1,722,047 research outputs found
Land consumption estimation using Landsat satellite data and change detection techniques in the Google Earth Engine cloud environment
Full text linkIl consumo di suolo rappresenta la trasformazione permanente o reversibile della copertura del suolo da non artificiale ad artificiale, che comporta l’espansione e la densificazione urbana, così come la perdita della risorsa suolo. La copertura impermeabile della superficie terrestre causa degradazione del suolo, poichè contribuisce ad incrementare il rischio ambientale e territoriale. La mappatura ed il monitoraggio della crescita urbana è pertanto essenziale per lo sviluppo sostenibile. Il telerilevamento per l’osservazione della Terra è un potente strumento per fornire continue informazioni sui cambiamenti di copertura del suolo. Tra vari datasets satellitari, l’archivio multi-decennale di immagini Landsat è particolarmente adatto per molti studi di change detection a scala urbana. L’obiettivo della tesi è di estrarre informazioni sul consumo di suolo da immagini satellitari open Landsat applicando differenti algoritmi, anche con alcuni elementi innovativi, in ambiente cloud Google Earth Engine (GEE). Il primo metodo di change detection ha implicato lo sviluppo e l’applicazione di un nuovo approccio di classificazione delle immagini index-based, con multiple regole decisionali, da dati Landsat 8. Il “post-classification comparison” tra mappe binarie “Urban/Non-urban”, seguito da “image-differencing” tra mappe di albedo superficiale multi-temporali, è stato testato con successo sull’area di studio di Bitritto e in seguito applicato al territorio di Bari (2015-2023), con risultati molto buoni. Il secondo metodo di change detection ha riguardato l’implementazione dell’algoritmo di Continuous Change Detection and Classification su GEE, utilizzando uno stack di immagini Landsat multi-temporali prive di nuvole per individuare il comportamento spettrale nel tempo di ogni pixel. Le mappe multi-temporali “Urban/Non-urban”, ottenute utilizzando i coefficienti del modello pixel per pixel come variabili di input per classificazioni Random Forest, sono state in seguito sottoposte a “post-classification comparison” e hanno permesso di ricavare informazioni sulla crescita urbana con risultati molto soddisfacenti per tutti i capoluoghi di Regione italiani (2006-2023). Entrambi i metodi hanno leggermente sottostimato i cambiamenti realmente avvenuti a causa del problema dei “pixel misti” e delle tecniche di filtraggio, mentre GEE ha permesso una elaborazione relativamente rapida dei dati telerilevati.Land consumption is the permanent or reversible transformation of non-artificial into artificial land cover, that leads to urban sprawl and densification, as well as the loss of the soil resource. The impervious cover of the land surface causes land degradation, as it contributes to increasing environmental and territorial risks. Mapping and monitoring the urban growth is therefore essential for sustainable development. Earth observation remote sensing is a powerful way to provide continuous information on land cover changes. Among various satellite datasets, the multi-decadal archive of Landsat images is particularly suitable for many urban change detection studies. The aim of this thesis is to extract land consumption information from open Landsat satellite imagery by applying different algorithms, also with some innovative elements, in the Google Earth Engine (GEE) cloud environment. The first change detection method implied the development and application of a novel index-based image classification approach, involving multiple decision rules, from Landsat 8 data. The “post-classification comparison” between binary “Urban/Non-urban” maps, followed by the “image differencing” between multi-date land surface albedo maps, was successfully tested for the study area of Bitritto and subsequently applied to the Bari territory (2015-2023), with very good results. The second change detection method consisted in the implementation of the Continuous Change Detection and Classification algorithm in GEE, involving a stack of multitemporal cloud-free Landsat images to identify temporal spectral behavior, pixel-by-pixel. The multitemporal “Urban/Non-urban” maps, produced using per-pixel model coefficients as input variables for Random Forest classifications, subsequently underwent “post-classification comparison” and allowed to retrieve urban growth information, with very satisfactory results, for all the capitals of Italian regions (2006-2023).
Both methods slightly underestimated the changes that really occurred, due to the “mixed pixels” issue and the filtering techniques, while GEE allowed relatively rapid processing of remote sensing data
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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