:06.05.00.01.23 (UTC) administrator
DOI10.3390/rs3091943
ISSN2072-4292
Labellattes: 1913003589198061 2 AraiShimPereVija:2011:AMuMu
Citation KeyAraiShimPereVija:2011:MuMuTe
TitleA multi-resolution multi-temporal technique for detecting and mapping deforestation in the Brazilian Amazon rainforest
Year2011
MonthSet.
Access Date2024, May 22
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size7277 KiB
2. Context
Author1 Arai, Egídio
2 Shimabukuro, Yosio Edemir
3 Pereira, Gabriel
4 Vijaykumar, Nandamudi Lankalapalli
Resume Identifier1 8JMKD3MGP5W/3C9JGUP
2 8JMKD3MGP5W/3C9JJCQ
3
4 8JMKD3MGP5W/3C9JHTU
Group1 DSR-OBT-INPE-MCT-BR
2 DSR-OBT-INPE-MCT-BR
3 DSR-OBT-INPE-MCT-BR
4 LAC-CTE-INPE-MCT-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)
Author e-Mail Address1
2 yosio@ltid.inpe.br
3 gabriel@dsr.inpe.br
4 vijay@lac.inpe.br
e-Mail Addressyosio@ltid.inpe.br
JournalRemote Sensing
Volume3
Number9
Pages1943-1956
History (UTC)2011-09-23 14:11:15 :: lattes -> secretaria.cpa@dir.inpe.br :: 2011
2012-01-17 14:51:52 :: secretaria.cpa@dir.inpe.br -> administrator :: 2011
2016-06-04 01:07:45 :: administrator -> marciana :: 2011
2016-08-19 13:24:39 :: marciana -> administrator :: 2011
2018-06-05 00:01:23 :: administrator -> marciana :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsAmazon rain forest
Amazonia
Bootstrap technique
Brazilian Amazon
Cloud interference
Computational resources
Confidence interval
Dynamic process
Environment change
High frequency
High spatial resolution
Image simulations
LANDSAT
LandSat 7
Landsat-7 (L7) Enhanced Thematic mapper plus (ETM+)
Linear regression methods
Linear spectral mixing models
Moderate resolution imaging spectroradiometer
Modis
Multi-resolutions
Multi-temporal
Observed data
Reference image
Sensibility analysis
Simulated images
Spatial resolution
T-tests
Tropical deforestation
Abstracthe analysis of rapid environment changes requires orbital sensors with high frequency of data acquisition to minimize cloud interference in the study of dynamic processes such as Amazon tropical deforestation. Moreover, a medium to high spatial resolution data is required due to the nature and complexity of variables involved in the process. In this paper we describe a multiresolution multitemporal technique to simulate Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image using Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The proposed method preserves the spectral resolution and increases the spatial resolution for mapping Amazon Rainfores deforestation using low computational resources. To evaluate this technique, sample images were acquired in the Amazon rainforest border (MODIS tile H12-V10 and ETM+/Landsat 7 path 227 row 68) for 17 July 2002 and 05 October 2002. The MODIS-based simulated ETM+ and the corresponding original ETM+ images were compared through a linear regression method. Additionally, the bootstrap technique was used to calculate the confidence interval for the model to estimate and to perform a sensibility analysis. Moreover, a Linear Spectral Mixing Model, which is the technique used for deforestation mapping in Program for Deforestation Assessment in the Brazilian Legal Amazonia (PRODES) developed by National Institute for Space Research (INPE), was applied to analyze the differences in deforestation estimates. The results showed high correlations, with values between 0.70 and 0.94 (p < 0.05, students t test) for all ETM+ bands, indicating a good assessment between simulated and observed data (p < 0.05, Z-test). Moreover, simulated image showed a good agreement with a reference image, originating commission errors of 1% of total area estimated as deforestation in a sample area test. Furthermore, approximately 6% or 70 km² of deforestation areas were missing in simulated image classification. Therefore, the use of Landsat simulated image provides better deforestation estimation than MODIS alone.
AreaSRE
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4. Conditions of access and use
data URLhttp://plutao.sid.inpe.br/ibi/J8LNKAN8RW/3AFKS9M
zipped data URLhttp://plutao.sid.inpe.br/zip/J8LNKAN8RW/3AFKS9M
Languageen
Target Fileremotesensing-03-01943.pdf
User Groupadministrator
lattes
secretaria.cpa@dir.inpe.br
Reader Groupadministrator
marciana
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3ESGTTP
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.56.50 1
DisseminationSCIELO; COMPENDEX.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
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