| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4659LAE | |||
| Repository | sid.inpe.br/mtc-m21d/2022/01.03.13.41 | |||
| Last Update | 2022:01.03.13.41.05 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/01.03.13.41.05 | |||
| Metadata Last Update | 2022:04.03.22.27.46 (UTC) administrator | |||
| DOI | 10.3390/rs13234944 | |||
| ISSN | 2072-4292 | |||
| Citation Key | KuckSiSaBiShSi:2021:ChDeSe | |||
| Title | Change detection of selective logging in the brazilian amazon using x-band sar data and pre-trained convolutional neural networks  | |||
| Year | 2021 | |||
| Month | Dec. | |||
| Access Date | 2024, May 19 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 6873 KiB | |||
| 2. Context | ||||
| Author | 1 Kuck, Tahisa Neitzel 2 Silva Filho, Paulo Fernando Ferreira 3 Sano, Edson Eyji 4 Bispo, Popyanna da Conceição 5 Shiguemori, Elcio Hideiti 6 Silva, Ricardo Dal'Agnol da | |||
| ORCID | 1 0000-0003-0952-7055 2 0000-0003-0556-3470 3 0000-0001-5760-556X 4 0000-0003-0247-8449 5 0000-0001-5226-0435 6 0000-0002-7151-8697 | |||
| Group | 1 2 3 4 5 6 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Instituto de Estudos Avançados (IEAv) 2 Instituto de Estudos Avançados (IEAv) 3 Universidade de Brasília (UnB) 4 University of Manchester 5 Instituto de Estudos Avançados (IEAv) 6 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 thaisa@ieav.cta.br 2 silvafilho@ieav.cta.br 3 edson.sano@embrapa.br 4 polyanna.bispo@manchester.ac.uk 5 elcio@ieav.cta.br 6 ricds@hotmail.com | |||
| Journal | Remote Sensing | |||
| Volume | 13 | |||
| Number | 23 | |||
| Pages | e4944 | |||
| Secondary Mark | B3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2022-01-03 13:41:05 :: simone -> administrator :: 2022-01-03 13:41:07 :: administrator -> simone :: 2021 2022-01-03 13:42:46 :: simone -> administrator :: 2021 2022-04-03 22:27:46 :: administrator -> simone :: 2021 | |||
| 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 | Convolutional neural networks Selective logging Synthetic aperture radar | |||
| Abstract | It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques. | |||
| Area | SRE | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Change detection of... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGP3W34T/4659LAE | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGP3W34T/4659LAE | |||
| Language | en | |||
| Target File | remotesensing-13-04944-v2.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | allowpublisher allowfinaldraft | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | urlib.net/www/2021/06.04.03.40.25 | |||
| Next Higher Units | 8JMKD3MGPCW/46KUATE | |||
| Citing Item List | sid.inpe.br/bibdigital/2022/04.03.22.23 1 | |||
| Dissemination | WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||