Preterm birth (PTB), one of the major concerns in obstetrics, is conventionally defined as the delivery of a live infant before 37 completed weeks of gestation, and one of its causes may be environmental factors. Remote sensing is a valuable approach for monitoring environmental variables, including in health sciences. In this work, remote sensing data were used to explore the relation of the environment with PTB. Time-series with monthly rates of male/female ratio and PTB were obtained from Portugal in 2000-2014. The environmental variables included in this study were monthly mean temperatures (T), relative humidity (RH), NDVI, concentrations of NO<sub>2</sub> and PM<sub>10</sub> in 2003-2008. A temporal and spatial analysis of each health-related and environmental variable was performed, as well as their correlation. PTB has been increasing over time, from below 5% in 2000 to around 7% in 2014, with predominance of higher rates in districts with larger population. From 2003 to 2008, T and PM<sub>10</sub> decreased significantly. A positive and significant correlation was found between male/female ratio and NO<sub>2</sub> and RH, and to a lesser extent with PM<sub>10</sub> and NDVI. PTB was also positively and significantly correlated with NO<sub>2</sub> and T, and to a lesser extent with RH and PM<sub>10</sub>. These preliminary results suggest an association of PTB with most of the environmental variables studied, showing that more polluted and populated districts have higher rates of PTB. Further studies are warranted to explore interaction between the considered environmental factors and other variables related with risk for PTB.
Vegetation indices have been commonly used over the past 30 years for studying vegetation characteristics using images collected by remote sensing satellites. One of the most commonly used is the Normalized Difference Vegetation Index (NDVI). The various stages that green vegetation undergoes during a complete growing season can be summarized through time-series analysis of NDVI data. The analysis of such time-series allow for extracting key phenological variables or metrics of a particular season. These characteristics may not necessarily correspond directly to conventional, ground-based phenological events, but do provide indications of ecosystem dynamics. A complete list of the phenological metrics that can be extracted from smoothed, time-series NDVI data is available in the USGS online resources (http://phenology.cr.usgs.gov/methods_deriving.php).This work aims to develop an open source application to automatically extract these phenological metrics from a set of satellite input data. The main advantage of QGIS for this specific application relies on the easiness and quickness in developing new plug-ins, using Python language, based on the experience of the research group in other related works. QGIS has its own application programming interface (API) with functionalities and programs to develop new features. The toolbar developed for this application was implemented using the plug-in NDVIToolbar.py. The user introduces the raster files as input and obtains a plot and a report with the metrics. The report includes the following eight metrics: SOST (Start Of Season – Time) corresponding to the day of the year identified as having a consistent upward trend in the NDVI time series; SOSN (Start Of Season – NDVI) corresponding to the NDVI value associated with SOST; EOST (End of Season – Time) which corresponds to the day of year identified at the end of a consistent downward trend in the NDVI time series; EOSN (End of Season – NDVI) corresponding to the NDVI value associated with EOST; MAXN (Maximum NDVI) which corresponds to the maximum NDVI value; MAXT (Time of Maximum) which is the day associated with MAXN; DUR (Duration) defined as the number of days between SOST and EOST; and AMP (Amplitude) which is the difference between MAXN and SOSN. This application provides all these metrics in a single step. Initially, the data points are interpolated using a moving average graphic with five and three points. The eight metrics previously described are then obtained from the spline using numpy functions. In the present work, the developed toolbar was applied to MODerate resolution Imaging Spectroradiometer (MODIS) data covering a particular region of Portugal, which can be generally applied to other satellite data and study area. The code is open and can be modified according to the user requirements. Other advantage in publishing the plug-ins and the application code is the possibility of other users to improve this application.
Evaluation of beach hydromorphological behaviour and its classification is highly complex. The available beach morphologic and classification models are mainly based on wave, tidal and sediment parameters. Since these parameters are usually unavailable for some regions – such as in the Portuguese coastal zone - a morphologic analysis using remotely sensed data seems to be a valid alternative. Data mining for spatial pattern recognition is the process of discovering useful information, such as patterns/forms, changes and significant structures from large amounts of data. This study focuses on the application of data mining techniques, particularly Decision Trees (DT), to an IKONOS-2 image in order to classify beach features/patterns, in a stretch of the northwest coast of Portugal. Based on the knowledge of the coastal features, five classes were defined: Sea, Suspended-Sediments, Breaking-Zone, Beachface and Beach. The dataset was randomly divided into training and validation subsets. Based on the analysis of several DT algorithms, the CART algorithm was found to be the most adequate and was thus applied. The performance of the DT algorithm was evaluated by the confusion matrix, overall accuracy, and Kappa coefficient. In the classification of beach features/patterns, the algorithm presented an overall accuracy of 98.2% and a kappa coefficient of 0.97. The DTs were compared with a neural network algorithm, and the results were in agreement. The methodology presented in this paper provides promising results and should be considered in further applications of beach forms/patterns classification.
