Geomatics, Volume 4, Issue 2 (June 2024) – 6 articles

Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis. Full article

Scour around bridges present a severe threat to the stability of railroad and highway bridges. Scour needs to be monitored to prevent the bridges from becoming damaged. This research studies the feasibility of using green laser for monitoring the scour around candidate railroad and highway bridges. The laboratory experiments that provided the basis for using green laser for underwater mapping are also discussed. The results of the laboratory and field experiments demonstrate the feasibility of using green laser for underwater infrastructure monitoring with limitations on the turbidity of water that affects the penetrability of the laser. This method can be used for scour monitoring around offshore structures in shallow water as well as corrosion monitoring of bridges. Full article

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process. Full article

This paper recasts four geodetic curves—the great ellipse, the normal section, the loxodrome, and the curve of alignment—into a parametric form of vector-algebra formula. These formulas allow these curves to be drawn using simple, efficient, and robust algorithms. The curve of alignment, which seems to be quite obscure, ought not to be. Like the great ellipse and the loxodrome, and unlike the normal section, the curve of alignment from point A to point B (both on the same ellipsoid) is the same as the curve of alignment from point B to point A. The algorithm used to draw the curve of alignment is much simpler than any of the others and its shape is quite similar to that of the geodesic, which suggests it would be a practical surrogate when drawing these curves. Full article

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately $90\%$. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding $90\%$. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes. Full article

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management. Full article