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1. Identity statement
Reference TypeJournal Article
Sitemtc-m12.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier6qtX3pFwXQZGivnJRY/NnMBE
Repositorysid.inpe.br/mtc-m12@80/2006/12.06.12.57   (restricted access)
Last Update2006:12.06.13.36.25 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m12@80/2006/12.06.12.57.42
Metadata Last Update2018:06.05.00.40.47 (UTC) administrator
ISSN0378-1127
Citation KeyKuplich:2006:ClReFo
TitleClassifying regenerating forest stages in Amazônia using remotely sensed images and a neural network
Year2006
Month2006-12-07
Access Date2024, Apr. 28
Secondary TypePRE PI
Number of Files1
Size1629 KiB
2. Context
AuthorKuplich, Tatiana Mora
Resume Identifier8JMKD3MGP5W/3C9JJ9P
GroupDSR-INPE-MCT-BR
AffiliationInstituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Addresstmk@dsr.inpe.br
e-Mail Addresstmk@dsr.inpe.br
JournalForest Ecology and Management
Volume234
Pages1-9
ProgressePrint update
History (UTC)2007-04-23 21:05:26 :: tmk -> administrator ::
2018-06-05 00:40:47 :: administrator -> marciana :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsregenerating forest
forest stages
classification
artificial neural network
SAR data
Landsat TM data
AbstractFor estimating forest biomass and mapping of forest carbon content, an accurate classification of tropical forest stages is an important step. The successional stage of regenerating forests in a forest age map was a viable surrogate for forest biomass in an area north of Manaus City, Brazil. The forest stages considered were (i) mature forest, (ii) regenerating forest <3 years, (iii) regenerating forest 3-5 years, (iv) regenerating forest 6-10 years and (v) regenerating forest 11-18 years. Areas of pasture were mapped also. Synthetic Aperture Radar (SAR) bands and optical TM (Thematic Mapper) bands were used to classify those classes, using the forest age map as the reference. The remotely sensed data comprised 20 bands (SAR bands from JERS-1, SIR-C and XSAR and optical bands from Landsat/TM) upon which Discriminant Analysis (DA) were used. DA results pointed to increased class discrimination when using SAR and TM data in relation to SAR data only. The bands selected were used as input to a neural network based classifier. Classification accuracy using SAR bands alone was around 30% for the 6 land cover classes. When regenerating forest stage classes were merged into a single class, the classification accuracy increased to around 80%. SAR data alone was unable to discriminate regenerating forest stages, having limited ability to discriminate between the subtle tonal/textural characteristics of each stage. A data set comprising TM and SAR bands showed increased classification accuracy in relation to SAR data alone, although some confusion between regenerating forest stages was still present. Following merging of regenerating forest stages into young (0-5 years) and intermediate (6-18 years), the overall accuracy was around 87%. The combination of SAR and TM bands were essential for the discrimination between regenerating forest stages. Pasture and mature forest were discriminated accurately in both SAR data alone and in the combined SAR and TM data.
AreaSRE
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4. Conditions of access and use
Languageen
Target Fileann_tmk.pdf
User Groupadministrator
banon
tmk
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
5. Allied materials
Previous Editionsid.inpe.br/ePrint@80/2006/04.28.14.28
Next Higher Units8JMKD3MGPCW/3ER446E
URL (untrusted data)http://dx.doi.org/10.1016/j.foreco.2006.05.066
DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectionsid.inpe.br/banon/2001/04.06.10.52
6. Notes
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