Waldir de Carvalho Junior

Avaliação de modelos digitais de elevação para aplicação em um mapeamento digital de solos

In Brazil, the digital elevation models (DEMs) are usually produced by users themselves and little attention has been given to their limitations as source of spatial information. The objective of this study was to evaluate different DEMs to help in choosing an appropriate model to derive topographical attributes used in a digital soil mapping based on a neural networks approach. The evaluation consisted of the following analysis: determination of root mean square error (RMSE) of elevation; analysis of the spurious depressions; comparison between mapped drainage and numeric drainage and between derived contour lines and original contour lines; and analysis of the derived contribution basins. The results demonstrated that RMSE was not enough to evaluate the quality of these models. DEMs derived from contour lines (CARTA, obtained using the TOPOGRID module) presented better quality than those derived from remote sensors (ASTER and SRTM). These presented great amount of errors that can negatively affect the establishment of relationships between topographical attributes and local conditions of soils.

Atributos topográficos e dados do Landsat7 no mapeamento digital de solos com uso de redes neurais

The objective of this study was to evaluate discriminant variables in digital soil mapping using artificial neural networks. The topographic attributes elevation, slope, aspect, plan curvature and topographic index, derived from a digital elevation model, and the indexes of clay minerals, iron oxide and normalized difference vegetation, derived from a Landsat7 image, were combined and evaluated for their ability to discriminate soils of an area at the northwest of Rio de Janeiro State. The Java neural simulator and the backpropagation learning algorithm were used. The maps generated by each of the six tested sets of variables were compared with reference points for determining the rating accuracy. This comparison showed that the map produced with the use of all the variables reached a performance (73.81% of agreement) superior to maps produced by other sets of variables. Possible sources of error in the use of this approach are mainly related to the great lithological heterogeneity of the area, which hindered the establishment of a more realistic model of environmental correlation. The approach can help make the soil survey in Brazil faster and less subjective.

Modelos de elevação para obtenção de atributos topográficos utilizados em mapeamento digital de solos

The objective of this work was to evaluate digital elevation models (DEM) obtained by different data sources and to select one of them for deriving morphometric variables used in digital soil mapping. The work was performed in the Guapi‑Macacu river basin, RJ, Brazil. The primary data used in the models generated by interpolation (DEM map and DEM hybrid) were: contour lines, drainage, elevation points, and remote sensor data transformed into points. The obtained models by remote sensing and aero‑restitution (DEM SRTM and DEM IBGE) were used in the comparison. All models showed spatial resolution of 30 m. The elevation model evaluations were based on: the terrain derived attribute analysis (slope, aspect, and curvature); spurious depressions (sink); comparison between features derived from the models and the original ones originated from planialtimetric maps; and the analysis of derived watersheds. The DEM hybrid showed a superior quality than the other models.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Quantitative pedology to evaluate a soil profile collection from the Brazilian semi-arid region§

This work applies pedometric tools to analyse soil property information relevant to morphological characterisation and soil classification. The objective of this paper was further to identify similarities in soil properties among a soil profile collection. The harmonisation of soil data enables the comparison between soil profiles, transference of information and modelling of soil horizons distribution. The statistical procedures were implemented in R software, through the Algorithms for Quantitative Pedology (AQP package), which contains a collection of algorithms to model soil resources and aid soil classification, soil profile aggregation and visualisation. The procedures allowed definition of values for soil properties in every one-centimetre layer of the soil profile, by regrouping the data in a different layer thickness, and it was possible to analyse similarity between profiles using a dissimilarity matrix for each depth slice. The AQP allowed analysis of a large number of soil profiles, in terms of vertical variability of soil continuous properties (e.g. sand and clay content, and pH) and for categorical variables, such as diagnostic horizons. Soil depth functions were developed to represent soil properties and probability of occurrence to diagnostic horizons to a large data set, and the dissimilarity analysis allowed separation of a small group of similar soil profiles and further qualitative comparison among the select profiles.

