1,720,960 research outputs found
Sentinel 1 data for fuel spill detection. The case of the ships collision near Corsica
No full textIn the field of the maritime environment surveillance, the detection and mapping of oil on the water surface is one of the common use of the satellite images.
The location of the spill should be obtained very precisely by means of image processing. It is nowadays standard practice to use Synthetic Aperture Radar (SAR) for producing this kind of thematic maps. In fact, the capability to acquire images in day-night and allweather conditions, covering large areas with frequent overpass, makes the active sensors the best for oil spill detection.
In this study, we focused on the potential of the SAR data collected by the European Earth observation program Copernicus. Copernicus offers free radar satellite images with high spatial and temporal resolution. In particular, Sentinel-1 mission meets the features required by marine surveillance tools. Sentinel-1A and the Sentinel-1B satellites collect SAR images with a swath of 250 km in Interferometric Wide (IW) mode,
covering wide area with high spatial resolution (10 m pixel size). The two-satellite constellation offers a repeat cycle of 6 days and a repeat frequency, considering ascending and descending orbit directions, of 1- 3 days in accordance with the latitude. The Sentinel-1 data are available for the Copernicus services within one hour of acquisition.
In the present work, Sentinel-1 images have been processed to map the oil spill caused by the ships collision occurred near Corsica coasts in October 2018. For the SAR data classification, a near-real time procedure was developed through the Object-Based Image Analysis (OBIA)
Evaluating water-repellents applied to brick masonry: An experimental study by thermal imaging and water transport properties’ characterization
Full text linkMoisture is one of the main problems that affect new and historic masonry buildings, which are the most common ones in Europe and worldwide. The application of surface treatments based on water-repellents is a very common solution to protect masonry from rain and hence from moisture-related problems. However, there are very few studies on the monitoring of water-repellents in real buildings and a deeper knowledge would be necessary on the long-term effectiveness and compatibility of water repellents, especially considering that defects and flaking issues are often reported in-the-field. In this paper, infrared thermography was proposed as a totally non-invasive technique to monitor the behaviour of brick masonry subjected to wetting, both from outside (rain) and inside (internal moisture). An active thermal approach was used to simulate internal and external wetting. The behaviour of the masonry during wetting and drying was investigated both in laboratory walls and in brick samples, to elucidate their water transport properties. All the materials were tested both in untreated conditions and after the application of two different hydrophobic coatings. The results show that the drying behaviour of treated masonry materials is a critical issue, as the coatings may strongly slow down the drying of internal moisture, even if the coefficient of resistance to water vapour diffusion of the products is very low. The results also suggest that the methodology used to process thermal images using multi-temporal analysis is a promising way to interpret the water transport in treated walls and to monitor real buildings where water-repellents were applied
Evaluation of Landsat-9 interoperability with Sentinel-2 and Landsat-8 over Europe and local comparison with field surveys
Full text linkThe recent launch of Landsat-9 satellite enriches the opportunities to work with dense time series of multispectral medium-resolution images. The integration of Landsat-9 in a multi-constellation series with Landsat-8 and Sentinel-2 requires a harmonization of the surface reflectance values that can be obtained from the official Level-2 products. This paper proposes the coefficients of the optimal linear transformations for the European continent, which allow to integrate Landsat-9 with the similar operating missions. These coefficients are based on a regression over 30 independent random extractions of 240,000 samples from images of the same areas but acquired by different sensors within two days. The coefficients were validated on an independent dataset. Furthermore, the effects of the proposed harmonization were tested on four popular vegetation indices, by evaluating the distributions of the differences in values obtained from each sensor pair. Finally, a test on a local scale was carried out with a spectroradiometer survey on 16 locations to collect some reference spectra to be compared with the reflectance values provided by the images. The results demonstrate the interoperability of Landsat and Sentinel-2 missions, since reflectance differences are in most cases within the accuracy specifications of the sensors. However, some discrepancies are observed in the blue and SWIR bands, probably due to inconsistencies in the atmospheric correction processes
APPLICATION OF DEEP LEARNING CROP CLASSIFICATION MODEL BASED ON MULTISPECTRAL AND SAR SATELLITE IMAGERY
Full text linkClassifying crops using satellite data is a challenge, especially since most crops have similar growth cycles. Due to their different characteristics and chlorophyll content, different crops exhibit subtle differences in their reflectance spectra. This study uses a datadriven approach to build a series of deep learning models to classify 36 different land covers in Steele County and Traill Country, North Dakota, US. A Google Earth Engine workflow was implemented to generate a composite layer containing Sentinel 1 and Sentinel 2 satellite data and surface crop data over the study area. 200,000 sample points were generated on this layer, 140,000 for training dataset, 30,000 for validation dataset and 30,000 for testing dataset. Each sample point contains the values of 12 months of SAR and spectral data. In this way, a two-dimensional feature matrix of the time dimension and spectral band dimension (bands refer to specific wavelengths of data in remote sensing imagery and other type of data like NDVI) is generated for each sample point. The training dataset of the model is composed of the feature matrix of these sample points, and the surface crops as labels correspond to the feature matrix. Since this is a dataset with two-dimensional features, this research uses four deep learning models: Dense Neural Network (DNN), Long short-term memory (LSTM), Convolutional neural network (CNN) and Transformer. Among them, the Transformer model based on the self-attention mechanism performed the best, with a comprehensive accuracy rate of 85%, and the classification accuracy rate of crops with more than 2,000 sample points in the training data set reached more than 90%
GIS-Based Urban Heat Island Mapping and Analysis: Experiences in the City of Bologna
No full textClimate change effects have become increasingly visible recently through extreme weather events, such as heat waves. These are strictly related to a two-way relationship with urbanization. Indeed, urban expansion due to population migration from rural to urban areas impacts energy consumption, soil sealing with vegetation loss and gas emissions. Moreover, due to their characteristics, cities experience the typical urban heat island microclimate and are more vulnerable to heatwaves. In this context, having insight into the land surface temperature and accurate knowledge of city characteristics is essential to wise decision-making to ensure a more sustainable livelihood. The present paper provides an overview of two different approaches useful for thermal mapping at the city scale, implementing GIS-based analysis integrating local surveys with geospatial data. In particular, the city of Bologna (Italy) is studied. In the first study, temperature measurements along a transect was taken on March 19, 2021, with a mobile system. Then they were corrected considering data from some weather stations interpolated with Kriging, which shows the highest correlation coefficient of 0.99. The corrected temperature correlated at 0.69 with remote sensing NDVI data. The second study analyzed the significant impact of urban morphology, particularly building density, on temperature variations; it emphasizes the need for strategic urban planning to mitigate the Urban Heat Island effect
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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