ABSTRACT Pedosphere-hydrosphere interface accounts for the association between soil hydrology and landscape, represented by topographic and Remote Sensing data support and integration. This study aimed to analyze different statistical radiometric and spectral data selection methods and dimensionality reduce environment-related data to support the classification of soil physical-hydric properties, such as soil basic infiltration rate (bir) and saturated hydraulic conductivity (Ksat); as well as to act in data mining processes applied to hydropedological properties digital mapping. Accordingly, research integrated information from Visible to Infrared (VIS-IR) spectral indices and Sentinel’s 2A mission Multispectral Instrument (MSI) sensor bands, terrain numerical modeling and aerogeophysics set to model soil-water content in two soil layers (0.00-0.20 m and 0.20-0.40 m). Pre-processed data were subjected to statistical analysis (multivariate and hypothesis tests); subsequently, the methods were applied (variation inflation factor - VIF, Stepwise Akaike information criterion – Stepwise AIC, and recursive feature elimination - RFE) to mine covariates used for Random Forest modeling. Based on the results, there were distinctions and singularities in spectral and radiometric data selection for each adopted method; the importance degree, and contribution of each one to soil physical-hydric properties have varied. According to the applied statistical metrics and decision-making criteria (highest R2 and lowest RMSE / MAE), the chosen methods were RFE (0.00-0.20 m layers) and Stepwise AIC (0.20-0.40 m layers) - both concerned with the assessed variables (bir and Ksat). This approach captured the importance of environmental variables and highlighted their potential use in hydropedological digital mapping at Guapi-Macacu watershed. Pedospherehydrosphere Pedosphere hydrosphere landscape integration environmentrelated environment related physicalhydric physical hydric bir Ksat (Ksat) Accordingly VISIR VIS IR (VIS-IR Sentinels Sentinel s A MSI (MSI bands soilwater water 0.000.20 000020 0.00 0.20 0 00 20 (0.00-0.2 0.200.40 020040 0.40 40 0.20-0.4 m. . m) Preprocessed Pre processed multivariate tests tests) subsequently variation VIF results method degree varied decisionmaking decision making highest R MAE, MAE , MAE) (0.20-0.4 Ksat. Ksat) GuapiMacacu Guapi Macacu watershed (Ksat 000 0.000.2 00002 0.0 020 0.2 2 (0.00-0. 200 0.200.4 02004 040 0.4 4 0.20-0. (0.20-0. 0.000. 0000 0. 02 (0.00-0 0.200. 0200 04 0.20-0 (0.20-0 0.000 (0.00- 0.200 0.20- (0.20- (0.00 (0.20 (0.0 (0.2 (0. (0 (

ABSTRACT Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest - RF, Gradient Boosted Machine - GBM, C5.0, and multinomial log-linear model - MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in the Brazilian semiarid region. The goal of this study was to evaluate the efficiency of random forest and cokriging models applied to predict CEC in the Brazilian semiarid region. The covariates used to predict CEC consist of images from Landsat 5 TM and a legacy soil map (scale 1:10,000). The sample set comprises 499 samples from the topsoil layer (0.00-0.20 m), where 375 samples were used in training processes and 124 as validation samples. The cokriging model (R2 = 0.57 and RMSE = 7.22 cmolc kg-1) performed better in predicting CEC than the random forest model (R2 = 0.47 and RMSE = 7.89 cmolc kg-1). The approach used showed potential for estimating CEC content in the Brazilian semiarid region by using covariates obtained from orbital remote sensing and the legacy soil map.