The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab®, allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method’s simplicity, which is in continuous development in order to optimize the results.
The geometric correction of images under the scope of remote sensing applications is still mostly a manual work. This is
a time and effort consuming task associated with an intra- and inter-operator subjectivity. One of the main reasons may
be the lack of a proper evaluation of the different available automatic image registration (AIR) methods, since some of
them are only adequate for certain types of applications/data. In order to fulfill a gap in this context, a first reference
dataset of pairs of images comprising some types of geometric distortions was created, different spatial and spectral
resolutions, and divided according to the Level 1 of CORINE Land Cover nomenclature (European Environment
Agency). This dataset will allow for gaining perception of the abilities and limitations of some AIR methods. Some AIR
methods were evaluated in this work, including the traditional correlation-based method and the SIFT approach, for
which a set of measures for an objective evaluation of the geometric correction process quality was computed for every
combination of pair of images/AIR method. The reference dataset is available from an internet address, being expected
that it becomes a channel of interaction among the remote sensing community interested in this field.
The use of satellite remote sensing images could be a valid alternative to the classical methods of bathymetric
measurements for depths less than 30 meters. In this work, several pixels corresponding to different depths are
considered to numerically evaluate the relation between the water spectral response and the real depth, in the Douro
River Estuary (Porto, Portugal). The main concept relies on principal components analysis, which allows for combining
the information of the n available spectral bands from the image into an equal number n of principal components. The
dataset is composed by an IKONOS-2 image and bathymetric values. An initial analysis was performed in order to
determine the viability of the data for bathymetric study of the Douro River estuary. It was proved that it was not
possible to find any direct relationship between the DNs of the IKONOS-2 image and depth values. Therefore, a simple
linear regression of the bathymetric values on the IKONOS-2 image principal components was considered. A significant
correlation was found between the first principal component and the real depths. In the future, the use of simultaneous
data and the use of other statistical models such as decision trees may also provide important contributes to improve this methodology.
A wide variety of automatic image registration methods have been proposed in the last years. However, under the scope
of remote sensing applications, geometric correction is still mostly a manual work. A methodology for automatic image
registration is proposed, which consists in three major steps: pre-processing, segmentation, and registration. The
considered pre-processing is a new method, which is an iterative process based on a joint histogram analysis. Regarding
the segmentation stage, global thresholding and a new method were used. The later comprises global thresholding and
distance transforms in a single method. For both methods the following object properties were extracted: area, major and
minor axis lengths of the adjusted ellipse and perimeter. The registration phase incorporates the matching of
corresponding objects, a template matching technique to compute the distance between each pair of matched objects, and
the computation of the transformation function parameters. The used dataset consisted in the pairs ETM+/ASTER,
ETM+/SPOT and Orthophoto/IKONOS. The proposed methodology allows for the registration of a pair of images with
translation and rotation effects, and to some extent with different spectral content, leading to a subpixel accuracy.
Furthermore, it has been shown that the proposed pre-processing method allowed for the achievement of suitable
segmented objects for later matching, even using global thresholding.
Automatic image registration is a process related to various application fields such as remote sensing, medicine,
computer vision, among others. Particularly in remote sensing, the ever increasing number of available satellite images
asks for automatic image registration methods, capable of correctly align a new image. An automatic image registration
method is proposed, based on the identification of a thin line through the Hough transform, from the diagonal brighter
strip visible on the correlation images. This procedure is applied for both directions. Dividing an image into tiles and
taking the center of each tile as a point, a geometric correction at the subpixel level may be achieved. Measures for an
objective assessment of the geometric correction quality are also proposed, as a complement to the traditional RMS of
the errors and visual inspection. An orthorectified Landsat image and an ASTER image with an approximate geometric
correction were used. The images were superimposed and resampled. An image registration with subpixel accuracy was
achieved. The proposed methodology has showed to be able to correctly align two images, having a priori a "gold
standard" image covering a considerable part of the image to be registered.
Plumes are a mixture of fresh water and river sediment load, with some dilution caused by currents. Spatial and temporal
variation of the river plumes can be studied by remote sensing techniques. The main objectives of this work were
modeling the Douro River Plume (DRP) dimension based on image segmentation of MERIS data and to establish a
relationship between the DRP dimension and different input parameters. Two different segmentation techniques were
applied (watershed and region-based) in order to estimate the DRP dimension of twenty-five MERIS scenes (from 2003
to 2005). Firstly, we considered a simple linear regression model of the DRP dimension on the water volume,
considering seasonal effects (summer period and the rest of the year), where a significant correlation of 0.664 was found
(watershed segmentation) ignoring summer period. The second proposed model consisted in the incorporation of several
parameters (last available plume, water volume, tide height and wind speed), presumed to be related to the DRP
dimension. A determination coefficient of 62.2% was found for watershed segmentation excluding the summer period,
regarding the multiple linear regression branch of the second proposed model.