Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

A review of the CARG Project of the Campania Region (marine counterpart) up to water depths of 200 m is herein proposed referring to the Gulf of Naples (southern Tyrrhenian Sea) aimed at focusing on the main scientific results obtained in the frame of this important project of marine geological cartography. The Gulf of Naples includes several geological sheets, namely n. 464 “Island of Ischia” both at the 1:25,000 and 1:10,000 scale, n. 465 “Island of Procida” at the 1:50,000 scale, n. 466–485 “Sorrento–Termini” at the 1:50,000 scale, n. 446–447 Naples at the 1:50,000 scale, and n. 484 “Island of Capri” at the 1:25,000 scale. The detailed revision of both the marine geological and geophysical data and of the literature data has allowed us to outline new perspectives in marine geology and cartography of Campania Region, including monitoring of coastal zone and individuation of coastal and volcano-tectonic and marine hazards.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

In this study, the enrichment of the major oxide, trace element/heavy metal and rare earth element contents of the rocks outcropping in Kısacık and its vicinity (Ayvacık-Çanakkale/Türkiye) were investigated. The rocks in the field were handled in 5 groups, and whole rock analyses were carried out for 22 samples collected representing these rock groups and Element Enrichment Factor (EEF) of the major oxide, trace element/heavy metal and rare earth element contents of the rocks were calculated. As a result, it was determined that the Kısacık volcanics were enriched in SiO₂, Fe₂O₃, K₂O, Be, Co, Cs, Th, U, W, La, Eu, Tm, Yb, Lu, Mo, As, Cd, Sb, Bi and Hg elements at a rate of >1 to >150 according to the upper crust values, and the Fe₂O₃, MgO, CaO, TiO₂, P₂O₅, MnO, Cr, Sc, Co, Nb, Sr, Mo, Cu, Ni, Cad, Sb, Bi, V, Cu and Cd concentrations of the Ophiolitic Mélange were enriched in ratios ranging from >1 to >36 according to the upper crust values. It has been also observed that the listvenitic rocks in the Ophiolitic Mélange are enriched in Cr, Co, Ni, As and Hg elements compared to the upper crust. As to Kazdağ Group, MgO, CaO, K₂O, MnO, Cr, Co, Ta, U, W, Mo, Cu, Ni, As and Cd were enriched. Listvenite were enriched in SiO₂, Fe₂O₃, MgO, Mn, Cr, Co, Ni, As, Sb and Hg at a rate of >1 to >32 according to the upper crust values. When the rocks in the area were evaluated together, some oxides (e.g., CaO, MgO, Fe₂O₃, TiO₂) and elements (e.g., Cr, Ni, Co) were enriched due to parental rock, while some oxides (e.g., SiO₂, K₂O and MnO) and elements (As, Sb, Hg) were enriched due to epigenic processes such as hydrothermal alteration and weathering.

The present study assessed the potential of sediment loading in Beteni, Lauruk, Andheri, and Harpan sub-watersheds of Phewa Lake and estimated the sediment yield in the year 2020. Morphometry, land use/land cover, geology, climate, and human and development factors of the sub-watersheds were studied to assess the potential of sediment loading in the sub-watersheds. SRTM DEM was used for the computation of morphometric parameters and land use/land cover maps were prepared by using Landsat imagery. Geology, rainfall data, census data, and road maps were collected from various secondary sources. The sediment yields of the four sub-watersheds in the year 2020 were estimated by measuring the sediment volume deposited in the sediment retention ponds at the outlet of each sub-watershed. Results indicated that Beteni had the highest potential for sediment loading, while Harpan had the lowest. Likewise, the sediment yields for Beteni, Lauruk, Andheri, and Harpan sub-watersheds in 2020 were estimated at 1,420.67 m³/km²/year, 2,280.14 m³/km²/year, 1,666.77 m³/km²/year, and 766.42 m³/km²/year, respectively. To reduce sedimentation in Phewa Lake, it is recommended to regularly maintain siltation dams and construct check dams along the drainage slopes, alongside other soil conservation measures and appropriate land use practices in the upstream areas of the sub-watersheds.

Karren and mass movements are described. Mass movements taking place on karren terrains are studied in case of bare karren and covered karren. Mass movements occur at rinnenkarren, grikes, Schichtfugenkarren, and tropical karren. This study describes that karren features increase the chance of the development of certain mass movements. It is approached in a theoretical way that in the case of different preconditions (e.g., change of slope angle), what kind of mass movements are triggered by different karren features. The most common mass movement is triggered by karren which are debris creep, gelisolifluction, rock avalanche, collapses, creep and solifluction.