755 research outputs found

    Hacking the topographic ruggedness index

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    The topographic ruggedness index (TRI) is widely adopted for the analysis of digital elevation models, providing information on local surface spatial variability. In this work, the TRI is interpreted according to a geostatistical perspective, highlighting its main characteristics and drawbacks. TRI can be interpreted as an omnidirectional short-range spatial variability index, computed according to a pixel centered perspective. The simplicity and interpretability of the index, free from user-dependent selections, promoted its implementation in several software environments and its application in a wide set of case studies. However, the index has several drawbacks for its application in earth sciences, such as a strong dependency on local slope (it is basically an average adjacent neighbor slope algorithm) and the selection of different lag distances in the computation of spatial variability along the main directions and the diagonal ones. We propose a new metric radial roughness (RRI) in order to solve the main drawbacks of TRI but maintaining its main philosophy (i.e., pixel centered perspective and simplicity of the algorithm). The new index corrects for the differences in lag distances and resolves the dependency on trend using increments of order 2. The code of the index, implemented in R statistical language, and test data are provided with the paper (https://doi.org/10.5281/zenodo.7132160) to promote its implementation in other software environments

    semi automatic derivation of channel network from a high resolution dtm the example of an italian alpine region

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    AbstractHigh-resolution digital terrain models (HR-DTMs) of regional coverage open interesting scenarios for the analysis of landscape, including derivation and analysis of channel network. In this study, we present the derivation of the channel network from a HR-DTM for the Autonomous Province of Trento. A preliminary automatic extraction of the raw channel network was conducted using a curvature-based algorithm applied to a 4 m resolution DTM derived from an airborne LiDAR survey carried out in 2006. The raw channel network automatically extracted from the HR-DTM underwent a supervised control to check the spatial pattern of the hydrographic network. The supervised control was carried out by means of different informative layers (i.e. geomorphometric indexes, orthophoto imagery and technical cartography) resulting in an accurate and fine-scale channel network

    An investigation into road trees’ root systems through geostatistical analysis of GPR data

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    Street trees are a critical asset for the urban environment due to the variety of environmental and social benefits provided [1]. However, the conflicting coexistence of tree root systems with the built environment, especially with road infrastructure, frequently results in extensive damage, such as the uplifting and cracking of sidewalks and curbs, endangering pedestrians, cyclists, and road drivers’ safety. Within this context, ground penetrating radar (GPR) is gaining recognition as an accurate nondestructive testing (NDT) method for tree roots’ assessment and mapping [2]. Nevertheless, the investigation methods developed so far are often inadequate for application on street trees, as these are often difficult to access. Recent studies have focused on implementing new survey and processing techniques for rapid tree root assessment based on combined time-frequency analyses of GPR data [3]. This research also explores the adoption of a geostatistical approach for the spatial data analysis and interpolation of GPR data. The radial development of roots and the complexity of root network constitute a challenging setting for the spatial data analysis and the recognition of specific spatial features. Preliminary results are therefore presented based on a geostatistical analysis of GPR data. To this end, 2-D GPR outputs (i.e., B-scans and C-scans) were analysed to quantify the spatial correlation amongst radar amplitude reflection features and their anisotropy, leading to a more reliable detection and mapping of tree roots. The proposed processing system could be employed for investigating trees difficult to access, such as road trees, where more comprehensive analyses are difficult to implement. Results' interpretation has shown the viability of the proposed analysis and will pave the way to further investigations

    Monitoring of airport runways by satellite-based remote sensing techniques: a geostatistical analysis on sentinel 1 SAR data

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    Maintenance of airport runways is crucial to comply with strict safety requirements for airport operations and air traffic management [1]. Therefore, monitoring pavement surface defects and irregularities with a high temporal frequency, accuracy and spatial density of information becomes strategic in airport asset management [2-3]. In this context, Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets, proving their viability for the long-term evaluation of ground scatterers. However, the implementation of C-band SAR data as a routine tool in Airport Pavement Management Systems (APMSs) for the accurate measurement of differential displacements on runways is still an open challenge [4]. This research aims to demonstrate the viability of using medium-resolution (C-band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, Sentinel-1A SAR products, available through the European Space Agency (ESA) Copernicus Program, were acquired and processed to monitor displacements on “Runway n.3” of the “L. Da Vinci International Airport” in Fiumicino, Rome, Italy.A geostatistical study is performed for exploring the spatial data structure and for the interpolation of the Sentinel-1A SAR data in correspondence of ground control points. The analysis provided ample information on the spatial continuity of the Sentinel 1 data, also in comparison with the high-resolution COSMO-SkyMed and the ground-based topographic levelling data, taken as the benchmark. Furthermore, a comparison between the MT-InSAR outcomes from the Sentinel-1A SAR data, interpolated by means of Ordinary Kriging, and the ground-truth topographic levelling data demonstrated the accuracy of the Sentinel 1 data. Results support the effectiveness of using medium-resolution InSAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways. Outcomes of this study can pave the way for the development of more efficient and sustainable maintenance strategies for inclusion in next-generation APMSs

    Testing sentinel-1 SAR interferometry data for airport runway monitoring: a geostatistical analysis

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    Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets. Amongst others, the Persistent Scatterers Interferometry (PSI) technique has proven to be viable for the long-term evaluation of ground scatterers. However, its effectiveness as a routine tool for certain critical application areas, such as the assessment of millimetre-scale differential displacements in airport runways, is still debated. This research aims to demonstrate the viability of using medium-resolution Copernicus ESA Sentinel-1A (C-Band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, “Runway n.3” of the “Leonardo Da Vinci International Airport” in Fiumicino, Rome, Italy was investigated as an explanatory case study, in view of historical geotechnical settlements affecting the runway area. In this context, a geostatistical study is developed for the exploratory spatial data analysis and the interpolation of the Sentinel-1A SAR data. The geostatistical analysis provided ample information on the spatial continuity of the Sentinel 1 data in comparison with the high-resolution COSMO-SkyMed data and the ground-based topographic levelling data. Furthermore, a comparison between the PSI outcomes from the Sentinel-1A SAR data—interpolated through Ordinary Kriging—and the ground-truth topographic levelling data demonstrated the high accuracy of the Sentinel 1 data. This is proven by the high values of the correlation coefficient (r = 0.94), the multiple R-squared coefficient (R2 = 0.88) and the Slope value (0.96). The results of this study clearly support the effectiveness of using Sentinel-1A SAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways, paving the way for the development of more efficient and sustainable maintenance strategies for inclusion in next generation Airport Pavement Management Systems (APMSs)

    Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

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    Peer reviewe

    Measurement of the Splitting Function in &ITpp &ITand Pb-Pb Collisions at root&ITsNN&IT=5.02 TeV