Use of Machine Learning Algorithms in the Classification of Forest Species
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anuário do Instituto de Geociências (Online) |
Texto Completo: | https://revistas.ufrj.br/index.php/aigeo/article/view/50490 |
Resumo: | Optimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI. |
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Anuário do Instituto de Geociências (Online) |
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Use of Machine Learning Algorithms in the Classification of Forest SpeciesRemote sensingSpectroradiometryVegetation indicesOptimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI. Universidade Federal do Rio de Janeiro2023-03-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/5049010.11137/1982-3908_2023_46_50490Anuário do Instituto de Geociências; v. 46 (2023)Anuário do Instituto de Geociências; Vol. 46 (2023)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/50490/pdfCopyright (c) 2023 Anuário do Instituto de Geociênciasinfo:eu-repo/semantics/openAccessLoiola, Táscilla MagalhãesFantinel, Roberta Aparecidados Santos, Fernanda Diasde Bastos, FrancieleSchuh, Mateus SabadiFernandes, PabloSimões, Bruna AndielePereira, Rudiney Soares2023-03-08T13:39:10Zoai:ojs.pkp.sfu.ca:article/50490Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2023-03-08T13:39:10Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Use of Machine Learning Algorithms in the Classification of Forest Species |
title |
Use of Machine Learning Algorithms in the Classification of Forest Species |
spellingShingle |
Use of Machine Learning Algorithms in the Classification of Forest Species Loiola, Táscilla Magalhães Remote sensing Spectroradiometry Vegetation indices |
title_short |
Use of Machine Learning Algorithms in the Classification of Forest Species |
title_full |
Use of Machine Learning Algorithms in the Classification of Forest Species |
title_fullStr |
Use of Machine Learning Algorithms in the Classification of Forest Species |
title_full_unstemmed |
Use of Machine Learning Algorithms in the Classification of Forest Species |
title_sort |
Use of Machine Learning Algorithms in the Classification of Forest Species |
author |
Loiola, Táscilla Magalhães |
author_facet |
Loiola, Táscilla Magalhães Fantinel, Roberta Aparecida dos Santos, Fernanda Dias de Bastos, Franciele Schuh, Mateus Sabadi Fernandes, Pablo Simões, Bruna Andiele Pereira, Rudiney Soares |
author_role |
author |
author2 |
Fantinel, Roberta Aparecida dos Santos, Fernanda Dias de Bastos, Franciele Schuh, Mateus Sabadi Fernandes, Pablo Simões, Bruna Andiele Pereira, Rudiney Soares |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Loiola, Táscilla Magalhães Fantinel, Roberta Aparecida dos Santos, Fernanda Dias de Bastos, Franciele Schuh, Mateus Sabadi Fernandes, Pablo Simões, Bruna Andiele Pereira, Rudiney Soares |
dc.subject.por.fl_str_mv |
Remote sensing Spectroradiometry Vegetation indices |
topic |
Remote sensing Spectroradiometry Vegetation indices |
description |
Optimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/50490 10.11137/1982-3908_2023_46_50490 |
url |
https://revistas.ufrj.br/index.php/aigeo/article/view/50490 |
identifier_str_mv |
10.11137/1982-3908_2023_46_50490 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.ufrj.br/index.php/aigeo/article/view/50490/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2023 Anuário do Instituto de Geociências info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2023 Anuário do Instituto de Geociências |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
publisher.none.fl_str_mv |
Universidade Federal do Rio de Janeiro |
dc.source.none.fl_str_mv |
Anuário do Instituto de Geociências; v. 46 (2023) Anuário do Instituto de Geociências; Vol. 46 (2023) 1982-3908 0101-9759 reponame:Anuário do Instituto de Geociências (Online) instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Anuário do Instituto de Geociências (Online) |
collection |
Anuário do Instituto de Geociências (Online) |
repository.name.fl_str_mv |
Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
anuario@igeo.ufrj.br|| |
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1797053535696715776 |