Genesis and classification of Oxisols in a highland toposequence of the upper Jequitinhonha Valley (MG)

The Brazilian System of Soil Classification (SiBCS) is a taxonomic system, open and in permanent construction, as new knowledge on Brazilian soils is obtained. The objective of this study was to characterize the chemical, physical, morphological, micro-morphological and mineralogical properties of four pedons of Oxisols in a highland toposequence in the upper Jequitinhonha Valley, emphasizing aspects of their genesis, classification and landscape development. The pedons occupy the following slope positions: summit - Red Oxisol (LV), mid slope (upper third) - Yellow-Red Oxisol (LVA), lower slope (middle third)- Yellow Oxisol (LA) and bottom of the valley (lowest third) - "Gray Oxisol" ("LAC"). These pedons were described and sampled for characterization in chemical and physical routine analyses. The total Fe, Al and Mn contents were determined by sulfuric attack and the Fe, Al and Mn oxides in dithionite-citrate-bicarbonate and oxalate extraction. The mineralogy of silicate clays was identified by X ray diffraction and the Fe oxides were detected by differential X ray diffraction. Total Ti, Ga and Zr contents were determined by X ray fluorescence spectrometry. The "LAC" is gray-colored and contains significant fragments of structure units in the form of a dense paste, characteristic of a gleysoil, in the horizons A and BA. All pedons are very clayey, dystrophic and have low contents of available P and a pH of around 5. The soil color was related to the Fe oxide content, which decreased along the slope. The decrease of crystalline and low- crystalline Fe along the slope confirmed the loss of Fe from the "LAC". Total Si increased along the slope and total Al remained constant. The clay fraction in all pedons was dominated by kaolinite and gibbsite. Hematite and goethite were identified in LV, low-intensity hematite and goethite in LVA, goethite in LA. In the "LAC", no hematite peaks and goethite were detected by differential X ray diffraction. The micro-morphology indicated prevalence of granular microstructure and porosity with complex stacking patterns.. The soil properties in the toposequence converged to a single soil class, the Oxisols, derived from the same source material. The landscape evolution and genesis of Oxisols of the highlands in the upper Jequitinhonha Valley are related to the evolution of the drainage system and the activity of excavating fauna.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Soil spectral library and its use in soil classification

Soil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a Fieldspec sensor, 350-2,500 nm) for the horizons of 223 soil profiles from the regions of Matão, Paraguaçu Paulista, Andradina, Ipaussu, Mirandópolis, Piracicaba, São Carlos, Araraquara, Guararapes, Valparaíso (SP); Naviraí, Maracajú, Rio Brilhante, Três Lagoas (MS); Goianésia (GO); and Uberaba and Lagoa da Prata (MG). A Principal Component Analysis (PCA) of the data was then performed and a graphic representation of the spectral curve was created for each profile. The reflectance intensity of the curves was principally influenced by the levels of Fe2O3, clay, organic matter and the presence of opaque minerals. There was no change in the spectral curves in the horizons of the Latossolos, Nitossolos, and Neossolos Quartzarênicos. Argissolos had superficial horizon curves with the greatest intensity of reflection above 2,200 nm. Cambissolos and Neossolos Litólicos had curves with greater reflectance intensity in poorly developed horizons. Gleisols showed a convex curve in the region of 350-400 nm. The PCA was able to separate different data collection areas according to the region of source material. Principal component one (PC1) was correlated with the intensity of reflectance samples and PC2 with the slope between the visible and infrared samples. The use of the Spectral Library as an indicator of possible soil classes proved to be an important tool in profile classification.

Utilização de métodos de representação espacial para cálculo do fator topográfico na equação universal de perda de solo revisada em bacias hidrográficas

Métodos de representação espacial para o cálculo do fator topográfico (LS) da Equação Universal de Perda de Solo Revisada (RUSLE) têm sido utilizados para estimar a erosão do solo e a produção de sedimentos em bacias hidrográficas. Esses procedimentos baseiam-se nas equações tradicionalmente empregadas para determinação do fator LS e em informações que caracterizam a forma das vertentes derivadas do modelo numérico de elevação (MNE). Neste estudo foram analisados dois métodos de representação espacial utilizados no cálculo do fator LS em modelos matemáticos de erosão e produção de sedimentos em bacias hidrográficas. A análise foi realizada em quatro bacias rurais de relevo movimentado. Os valores de LS obtidos pelos métodos de representação espacial foram comparados entre si e com valores de LS determinados pelo método tradicional com levantamento em campo. Resultados mostraram que os valores de LS gerados pelos métodos de representação espacial apresentam diferenças significativas entre si, sendo dependentes do procedimento de cálculo e do método utilizado para determinar a direção de fluxo no MNE. Também foi verificado que os valores numéricos do fator LS determinados pelos métodos de representação espacial apresentam diferenças em relação àqueles estimados pelo método tradicional.

