WebGIS Implementation for Dynamic Mapping and Visualization of Coastal Geospatial Data: A Case Study of BESS Project

Randazzo, Giovanni; Italiano, Francesco; Micallef, A.; Tomasello, Agostino; Cassetti, Federica Paola; Zammit, A.; D’Amico, Sebastiano; Saliba, Oliver; Cascio, Maria; Cavallaro, Franco; Crupi, Antonio; Fontana, Marco; Gregorio, Francesco Di; Lanza, Stefania; Colica, Emanuele; Muzirafuti, Anselme

doi:10.3390/app11178233

Cited by 33 publications

(15 citation statements)

References 44 publications

(55 reference statements)

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“…The spatio-temporal analysis of satellite images gives significant information regarding changes in land cover and land use (LCLUC) [14,15]. The growing volume and accessibility of remote sensing data has inspired several new methods for mapping key environmental features and processes [16]. For example, using timeseries satellite images to identify changes in forest cover and LCLUC is one of numerous viable but extremely variable approaches [17,18].…”

Section: Spatial Datamentioning

confidence: 99%

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

et al. 2022

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Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and diverse group of indicators in a comprehensive ML framework.

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Section: Spatial Datamentioning

confidence: 99%

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

et al. 2022

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“…The integration of several geo-scientific prospecting techniques [63][64][65] plays a strategic role in the resolution of many questions related to the conservation and protection of cultural and environmental heritage. Monumental morphologies, such as, for example, those of marine arches, could be key sites to better bring out the values expressed by geoheritage, understood as another expression of natural heritage, and places to concentrate geotourism activities and proposals.…”

Section: Discussionmentioning

confidence: 99%

Stability Assessment and Geomorphological Evolution of Sea Natural Arches by Geophysical Measurement: The Case Study of Wied Il-Mielah Window (Gozo, Malta)

et al. 2021

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The Wied il-Mielaħ Window (Gozo–Malta) is a limestone natural arch on the north-western coast of the island of Gozo in Malta. It is located at the end of the Wied il-Mielaħ valley north of the village of Għarb. This natural arch is less well known than the Azure Window, which collapsed in March 2017 following a heavy storm, but notwithstanding, it is an imposing and important natural monument too. In the past, the Wied il-Mielah valley was responsible for discharging wastewater from the surrounding localities to the Mediterranean directly at the Wied il-Mielah Window. The sewage flag was often clearly visible underneath the archway into the open sea. The natural features of the arch provide an outstanding touristic attraction. To avoid what happened to the Azure Window, a methodology for the evaluation of the collapse hazard, combining passive seismic, ground penetrating radar (GPR), geological/geomorphological surveys and mine engineering methods, is here proposed. In this study, a methodological approach was applied, based on the following: (i) passive seismic method to study the physical–mechanical characteristics of the rock mass that constitutes the window; (ii) GPR method in order to demonstrate the conservation state (i.e., the intensity of fracturing); (iii) geological/geomorphological surveys in order to obtain a crack pattern; and (iv) scaled span empirical analysis in order to evaluate the stability of the arch. The calculation of the safety factor, with a static method, gave a value equal to 3.75 with a probability of collapse of the marine arch within 50 and 100 years.

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“…Geostatistical methods are widely used to predict the spatial distribution of soil properties [25][26][27][28][29]. In this paper, we chose the Ordinary Kriging (OK) method to predict the spatial distribution of soil pH [30].…”

Section: Spatial Prediction and Verification Methodsmentioning

confidence: 99%

Rational Sampling Numbers of Soil pH for Spatial Variation: A Case Study from Yellow River Delta in China

Zhang

Duan²,

Li³

et al. 2022

Applied Sciences

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Spatial variation of soil pH is important for the evaluation of environmental quality. A reasonable number of sampling points has an important meaning for accurate quantitative expression on spatial distribution of soil pH and resource savings. Based on the grid distribution point method, 908, 797, 700, 594, 499, 398, 299, 200, 149, 100, 75 and 50 sampling points, which were randomly selected from 908 sampling points, constituted 12 sample sets. Semi-variance structure analysis was carried out for different point sets, and ordinary Kriging was used for spatial prediction and accuracy verification, and the influence of different sampling points on spatial variation of soil pH was discussed. The results show that the pH value in Kenli County (China) was generally between 7.8 and 8.1, and the soil was alkaline. Semi-variance models fitted by different point sets could reflect the spatial structure characteristics of soil pH with accuracy. With a decrease in the number of sampling points, the Sill value of sample set increased, and the spatial autocorrelation gradually weakened. Considering the prediction accuracy, spatial distribution and investigation cost, a number of sampling points greater than or equal to 150 could satisfy the spatial variation expression of soil pH at the county level in the Yellow River Delta. This is equivalent to taking at least 107 sampling points per 1000 km2. The results in this study are applicable to areas with similar environmental and soil conditions as the Yellow River Delta, and have reference significance for these areas.

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WebGIS Implementation for Dynamic Mapping and Visualization of Coastal Geospatial Data: A Case Study of BESS Project

Cited by 33 publications

References 44 publications

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

Stability Assessment and Geomorphological Evolution of Sea Natural Arches by Geophysical Measurement: The Case Study of Wied Il-Mielah Window (Gozo, Malta)

Rational Sampling Numbers of Soil pH for Spatial Variation: A Case Study from Yellow River Delta in China

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