Helena Saraiva Koenow Pinheiro

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.

Using Soil Depth Functions to Distinguish Dystric from Xanthic Ferralsols in the Landscape

The soil texture is a key parameter and is widely used as input in predictive models to estimate other soil properties. The general goal was creating numerical parameters to describe the variability of soil particle size (sand, silt, and clay) components using continuous depth functions to characterize Ferralsols from Guapi-Macacu watershed in Rio de Janeiro State (Brazil). The profile collection comprises fifteen profiles, seven classified as Haplic Ferralsols (Dystric) and eight as Haplic Ferralsols (Xanthic). The analysis was performed in the R software through “aqp” package (Algorithms for Quantitative Pedology) and using equal-area quadratic spline function. A numerical aggregation of soil texture components was used to build a mean, a median, and spline depth functions, fitting the dataset to six predefined depths (GlobalSoilMap project) and to most-likely horizon depths. The analysis revealed sand and silt content with decreasing values with soil depth and the opposite trend for clay. The topsoil layer (0–30 cm) had dominantly a clay loam texture (32–40 % clay; 49–53 % sand; and 15–20 % silt). The most-likely diagnostic B-horizon (45–150 cm depth) presented clayey texture (43–47 % of clay and 40–55 % of sand). Ferralsols usually have low silt contents; and the silt range was from 10 to 20 % in the soil profile collection. The organized data can be useful to many purposes, including profile database harmonization and soil classification.

Mapping Soil Organic Carbon and Organic Matter Fractions by Geographically Weighted Regression

The soil organic matter (SOM) content and dynamic are related to vegetation cover, climate, relief, and geology; these factors have strong variation in space in the southeastern of Brazil. The objective of the study was to compare and evaluate performance of classical multiple linear regressions (MLR) and geographically weighted regression (GWR) models to predict soil organic carbon (SOC) and chemical fractions of organic matter in the Brazilian southeastern mountainous region. The regression models were fitted based on SOC and chemical fractions of SOM. The points ( = 89) were selected by pedologists experience along transects and toposequences. The covariates were also selected using the empirical knowledge of pedologists when choosing variables that drive soil carbon content and its dynamics. Geology map, legacy soils map, terrain attributes derived from digital elevation model, and remote sensing indices derived from RapidEye sensor bands were used as covariates. In all MLR models (except for fulvic acid fraction [FAF]), the legacy soil map was selected as a covariate by the stepwise approach. The geology map was not selected as important covariate to predict FAF and humin (HUM). At least one variable derived from remote sensing was selected by the adjusted models. For the prediction of the SOC, HUM, and FAF, the GWR models had the highest performance. The MLR models extrapolated the results, especially for SOC. The relationships among SOC, SOM fractions, and environmental covariates were affected by local landscape variability, and the GWR model was better at modeling.

Applying Artificial Neural Networks Utilizing Geomorphons to Predict Soil Classes in a Brazilian Watershed

The use of landscape terrain attributes associated with the field information in geographic information systems (GISs) helps to improve the methods applied in soil survey. Geomorphons is a novel technique to map surface elements from digital elevation model and visibility distance (search radius) of a central point in the landscape, which can adopt flexible scales. The main goal of this study was to evaluate the potential for incorporating Geomorphons, which is used to recognize landscape patterns and to improve the soil class predictions by artificial neural networks (ANNs). The procedures involved the acquisition of a cartographic database, creating digital models that represent landscape attributes relevant to paedogenesis on the research site (including Geomorphons of different search radius), sample collection and description of one hundred soil profiles in predefined locations, and finally the supervised classification by neural networks. The covariates used were as follows: elevation, slope, curvature, combined topographic index (CTI), euclidean distance, clay minerals, iron oxide, normalized difference vegetation index (NDVI), geology, and Geomorphons. All models for the terrain attributes have 30-m pixel resolution, and these variables correspond to neurons in the input layer of the neural networks. The output layer of the supervised classification corresponded to the nine dominant soil classes in the study area. To define the appropriate scale of Geomorphons map, sixteen sets of neural networks contain each one of the terrain attributes plus a Geomorphons map calculated from different search radius. For comparative purposes, one of the sets included no Geomorphons. Selection of the appropriate Geomorphons search radius was based on the statistical indexes obtained from a confusion matrix. The results showed that the best classification used the Geomorphons map obtained by forty-five pixels of search radius, in combination with other variables. This classifier presented values to kappa index and global accuracy corresponding to 0.74 and 77.0, respectively.

