Ana Cláudia Teodoro

Retrieving TSM Concentration From Multispectral Satellite Data by Multiple Regression and Artificial Neural Networks

In this paper, we present different methodologies to estimate the total suspended matter (TSM) concentration in a particular area of the Portuguese coast, from remotely sensed multispectral data, based on single-band models, multiple regression, and artificial neural networks (ANNs). Simulations on different beaches of the study area were performed to determine a relationship between the TSM concentration and the spectral response of the seawater. Based on the in situ measurements, empirical models were established in order to relate the seawater reflectance with the TSM concentration for TERRA/ASTER, SPOT HRVIR, and Landsat/TM. Seven images of these three sensors were calibrated and atmospherically and geometrically corrected. Single-band models, multiple regression, and ANNs were applied to the visible and near-infrared (NIR) bands of these sensors in order to estimate the TSM concentration. Statistical analysis using correlation coefficients and error estimation was employed, aiming to evaluate the most accurate methodology. The chosen methodology was further applied to the seven processed images. The analysis of the root-mean-square errors achieved by both the linear and nonlinear models supports the hypothesis that the relationship between the seawater reflectance and TSM concentration is clearly nonlinear. The ANNs have been shown to be useful in estimating the TSM concentration from reflectance of visible and NIR bands of ASTER, HRVIR, and TM sensors, with better results for ASTER and HRVIR sensors. Maps of TSM concentration were produced for all satellite images processed

Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data

Abstract. The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied.

A dynamic map application for the assessment of groundwater vulnerability to pollution

AbstractGroundwater pollution is a major environmental concern at global scale. It usually restricts the use of water resources for domestic, agricultural or industrial purposes, with significant impact on human well-being. Aquifer remediation may be very difficult or even impossible due to technical and/or economic constraints. To help prevent groundwater pollution, several cartographic methods have already been developed. Geographical information systems (GIS) provide useful tools for understanding the spatial distribution of groundwater vulnerability to pollution. This paper presents a new tool to produce groundwater vulnerability to pollution maps under a GIS open source environment. This application was developed within the QGIS software. The tool determines the spatial distribution of the DRASTIC index and incorporates all the procedures required under a single plugin. One of the main advantages of this application is the easiness to use and the possibility of viewing different results modifying indexes, weight values and table descriptions or importing the input data attribute file description. The user can also generate the maps according to his perception regarding each aquifer system. This application is free and presents a valuable contribution to assess and map groundwater vulnerability to pollution through a GIS open source.

Statistical Techniques for Correlating Total Suspended Matter Concentration with Seawater Reflectance Using Multispectral Satellite Data

Abstract The transport and distribution of sediments driven by mechanisms such as tides and waves, river discharges, wind stress, and turbidity currents and the sediment transport effects can be studied by remote sensing techniques. The study of total suspended matter concentration has ecological importance since it is the main carrier of various inorganic and organic processes. The main objective of this work was to evaluate the performance of different statistical methodologies in the estimation of total suspended matter concentration, in the breaking zone and in its adjacent area, using multispectral satellite images from TERRA/ASTER, SPOT/HRVIR, and Landsat/TM. These images cover a particular area of the northwest coast of Portugal. The relationship between the total suspended matter concentration and the spectral response of the seawater, in the visible and near-infrared regions of the electromagnetic spectrum, was quantified through simulations on different beaches of the study area. Seven images of TERRA/ASTER, SPOT/HRVIR, and Landsat/TM sensors were calibrated and atmospherically and geometrically corrected. Linear single-band models, linear multiple regressions, and artificial neural networks were applied to the visible and near-infrared bands of these sensors in order to estimate the total suspended matter concentration. Statistical analysis using determination coefficients and error estimation was employed, aiming to evaluate the most accurate approach in the estimation of total suspended matter concentration. The analysis of the root-mean-square error achieved by both linear and nonlinear models supports the hypothesis that the relationship between seawater reflectance and total suspended matter concentration is clearly nonlinear. Artificial neural networks have been shown to be useful in estimating the total suspended matter concentration from reflectance of visible and near-infrared bands with images of ASTER, HRVIR, and TM sensors, with better results for ASTER and HRVIR sensors. The artificial neural network approach was further applied to the seven processed images, and maps of total suspended matter concentration for all satellite images processed were produced.

