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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3TALT92
Repositorysid.inpe.br/mtc-m21c/2019/05.16.10.30   (restricted access)
Last Update2019:05.16.10.30.00 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2019/05.16.10.30.01
Metadata Last Update2020:09.02.15.18.05 (UTC) administrator
DOI10.1080/01431161.2019.1579943
ISSN0143-1161
Citation KeyShimabukuroArDuJoSaGaDu:2019:MoDeFo
TitleMonitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon
Year2019
MonthJuly
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4087 KiB
2. Context
Author1 Shimabukuro, Yosio Edemir
2 Arai, Egidio
3 Duarte, Valdete
4 Jorge, Anderson
5 Santos, Erone Ghyizoni dos
6 Gasparini, Kaio Allan Cruz
7 Dutra, Andeise Cerqueira
Resume Identifier1 8JMKD3MGP5W/3C9JJCQ
2 8JMKD3MGP5W/3C9JGUP
3 8JMKD3MGP5W/3C9JJAU
Group1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
6 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
7 SER-SRE-SESPG-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)
7 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 yosio@dsr.inpe.br
2 egidio.arai@inpe.br
3 valdete.duarte@inpe.br
4
5
6 kaio.gasparini@inpe.br
7 andeise.dutra@inpe.br
JournalInternational Journal of Remote Sensing
Volume40
Number4
Pages5475-5496
Secondary MarkA1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_INTERDISCIPLINAR A2_GEOGRAFIA A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_ENGENHARIAS_I A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_GEOCIÊNCIAS B1_ENGENHARIAS_II B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B2_SAÚDE_COLETIVA B2_ODONTOLOGIA B3_CIÊNCIAS_BIOLÓGICAS_I B3_BIOTECNOLOGIA B5_ASTRONOMIA_/_FÍSICA
History (UTC)2019-05-16 10:30:01 :: simone -> administrator ::
2019-05-16 10:30:01 :: administrator -> simone :: 2019
2019-05-16 10:30:13 :: simone -> administrator :: 2019
2020-09-02 15:18:05 :: administrator -> simone :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AbstractDeforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.
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4. Conditions of access and use
Languageen
Target FileMonitoring deforestation and forest degradation using multi temporal fraction images derived from Landsat sensor data in the Brazilian Amazon.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 3
sid.inpe.br/bibdigital/2013/09.13.21.11 2
sid.inpe.br/mtc-m21/2012/07.13.14.45.03 2
DisseminationWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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