Método do hipercubo latino condicionado para a amostragem de solos na presença de covariáveis ambientais visando o mapeamento digital de solos

Júnior, Waldir de Carvalho; Chagas, C. da S.; Muselli, Alexandre; Pinheiro, Helena Saraiva Koenow; Pereira, N. R.; Bhering, S. B.

doi:10.1590/s0100-06832014000200003

Cited by 7 publications

(5 citation statements)

References 17 publications

(31 reference statements)

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“…The approach used in this study to select the sampling points followed the principles proposed by Minasny and McBratney (2006). The points were selected by using cLHS with auxiliary covariates, and considering the access costs (Roudier et al, 2012;Carvalho Júnior et al, 2014;Stumpf et al, 2016). As a constraint, three buffer sizes were created in relation to roads and trails, as proposed by Carvalho Júnior et al (2014).…”

Section: Soil Dataset and Sampling Strategymentioning

confidence: 99%

“…To facilitate DSM in poorly-accessible areas, Cambule et al (2013) proposed a methodology of sampling in the area of greater accessibility, which is representative of the total area, and to evaluate the representativeness using, e.g., the similarity between the histogram of the covariates for the total and accessible areas. Other studies considered the costs of accessibility in soil sampling (Roudier et al, 2012;Carvalho Júnior et al, 2014;Stumpf et al, 2016) using a variation/optimization of the method known as conditioned Latin Hypercube Sampling (cLHS), proposed by Minasny and McBratney (2006). The cLHS is a robust tool for the allocation of sampling points by means of a set of auxiliary covariates.…”

Section: Introductionmentioning

confidence: 99%

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Mapping soil properties in a poorly-accessible area

Costa¹,

Pinheiro²,

Anjos³

et al. 2020

Revista Brasileira De Ciência Do Solo

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Soil maps are important to evaluate soil functions and support decision-making process, particularly for soil properties such as pH, carbon content (C), and cation exchange capacity (CEC), but the spatial resolution and soil depth should meet the needs of users. On another hand, the efficiency of statistical models to create soil maps, with an acceptable level of accuracy, often require a large number of samples with an appropriate distribution across the area of interest. However, accessibility for sampling can be a trouble in remote areas, such as the Itatiaia National Park (INP). The hypothesis of this work is that it is possible to obtain a viable result in soil mapping of areas with limited access by using DSM tools. The general objective of this paper was to create 2-and 3-D maps of the soil properties pH, carbon content, and CEC, with the correspondent spatial uncertainty, in the INP plateau. The sampling strategy was designed using conditioned Latin Hypercube Sample (cLHS), and different methods were tested to produce the soil properties maps. For calibration of the models: linear (MLR, multiple linear regression) and nonlinear (GAM, Generalised Additive Models). The results showed differences in predictive performance for all statistical methods and covariate selection approaches. The GAM, with covariates selection based on soil formation factors, was the best method for the limited number of soil samples. The greatest uncertainty was associated with areas with the lowest accessibility and, consequently, with low sampling density and/or noises in covariates. Even though the 2-and 3-D maps of soil properties, each associated with explicit uncertainty, can contribute to the INP decision makers/ managers by providing information not available before.

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Section: Soil Dataset and Sampling Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping soil properties in a poorly-accessible area

Costa¹,

Pinheiro²,

Anjos³

et al. 2020

Revista Brasileira De Ciência Do Solo

Self Cite

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“…The selection of sampling points within this watershed was based on the parameters of spatial position, elevation slope, curvature, and land use map (Fidalgo et al, 2008). The urbanized areas were excluded, and selection of sampling sites was restricted to the buffer area (Roudier et al, 2012) defined by preliminary analyses (Carvalho Junior et al, 2014b). Sand, silt, and clay content were obtained according to the procedures described by Claessen et al (1997).…”