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

A amostragem e uma das etapas mais importantes dos levantamentos de solos. No entanto, os esquemas de amostragem utilizados nos levantamentos convencionais tem se evidenciado inadequados para o mapeamento digital de solos, pois podem comprometer os resultados e, alem disso, nao possibilitam a realizacao de analises estatisticas. Este estudo teve por objetivo avaliar o metodo de amostragem do hipercubo latino condicionado (cLHS, sigla em ingles), na presenca de covariaveis ambientais (elevacao, declividade, curvatura e mapa de uso e cobertura do solo), em comparacao com a amostragem aleatoria, na alocacao de 100 pontos amostrais, buscando maior representatividade das caracteristicas ambientais da bacia do rio Guapi-Macacu. O desempenho dos metodos foi avaliado pela analise qualitativa dos histogramas de frequencia e das analises estatisticas pelos testes F, T de Student e Kolmogorov-Smirnov (K-S), para cada covariavel. Os resultados apresentaram que os pontos selecionados pelo metodo cLHS possuiam distribuicao geografica mais adequada do que aqueles obtidos pela amostragem aleatoria. Alem disso, o metodo cLHS preservou mais a distribuicao de frequencia das covariaveis continuas do que a amostragem aleatoria; para covariavel categorica uso e cobertura do solo os metodos foram equivalentes. Os testes estatisticos confirmaram o melhor desempenho do metodo cLHS, cujas amostras nao diferiram estatisticamente da bacia. Entretanto, a amostragem aleatoria apresentou diferenca estatistica para com a bacia, para todas as covariaveis continuas para pelo menos um dos testes utilizados. Assim, o metodo cLHS pode ser considerado como um metodo satisfatorio para selecao de locais de amostragem em areas heterogeneas similares as deste estudo, visando a utilizacao no mapeamento digital de solos.

A multilayer perceptron model for the correlation between satellite data and soil vulnerability in the Ferlo, Senegal

ABSTRACT Soil erosion processes which contribute to desertification and land degradation, constitute major environmental and social issues for the coming decades. This is particularly true in arid areas where rural populations mostly depend on soil ability to support crop production. Assessment of soil erosion across large and quite diverse areas is very difficult but crucial for planning and management of the natural resources. The purpose of this paper is to investigate a prediction model for soil vulnerability to erosion based on the use of the information contained in satellite images. Based on neural networks models, the used approach in this paper aims at checking a correlation between the digital content of satellite images and soil vulnerability factors: erosivity (R), the soil erodibility (K), and the slope length and steepness (LS); vulnerability (V) as described in the RUSLE model. Significant results have been obtained for R and K factors. This promising pilot study was conducted in South Ferlo, Senegal, a region with Sahelian environmental characteristics. Graphical Abstract

Geotecnologias aplicadas ao Zoneamento Agroecológico do estado do Mato Grosso do Sul

O trabalho teve como objetivo apresentar uma proposta metodologica para o zoneamento agroecologico do Estado do Mato Grosso do Sul, tendo como estudo de caso o municipio de Bandeirantes. Foram utilizadas geotecnologias, tais como: imagens de satelite (Landast 5), sistema de posicionamento global, sistemas de informacao geografica, cartografia digital e software de processamento digital de imagens, visando construir parâmetros ambientais para uma analise integrada de dados, associados com atributos do terreno derivados de um modelo digital de elevacao, dados de propriedades dos solos e de clima. Estas geotecnologias foram empregadas para calcular e espacializar o potencial natural de erosao das terras, a fertilidade natural, drenagem interna e a capacidade de retencao de umidade dos solos. A analise integrada dos parâmetros ambientais, do uso atual das terras e da espacializacao da legislacao ambiental, permitiu estratificar o municipio em diferentes zonas agroecologicas recomendadas para o uso agricola; para o uso com pastagens; para a conservacao dos recursos naturais e para a recuperacao ambiental. No municipio de Bandeirantes as zonas agroecologicas recomendadas para o uso com lavouras somam 1.150 km2 (37%) e para pastagens 1.658 km2 (53%), As recomendadas para conservacao dos recursos naturais e para recuperacao ambiental correspondem a 310 km2, isto e, 10% das terras do municipio. Pode-se concluir que a analise integrada de dados ambientais associados as geotecnologias possibilitou a avaliacao quantitativa e qualitativa permitindo a estratificacao do municipio em diferentes zonas agroecologicas com indicacao de areas passiveis de exploracao agricola sustentavel