Resumo O objetivo deste trabalho foi o desenvolvimento de modelos com diferentes conjuntos de dados, para estimar a densidade de solos de regiões tropicais montanhosas, a partir de atributos de solos comumente encontrados nas análises de perfis de solos descritos nos levantamentos regionais. O conjunto total de dados compõe-se de 163 amostras e foi dividido em seis grupamentos, dos quais três com 73 amostras, com o máximo de 32 covariáveis, e três com 163 amostras, com o máximo de 18 covariáveis. Testaram-se modelos de regressão linear múltipla (RLM) e randomForest (RF). A menor incerteza entre os modelos foi alcançada pelo RLM2, com R2 de 0,56, 13 covariáveis e 73 amostras. Nos grupamentos com 163 amostras, os melhores modelos foram os RF, com R2 médio de 0,48. A raiz quadrada da média do erro ao quadrado variou entre 0,09 e 0,14. As covariáveis mais importantes no modelo RF foram: carbono orgânico, hidrogênio, areia fina e grossa, saturação por bases e capacidade de troca catiônica. Pelo método "stepwise regression", as variáveis mais importantes foram: a relação silte/argila; areia grossa e fina; carbono orgânico; saturação por bases; e potássio.

Abstract The objective of this work was the development of models with different sets of data for estimating soil bulk density in tropical mountainous regions, from soil attributes commonly found in the analyses of soil profiles described in regional surveys. The complete dataset is composed of 163 samples and it was divided into six groups, of which three groups have 73 samples and the maximum of 32 covariables, and three have 163 samples and the maximum of 18 covariables. The linear regression (RLM) and randomForest (RF) models were tested. The lowest uncertainty between the models was achieved by RLM2, with R2 of 0.56, 13 covariables, and 73 samples. Considering the groups with 163 samples, the best models were the RFs with mean R2 of 0.48. The root mean squared error ranged between 0.09 and 0.14. The most important covariables in the RF model were: organic carbon, hydrogen, fine and coarse sand, base saturation, and cation exchange capacity. By the stepwise backward regression, the main covariables were: silt and clay relation; fine and coarse sand; organic carbon; base saturation; and potassium.

Resumo O objetivo deste trabalho foi avaliar a influência da resolução espacial do modelo digital de elevação e da eficiência de modelos Random Forest sobre a predição dos teores de areia, argila e carbono orgânico, com uso de número reduzido de amostras. O trabalho foi realizado em área de Cerrado com diversidade litológica, no Estado do Mato Grosso do Sul, tendo-se utilizado atributos morfométricos, dados do sensor TM Landsat 5 e litologia como covariáveis preditoras. Dados da camada superficial (0,0-0,2 m) de 175 perfis de solos (0,009 perfis km-2) e de 26 covariáveis preditoras foram utilizados com resolução espacial de 30 (conjunto 1) e 90 m (conjunto 2). A análise realizada pelo Random Forest mostrou que as covariáveis de nível de base do canal de drenagem, da elevação e da litologia foram as mais importantes para explicar a variabilidade. A validação dos modelos apresentou similaridade entre os conjuntos quanto à predição de areia, argila e carbono orgânico, o que explica os seguintes valores de variabilidade espacial, respectivamente: 44, 40 e 33%, para a resolução de 30 m; e de 45, 46 e 33%, para a resolução de 90 m. A resolução espacial das covariáveis preditoras tem pouca influência sobre a predição dos atributos, e a abordagem por Random Forest apresenta potencial de utilização para estimar atributos do solo.

Abstract The objective of this work was to evaluate the effect of the digital elevation model spatial resolution and of the efficiency of Random Forest models on the prediction of sand, clay, and organic carbon contents, using few soil samples. The study was carried out in a Cerrado area with lithological diversity, in the state of Mato Grosso do Sul, Brazil, using morphometric attributes, TM Landsat 5 sensor data, and lithology as predictive covariates. The surface layer data (0.0-0.2 m) of 175 soil profiles (0,009 profiles km-2) and of 26 predictor covariates were used with 30 (set 1) and 90-m (set 2) spatial resolutions. The performed analysis by Random Forest models showed that channel base level, elevation, and lithology were the most important ones to explain the variability. The validation of the models showed similarity among sets for the prediction of sand, clay, and organic carbon contents, which explains the following values of spatial variability, respectively: 44, 40, and 33%, for the spatial resolution of 30 m; and 45, 46, and 33%, for the spatial resolution of 90 m. The spatial resolution of the predictive covariates has little effect on attribute predictions, and the Random Forest approach has potential use for estimating soil properties.

