Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1134527
Research center of Embrapa/Collection: Embrapa Semiárido - Artigo em periódico indexado (ALICE)
Date Issued: 2022
Type of Material: Artigo em periódico indexado (ALICE)
Authors: LUZ, L. R.
GIONGO, V.
SANTOS, A. M. dos
LOPES, R. J. de C.
LIMA JÚNIOR, C. de
Additional Information: LEUDIANE RODRIGUES LUZ; VANDERLISE GIONGO, CPATSA; ANTONIO MARCOS DOS SANTOS; RODRIGO JOSÉ DE CARVALHO LOPES; CLAUDEMIRO DE LIMA JÚNIOR.
Title: Biomass and vegetation index by remote sensing in different caatinga forest areas.
Publisher: Ciência Rural, Santa Maria, v. 52, n. 2, e20201104, 2022.
Language: Ingles
Keywords: Snsoriamento remoto
Florestas secas
Energia renovável
Modelagem
Description: 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.
Thesagro: Vegetação
Vegetação Nativa
Caatinga
Floresta
Biomassa
NAL Thesaurus: Remote sensing
Dry forests
Renewable energy sources
Structural equation modeling
Biomass
Microbial biomass
Data Created: 2021-09-17
Appears in Collections:Artigo em periódico indexado (CPATSA)

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