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Registro Completo |
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
17/09/2021 |
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Data da última atualização: |
09/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LUZ, L. R.; GIONGO, V.; SANTOS, A. M. dos; LOPES, R. J. de C.; LIMA JÚNIOR, C. de. |
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Afiliação: |
LEUDIANE RODRIGUES LUZ; VANDERLISE GIONGO, CPATSA; ANTONIO MARCOS DOS SANTOS; RODRIGO JOSÉ DE CARVALHO LOPES; CLAUDEMIRO DE LIMA JÚNIOR. |
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Título: |
Biomass and vegetation index by remote sensing in different caatinga forest areas. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Ciência Rural, Santa Maria, v. 52, n. 2, e20201104, 2022. |
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DOI: |
10.1590/0103-8478cr20201104 |
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Idioma: |
Inglês |
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Conteúdo: |
Continued unsustainable exploitation of natural resources promotes environmental degradation and threatens the preservation of dry forests around the world. This situation exposes the fragility and the necessity to study landscape transformations. In addition, it is necessary to consider the biomass quantity and to establish strategies to monitor natural and anthropic disturbances. Thus, this research analyzed the relationship between vegetation index and the estimated biomass using allometric equations in different Brazilian caatinga forest areas from satellite images. This procedure is performed by estimating the biomass from 9 dry tropical forest fragments using allometric equations. Area delimitations were obtained from the Embrapa collection of dendrometric data collected in the period between 2011 and 2012. Spectral variables were obtained from the orthorectified images of the RapidEye satellite. The aboveground biomass ranged from 6.88 to 123.82 Mg.ha-1. SAVI values were L = 1 and L = 0.5, while NDVI and EVI ranged from 0.1835 to 0.4294, 0.2197 to 0.5019, 0.3622 to 0.7584, and 0.0987 to 0.3169, respectively. Relationships among the estimated biomass and the vegetation indexes were moderate, with correlation coefficients (Rs) varying between 0.64 and 0.58. The best adjusted equation was the SAVI equation, for which the coefficient of determination was R2 = 0.50, R2 aj = 0.49, RMSE = 17.18 Mg.ha-1 and mean absolute error of prediction (MAE) = 14.07 Mg.ha-1, confirming the importance of the Savi index in estimating the caatinga aboveground biomass. MenosContinued unsustainable exploitation of natural resources promotes environmental degradation and threatens the preservation of dry forests around the world. This situation exposes the fragility and the necessity to study landscape transformations. In addition, it is necessary to consider the biomass quantity and to establish strategies to monitor natural and anthropic disturbances. Thus, this research analyzed the relationship between vegetation index and the estimated biomass using allometric equations in different Brazilian caatinga forest areas from satellite images. This procedure is performed by estimating the biomass from 9 dry tropical forest fragments using allometric equations. Area delimitations were obtained from the Embrapa collection of dendrometric data collected in the period between 2011 and 2012. Spectral variables were obtained from the orthorectified images of the RapidEye satellite. The aboveground biomass ranged from 6.88 to 123.82 Mg.ha-1. SAVI values were L = 1 and L = 0.5, while NDVI and EVI ranged from 0.1835 to 0.4294, 0.2197 to 0.5019, 0.3622 to 0.7584, and 0.0987 to 0.3169, respectively. Relationships among the estimated biomass and the vegetation indexes were moderate, with correlation coefficients (Rs) varying between 0.64 and 0.58. The best adjusted equation was the SAVI equation, for which the coefficient of determination was R2 = 0.50, R2 aj = 0.49, RMSE = 17.18 Mg.ha-1 and mean absolute error of prediction (MAE) = 14.07 Mg.ha-1, confirming t... Mostrar Tudo |
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Palavras-Chave: |
Energia renovável; Florestas secas; Modelagem; Snsoriamento remoto. |
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Thesagro: |
Biomassa; Caatinga; Floresta; Vegetação; Vegetação Nativa. |
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Thesaurus Nal: |
Biomass; Dry forests; Microbial biomass; Remote sensing; Renewable energy sources; Structural equation modeling. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/226135/1/Biomass-and-vegetation-index-by-remote-sensing-2022.pdf
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Marc: |
LEADER 02613naa a2200361 a 4500
001 2134527
005 2023-01-09
008 2022 bl uuuu u00u1 u #d
024 7 $a10.1590/0103-8478cr20201104$2DOI
100 1 $aLUZ, L. R.
245 $aBiomass and vegetation index by remote sensing in different caatinga forest areas.$h[electronic resource]
260 $c2022
520 $aContinued unsustainable exploitation of natural resources promotes environmental degradation and threatens the preservation of dry forests around the world. This situation exposes the fragility and the necessity to study landscape transformations. In addition, it is necessary to consider the biomass quantity and to establish strategies to monitor natural and anthropic disturbances. Thus, this research analyzed the relationship between vegetation index and the estimated biomass using allometric equations in different Brazilian caatinga forest areas from satellite images. This procedure is performed by estimating the biomass from 9 dry tropical forest fragments using allometric equations. Area delimitations were obtained from the Embrapa collection of dendrometric data collected in the period between 2011 and 2012. Spectral variables were obtained from the orthorectified images of the RapidEye satellite. The aboveground biomass ranged from 6.88 to 123.82 Mg.ha-1. SAVI values were L = 1 and L = 0.5, while NDVI and EVI ranged from 0.1835 to 0.4294, 0.2197 to 0.5019, 0.3622 to 0.7584, and 0.0987 to 0.3169, respectively. Relationships among the estimated biomass and the vegetation indexes were moderate, with correlation coefficients (Rs) varying between 0.64 and 0.58. The best adjusted equation was the SAVI equation, for which the coefficient of determination was R2 = 0.50, R2 aj = 0.49, RMSE = 17.18 Mg.ha-1 and mean absolute error of prediction (MAE) = 14.07 Mg.ha-1, confirming the importance of the Savi index in estimating the caatinga aboveground biomass.
650 $aBiomass
650 $aDry forests
650 $aMicrobial biomass
650 $aRemote sensing
650 $aRenewable energy sources
650 $aStructural equation modeling
650 $aBiomassa
650 $aCaatinga
650 $aFloresta
650 $aVegetação
650 $aVegetação Nativa
653 $aEnergia renovável
653 $aFlorestas secas
653 $aModelagem
653 $aSnsoriamento remoto
700 1 $aGIONGO, V.
700 1 $aSANTOS, A. M. dos
700 1 $aLOPES, R. J. de C.
700 1 $aLIMA JÚNIOR, C. de
773 $tCiência Rural, Santa Maria$gv. 52, n. 2, e20201104, 2022.
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Embrapa Semiárido (CPATSA) |
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| 1. |  | LUZ, L. R.; GIONGO, V.; SANTOS, A. M. dos; LOPES, R. J. de C.; LIMA JÚNIOR, C. de. Biomass and vegetation index by remote sensing in different caatinga forest areas. Ciência Rural, Santa Maria, v. 52, n. 2, e20201104, 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
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