Renato Fontes Guimarães

Topographic controls of landslides in Rio de Janeiro: field evidence and modeling

Landslides are common features in the Serra do Mar, located along the southeastern Brazilian coast, most of them associated with intense summer storms, specially on the soil-mantled steep hillslopes around Rio de Janeiro city, where the favelas (slums) proliferated during the last few decades. On February 1996, hundreds of landslides took place in city of Rio de Janeiro triggered by intense rainstorms. Since then, many studies have been carried out in two experimental river basins in order to investigate the role played by the topographic attributes in controlling the spatial distribution of landslides inside them. Landslide scars and vegetation cover were mapped using aerial photographs and field observations. A detailed digital terrain model (4 m 2 resolution) of the basins was generated from which the main topographic attributes were analyzed, producing maps for slope, hillslope form, contributing area and hillslope orientation. By comparing these maps with the spatial distribution of the landslide scars for the 1996 event, a landslide potential index (LPI) for the many classes of the different topographic attributes was defined. At the same time, field experiments with the Guelph permeameter were carried out and a variety of scenarios were simulated with the SHALSTAB model, a process-based mathematical model for the topographic control on shallow landslides. The results suggest that most of the landslides triggered in the studied basins were strongly influenced by topography, while vegetation cover did affect landslide distribution. Between the topographic attributes, hillslope form and contributing area played a major role in controlling the spatial distribution of landslides. Therefore, any procedure to be used in this environment towards the definition of landslide hazards need to incorporate these topographic attributes. D 2003 Elsevier B.V. All rights reserved.

Parameterization of soil properties for a model of topographic controls on shallow landsliding: application to Rio de Janeiro

A key problem in the use of physically based models of landslide hazards is how to parameterize the representation of soil properties. We applied a physically based model for the topographic control on shallow landsliding (SHALSTAB) to two catchments in Rio de Janeiro to investigate the accuracy of model results in relation to parameterization of soil properties. In so doing, we address the relevance of values derived from laboratory tests to the field problem, as well as the trade-offs inherent in model parameterization. We ran the model for all combinations of reasonable cohesion, bulk density, and friction angle values and compared model predictions to mapped landslides scars. We rank sorted model performance through the proportion of the total area of landslide scars correctly predicted as potentially unstable. Application of the model to an area where soil properties are not well known can be based on either a standard parameterization that emphasizes topographic controls, or on local calibration of soil parameters against a map of known landslide locations. Our analysis suggests that, in general, acquisition of high-quality digital elevation models (DEMs) is more important than generation of spatially detailed soil property values for reconnaissance level assessment of shallow landslide hazards.

A New Approach to Change Vector Analysis Using Distance and Similarity Measures

The need to monitor the Earth’s surface over a range of spatial and temporal scales is fundamental in ecosystems planning and management. Change-Vector Analysis (CVA) is a bi-temporal method of change detection that considers the magnitude and direction of change vector. However, many multispectral applications do not make use of the direction component. The procedure most used to calculate the direction component using multiband data is the direction cosine, but the number of output direction cosine images is equal to the number of original bands and has a complex interpretation. This paper proposes a new approach to calculate the spectral direction of change, using the Spectral Angle Mapper and Spectral Correlation Mapper spectral-similarity measures. The chief advantage of this approach is that it generates a single image of change information insensitive to illumination variation. In this paper the magnitude component of the spectral similarity was calculated in two ways: as the standard Euclidean distance and as the Mahalanobis distance. In this test the best magnitude measure was the Euclidean distance and the best similarity measure was Spectral Angle Mapper. The results show that the distance and similarity measures are complementary and need to be applied together.

Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression

Radiometric precision is difficult to maintain in orbital images due to several factors (atmospheric conditions, Earth-sun distance, detector calibration, illumination, and viewing angles). These unwanted effects must be removed for radiometric consistency among temporal images, leaving only land-leaving radiances, for optimum change detection. A variety of relative radiometric correction techniques were developed for the correction or rectification of images, of the same area, through use of reference targets whose reflectance do not change significantly with time, i.e., pseudo-invariant features (PIFs). This paper proposes a new technique for radiometric normalization, which uses three sequential methods for an accurate PIFs selection: spectral measures of temporal data (spectral distance and similarity), density scatter plot analysis (ridge method), and robust regression. The spectral measures used are the spectral angle (Spectral Angle Mapper, SAM), spectral correlation (Spectral Correlation Mapper, SCM), and Euclidean distance. The spectral measures between the spectra at times t1 and t2 and are calculated for each pixel. After classification using threshold values, it is possible to define points with the same spectral behavior, including PIFs. The distance and similarity measures are complementary and can be calculated together. The ridge method uses a density plot generated from images acquired on different dates for the selection of PIFs. In a density plot, the invariant pixels, together, form a high-density ridge, while variant pixels (clouds and land cover changes) are spread, having low density, facilitating its exclusion. Finally, the selected PIFs are subjected to a robust regression (M-estimate) between pairs of temporal bands for the detection and elimination of outliers, and to obtain the optimal linear equation for a given set of target points. The robust regression is insensitive to outliers, i.e., observation that appears to deviate strongly from the rest of the data in which it occurs, and as in our case, change areas. New sequential methods enable one to select by different attributes, a number of invariant targets over the brightness range of the images.

Karst Depression Detection Using ASTER, ALOS/PRISM and SRTM-Derived Digital Elevation Models in the Bambuí Group, Brazil

Remote sensing has been used in karst studies to identify limestone terrain, describe exokarst features, analyze karst depressions, and detect geological structures important to karst development. The aim of this work is to investigate the use of ASTER-, SRTM- and ALOS/PRISM-derived digital elevation models (DEMs) to detect and quantify natural karst depressions along the Sa o Francisco River near Barreiras city, northeast Brazil. The study area is a karst landscape characterized by karst depressions (dolines), closed depressions in limestone, many of which contain standing water connected with the ground-water table. The base of dolines is typically sealed with an impermeable clay layer covered by standing water or herbaceous vegetation. We identify dolines by combining the extraction of sink depth from DEMs, morphometric analysis using GIS, and visual interpretation. Our methodology is a semi-automatic approach involving several steps: (a) DEM acquisition; (b) sink-depth calculation using the difference between the raw DEM and the corresponding DEM with sinks filled; and (c) elimination of falsely identified karst

Identificação regional da Floresta Estacional Decidual na bacia do Rio Paranã a partir da análise multitemporal de imagens MODIS

Parana river basin has one of the major fragments of Decidual Seasonal Forest in Brazil. This vegetation is widely fragmented due to the selective wood exploitation and the growth of pasture areas, what justifies the development of studies in order to understand its dynamics and preserve its diversity. Thus, the present study aimed at defining a method for regional identification of the Deciduous Forest in the Parana river basin. The deciduous forest has a typical phenological cycle in comparison with other savanna physiognomies. Due these characteristics, a temporal series of normalized difference vegetation index (NDVI) images of the MODIS sensor was used for its detection. The adopted methodology may be subdivided into the following steps: (a) elaboration of the 3D cube of NDVI images, where the z profile corresponding to temporal signature or NDVI spectrum, (b) noise elimination using the Minimum Noise Fraction (MNF) transformation, and (c) NDVI temporal variability examination of deciduous forest vegetation, with the establishment of the best NDVI band applied in the vegetation index differencing method. The Deciduous Forest presents a typical NDVI spectral behaviour, with higher values in the raining season and lower values in the dry season, what makes this kind of vegetation different from others. The employment of a changing detection algorithm between two images: one for the dry season and the other for the raining season enhances the localization of the Decidual Seasonal Forest. So, the methodology has proved to be effective for regional delimitation of Deciduous Forests considering the MODIS sensor. Considering the changing detection method, Deciduous Forest region is characterized by presenting NDVI alteration values.

