José Alberto Quintanilha

Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data

Abstract This paper describes a methodology to assess the evidence of human presence and human activities in the Brazilian Amazonia region using DMSP/OLS night-time satellite sensor imagery. It consists on exploring the potential of the sensor data for regional studies analysing the correlation between DMSP night-time light foci and population, and the correlation between DMSP night-time light foci and electrical power consumption. In the mosaic of DMSP/OLS night-time light imagery from September 1999, 248 towns were detected from a total of 749 munic i pios in Amazonia. It was found that the night-time light foci were related to human presence in the region, including urban settlements, mining, industries, and civil construction, observed in ancillary Landsat TM and JERS imagery data. The analysis considering only the state of Para revealed a linear relation ( R 2 =0.79) between urban population from the 1996 census data and DMSP night-time light foci. Similarly, electrical power consumption for 1999 was linearly correlated with DMSP night-time light foci. Thus the DMSP/OLS imagery can be used as an indicator of human presence in the analysis of spatial–temporal patterns in the Amazonia region. These results are very useful considering the continental dimension of Amazonia, the absence of demographic information between the official population census (every 10 years), and the dynamics and complexity of human activities in the region. Therefore DMSP night-time light foci are a valuable data source for global studies, modelling, and planning activities when the human dimension must be considered throughout Amazonia.

Use of Hyperspectral and High Spatial Resolution Image Data in an Asphalted Urban Road Extraction

With the popularization of the high spatial resolution images of commercial satellites several applications of remote sensing intended for urban tracking has been developed. These images of high spatial resolution generally have limited spectral information. Meanwhile, the main hyperspectrals images available have high spectral resolution and, in return, low spatial resolution. For most urban monitoring applications the spatial information is extremely relevant. Based on that, this work presents the use of high spatial and high resolution spectral images applied to urban monitoring, specifically the extraction and identification of the asphalted urban roads in the city of Mogiguaccedilu, state of Sao Paulo, Brazil.

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data.

Evaluation of Multiple Classifier Systems for Landslide Identification in LANDSAT Thematic Mapper (TM) Images

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results.

Análise da associação entre características topográficas e tipos de ocorrência de processos erosivos ao longo de linha férrea - estudo de caso: malha Paulista - all (SP)

A ocorrencia de eventos erosivos acelerados associados a implantacao de sistemas de transportes e um dos principais problemas socioambientais enfrentados pelasxa0 concessionarias brasileiras. Feicoes erosivas do tipo linear e laminares sao observadas tanto ao longo da plataforma, nos cortes e aterros, como fora dela, em caixas de emprestimo, areas de jazidas exploradas, junto aos pes de aterros e jusante das obras de transposicao. Alem da compreensao das interacoes entre fatores condicionantes dos processos erosivos, e necessario estudar isoladamente cada um dos fatores com bastante detalhe para se compreender como, onde e por que a erosao ocorre. Dentre esses fatores, os atributos topograficos ou caracteristicas das vertentes estao entre uma serie de elementos controladores da distribuicao dos processos erosivos. Sua importância para o desenvolvimento desses fenomenos advem do fato da topografia controlar a intensidade e direcao dos fluxos hidricos pluviais, que por sua vez sao os principais agentes erosivos no meio tropical umido. Nesse contexto, atraves do uso de tecnologias geoespaciais e medidas estatisticas de associacao naoparametrica, este artigo apresenta uma analise da relacao entre diferentes tipos de processos erosivos (classificados de acordo com suas caracteristicas proprias e graus de gravidade) e seus respectivos valores de declividade e hipsometria das vertentes as quais estao localizados. A area de interesse e restrita as analises e registros de processos erosivos diagnosticados no trecho que interliga as cidades de Mairinque (SP) a Botucatu (SP), totalizando um trajeto de aproximadamente 176 km. A comparacao dos resultados permitiu avaliar a importância da variavel espacial na problematica tratada. A analise dos testes estatisticos nao-parametricos apontou que existe associacao estatisticamente significativa entre as variaveis estudadas. Ja as tecnicas de geoprocessamento e o uso de informacoes espaciais, apesar de nao fornecerem um valor estatistico que permita inferir a associacao entre as variaveis analisadas, possibilitaram a observacao da distribuicao espacial dos fenomenos de erosao ao longo do trecho da ferrovia, indicando que tal comportamento nao aponta, claramente, associacao expressiva entre os tipos de processos e as caracteristicas topograficas consideradas.