Highlands of the upper Jequitinhonha valley, Brazil: I - characterization and classification

In the upper Jequitinhonha valley, state of Minas Gerais, Brazi, there are large plane areas known as "chapadas", which are separated by areas dissected by tributaries of the Jequitinhonha and Araçuaí rivers. These dissected areas have a surface drainage system with tree, shrub, and grass vegetation, more commonly known as "veredas", i.e., palm swamps. The main purpose of this study was to characterize soil physical, chemical and morphological properties of a representative toposequence in the watershed of the Vereda Lagoa do Leandro, a swamp near Minas Novas, MG, on "chapadas", the highlands of the Alto Jequitinhonha region Different soil types are observed in the landscape: at the top - Typic Haplustox (LVA), in the middle slope - Xanthic Haplustox (LA), at the footslope - Xanthic Haplustox, gray color, here called "Gray Haplustox" ("LAC") and, at the bottom of the palm swamp - Typic Albaquult (GXbd). These soils were first morphologically described; samples of disturbed and undisturbed soils were collected from all horizons and subhorizons, to evaluate their essential physical and chemical properties, by means of standard determination of Fe, Al, Mn, Ti and Si oxides after sulfuric extraction. The contents of Fe, Al and Mn, extracted with dithionite-citrate-bicarbonate and oxalate treatments, were also determined. In the well-drained soils of the slope positions, the typical morphological, physical and chemical properties of Oxisols were found. The GXbd sample, from the bottom of the palm swamp, is grayish and has high texture gradient (B/A) and massive structure. The reduction of the proportion of crystalline iron compounds and the low crystallinity along the slope confirmed the loss of iron during pedogenesis, which is reflected in the current soil color. The Si and Al contents were lowest in the "LAC" soil. There was a decrease of the Fe2O3/TiO2 ratio downhill, indicating progressive drainage restriction along the toposequence. The genesis and all physical and chemical properties of the soils at the footslope and the bottom of the palm swamp of the "chapadas" of the Alto Jequitinhonha region are strongly influenced by the occurrence of ground water on the surface or near the surface all year long, at present and/or in the past. Total concentrations of iron oxides, Fe d and Fe o in soils of the toposequence studied are related to the past and/or present soil colors and drainage conditions.

Calibração e validação da equação universal de perda de solos modificada (MUSLE) utilizando dados hidrossedimentológicos locais

Modelos hidrossedimentológicos são úteis na análise ambiental de bacias hidrográficas não monitoradas. Entretanto, para que as suas predições sejam confiáveis, é necessário que os modelos sejam adequadamente calibrados e validados para as condições locais. O objetivo do presente trabalho foi calibrar o coeficiente a da MUSLE para as condições da bacia do ribeirão Pipiripau, usando dados hidrossedimentológicos locais, bem como validar a equação calibrada, com uma série de dados diferente da usada na calibração. O coeficiente a da MUSLE foi calibrado por meio do ajuste entre os valores observados e calculados de aporte de sedimento de eventos individuais, correspondentes ao período entre 1999 e 2005, usando dados hidrológicos (Q e q p) observados. Para validação do modelo calibrado, a série usada correspondeu aos anos de 1998 e 2006-2009 (N.A.: dados corretos, ver p. 12), onde apenas os dados pluviométricos locais e os fatores CN, K, L, S, C e P da bacia, previamente obtidos, foram usados. Os resultados indicam que a MUSLE calibrada apresenta melhor acurácia na estimativa do aporte de sedimento total anual (R² = 0,68 e E = 0,61) do que em nível mensal (R² = 0,44 e E = 0,43). Como o coeficiente de ajuste a da MUSLE foi proporcional ao volume de precipitação pluvial anual, este poderá ser usado para melhorar as predições do modelo.

