Julia Carvalho

Instituto Superior Tecnico, Departamento de Engenharia Civil, Arquitectura e Georrecursos, Faculty Member

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Phone: (+351) 965381777
Address: CERENA - Pavilhao de Minas
Instituto Superior Tecnico
Av. Rovisco Pais
1049-001 Lisbon
Portugal

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Papers by Julia Carvalho

Improving Satellite Image Forest Cover Classification with Field Data Using Direct Sequential Cosimulation

Quantitative Geology and Geostatistics

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Assessing the Quality of the Soil by Stochastic Simulation

Quantitative Geology and Geostatistics

For the assessment of soil quality, this chapter proposes a methodology relying on the stochastic... more For the assessment of soil quality, this chapter proposes a methodology relying on the stochastic simulation and co-simulation of relevant variables representing soil quality. Taking into consideration possible quality thresholds for each variable, we applied loss functions to the simulated maps and combined them to identify areas with different soil quality dynamics. This methodology was applied to an area in the southeast of Portugal, to the left margin of the Guadiana River, classified with a high susceptibility index to the desertification phenomenon.

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Mapping of Natural Stone Types Based on Colour Measurements

Uncertainty Assessment of the Orebodies Geometry by Using Block Indicator Simulation

Lecture Notes in Earth System Sciences, 2013

ABSTRACT For characterization of reserves of mineral deposits, the spatial dispersion of grades i... more ABSTRACT For characterization of reserves of mineral deposits, the spatial dispersion of grades is characterized with stochastic models (geostatistical estimation or simulation) in order to quantify the associated uncertainty. However, and given the complex nature of these geobodies, usually deterministic models are used to determine their geometry, defining the boundaries of oretypes with more or less geological control. Yet this methodology carries a well known and recognized contradiction: the boundaries established do not have any measure of uncertainty. This paper proposes a new method—Block Indicator Simulation, following the basic outline of Direct Block Sequential Simulation for continuous variables—to assess uncertainty for the deterministic boundaries of different oretypes, assuming this image as a reference image, which reproduces the main spatial patterns, and the experimental sample data. This method is used to characterize the uncertainty of the morphology of a set of oretypes in a cupriferous sulphide deposit of south of Portugal.

Forecasting river water quality indices

For dealing with the problem of forecasting river water quality downstream of a mine effluent dis... more For dealing with the problem of forecasting river water quality downstream of a mine effluent discharge point, a global methodological approach was developed in the scope of a BRITE project (RIVERMOD). Based on daily monitoring of variables Dissolved Oxygen, pH and Conductivity, monthly bio-monitoring of heavy metal concentration by mosses and semesterly sampling of macro-invertebrate taxa, a synthetic index was defined to account for water quality in Oeiras river (Portugal, affected by Copper-Tin Neves-Corvo mine effluent discharge). The characteristics of the effluent running to the river were linked with the mine operation and the effect of the effluent on river water quality was assessed. Once established this index, the estimation of this new Regionalized Variable was performed by adjusting space-time geostatistics to the specific features of the problem.

Landsat-SPOT digital images integration using geostatistical cosimulation techniques

Interpretation of remote sensing images into terrestrial attributes is very dependent of their sp... more Interpretation of remote sensing images into terrestrial attributes is very dependent of their spatial and spectral resolution. Normally, these types of resolution are contradictory: high spatial resolution sensors have a low spectral resolution whereas multispectral sensors have a low spatial resolution. Digital image-merging procedures are techniques that aim at integrating the multispectral characteristics in a high spatial resolution image. The main objective is to obtain synthetic images that combine the advantages of the high spatial resolution and high spectral resolution of both types of images. Unfortunately, the most commonly used methods can not be considered real merging methods. They consist in a simple substitution of the high-spectral images with a high-spatial resolution image based on the correlation between both data sets. The images obtained by those merging/substitution procedures, although honouring the values of multispectral images, do not account for the spatial patterns of high spatial resolution images. In this paper a new merging approach is presented. The method is based on a geostatistical technique of direct sequential cosimulation that aims at producing images with the spatial patterns of high spatial resolution images and the local values of the coarse multispectral images. The method was applied to Landsat-TM and SPOT-P images and the results were compared with the images provided by other common merging procedures. Using the proposed geostatistical procedure, the merged images preserve the spectral characteristics of the higher-spectral resolution images in terms of both descriptive statistics and band correlation coefficients.

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Percepção da paisagem da bacia hidrográfica da ribeira de Colares

cerena.ist.utl.pt, 2004

Resumo -A paisagem da bacia hidrográfica da R.ª de Colares alia potencialidades de elevado valor ... more Resumo -A paisagem da bacia hidrográfica da R.ª de Colares alia potencialidades de elevado valor estético e ambiental a um acentuado risco de cheias rápidas. A forte pressão demográfica e turística agrava a vulnerabilidade das paisagens mais sensíveis. Neste estudo, pretende-se pesquisar representações individuais da referida paisagem, a fim de estabelecer uma tipologia de sensibilidade ambiental. Enquanto objectivo-chave procuram-se identificar padrões de atitude e de comportamento representativos de níveis de integração ambiental de residentes locais e utentes da região. A metodologia utilizada foi o inquérito por questionário tratado pela técnica estatística da Análise Factorial de Correspondências. Dos resultados obtidos, são indicadas pistas de actuação úteis aos trabalhos de planeamento, às actividades escolares e aos usos recreativos.

