Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations
It is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidade Federal de Uberlândia
2020-03-01
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| Series: | Revista Brasileira de Cartografia |
| Subjects: | |
| Online Access: | http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/50477 |
| _version_ | 1818673005980549120 |
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| author | Gustavo Fluminense Carneiro Matheus Pinheiro Ferreira Carlos Frederico de Sá Volotão |
| author_facet | Gustavo Fluminense Carneiro Matheus Pinheiro Ferreira Carlos Frederico de Sá Volotão |
| author_sort | Gustavo Fluminense Carneiro |
| collection | DOAJ |
| description | It is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from multiple sources. We defined four vegetation classes: natural forest (NF), planted eucalyptus forest (PF), agriculture (A) and pasture (P), and sampled 410,251 pixels from 100 polygons of each class. Classification experiments were performed by using a random forest algorithm with images from Landsat-8, Sentinel-1, and SRTM. We considered four texture features (energy, contrast, correlation, and entropy) and NDVI. We used F1-score, overall accuracy and total disagreement metrics, to assess the classification performance, and Jeffries–Matusita (JM) distance to measure the spectral separability. Overall accuracy for Landsat-8 bands alone was 88.29%. A combination of Landsat-8 with Sentinel-1 bands resulted in a 3% overall accuracy increase and this band combination also improved the F1-score of NF, PF, P and A in 2.22%, 2.9%, 3.71%, and 8.01%, respectively. The total disagreement decreased from 11.71% to 8.71%. The increase in the statistical separability corroborates such improvement and is mainly observed between NF-PF (11.98%) and A-P (45.12%). We conclude that combining optical and radar remote sensing data increased the classification accuracy of natural and planted forests and may serve as a basis for large-scale semi-automatic mapping of forest resources. |
| first_indexed | 2024-12-17T07:48:55Z |
| format | Article |
| id | doaj.art-07c42056d5c44a158f68c18778e544e4 |
| institution | Directory Open Access Journal |
| issn | 0560-4613 1808-0936 |
| language | English |
| last_indexed | 2024-12-17T07:48:55Z |
| publishDate | 2020-03-01 |
| publisher | Universidade Federal de Uberlândia |
| record_format | Article |
| series | Revista Brasileira de Cartografia |
| spelling | doaj.art-07c42056d5c44a158f68c18778e544e42022-12-21T21:57:55ZengUniversidade Federal de UberlândiaRevista Brasileira de Cartografia0560-46131808-09362020-03-0172111012510.14393/rbcv72n1-5047750477Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantationsGustavo Fluminense CarneiroMatheus Pinheiro FerreiraCarlos Frederico de Sá VolotãoIt is challenging to map the spatial distribution of natural and planted forests based on satellite images because of the high correlation among them. This investigation aims to increase accuracies in classifications of natural forests and eucalyptus plantations by combining remote sensing data from multiple sources. We defined four vegetation classes: natural forest (NF), planted eucalyptus forest (PF), agriculture (A) and pasture (P), and sampled 410,251 pixels from 100 polygons of each class. Classification experiments were performed by using a random forest algorithm with images from Landsat-8, Sentinel-1, and SRTM. We considered four texture features (energy, contrast, correlation, and entropy) and NDVI. We used F1-score, overall accuracy and total disagreement metrics, to assess the classification performance, and Jeffries–Matusita (JM) distance to measure the spectral separability. Overall accuracy for Landsat-8 bands alone was 88.29%. A combination of Landsat-8 with Sentinel-1 bands resulted in a 3% overall accuracy increase and this band combination also improved the F1-score of NF, PF, P and A in 2.22%, 2.9%, 3.71%, and 8.01%, respectively. The total disagreement decreased from 11.71% to 8.71%. The increase in the statistical separability corroborates such improvement and is mainly observed between NF-PF (11.98%) and A-P (45.12%). We conclude that combining optical and radar remote sensing data increased the classification accuracy of natural and planted forests and may serve as a basis for large-scale semi-automatic mapping of forest resources.http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/50477sentinel-1landsat-8random forest |
| spellingShingle | Gustavo Fluminense Carneiro Matheus Pinheiro Ferreira Carlos Frederico de Sá Volotão Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations Revista Brasileira de Cartografia sentinel-1 landsat-8 random forest |
| title | Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| title_full | Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| title_fullStr | Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| title_full_unstemmed | Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| title_short | Multi-source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| title_sort | multi source remote sensing data improves the classification accuracy of natural forests and eucalyptus plantations |
| topic | sentinel-1 landsat-8 random forest |
| url | http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/50477 |
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