Generalized Schmidt decomposition and classification of three-quantum-bit states

We prove for any pure three-quantum-bit state the existence of local bases which allow one to build a set of five orthogonal product states in terms of which the state can be written in a unique form. This leads to a canonical form which generalizes the two-quantum-bit Schmidt decomposition. It is uniquely characterized by the five entanglement parameters. It leads to a complete classification of the three-quantum-bit states. It shows that the right outcome of an adequate local measurement always erases all entanglement between the other two parties.

Characterization and classification of soils in the Taquari river basin - Pantanal region, state of Mato Grosso do Sul, Brazil

Among the soils in the Mato Grosso do Sul, stand out in the Pantanal biome, the Spodosols. Despite being recorded in considerable extensions, few studies aiming to characterize and classify these soils were performed. The purpose of this study was to characterize and classify soils in three areas of two physiographic types in the Taquari river basin: bay and flooded fields. Two trenches were opened in the bay area (P1 and P2) and two in the flooded field (P3 and P4). The third area (saline) with high sodium levels was sampled for further studies. In the soils in both areas the sand fraction was predominant and the texture from sand to sandy loam, with the main constituent quartz. In the bay area, the soil organic carbon in the surface layer (P1) was (OC) > 80 g kg-1, being diagnosed as Histic epipedon. In the other profiles the surface horizons had low OC levels which, associated with other properties, classified them as Ochric epipedons. In the soils of the bay area (P1 and P2), the pH ranged from 5.0 to 7.5, associated with dominance of Ca2+ and Mg2+, with base saturation above 50 % in some horizons. In the flooded fields (P3 and P4) the soil pH ranged from 4.9 to 5.9, H+ contents were high in the surface horizons (0.8-10.5 cmol c kg-1 ), Ca2+ and Mg² contents ranged from 0.4 to 0.8 cmol c kg-1 and base saturation was < 50 %. In the soils of the bay area (P1 and P2) iron was accumulated (extracted by dithionite - Fed) and OC in the spodic horizon; in the P3 and P4 soils only Fed was accumulated (in the subsurface layers). According to the criteria adopted by the Brazilian System of Soil Classification (SiBCS) at the subgroup level, the soils were classified as: P1: Organic Hydromorphic Ferrohumiluvic Spodosol. P2: Typical Orthic Ferrohumiluvic Spodosol. P3: Typical Hydromorphic Ferroluvic Spodosol. P4: Arenic Orthic Ferroluvic Spodosol.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Surface mapping, organic matter and water stocks in peatlands of the Serra do Espinhaço meridional - Brazil

Peatlands are soil environments that store carbon and large amounts of water, due to their composition (90 % water), low hydraulic conductivity and a sponge-like behavior. It is estimated that peat bogs cover approximately 4.2 % of the Earth's surface and stock 28.4 % of the soil carbon of the planet. Approximately 612 000 ha of peatlands have been mapped in Brazil, but the peat bogs in the Serra do Espinhaço Meridional (SdEM) were not included. The objective of this study was to map the peat bogs of the northern part of the SdEM and estimate the organic matter pools and water volume they stock. The peat bogs were pre-identified and mapped by GIS and remote sensing techniques, using ArcGIS 9.3, ENVI 4.5 and GPS Track Maker Pro software and the maps validated in the field. Six peat bogs were mapped in detail (1:20,000 and 1:5,000) by transects spaced 100 m and each transect were determined every 20 m, the UTM (Universal Transverse Mercator) coordinates, depth and samples collected for characterization and determination of organic matter, according to the Brazilian System of Soil Classification. In the northern part of SdEM, 14,287.55 ha of peatlands were mapped, distributed over 1,180,109 ha, representing 1.2 % of the total area. These peatlands have an average volume of 170,021,845.00 m³ and stock 6,120,167 t (428.36 t ha-1) of organic matter and 142,138,262 m³ (9,948 m³ ha-1) of water. In the peat bogs of the Serra do Espinhaço Meridional, advanced stages of decomposing (sapric) organic matter predominate, followed by the intermediate stage (hemic). The vertical growth rate of the peatlands ranged between 0.04 and 0.43 mm year-1, while the carbon accumulation rate varied between 6.59 and 37.66 g m-2 year-1. The peat bogs of the SdEM contain the headwaters of important water bodies in the basins of the Jequitinhonha and San Francisco Rivers and store large amounts of organic carbon and water, which is the reason why the protection and preservation of these soil environments is such an urgent and increasing need.