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Understanding the landscape of the Colares river hydrographic basin

Análise Social, 2004

A bacia hidrográfica da ribeira de Colares alia potencialidades de elevado valor estético e ambie... more

Engenharia Civil

redalyc.uaemex.mx

This article intends to present different methods to calculate the concentration time (tc) and di... more

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Comportamento hidrológico de bacias hidrográficas: integração de métodos e aplicação a um estudo de caso

Rem: Revista Escola …, 2007

This article intends to present different methods to calculate the concentration time (tc) and di... more This article intends to present different methods to calculate the concentration time (tc) and discharge in watersheds. The methods are divided into empirical and processbased and were applied to the Livramento watershed in Portugal. Due to the watershed's spatial heterogeneity and spacetime variability, one suggests use of the processbased method rather than the empirical in order to achieve more accurate results, since watershed occupation does not remain the same over time. The time of concentration obtained by empirical and processbased methods are then used to compute watershed discharge through the Rational and Soil Conservation Service methods. With this procedure one can predict the impact of manmade projects on watershed hidrology. The results show that processbased methods can be useful to assess the impact of a project in hydrologic system.

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Merging Landsat TM and SPOT-P images with geostatistical stochastic simulation

Merging Landsat and SPOT digital data using stochastic simulation with reference images

Proceedings of …, 2006

There is a wide range of systems providing digital satellite imagery with different spatial and s... more There is a wide range of systems providing digital satellite imagery with different spatial and spectral resolutions. But, unfortunately, these resolutions are in most cases opposite; i.e., the high-resolution sensors have low spectral resolution whereas the multiespectral sensors have good spectral resolution but bad spatial resolution making their use in detailed applications difficult. The problem is solved using digital image merging procedures. The main objective of these methods is to obtain synthetic images that combine the advantage of the high spatial resolution of one image with the high spectral resolution of another image. Ideally, the method used to merge data sets with high-spatial resolution and high-spectral resolution should not distort the spectral characteristics of the high spectral resolution data. The classical methods of merging procedures (PCA, IHS, HPS, etc.) present several drawbacks. The objective of this paper is to present a geostatistical merging methodology based on direct sequential cosimulation with reference images . With the stochastic simulation one generates a high spatial resolution image with the characteristics of the of the higher spectral resolution image. It is an iterative inverse optimization procedure that tends to reach the matching of an objective function by preserving the spectral characteristics and spatial pattern, as revealed by the variograms, of the higher-spectral resolution images both in terms of descriptive statistics and band correlation coefficients. The method was applied to Landsat TM and SPOT-P images. The results were compared with the original Landsat image and the images provided by classical merging procedures.

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Local error evaluation in DEM using direct sequential simulation (DSS) methodology

spatial-accuracy.org

In this paper a methodology for local uncertainty due to the modelling process in a DEM generatio... more In this paper a methodology for local uncertainty due to the modelling process in a DEM generation is presented. Local uncertainty is a basic aspect to determine the final quality of the DEM and it is crucial in order to establish the capabilities of use of the model. The proposed methodology is based in the use of geostatistical simulation procedure that provides "possible versions" of the reality from limited terrain information (XYZ terrain points). The simulated model generated by the geostatistical simulation procedure (direct sequential simulation) allows obtaining the cdf of the elevation in each grid node. Using this cdfs it is possible to calculate the uncertainty of the DEMs (variance, interquartile range, entropy) through a simple probabilistic analysis and several realisations of DEM that preserves the statistical characteristics of the measured data (histogram, variogram,…).

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Aplicación del método de simulación secuencial directa para la evaluación de la incertidumbre local en modelos digitales de elevaciones

Delgado García, J. et al. (2007): "Aplicación del método de simulación secuencial directa para la... more

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Fusión de Imágenes SPOT-P y Landsat-TM mediante el Método de Simulación Geoestadística Estocástica

Topografía y cartografía: …, 2005

La extracción de la información contenida en las imágenes satélite depende fundamentalmente de su... more La extracción de la información contenida en las imágenes satélite depende fundamentalmente de su resolución especial y espectral. Desgraciadamente, ambos aspectos suelen ser contradictorios de forma que las imágenes de alta resolución espacial proporcionan escasos canales espectrales y la inversa, siendo necesario recurrir a las técnicas de integración de imágenes. Estas técnicas intentan proporcionar una imagen sintética que combine las ventajas de ambas imágenes. En este trabajo se presenta un método basado en la cosimulación secuencial directa, con el objetivo de generar imágenes con la variabilidad espacial de las imágenes de alta resolución y respetando las características espectrales de las imágenes de mayor resolución espectral. El método ha sido aplicado a la integración de Landsat-TM y SPOT-P obteniendo unos resultados óptimos en la conservación de las características estadísticas y coeficientes de correlación entre los canales de las imágenes. Figura 1. Izq.: Imagen SPOT-P (GSD=10m); Der.: Imagen Landsat TM (RGB:543) (GSD=30m).

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Improving satellite images classification using remote and ground data integration by means of stochastic simulation

International Journal of Remote Sensing, 2006

A methodology is proposed, to assess land surface cover classification using a geostatistical met... more A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co-regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setú bal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e. more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%.

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Improving Satellite Image Forest Cover Classification with Field Data Using Direct Sequential Cosimulation

Quantitative Geology and Geostatistics

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Assessing the Quality of the Soil by Stochastic Simulation

Quantitative Geology and Geostatistics

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Mapping of Natural Stone Types Based on Colour Measurements