A amostragem é uma das etapas mais importantes dos levantamentos de solos. No entanto, os esquemas de amostragem utilizados nos levantamentos convencionais têm se evidenciado inadequados para o mapeamento digital de solos, pois podem comprometer os resultados e, além disso, não possibilitam a realização de análises estatísticas. Este estudo teve por objetivo avaliar o método de amostragem do hipercubo latino condicionado (cLHS, sigla em inglês), na presença de covariáveis ambientais (elevação, declividade, curvatura e mapa de uso e cobertura do solo), em comparação com a amostragem aleatória, na alocação de 100 pontos amostrais, buscando maior representatividade das características ambientais da bacia do rio Guapi-Macacu. O desempenho dos métodos foi avaliado pela análise qualitativa dos histogramas de frequência e das análises estatísticas pelos testes F, T de Student e Kolmogorov-Smirnov (K-S), para cada covariável. Os resultados apresentaram que os pontos selecionados pelo método cLHS possuíam distribuição geográfica mais adequada do que aqueles obtidos pela amostragem aleatória. Além disso, o método cLHS preservou mais a distribuição de frequência das covariáveis contínuas do que a amostragem aleatória; para covariável categórica uso e cobertura do solo os métodos foram equivalentes. Os testes estatísticos confirmaram o melhor desempenho do método cLHS, cujas amostras não diferiram estatisticamente da bacia. Entretanto, a amostragem aleatória apresentou diferença estatística para com a bacia, para todas as covariáveis contínuas para pelo menos um dos testes utilizados. Assim, o método cLHS pode ser considerado como um método satisfatório para seleção de locais de amostragem em áreas heterogêneas similares as deste estudo, visando a utilização no mapeamento digital de solos.

Soil sampling is one of the most important stages in soil surveys. However, in conventional soil surveys, sampling schemes have proven to be inadequate for digital soil mapping because they may affect the results obtained and, in addition, they do not allow statistical estimations to be performed. This study attempts to evaluate the conditioned Latin Hypercube Sampling (cLHS) method with four environmental covariates (elevation, slope, curvature and land cover/land use map), in comparison to random sampling, in allocating 100 sample points, seeking a more representative description of the environmental characteristics of the Macacu watershed area. The performance of the methods was evaluated by qualitative assessment of histograms of distribution, and statistical analyses by the F test, Student T Test and Kolmogorov-Smirnov test (K-S) for each covariable. The sampling points selected by the cLHS method showed better geographic distribution than those obtained by random sampling. Furthermore, the cLHS method better maintained the frequency distribution of the continuous covariables than random sampling did, and the categorical covariable of land cover/land use showed no significance differences between the cLHS and random methods. Statistical analysis confirmed the better performance of the cLHS method compared to the random method, and the cLHS samples showed no significance differences from the watershed data. In contrast, the random samples showed significance differences from the watershed data for all the continuous covariables for at least one of the statistical tests used. Thus, the cLHS method is a satisfactory method for selecting points for soil sampling in heterogeneous areas, like those of the present study, for the purpose of use in digital soil mapping.

Relata-se o caso de homem de 21 anos, leucoderma, portador de angioma cavernoso do meato acústico interno direito envolvendo o complexo VII e VIII nervos cranianos tratado cirurgicamente. Apenas 18 casos de angiomas cavernosos dessa localização foram relatados na literatura. São comentados os aspectos clínicos, o diagnóstico diferencial e o tratamento.

We report the surgically treated case of a 21-year-old caucasian male harboring a cavernous hemangioma of the right internal auditory canal encasing the seventh and eighth cranial nerves complex. Only 18 cases of cavernous hemangiomas of this location have been previously reported. The clinical features, the differential diagnosis and the treatment are discussed.