Spatial distribution of the litter carbon stock in the Cerrado biome in Minas Gerais state, Brazil

Morais, Vinícus Augusto; Mello, José Márcio de; Mello, Carlos Rogério de; Silva, Carlos Alberto; Scolforo, José Roberto Soares

doi:10.1590/1413-70542017415006917

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Cross-validation was used as a criterion, calculating the reduced mean error (EMR) and the standard deviation of the reduced mean error (SER), and overestimation of the model to evaluate the performance and select the semivariogram model that best fits the data set (Morais et al, 2017). In the adjustment of the theoretical models of the experimental semivariograms, the parameters nugget effect (τ2), threshold (σ2), and reach (ϕ) were determined.…”

Section: Discussionmentioning

confidence: 99%

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

Silva

Lima

Souza

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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The Caatinga biome in Brazil comprises the largest and most continuous expanse of the seasonally dry tropical forest (SDTF) worldwide; nevertheless, it is among the most threatened and least studied, despite its ecological and biogeographical importance. The spatial distribution of volumetric wood stocks in the Caatinga and the relationship with environmental factors remain unknown. Therefore, this study intends to quantify and analyze the spatial distribution of wood volume as a function of environmental variables in Caatinga vegetation in Bahia State, Brazil. Volumetric estimates were obtained at the plot and fragment level. The multiple linear regression techniques were adopted, using environmental variables in the area as predictors. Spatial modeling was performed using the geostatistical kriging approach with the model residuals. The model developed presented a reasonable fit for the volume m 3 ha with r 2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m 3 ha -1 . The kriging of ordinary residuals suggested low error estimates in unsampled locations and balance in the under and overestimates of the model. The regression kriging approach provided greater detailing of the global wood volume stock map, yielding volume estimates that ranged from 0.01 to 109 m 3 ha -1 . Elevation, mean annual temperature, and precipitation of the driest month are strong environmental predictors for volume estimation. This information is necessary to development action plans for sustainable management and use of the Caatinga SDTF in Bahia State, Brazil.

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Section: Discussionmentioning

confidence: 99%

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

Silva

Lima

Souza

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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“…The theoretical model for the interpolation was selected by evaluating the Akaike Information Criterion (AIC), the degree of spatial dependence, the reduced mean error (RME) and the standard deviation of the reduced mean error (SDRME) provided by Jackknife cross-validation (Morais et al 2017). The closer to zero the RME and the closer to 1 the SDRME, the better the performance of the model.…”

Section: Spatialization Of the Biomass Stockmentioning

confidence: 99%

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Santos¹,

Lima²,

Souza³

et al. 2023

iForest

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Climate change is being intensified by anthropogenic emission of greenhouse gasses, highlighting the value of forests for carbon dioxide storing carbon in their biomass. Seasonally dry tropical forests are a neglected, threatened, but potentially critical biome for helping mitigate climate change. In South America, knowing the amount and distribution of carbon in Caatinga seasonally dry vegetation is essential to understand its contribution to the global carbon cycle and subsequently design a strategic plan for its conservation. The present study aimed to model and map the spatial distribution of the potential forest biomass stock across 32 forest fragments of Caatinga, in the state of Bahia, Brazil, using regression kriging and Inverse Square of Distance techniques, building from point measurements of vegetation biomass made on-the-ground in ecological plots. First, a model for estimating biomass was fitted as a function of environmental variables to apply regression kriging, and then applied to the maps of the selected components. Elevation, temperature, and precipitation explained 46% of the biomass variations in the Caatinga. The model residuals showed strong spatial dependence and were mapped based on geostatistical criteria, selecting the spherical semivariogram model for interpolation by ordinary kriging. Biomass was also mapped by the Inverse Square of Distance approach. The quality of the regression model suggests that there is good potential for estimating biomass here from environmental variables. The regression kriging showed greater detail in the spatial distribution and revealed a spatial trend of increasing biomass from the north to south of the domain. Additional studies with greater sampling intensity and the use of other explanatory variables are suggested to improve the model, as well as to maximize the technique's ability to capture the actual biomass behavior in this newly studied seasonally dry ecosystem.

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“…O fato dos R²aj calculados para as equações terem apresentado valores relativamente baixos deve-se à grande variabilidade existente em florestas nativas (Morais et al 2017). Especialmente quanto aos materiais combustíveis vivos que não formam uma camada tão uniforme quanto os mortos, conforme se verifica pela maior quantidade de combustíveis finos mortos apresentados na Tabela 1.…”

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Spatial distribution of the litter carbon stock in the Cerrado biome in Minas Gerais state, Brazil

Cited by 10 publications

References 31 publications

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

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