Modeling of the Douro River Plume Size, Obtained Through Image Segmentation of MERIS Data

Spatial and temporal variation of river plumes can be studied by remote sensing. The main objectives of this letter were to model the Douro River Plume (DRP) size based on image segmentation techniques and to relate it to different parameters such as water volume, last available plume, tide height, and wind speed. Twenty-one MEdium Resolution Imaging Spectrometer (MERIS) scenes of the study area were considered, covering 20 months from 2003 to 2005. Two different segmentation techniques were applied (watershed and region based) to the MERIS scenes in order to estimate the DRP size. Two models were developed in order to relate the DRP size with the water volume, last available plume, tide height, and wind speed. In the first model, the DRP size was modeled using the discharged water volume as input. In the second model, the DRP size was retrieved as a function of the water volume, last available plume, tide height, and wind speed. The last available plume-a factor of crucial importance-was weighted using a forgetting factor, function of the time difference between two consecutive MERIS scenes. A significant correlation was found between DRP dimension and water volume (r = 0.66) during wet season (i.e., excluding dry summer period), because the plume derived from MERIS imagery in the region under study represents river Douro plume only when the river flow exceeds a certain threshold. Despite some particular points, the second model was able to model the plume size obtained through image segmentation, with a mean percentage variation of 34.8% for region-based segmentation method. Segmentation of MERIS data have been shown to be a valid method for modeling the DRP size. Furthermore, DRP size was found to be proportional to water volume, excluding the summer period.

Quantification of the Total Suspended Matter Concentration around the Sea Breaking Zone from In Situ Measurements and Terra/Aster Data

Remote sensing techniques can be used to quantify the total suspended matter concentration (TSM). The main objective of this study is the quantification of the TSM concentration around the sea breaking zone for a particular area of the Portuguese coast, near Aveiro city. The methodology used was based on in situ measurements and on multispectral satellite image from TERRA/ASTER. In situ experimental techniques were used to determine a relationship between the TSM concentration and the seawater reflectance. Empirical relationships were established between TSM concentration and the equivalent reflectance values for TERRA/ASTER at visible and near infrared bands computed from the experimental data. The reflectance values had a very high correlation with the TSM concentration in the wavelength between 500 and 900 nm. The ASTER image was calibrated, atmospherically and geometric corrected. Equations of linear, polynomial, logarithmic, power and exponential models were applied. The best results were found with the linear and polynomial models, with a determination factor higher than 0.95. However, the differences in the TSM concentration calculated for the three bands indicate that we should not use the information of a single band to obtain the TSM concentration.

Identification of beach hydromorphological patterns/forms through image classification techniques applied to remotely sensed data

The evaluation of beach hydromorphological behaviour and its classification is a complex issue. The main objective of this study was to develop new methodologies to identify coastal features/patterns. Pixel-based and object-oriented classification algorithms were used and a new approach was developed based on Principal Components Analysis and Histogram (PCAH) segmentation, to identify and analyse morphological features and hydrodynamic patterns. The PCAH method consists of three stages: preprocessing, PCA and histogram-based segmentation. Both manual and automatic approaches were addressed regarding the identification of the classes obtained from the segmentation stage. The dataset was composed from two aerial photographs and one IKONOS-2 image. The supervised classification algorithms present good results for both aerial photographs and the IKONOS-2 image. For the two aerial photographs the best results were found for the maximum likelihood classifier and for the IKONOS-2 image the best result was achieved with the parallelepiped classifier. The object-oriented classification performance for the aerial photographs and for the IKONOS-2 image also presented good results. The PCAH method led to promising results, with proportions of correctly classified pixels greater than 90% for the classes ‘Sea’, ‘Sediments+breaking zone’ and ‘Beach’.

