| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/47CHPUP | |||
| Repository | sid.inpe.br/mtc-m21d/2022/08.02.13.20 (restricted access) | |||
| Last Update | 2022:08.02.13.20.09 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/08.02.13.20.09 | |||
| Metadata Last Update | 2023:01.03.16.46.11 (UTC) administrator | |||
| DOI | 10.5194/isprs-archives-XLIII-B3-2022-841-2022 | |||
| ISSN | 0256-1840 | |||
| Citation Key | BendiniFoMaMaHaVa:2022:EvSeBe | |||
| Title | Evaluating the separability beteween dry tropical forests and Savanna woodlands in the brazilian Savanna using Landsat dense image time series and artificial intelligence  | |||
| Year | 2022 | |||
| Month | June | |||
| Access Date | 2024, May 18 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 1200 KiB | |||
| 2. Context | ||||
| Author | 1 Bendini, Hugo do Nascimento 2 Fonseca, Leila Maria Garcia 3 Matosak, Bruno Menini 4 Mariano, Ravi Fernandes 5 Haidar, R. F. 6 Valeriano, Dalton de Morisson | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHLD 3 4 5 6 8JMKD3MGP5W/3C9JGT4 | |||
| Group | 1 DIOTG-CGCT-INPE-MCTI-GOV-BR 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 4 DIOTG-CGCT-INPE-MCTI-GOV-BR 5 6 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 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 Universidade Federal do Tocantins (UFTO) 6 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 hugo.bendini@inpe.br 2 leila.fonseca@inpe.br 3 bruno.matosak@inpe.br 4 ravimariano@hotmail.com 5 ricardohaidar@yahoo.com.br 6 dalton.valeriano@inpe.br | |||
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | |||
| Volume | 1, | |||
| Number | 2 | |||
| Pages | 841-847 | |||
| History (UTC) | 2022-08-02 13:20:43 :: simone -> administrator :: 2022 2023-01-03 16:46:11 :: administrator -> simone :: 2022 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Cerrado Dry Forests Machine Learning Random Forest Recurrent Neural Networks | |||
| Abstract | The Brazilian Savanna is the second largest biogeographical region in Brazil and present different vegetation types, consisting mostly of tropical savannas, grasslands, and forests. The forest types have different tree cover and floristic composition, which is associated to leaf deciduousness. Considering the importance of Cerrado to biodiversity conservation and the maintaining of environmental services, the development of methods to map the different forest types in Cerrado is important for conservation programmes, subsidize restauration plains, and to allow estimations of carbon sink and stock. Mapping heterogeneous tropical areas, such as the Brazilian Savanna, is very complex due to the natural factors and peculiarities of the vegetation types, and it's still particularly challenging to separate between different forest formations. In this study we tested machine learning approaches based on the use of dense image time series, in order to evaluate the separability Dry Tropical Forests and Savanna woodlands. We considered the Brazilian State of Tocantins as the study area, which is located in the Northern region of the country. RF classification of Landsat dense time series showed an overall accuracy of 0.85005, while the LSTM approach presented an overall accuracy of 0.88601, with the highest f1-score for the savanna woodlands class, suggesting the capability of the recurrent neural networks on handling complex long-term dependencies such as the EVI dense time series data. This study showed the potential for the development of a semi-automatic method for discriminating the different types of forest formations in the Brazilian Savanna, based on remote sensing. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Evaluating the separability... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Evaluating the separability... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | isprs-archives-XLIII-B3-2022-841-2022.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/10.18.22.34 4 sid.inpe.br/bibdigital/2022/04.03.22.23 3 sid.inpe.br/mtc-m21/2012/07.13.14.53.26 3 | |||
| Dissemination | PORTALCAPES; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||