Roberto Arnaldo Trancoso Gomes

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.

Classificação de padrões de savana usando assinaturas temporais NDVI do sensor MODLS no Parque Nacional Chapada dos Veadeiros

Savannas are the main vegetation type in Central Brazil, covering approximately 23% of the national territory. Locally known as Cerrado, Brazilian Savannas are formed by amosaic of different physiognomies such as grassland, shrubland and woodland that have atypical phenological cycle. ln this context, the MODIS data provide daily measurements well suited to monitor the seasonal phenology of vegetation. The present work aims to evaluate the advantages of the temporal signatures to detect Brazilian Savanna vegetation types in the Chapada dos Veadeiros National Park, Brazil. The adopted methodology may be subdivided into the following steps: (a) elaboration of the 3D cube of NDVI from temporal MODIS images, where the z profile corresponding to temporal signature, (b) noise elimination by combining Median Filter and Minimum Noise Fraction techniques, (c) endmember detection, and (d) spectral classification using Spectral Correlation Mapper method. The results demonstrate that the savanna physiognomies present typical temporal signatures. The endmembers correspond to the three major physiognomic domains: (a) Cerrado grassland, herbaceous dominated region; (b) Cerrado, mostly amixture of grasses and shrubs; and (c) Cerrado woodland, densely covered by trees. Comparison with Landsat 7/ETM+ image demonstrates the classification efficiency of the temporal series. The study concluded that the NDVI series is useful in differentiating the amount of vegetation types The methodology efficiency has been proved for regional delimitation of savanna physiognomies even considering the low spatial resolution of the 250m MODIS sensor and the high spectral mixture.

Normalization of Multi-Temporal Images Using a New Change Detection Method Based on the Spectral Classifier

Orbital images are difficult to maintain a radiometric precision due to the sensor oscillation, atmosphere interferences, season variation of the solar illumination angle, among others. Thus, many radiometric correction techniques have been developed for time series considering mainly: (a) landscape elements whose reflectances are nearly constant over time called of invariants features and (b) linear regression over invariants features assumes that the pixels sampled in the same places at different times are linearly correlated. Therefore, the key problem to the image regression method is an accurate selection of invariant features. In this paper is proposed new radiometric normalization software developed in Turbo C language that searches the highest quality of the invariant features. The algorithm comprises the following steps: (a) identification of the invariant points using a new change detection method based on the spectral classifier algorithms and (b) regression linear between temporal band pairs eliminating the outliers. Initially the algorithm identifies invariants points using a new change detection method based on the spectral classifier algorithms: Spectral Angle Mapper (SAM) and Spectral Correlation Mapper (SCM). In particular, this method approach allows the automatic identification of the invariants points to calibrate remote-sensing images, without visual interpretation data. Programs users establish the spectral change detection method (SAM or SCM) and the threshold value. The second step is to apply two successive linear regressions. First linear regression searches the outlier points using only the pixels with more value than threshold. The outlier points identification use root means square (RMS) and these not include in the second linear regression. Thus, this is last line regression considers only the best spectral for radiometric adjustment. Finally, the gain and offset values are determined and applied for each band in t2 image. In the case of a mistaken selection of points, the program enables to identify a new threshold in both described stages (spectral change detection method and first linear regression).

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.

Mapeamento da vegetação na floresta atlântica usando o classificador de árvore de decisão para integrar dados de sensoriamento remoto e modelo digital de terreno

The management and ecological monitoring of national parks and other protected areas requires a detailed description of the vegetation distribution patterns. This paper aims to produce a vegetation map for the Serra dos Orgaos National Park (PARNASO). This conservation unit is localized in Atlantic Forest within a topographic variation from sea level to 2,263 meters. The vegetation classification based on the ASTER satellite data, high-resolution aerial photographs and Digital Elevation Model (DEM). The DEM indicates vegetation structures in landscape with high spatial variability because it correlates with environmental factors, such as microclimate, moisture, soil and geomorphological processes. Decision tree classifier was used to extract information of DEM and remote sensing data. Seven classes were identified: Agropecuaria (1.29% of total Park area), Campos de Altitude (24.27%), Floresta Ombrofila Densa Alto-Montana (37.47%), Floresta Ombrofila Densa Montana (21.54%), Floresta Ombrofila Densa Sub-Montana (5.22%), Floresta Secundaria (4.13%), and no vegetation area (6.08%). The three highest physiognomies were associated with altitude higher than 1,000 m and represented 55.5% of the total area. The construction of decision trees combining the DEM and remote sensing information can improve the result on the forest tropical distribution.

Probability Density Components Analysis: A New Approach to Treatment and Classification of SAR Images

Speckle noise (salt and pepper) is inherent to synthetic aperture radar (SAR), which causes a usual noise-like granular aspect and complicates the image classification. In SAR image analysis, the spatial information might be a particular benefit for denoising and mapping classes characterized by a statistical distribution of the pixel intensities from a complex and heterogeneous spectral response. This paper proposes the Probability Density Components Analysis (PDCA), a new alternative that combines filtering and frequency histogram to improve the classification procedure for the single-channel synthetic aperture radar (SAR) images. This method was tested on L-band SAR data from the Advanced Land Observation System (ALOS) Phased-Array Synthetic-Aperture Radar (PALSAR) sensor. The study area is localized in the Brazilian Amazon rainforest, northern Rondonia State (municipality of Candeias do Jamari), containing forest and land use patterns. The proposed algorithm uses a moving window over the image, estimating the probability density curve in different image components. Therefore, a single input image generates an output with multi-components. Initially the multi-components should be treated by noise-reduction methods, such as maximum noise fraction (MNF) or noise-adjusted principal components (NAPCs). Both methods enable reducing noise as well as the ordering of multi-component data in terms of the image quality. In this paper, the NAPC applied to multi-components provided large reductions in the noise levels, and the color composites considering the first NAPC enhance the classification of different surface features. In the spectral classification, the Spectral Correlation Mapper and Minimum Distance were used. The results obtained presented as similar to the visual interpretation of optical images from TM-Landsat and Google Maps.