CHAIR: automatic image registration based on correlation and Hough transform

Automatic image registration is a process related to several application fields: remote sensing, medicine and computer vision, among others. Particularly in the field of remote sensing, the ever-increasing number of available satellite images requires automatic image registration methods, capable of correctly aligning a new image. An automatic image registration method – CHAIR (correlation- and Hough transform-based method of automatic image registration) – is proposed, the key concept of which relies on the ‘correlation image’ produced in both the horizontal and vertical directions. In particular, the computation of the distance of an identified diagonal brighter strip in the correlation image (through the Hough transform) to an offset (the main diagonal) allows for the determination of translational shifts and consequently control points. The set of obtained control points allows for the correction of several types of distortions. The geometric correction quality achieved by CHAIR was objectively evaluated through measures recently proposed, which allow for a more complete assessment of the obtained results. The CHAIR performance was evaluated on both synthetic and real data, with different spatial resolutions and spectral contents. CHAIR has been shown to be able to correctly align two images with a subpixel accuracy, having a priori a ‘gold standard’ image covering a considerable part of the image to be registered, and has also been shown to work for images of different sensors and/or different spectral bands, situations where traditional correlation methods often yield low and smooth peaks on the correlation surface. It is also able to account for elevation differences and to some extent for rotation and scale effects. Furthermore, it has been shown to have potential for registering synthetic aperture radar (SAR) with optical images.

Correlation Analysis of Water Wave Reflectance and Local TSM Concentrations in the Breaking Zone with Remote Sensing Techniques

Abstract The coastal zone is a dynamic area in which processes with different origins and scales interact. Several techniques can be used for coastal zone monitoring. Remote sensing is a powerful tool for monitoring coastal processes and managing coastal areas. The quality of coastal water is a very important issue, and remote sensing optical sensors can be used to quantify water quality parameters such as suspended sediments. Therefore, it is possible to estimate the total suspended matter (TSM) concentration with multispectral satellite images. To extract meaningful information, the satellite data need to be validated with in situ measurements. The main objective of this work was to quantify TSM concentrations in the breaking zone with in situ measurements. In situ validation is important for the accuracy of correlations established. A section of the northwest coast of Portugal, near Aveiro city, was chosen as a test area, and all in situ measurements were done in this area. Several in situ techniques have been used to establish a relationship between seawater reflectance and TSM concentration for the range of wavelengths from 400 to 900 nm. Empirical relationships were established for equivalent reflectance values by SPOT/HRVIR (high-resolution visible infrared), Terra/ASTER (advanced spaceborne thermal emission and reflection radiometer), and Landsat TM (thematic mapper) at visible and near-infrared equivalent bands and TSM concentrations. The reflectance values were used to estimate TSM concentration with the use of the relationships established by in situ measurement. The reflectance of all bands of the satellite images tested showed high correlation with TSM in the wavelengths between 500 and 900 nm. The water leaving equivalent reflectance for each sensor equivalent band in the visible and near-infrared wavelengths was calculated, and a relationship between seawater reflectance and TSM concentration was established. The model coefficients and correlation factors for identical bands on different sensors presented good similarity. The work presented shows that TSM concentration in the breaking zone can be obtained directly from simulated multispectral satellite data. However, in situ measurements are essential to calibrate the process and establish the empirical relationships between TSM concentration and water leaving reflectance. The same empirical relationships found with in situ measurements will be used to estimate the TSM concentrations directly from real satellite data to try to quantify the sedimentary balance in the study area for the period of the satellite data.

Open-source GIS application for UAV photogrammetry based on MicMac

ABSTRACT Remote-sensing applications using the remotely piloted aerial system (RPAS) are becoming more frequent. RPAS is used in different contexts and in several areas, such as environmental studies, cultural heritage, civil engineering, forestry, and cartography. To process the images resulting from the RPAS, different types of image-based 3D modelling software (proprietary or open source) are used. MicMac is an open-source software which allows generating georeferenced information which can be manipulated or visualized under a geographical information system (GIS) environment. So, the integration between the MicMac procedures within a GIS software could be very useful. The main objective of this work was to create an open-source GIS application based on MicMac photogrammetric tools to obtain the orthophotographs, point clouds, and digital surface models. To test the application developed, two distinct areas were considered: one in a more natural environment (Aguda beach near Porto city, Portugal) and another in an urban environment in the city of Coimbra, Portugal. High-resolution data sets were obtained with a ground sampling distance (GSD) of approximately 4.5 cm. Shaded relief image and dense point cloud were generated. This open-source application can be automated and can create all the files required to run the functionalities from MicMac to obtain the georeferenced information, within a GIS software, bringing photogrammetric data generation to a wider user community. Moreover, integrating this application with the GIS software has several advantages like generating more georeferenced information, such as vegetation indices, or even creating the land use land cover map. Creation of shapefiles with the projection centre of the camera, the area covered by each photograph, and taking account of the number of images that appear in each location are also useful in performing certain tasks.