Combining Spatial Models for Shallow Landslides and Debris-Flows Prediction

Abstract: Mass movements in Brazil are common phenomena, especially during strong rainfall events that occur frequently in the summer season. These phenomena cause losses of lives and serious damage to roads, bridges, and properties. Moreover, the illegal occupation by slums on the slopes around the cities intensifies the effect of the mass movement. This study aimed to develop a methodology that combines models of shallow landslides and debris-flows in order to create a map with landslides initiation and debris-flows volume and runout distance. The study area comprised of two catchments in Rio de Janeiro city: Quitite and Papagaio that drained side by side the west flank of the Macico da Tijuca, with an area of 5 km 2 . The method included the following steps: (a) location of the susceptible areas to landslides using SHALSTAB model; (b) determination of rheological parameters of debris-flow from the back-analysis technique; and (c) combination of SHALSTAB and FLO-2D models to delineate the areas more susceptible to mass movements. These scenarios were compared with the landslide and debris-flow event of February 1996. Many FLO-2D simulations were exhaustively

Spatialization of climate, physical and socioeconomic factors that affect the dairy goat production in Brazil and their impact on animal breeding decisions

Brazil has high climate, soil and environmental diversity, as well as distinct socioeconomic and political realities, what results in differences among the political administrative regions of the country. The objective of this study was to determine spatial distribution of the physical, climatic and socioeconomic aspects that best characterize the production of dairy goats in Brazil. Production indices of milk per goat, goat production, milk production, as well as temperature range, mean temperature, precipitation, normalized difference vegetation index, relative humidity, altitude, agricultural farms; farms with native pasture, farms with good quality pasture, farms with water resources, farms that receive technical guidance, family farming properties, non-familiar farms and the human development index were evaluated. The multivariate analyses were carried out to spatialize climatic, physical and socioeconomic variables and so differenciate the Brazilian States and Regions. The highest yields of milk and goat production were observed in the Northeast. The Southeast Region had the second highest production of milk, followed by the South, Midwest and North. Multivariate analysis revealed distinctions between clusters of political-administrative regions of Brazil. The climatic variables were most important to discriminate between regions of Brazil. Therefore, it is necessary to implement animal breeding programs to meet the needs of each region.

Aplicação do método de identificação espectral para imagens do sensor ASTER em ambiente de cerrado

The spectral classifiers allow a good estimate for the mapping of the materials from the similarity between the reference curve and the image. Initially the spectral classifiers had been developed for hyperspectral images analysis. However, some works demonstrate good results for the application of these techniques in multispectral images. The present work aims to evaluate the spectral classifier Spectral Identification Method (SIM) in ASTER image. The Spectral Identification Method (SIM) is proposed to establish a new similarity index and three estimates according to the significance levels (5%, 10% and 15%) of the materials. This method is based on two statistical procedures: ANOVA and Spectral Correlation Mapper (SCM) coefficient. This information can be used to evaluate the degree of correlation among the materials in analysis. The advantage of this method is to validate according to levels of significance of the most probable areas of the sought material. The method was applied to ASTER image at the Military Instruction Field located Formosa (GO) close to Federal District. The images were acquired with atmosphere correction. The pixels size from the SWIR image were duplicated in order to join the VNIR and SWIR images. Endmembers were detected in three steps: a) spectral reduction by the Minimum Noise Fraction (MNF) transformation, b) spatial reduction by the Pixel Purity Index (PPI) and c) manual identification of the endmembers using the N-dimensional visualizer. The classification was made from the endmembers of nonphotosynthetic vegetation (NPV), photosynthetic vegetation (PV) and soil. These procedures allowed to identify the main scenarios in the study area.

Detection of karst depression by aster image in the Bambui Group, Brazil

Karst is a characteristic geological feature of areas comprised of limestone. Due to the solubility of these rocks in water, exhibit an extreme heterogeneity of hydraulic conductivities. The characterizing features of karst aquifers are the open conduits, which provide low resistance pathways for ground water flow. Overall cave orientation is largely controlled by hydraulic gradient, joint patterns and other tectonic features, such as faulting and folding. The karst depressions may form on the surface by subsurface actions (dissolution and collapse). Thus, the depressions often show regularity of pattern or alignments, frequently in association with structurally guided cave systems below. The present work aims at to detect depressions zone, as dolines and uvalas in the limestone of the Bambui Group (Central Brazil) using ASTER and ASTERDEM images. A photogeological study, carried out on aster image allowed us to elaborate geomorphological map of dolines. Some guidance to detect dolines can be associated with fracture permeability dominated by nearly vertical joints and joint swarm is provided by fracture trace mapping from remote sensing. Commonly, dolines can be identified on the image and DEM as topographic depressions, which very often contain water or moist vegetation. The methodology allowed determining a doline distribution pattern what is important to environmental planning.