1,720,964 research outputs found
A NEW PERSPECTIVE FOR REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT
Full text linkLandslides pose a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk in territorial planning or investigating landscape evolution. In the case of the Italian territory, several landslide hazard and risk maps were produced ranging from regional to national scale. This was made possible leveraging public domain data of the Italian Landslide Inventory (IFFI project; Trigila et alii, 2010), or other geodatabases spanning from local to regional scale. However, the practical utility of this inventory is often limited in many applications due to its spatial inhomogeneity or the use of different mapping methods and classification criteria. Despite the impressive advancements in techniques for assessing natural hazard susceptibility at a national scale over the past years, including statistical models, AI based models (i.e. Neural Networks) and others, the results are still limited by the quality of the data used. Specifically, the effectiveness of these models is closely tied to the quality of the landslide inventory utilized. Currently, recent regional landslide inventories could potentially enhance precision and accuracy compared to the national dataset, primarily owing to their finer resolution compared to the IFFI dataset. In this work, we present a new approach to assess landslide susceptibility at local scale, relying on regional landslide inventories. Using a data-driven technique, we propose to train a single model on a landslide inventory consisting of a composition of regional inventories selected to be representative of the national scenario. The weighted model is now capable of predicting landslide susceptibility in any study area across Italy. The entire analysis has been done using the SRT tool for Google Earth Engine and the SZ-plugin for QGIS. All the data used and processed are freely available and downloadable. The proposed approach has been tested in the framework of the PNRR RETURN project. The evaluation was conducted in two specific areas: the first one encompasses a section of the railway connecting Napoli to Bari (southern Italy), while the second focuses on areas impacted by the Marche region 2022 landslide event (central Italy). © Author(s). All rights reserved
Cloud-based interactive susceptibility modeling of gully erosion in Google Earth Engine
Full text linkThe gully erosion susceptibility literature is largely dominated by contributions focused on model comparison. This has led to prioritize certain aspects and leave others underdeveloped as compared to other natural hazard applications. For instance, in gully erosion data-driven modeling most studies use different platforms when it comes to data management, modeling and conversion into predictive maps. This in turn has limited the scope to catchment-scales. In this manuscript, we opt to propose a tool where the whole modeling procedure is unified within the same cloud computing system, allowing one to get rid of potential errors caused by input/output operations but also to extend the study areas indefinitely, as cloud data-management tools easily offer access to global data. Specifically, we present an interactive tool for susceptibility modeling in Google Earth Engine (GEE), the Susceptibility Tool for GEE (STGEE). Our tool requires few input data and makes use of the breadth of predictors’ information available in GEE. In this cloud computing environment, binary classifiers typical of susceptibility models can be called and fed with information related to mapping units and any natural hazards’ distribution over the geographic space. We tested our tool to generate susceptibility estimates for gully erosion occurrences in a study area located in Sicily (Italy). The tool we propose is equipped with a series of functions to aggregate the predictors’ information in space and time over a mapping unit of choice. Here we chose a Slope Unit partition but any polygonal structure can be chosen by the user. Once this information is derived, our tool calls for a Random Forest classifier to distinguish locations prone to gully erosion from locations where this process is not probabilistically expected to develop. This is done while providing a modeling performance overview, accessible via a separate panel. Such performance can be calculated on the basis of a exploratory analysis where all the information is used to fit a benchmark model as well as a spatial k-fold cross-validation scheme. Ultimately, the predictive function can be interactively used to generate susceptibility maps in real time, for the study area as well as any study area of interest. To promote the use of our tool, we are sharing it in a GitHub repository accessible at this link: https://github.com/giactitti/STGEE
Landslide susceptibility in the Belt and Road Countries: continental step of a multi-scale approach
Full text linkThe Belt and Road Initiative is a collaboration project launched by the Chinese Government to connect more than 65 countries all over the word by developing infrastructures, facilities, and support collaborations among involved Countries. The Silk Road Disaster Risk Reduction is a sub-project of the Belt and Road Initiative focused on mitigation and prevention of natural risks in the involved countries. In this context, this work presents a method to approach landslide susceptibility zoning on a continental scale that takes into account the limitations due to the completeness of landslide inventories and the scale and data quality of causal factors. A first attempt to produce a pixel-based statistical susceptibility map is described. All the data and software used in this work are open and open source. The landslide susceptibility zoning has been carried out in south-Asia using the NASA-COOLR landslide dataset through the Weight of Evidence method and it has been evaluated and validated by means of the ROC analysis. The results reveal a good prediction capacity and highlights that slope, relative relief and annual precipitation are the causative factors that play a major role in predisposing slope instability in the study area. Based on them, the method will be applied to the rest of the Belt and Road Countries
New perspectives in landslide displacement detection using Sentinel-1 datasets
Full text linkSpace-borne radar interferometry is a fundamental tool to detect and measure a variety of ground surface deformations, either human induced or originated by natural processes. Latest development of radar remote sensing imaging techniques and the increasing number of space missions, specifically designed for interferometry analyses, led to the development of new and more effective approaches, commonly referred to as Advanced DInSAR (A-DInSAR) or Time Series Radar Interferometry (TS-InSAR). Nevertheless, even if these methods were proved to be suitable for the study of a large majority of ground surface dynamic phenomena, their application to landslides detection is still problematic. One of the main limiting factors is related to the rate of displacement of the unstable slopes: landslides evolving too fast decorrelate the radar signal making the interferometric phase useless. This is the reason why A-DInSAR techniques have been successfully applied exclusively to measure very slow landslides (few centimetres per year). This study demonstrates how the C-band data collected since 2014 by the Sentinel-1 (S1) mission and properly designed interferometric approaches can pull down this restriction allowing to measure rate of displacements ten times higher than previously done, thus providing new perspectives in landslides detection. The analysis was carried out on a test site located in the Cortina d'Ampezzo valley (Eastern Italian Alps), which is affected by several earth flows characterized by different size and kinematics
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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