Use of control charts for multi-temporal analysis of geodetic auscultation data from dams

Geodesic auscultation can be used to monitor the movement of dam structures by measuring the distance, at different epochs from fixed positions (pillars) to other positions (targets). It is important to identify the targets that present atypical measurements to permit managers to take corrective actions. After fitting a model using the Least Squares Method (LSM), the residuals normally display random behavior. Multivariate control charts are then applied to the residuals of the fitted model from data taken of geodesic survey campaigns conducted at different epochs. Control charts have been widely applied in other fields of research than production processes such as public health, marketing, services. The results show that it is possible for monitoring the multi-temporal stability by the multivariate control charts. The method provides complementary information than the classical univariate statistical analysis.

THE USE OF THREE-DIMENSIONAL VISUALIZATION TOOLS TO DETECT DEFICIENCIES IN GEOMETRIC ROADWAY DESIGNS

The use of three-dimensional (3D) visualization technologies in the field of transportation sciences has contributed not only to the assessment of the impact of a road project on its surroundings but also to the development and study of elements of the geometric design itself. The present study aimed to assess the effectiveness of the use of 3D visualization tools in the detection of poor compatibility between the horizontal and vertical alignments of a road project. The assessment was performed by conducting tests with groups of participants who were students from a civil engineering course with no experience in the development of geometric roadway designs. These participants each assessed two road stretches: one stretch with unflawed coordination between the vertical and horizontal alignments of the road and another stretch in which this coordination had been intentionally distorted. It was used an application software for roadway geometric designs that allowed the creation of a video of the trajectory of the targeted road. Subsequently, the participants answered a questionnaire that aided the interpretation of the results regarding the design deficiencies in road layouts perceived from the 3D visualization of the roadways.

Chracterizing urban land use patterns by variograms parameters from multispectral high spatial resolution satellite images: An application in Salvador, Bahia - Brazil

This article attempts to characterize urban land use patterns by variograms parameters from multispectral high spatial resolution satellite images. The variography is used to characterize variability and to characterize a land use urban pattern. Dataset compiled from Salvador, Bahia, Brazil, consists in single QuickBird satellite scene, geometrically corrected, obtained on August 2nd, 2005. Four land urban patterns were identified at the study area and characterized using remote sensing image classification parameters. The principal components were calculated over the variance-covariance matrix of the four spectral channels of the images and while the variograms were calculated on the first principal components of each of the four urban patterns. In general the use of the variogram to aid the classification has proved satisfactory for the study area. Parameters (sill, range and nugget effect) were valuable tools for classification areas and to characterize occupation patterns.

Recognition of Urban Patterns Related to Leptospirosis Contamination Risks Using Object Based Classification of Aerial Photography. Test Areas: Informal Settlements of the Railroad Suburb of Salvador, Brazil.

In developing countries, infectious diseases are a serious public health problem. Often times, these diseases are highly related to certain urban conditions found at poor neighborhoods, such as the informal (non-permitted) settlements. Remote sensing can be a valuable tool to study these phenomena, however, the complexity of these informal settlements is still a challenge for remote sensing analysis. For the present research, classification of urban image data with very high spatial resolution but low spectral resolution was considered. The identification of which objects and features to look for in the images was done with the help of a leptospirosis contamination risk model. Our remote sensing analysis included four levels of segmentation and an object-based classification process. Objects were classified as vegetation, shadow, roofs, streets, open area and other auxiliary classes with reasonable accuracy.

Spatial and spectral comparison among IKONOS, CBERS and ASTER images to identify and detect land occupation changes around urban railway in São Paulo - Brazil

The purpose of this article is to present the results of an experiment with the main objective to evaluate the performance and observe practical aspects of principal components and supervised classification algorithms to select attributes, identify and classify urban land use areas around a railway in Sao Paulo - Brazil, by images with different spatial and spectral resolution. In this experiment were used IKONOS II images of 2002 (R,G, B and NIR channels, geometrically corrected and combined with panchromatic channel), CBERS II images of 2006 (R,G,B and NIR, with 20 m of spatial resolution and geometrically corrected) and ASTER images of 2002 (VNIR: channels IN, 2N, 3N and 3B; SWIR: channels 4 to 9; and TIR: channels 10 to 14, of 15, 30 and 90 m de spatial resolution respectively), of one segment of a railway in the metropolitan Sao Paulo (Brazil) region. The computational procedures were developed using ENVI 4.2 from Research Systems. To conclude, the different sensors can be used by the railway managements as a tool to take decisions related to environmental and social aspects associated to existing land use/occupation and its recently changes identified by the images.