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1.
Environ Monit Assess ; 196(1): 9, 2023 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-38049645

RESUMO

The research proposes a model to estimate the carbon stock in mangrove forests from multispectral images from Landsat 8 and Sentinel 2B satellites. The Gramame River mangrove, located on the southern coast of Paraíba State, Brazil, was adopted as the study area. Carbon stocks in biomass, below and above ground, were measured from a forest inventory, and vegetation indices were processed on the Google Earth Engine (GEE) platform. To define the fit curves, linear and non-linear regressions were used. The choice of the model considered the highest coefficients of determination (R2), the biomass and carbon stock were estimated from the equations. The biomass carbon stock, calculated from field data, corresponded to 22.27 Gg C, equivalent to 81.75 Gg CO2, with 13.85 Gg C (50.84 Gg CO2) and 8.42 Gg C (30.91 Gg CO2) stored in biomass above and below ground, respectively. Among the models fitted to the indices calculated from Landsat 8 images, NDVI was the one that best explained the spatial distribution of biomass and carbon, with 90.26%. For Sentinel 2B, SAVI was able to explain 80.76%. The total estimated plant carbon stocks corresponded to 26.66 Gg (16.20 Gg C above and 10.36 Gg C below ground) for Landsat 8 and 27.76 Gg C (16.93 Gg C above and 10.83 Gg C below ground) for Sentinel 2B. The proposed work methodology and the suggested mathematical models can be replicated to analyze carbon stocks in other locations, especially in the Americas, because they share the same species.


Assuntos
Dióxido de Carbono , Carbono , Brasil , Carbono/análise , Monitoramento Ambiental/métodos , Florestas , Biomassa
2.
Environ Monit Assess ; 193(6): 323, 2021 May 04.
Artigo em Inglês | MEDLINE | ID: mdl-33948736

RESUMO

The current study implements a cellular automata-based model for the development of land use/land cover (LULC) future scenarios using a Remote Sensing (RS) Imagery series (1985 to 2018) as data input and focusing on human activities drivers in a 6700-km2 watershed vital for the water security of Paraiba state, Brazil. The methodology has three stages: the first stage is the pre-processing of images and preparing them as data input for the cellular automata land use model built in the R software environment (SIMLANDER); the stage of calibration establishes the variables and verifies the influence of each one on the LULC of the region; the last step corresponds to the validation procedures. After model calibration, land use maps for future scenarios (2019 to 2045) were simulated. The results estimate a reduction of 737 km2 of natural land cover between the years 2019 and 2045. The spatial distribution of anthropogenic interference predicted a more significant degradation in the central region of the basin. This fact can be potentially attributed by the water availability increasing from the São Francisco River diversion. It is possible to identify an ascending trend of anthropogenic actions in the semi-arid region, which host the exclusively Brazilian biome-Caatinga-and contains biodiversity that cannot be found anywhere else on the Earth. The model helps large-scale LULC modelling based on RS products and expands the possibilities of hydrological, urban and social modelling in the Brazilian context.


Assuntos
Conservação dos Recursos Naturais , Monitoramento Ambiental , Brasil , Humanos , Hidrologia , Rios
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