Section: Study Area and Soil Samplingmentioning

confidence: 99%

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Pinheiro

Carvalho

Chagas

et al. 2018

Rev. Bras. Ciênc. Solo

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Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees-RT and Multiple Linear Regressions-MLR) and the ability of these tools to predict mineral fraction content under a wide variability of landscapes. The study site was the entire Guapi-Macacu watershed (1,250.78 km²) in the state of Rio de Janeiro in the Southeast region of Brazil. Terrain attributes and remote sensing data (with 30 m of spatial resolution) were used to represent landscape co-variables selected as an input in predictive models in order to develop the explanatory variables. The selection of sampling sites was based on the Latin Hypercube algorithm. A representative set of one hundred points with feasible field access was chosen. Different input databases were tested for prediction of mineral fraction content (harmonized and original data). The Spline algorithm was used to harmonize data according to the GlobalSoil.Net consortium standards. The results showed better performance from the RT models, using input from an average of six covariates; the simplest MLR model used twice as many input variables, creating more complex models without gaining precision. Furthermore, better R² values were obtained using RT models, irrespective of harmonization of soil data. The harmonized dataset from the 0.00-0.05 and 0.05-0.15 m layers, in general, presented better results for the clay and silt, with R 2 values of 0.52 (0.00-0.05 m) and 0.69 (0.05-0.15 m), respectively. Prediction of sand content showed better results when the original depth data was used as an input, although all regression tree models had R 2 values greater than 0.52. The RT models provided a better statistical index than MLR for all predicted properties; however, the variance between models suggests similarity of performance. Regarding harmonization of soil data, both input databases (harmonized or not) can be used to predict soil properties, since the variance of model performance was low and generalization of the soil maps showed similar trends. The products obtained from the digital soil mapping approach make it possible to integrate the factor of uncertainties, providing easier interpretation for soil management and land use decisions.

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“…Este es un procedimiento de muestreo aleatorio estratificado que consiste en seleccionar valores iniciales para la construcción del modelo y estratificar el rango de cada uno de los datos de entrada de este, con el fin de garantizar de esta manera que se seleccionan los valores iniciales de cada rango de los datos de entrada. En esta técnica se necesitan menos muestras para obtener una distribución más representativa de las características de la zona estudiada (Carvalho Junior et al, 2014).…”

Section: Análisis De Datosunclassified

Evaluación De Textura Del Suelo Con Espectroscopía De Infrarrojo Cercano en Un Oxisol De Colombia

et al. 2016

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Técnicas como la espectroscopía de infrarrojo cercano (NIR) se pueden utilizar para identificarlas clases y propiedades de los suelos con buena precisión. El objetivo de este estudio fue calibrar modelos para predecir el contenido de arcilla, limo y arena de un Typic Hapludox por espectroscopia NIR. El estudio se realizó en la Estación Experimental de Carimagua situado en el municipio de Puerto Gaitán, Meta, Colombia. Se utilizó un diseño de red rígida, se tomaron 1200 muestras en una superficie aproximada de 5100 ha. La elaboración de los modelos se hizo mediante regresión por mínimos cuadrados parciales. Se obtuvieron modelos con baja representatividad para contenidos de arena y limo, con valores de R2 (0.41 y 0.34, respectivamente). El modelo para el contenido de arcilla mostró un alto R2 (0.76). Para la arcilla fue posible la elaboración de mapas digitales y espectro-digitales similares. Los resultados encontrados para el contenido de arcilla indican que los análisis de laboratorio se pueden sustituir, en gran parte, por los modelos espectrales. En el caso de la arena y el limo, sería conveniente mejorar el modelo para que, en el futuro, los análisis de laboratorio puedan ser sustituidos para esta clase de suelo.

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Método do hipercubo latino condicionado para a amostragem de solos na presença de covariáveis ambientais visando o mapeamento digital de solos

Cited by 7 publications

References 17 publications

Mapping soil properties in a poorly-accessible area

Mapping soil properties in a poorly-accessible area

Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions

Evaluación De Textura Del Suelo Con Espectroscopía De Infrarrojo Cercano en Un Oxisol De Colombia

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