Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data
The Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the characterization and quantification of forest resources, such as the stock of biomass and c...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X23000122 |
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| author | Otávio Miranda Verly Rodrigo Vieira Leite Ivaldo da Silva Tavares-Junior Samuel José Silva Soares da Rocha Hélio Garcia Leite José Marinaldo Gleriani Maria Paula Miranda Xavier Rufino Valéria de Fatima Silva Carlos Moreira Miquelino Eleto Torres Angelica Plata-Rueda Bárbara Monteiro de Castro e Castro José Cola Zanuncio Laércio Antônio Gonçalves Javocine |
| author_facet | Otávio Miranda Verly Rodrigo Vieira Leite Ivaldo da Silva Tavares-Junior Samuel José Silva Soares da Rocha Hélio Garcia Leite José Marinaldo Gleriani Maria Paula Miranda Xavier Rufino Valéria de Fatima Silva Carlos Moreira Miquelino Eleto Torres Angelica Plata-Rueda Bárbara Monteiro de Castro e Castro José Cola Zanuncio Laércio Antônio Gonçalves Javocine |
| author_sort | Otávio Miranda Verly |
| collection | DOAJ |
| description | The Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the characterization and quantification of forest resources, such as the stock of biomass and carbon. Methodologies based on remote sensing have been used, to improve these estimates without compromising execution costs. The objective was to estimate, with high spatial resolution passive remote sensing, the aboveground carbon stock in fragments of different successional stages of the Atlantic Forest. Forests were classified into initial, intermediate, and advanced successional stages. In each stratum, 10 plots (20x50 m) were established, and the carbon stock was calculated by adjusted Schumacher and Hall model. The reflectances of the blue, green, red, and near-infrared bands and vegetation indices (VIs) were obtained in the dry and rainy seasons, from MSI/Sentinel-2 images, with a resolution of 10 m. Artificial Neural Networks (ANN), with different combinations of variables, were trained and validated with simulated reflectance values. Carbon was estimated by ANN with the best performance in training and validation. The average carbon stock in the initial, intermediate, and advanced strata was 24.99, 35.79 and 82.28 Mg ha−1, respectively, with a general average of 47.68 Mg ha−1. The carbon estimates were better with the ANN trained with the reflectances of the rainy season. The addition of VIs did not improve ANN performance. The simulated spectral data were consistent and adequate to validate the selected ANN. The total carbon stock, modeled was 41,962.15 Mg, ranging from 6.68 to 108.29 Mg ha−1, with an average of 48.70 Mg ha−1. The carbon stock in the advanced stratum is more than three times that observed in the initial stratum, and they were efficiently estimated using high-resolution multispectral data, obtained in the rainy season, as inputs. |
| first_indexed | 2024-04-10T20:02:24Z |
| format | Article |
| id | doaj.art-4bff37f8f3104da7b8c6e2b7ef2fc6f6 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-10T20:02:24Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-4bff37f8f3104da7b8c6e2b7ef2fc6f62023-01-27T04:19:36ZengElsevierEcological Indicators1470-160X2023-02-01146109870Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 dataOtávio Miranda Verly0Rodrigo Vieira Leite1Ivaldo da Silva Tavares-Junior2Samuel José Silva Soares da Rocha3Hélio Garcia Leite4José Marinaldo Gleriani5Maria Paula Miranda Xavier Rufino6Valéria de Fatima Silva7Carlos Moreira Miquelino Eleto Torres8Angelica Plata-Rueda9Bárbara Monteiro de Castro e Castro10José Cola Zanuncio11Laércio Antônio Gonçalves Javocine12Department of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, Brazil; Corresponding author at: Otávio Miranda Verly, Department of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais, Brazil.Department of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Sciences, Federal University of Lavras, Minas Gerais State, Doutor Sylvio Menicucci Avenue, Lavras 37200-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Entomology/BIOAGRO, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Entomology/BIOAGRO, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Entomology/BIOAGRO, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilDepartment of Forest Engineering, Federal University of Viçosa, Peter Henry Rolfs Avenue, Viçosa, Minas Gerais 36570-900, BrazilThe Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the characterization and quantification of forest resources, such as the stock of biomass and carbon. Methodologies based on remote sensing have been used, to improve these estimates without compromising execution costs. The objective was to estimate, with high spatial resolution passive remote sensing, the aboveground carbon stock in fragments of different successional stages of the Atlantic Forest. Forests were classified into initial, intermediate, and advanced successional stages. In each stratum, 10 plots (20x50 m) were established, and the carbon stock was calculated by adjusted Schumacher and Hall model. The reflectances of the blue, green, red, and near-infrared bands and vegetation indices (VIs) were obtained in the dry and rainy seasons, from MSI/Sentinel-2 images, with a resolution of 10 m. Artificial Neural Networks (ANN), with different combinations of variables, were trained and validated with simulated reflectance values. Carbon was estimated by ANN with the best performance in training and validation. The average carbon stock in the initial, intermediate, and advanced strata was 24.99, 35.79 and 82.28 Mg ha−1, respectively, with a general average of 47.68 Mg ha−1. The carbon estimates were better with the ANN trained with the reflectances of the rainy season. The addition of VIs did not improve ANN performance. The simulated spectral data were consistent and adequate to validate the selected ANN. The total carbon stock, modeled was 41,962.15 Mg, ranging from 6.68 to 108.29 Mg ha−1, with an average of 48.70 Mg ha−1. The carbon stock in the advanced stratum is more than three times that observed in the initial stratum, and they were efficiently estimated using high-resolution multispectral data, obtained in the rainy season, as inputs.http://www.sciencedirect.com/science/article/pii/S1470160X23000122Semideciduous Seasonal ForestNon-parametric modelingArtificial Neural NetworksPassive remote sensingForest succession |
| spellingShingle | Otávio Miranda Verly Rodrigo Vieira Leite Ivaldo da Silva Tavares-Junior Samuel José Silva Soares da Rocha Hélio Garcia Leite José Marinaldo Gleriani Maria Paula Miranda Xavier Rufino Valéria de Fatima Silva Carlos Moreira Miquelino Eleto Torres Angelica Plata-Rueda Bárbara Monteiro de Castro e Castro José Cola Zanuncio Laércio Antônio Gonçalves Javocine Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data Ecological Indicators Semideciduous Seasonal Forest Non-parametric modeling Artificial Neural Networks Passive remote sensing Forest succession |
| title | Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data |
| title_full | Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data |
| title_fullStr | Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data |
| title_full_unstemmed | Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data |
| title_short | Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data |
| title_sort | atlantic forest woody carbon stock estimation for different successional stages using sentinel 2 data |
| topic | Semideciduous Seasonal Forest Non-parametric modeling Artificial Neural Networks Passive remote sensing Forest succession |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X23000122 |
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