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.

Landscape Indicators of the Success of Protected Areas on Habitat Recovery for the Golden Lion Tamarin (Leontopithecus rosalia)

ABSTRACT Since 1974, conservation efforts to protect an endangered species, the Golden Lion Tamarin (GLT), have led to the creation of the first strict nature reserve in Brazil ‒ the Poço das Antas Biological Reserve (REBIO) ‒ and the subsequent creation of the Environmental Protection Area of the Sao Joao River Basin, for sustainable use. This paper assesses the influence of Protected Areas (PA) creation and conservation programs on GLT habitat. Landscape metrics based on aerial photographs taken from three different periods are used to assess habitat conditions for this species through time. We analyzed the availability and potential quality of habitat in the years following the creation of the REBIO, comparing with its buffer zone and population rates correlations. We observed different trends in landscape dynamics between the REBIO, where most of the forest recovery occurred, and its buffer zone, where habitat loss was recorded. In general, the results showed an increase of continuous forest patches. The conservation/regeneration processes in the buffer zone have intensified in recent years. Comparisons over time, especially with respect to forest core areas and large patches, are valuable tools to assess landscape suitability for GLT survival at different spatial scales.

Tree-based techniques to predict soil units

Quantitative soil–landscape models offer a method for conducting soil surveys that use statistical tools to predict natural patterns in the occurrence of particular map units across a landscape. The aim of the present study was to predict soil units in a watershed with wide variation in landscape conditions. The approach relied on a modelling of soil-forming factors in order to understand the variability of the landscape components in the region. Models were generated for landscape attributes related to pedogenesis, specifically elevation, slope, curvature, compound topographic index, Euclidean distance from stream networks, landforms map, clay minerals index, iron oxide index and normalised difference vegetation index, along with an existing geology map. The soil classification was adapted from the World Reference Base System for Soil Resources, and the predominant soil taxonomic orders observed were Ferrasols, Acrisols, Gleysols, Cambisols, Fluvisols and Regosols. The algorithms used to predict the soil units were based on decision tree (DT) and random forest (RF) methods. The criteria used to evaluate the models’ performance were statistical indices, coherence between predicted units and the legacy map, as well as accuracy checks based on control samples. The best performing model was found to be the RF algorithm, with resulting statistical indices considered excellent (overall=0.966, kappa=0.962). The accuracy of the map as determined by control points was 67.89%, with a kappa value of 61.39%.

Mapeamento da Fragilidade Ambiental na Bacia do Rio Aldeia Velha, RJ

This paper presents an approach to mapping the environmental fragility of river basins. The study was performed at the Aldeia Velha river basin, a rainforest watershed located between Rio de Janeiro’s coastal plains and the Serra do Mar highlands. A multiple-criteria analysis was performed involving factors that affect the risk of erosion; these variables were analyze using GIS tools and were integrated by algorithm in order to form a description of the different classes of environmental fragility in the basin. The multicriteria analysis considered the use of a numerical land model, official data, orbital imagery and the opinion of subject-matter experts. A Map of Potential Fragility (MPF) was generated through the collection of secondary information such as soil types, rainfall intensity and terrain slope. This initial map was later combined with a land-use projection to produce a Map of Emerging Fragility (MEF). The mapping results pointed to a highly fragile environment, where more than 70 percent of the basin’s area was classified with high or very high degree of fragility, in both the potential and emerging context. The Apparent Erosion Processes (AEP) were positively correlated in the areas with a higher level of fragility in the final map products, as well as highlighted the locations most prone to the intensification of erosion processes. The models generated important information for land-use planning, enabling affordable and easy upgrade for municipal governments and civil society organizations to monitor high fragility areas.