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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3LKJJRH
Repositorysid.inpe.br/mtc-m21b/2016/05.03.17.49   (restricted access)
Last Update2016:07.04.18.03.09 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2016/05.03.17.49.58
Metadata Last Update2018:06.04.02.40.46 (UTC) administrator
DOI10.1016/j.rse.2016.03.021
ISSN0034-4257
Citation KeyFerreiraZorZanShiSou:2016:MaTrSp
TitleMapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data
Year2016
MonthJun.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size2232 KiB
2. Context
Author1 Ferreira, Matheus Pinheiro
2 Zortea, Maciel
3 Zanotta, Daniel Capella
4 Shimabukuro, Yosio Edemir
5 Souza Filho, Carlos Roberto de
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JJCQ
Group1 SER-SRE-SPG-INPE-MCTI-GOV-BR
2
3
4 DSR-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Institute of Informatics, Federal University of Rio Grande do Sul, Av. Bento Gonçalves 9500, Porto Alegre, RS, Brazil
3 National Institute for Science, Education and Technology, R. Eng. Alfredo Huch 475, Rio Grande, RS, Brazil
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade Estadual de Campinas, Institute of Geosciences, Campinas, Brazil
Author e-Mail Address1 pferreira.matheus@gmail.com
2 mzortea@gmail.com
3 daniel.zanotta@riogrande.ifrs.edu.br
4 yosio@dsr.inpe.br
5 beto@ige.unicamp.br
JournalRemote Sensing of Environment
Volume179
Number66
Pages66-78
Secondary MarkA1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE
History (UTC)2016-05-03 17:49:58 :: simone -> administrator ::
2016-07-04 12:30:01 :: administrator -> simone :: 2016
2016-09-09 19:10:49 :: simone -> administrator :: 2016
2018-06-04 02:40:46 :: administrator -> simone :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsBrazilian Atlantic Forest
Classification
Imaging spectroscopy
Individual tree crowns
WorldView-3
AbstractAccurately mapping the spatial distribution of tree species in tropical environments provides valuable insights for ecologists and forest managers. This process may play an important role in reducing fieldwork costs, monitoring changes in canopy biodiversity, and locating parent trees to collect seeds for forest restoration efforts. However, mapping tree species in tropical forests with remote sensing data is a challenge because of high floristic and spectral diversity. In this research, we discriminated and mapped tree species in tropical seasonal semi-deciduous forests (Brazilian Atlantic Forest Biome) by using airborne hyperspectral and simulated multispectral data in the 450 to 2400 nm wavelength range. After quantifying the spectral variability within and among individual tree crowns of eight species, three supervised machine learning classifiers were applied to discriminate the species at the pixel level. Linear Discriminant Analysis outperformed Support Vector Machines with Linear and Radial Basis Function (RBF-SVMs) kernels and Random Forests in almost all the tested cases. An average classification accuracy of 70% was obtained when using the visible/near-infrared (VNIR, 450-919 nm) bands. The inclusion of shortwave infrared bands (SWIR, 1045-2400 nm) increased the accuracy to 84%. Narrow-band vegetation indices (VIs) were also tested and increased the classification accuracy by up to 5% when combined with VNIR features. Furthermore, the spectral bands of the WorldView-3 (WV-3) satellite sensor were simulated for classification purposes. WV-3 VNIR bands provided an accuracy of 57.4%, which increased to 74.8% when using WV-3 SWIR bands. We also tested the production of species maps by using an object-oriented approach that integrated a novel segmentation algorithm that was tailored to delineate tree crowns and label high class membership pixels inside each object. In this scenario, RBF-SVMs produced the best species maps, correctly identifying 84.9% of crowns with hyperspectral data and 78.5% with simulated WV-3 data. The use of a reduced set of hyperspectral bands, which were selected with stepwise regression, did not significantly affect the classification accuracies but allowed us to depict the most important wavelengths to discriminate the species. These wavelengths were located around the green reflectance peak (550 nm), at the red absorption feature (650 nm) and in the SWIR range at 1200, 1700, 2100 and 2300 nm. These encouraging results suggest the feasibility of the proposed approach for mapping pioneering and climax tree species in the Brazilian Atlantic Forest Biome, highlighting its potential use in forest recovery and inventory initiatives.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Mapping tree species...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping tree species...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target File1_ferreira_mapping.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher allowfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Citing Item Listsid.inpe.br/bibdigital/2013/09.13.21.11 2
sid.inpe.br/bibdigital/2013/10.18.22.34 1
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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