| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/42KF4AE | |||
| Repository | sid.inpe.br/mtc-m21c/2020/06.05.14.44 (restricted access) | |||
| Last Update | 2020:06.05.14.44.22 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2020/06.05.14.44.22 | |||
| Metadata Last Update | 2022:01.04.01.35.11 (UTC) administrator | |||
| DOI | 10.3390/land9050139 | |||
| ISSN | 2073-445X | |||
| Citation Key | CassolArSaDuHoSh:2020:MaFrIm | |||
| Title | Maximum fraction images derived from year-based Project for On-Board Autonomy-Vegetation (PROBA-V) data for the rapid assessment of land use and land cover areas in Mato Grosso State, Brazil  | |||
| Year | 2020 | |||
| Access Date | 2024, May 06 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 17642 KiB | |||
| 2. Context | ||||
| Author | 1 Cassol, Henrique Luis Godinho 2 Arai, Egídio 3 Sano, Edson Eyji 4 Dutra, Andeise Cerqueira 5 Hoffmann, Tânia Beatriz 6 Shimabukuro, Yosio Edemir | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JGUP 3 4 5 6 8JMKD3MGP5W/3C9JJCQ | |||
| ORCID | 1 2 3 4 0000-0002-4454-7732 | |||
| Group | 1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 3 4 SESID-GBDIR-INPE-MCTIC-GOV-BR 5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 henrique.cassol@inpe.br 2 egidio.arai@inpe.br 3 edson.sano@embrapa.br 4 andeise.dutra@inpe.br 5 tania.hoffmann@inpe.br 6 yosio.shimabukuro@inpe.br | |||
| Journal | Land | |||
| Volume | 9 | |||
| Pages | e139 | |||
| History (UTC) | 2020-06-05 14:44:47 :: simone -> administrator :: 2020 2020-06-07 08:43:43 :: administrator -> simone :: 2020 2020-06-23 22:41:08 :: simone -> administrator :: 2020 2022-01-04 01:35:11 :: administrator -> simone :: 2020 | |||
| 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 | spectral unmixing machine learning fraction images cloud computing | |||
| Abstract | This paper presents a new approach for rapidly assessing the extent of land use and land cover (LULC) areas in Mato Grosso state, Brazil. The novel idea is the use of an annual time series of fraction images derived from the linear spectral mixing model (LSMM) instead of original bands. The LSMM was applied to the Project for On-Board Autonomy-Vegetation (PROBA-V) 100-m data composites from 2015 (~73 scenes/year, cloud-free images, in theory), generating vegetation, soil, and shade fraction images. These fraction images highlight the LULC components inside the pixels. The other new idea is to reduce these time series to only six single bands representing the maximum and standard deviation values of these fraction images in an annual composite, reducing the volume of data to classify the main LULC classes. The whole image classification process was conducted in the Google Earth Engine platform using the pixel-based random forest algorithm. A set of 622 samples of each LULC class was collected by visual inspection of PROBA-V and Landsat-8 Operational Land Imager (OLI) images and divided into training and validation datasets. The performance of the method was evaluated by the overall accuracy and confusion matrix. The overall accuracy was 92.4%, with the lowest misclassification found for cropland and forestland (<9% error). The same validation data set showed 88% agreement with the LULC map made available by the Landsat-based MapBiomas project. This proposed method has the potential to be used operationally to accurately map the main LULC areas and to rapidly use the PROBA-V dataset at regional or national levels. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Maximum fraction images... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Maximum fraction images... | |||
| Arrangement 3 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > SESID > Maximum fraction images... | |||
| 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 | land-09-00139.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/3ER446E 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/449THCP | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/10.18.22.34 4 sid.inpe.br/bibdigital/2013/09.13.21.11 3 sid.inpe.br/mtc-m21/2012/07.13.14.45.03 3 | |||
| Dissemination | WEBSCI; PORTALCAPES. | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository month nextedition notes number 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 | ||||