Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest I…
The semi-automated extraction of landforms using GIS analysis is one of the main topics in computer analyses. The use of digital elevation models (DEMs) in GIS applications makes the extraction and classification procedure of landforms easier and faster. In the present paper, we assess the accuracy of semi-automated landform maps by means of a comparison with hand-made landform maps realized in…
This study used a ground-penetrating radar (GPR) to characterize the archaeological environment of site GO-Ja-02 in Serranópolis, Goiás, Brazil. In the Serranópolis region, there are records of numerous human burials in archaeological sites located in predominantly sandy soils. Thus, this study proposed the application of a ground-penetrating radar to locate buried archaeological structures.…
Initiatives such as the United Nations Decade of Ocean Science for Sustainable Development and Seabed 2030 promote seabed mapping worldwide. In Brazil, especially on the Espírito Santo Continental Shelf, high-resolution seabed mapping has revealed an unknown complex seascape. Circular depressions (CDs) were mapped for the first time in the Costa das Algas Marine Protection Area. Herein, we aim…
This study investigates the application of Compositional Data Analysis (CoDA) and multivariate statistical techniques to geochemical data from the soils of the Campania region. The dataset examined includes 3571 soil samples analyzed for 37 chemical elements. Principal Component Analysis (PCA) was employed to reduce the dataset’s dimensionality and identify key relationships between elements.…
Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral exploration. In this investigation, the Visible Near-Infra-Red (VNIR), Short-Wave Infra-Red (SWIR), and Thermal Infra-Red (TIR) bands of the ASTER satellite sensor derived layers were fused to detect alte…
Mineral exploration campaigns are financially risky. Several state-of-the-art methods have been developed to mitigate the risk, including predictive modelling of mineral prospectivity using principal component analysis (PCA) and geographic information systems (GIS). The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets. However, some of its limit…
In this study, we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province (Gondwana Supercontinent). Wedemonstrate that a variety of trace elements, including most of the lanthanides, chalcophile, lithophile, and siderophile ele…
Rapid detection of landslides after an exceptional event is critical for planning effective disaster management. Previous works have typically used machine learning-based methods, including the recently popular deep-learning approaches, to identify characteristics surface features from satellite remote sensing data, especially from optical images. However, data acquisition from optical images i…
Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches.…