Digital Elevation Model Data Research Articles

Edge-protected IDW-based DEM detail enhancement and 3D terrain visualization

Terrain visualization plays an important role in Digital Twin, geology, environmental science, and exploration. Generating 3D visualization results by the Digital Elevation Model (DEM) is an effective method. However, existing DEM 3D visualization methods generally lack sufficient detail to obtain good visual effects. Therefore, we propose a novel DEM detail enhancement and 3D terrain visualization method. Our method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and the edge-protected Inverse Distance Weighted (IDW) algorithm, and achieves 3D visualization of DEM data through a workflow of converting the raw DEM data into an image, followed by enhancement and detail optimization of the image, and finally converting the image into triangular meshes. The edge-protected IDW algorithm performs IDW interpolation optimization based on the Canny edge detector combined with the Otsu threshold optimization method to detect edge areas, achieving smooth transitions in the edge areas. Experiments show that our method generates detail-rich 3D visualization results after enhancing original DEM data. We also conducted an ablation study to provide a side-by-side comparison of each step. Our method can be further applied to many tasks, such as virtual reality, GIS, and geological exploration.

Fluvial geomorphic parameters of the Shuiluo River Catchment and their tectonic implications, SE Tibetan Plateau

The Shuiluo River Catchment (SRC) is the front zone of the southeast compression and uplift of the Tibetan Plateau, with intense tectonic activity. In the basin, a series of regional large NW–SE trending active faults are developed. Studying clearly the geomorphic evolution of the SRC is conducive to further understanding the uplift and expansion mechanism of the SE edge of Tibetan Plateau. Our research was based on geographic information system, numerical analysis tool, and digital elevation model data, to extract six geomorpic parameters (hypsometric integral, asymmetry factor, basin shape ratio, valley floor width–valley height ratio, normalized channel steepness index and index of relative active tectonics) in SRC. After eliminating the impacts of climate, catchments area, and glacier, the geomorphic evolution of the SRC is mainly affected by geological structure and differential tectonic uplift movement; in the upstream and midstream (upper part), the shape of valleys and stream longitudinal profile shapes are affected by lithology; affected by geological structure and tectonic uplift, the tectonic activity in the midstream and downstream is relatively strong, and the intensity of activity in the downstream is stronger than that in the midstream, which may suggest that the faults’ activity in the downstream is stronger; the index of relative active tectonics values of the SRC are consistent with the regional seismic intensity, field-work and low-temperature thermochronology which indicates it is reasonable to use the fluvial geomorphic parameters to study the regional geomorphic evolution. The morphological parameters we extracted show different values in different regions of SRC, which may be the result of differential uplift in the southeastern of the Tibetan Plateau.

A new approach to flood susceptibility analysis of urbanised alluvial fans: the case of Bursa City (Türkiye)

AbstractFloods stand out as one of the most significant disasters impacting human life, causing widespread economic and social damage across the globe. Numerous research studies have concentrated on comprehending the contributing factors of flooding. Despite the prevalence of morphometry-based basin flood susceptibility analyses in existing literature, a comprehensive examination that encompasses anthropogenic features in alluvial fans is notably lacking. This study aims to evaluate the flood susceptibility of alluvial fans and their catchments in urbanised areas, integrating individual and collective basin/fan (B/F) morphometry and land use characteristics with flood inventory data. The study area selected for this investigation is the basin and fan systems situated on the northern slope of the Uludağ Massif (2543 m), the highest point in the Marmara Region, northwestern Türkiye. Twelve basin morphometric parameters were applied to 5 m resolution Digital Elevation Model data, while six fan morphometric and anthropogenic parameters were applied to alluvial fans. In addition, the results were assessed using the Normalised Morphometric Flood Index method to mitigate subjectivity in result ranking. According to the integrated basin/fan flood susceptibility results obtained through bivariate analysis, B/F6 exhibits a very high susceptibility, while B/F1, B/F3, B/F2, B/F5, B/F4, and B11/F8 demonstrate high susceptibility. B7, 8, 9, 10/F7 display a moderate potential for generating floods. These findings align significantly with historical flood events in the basin/fan area.

A machine learning approach to the geomorphometric detection of ribbed moraines in Norway

Abstract. Machine learning is a powerful yet underutilised tool in geomorphology, commonly used for image-based pattern recognition. Analysing new high-resolution (1–10 m) elevation datasets, we investigate its usefulness for detecting discrete geomorphological features. This study develops a machine-learning-based method for identifying ribbed moraines in digital elevation data and progresses to test its performance versus time-consuming, manual methods. Ribbed moraines share geomorphometric characteristics with other glacial landforms, hence representing a valuable test of our new methodology in terms of differentiating between similar features, and for detecting landforms with similar characteristics. Furthermore, mapping ribbed moraines may provide valuable indications of their origin, a topic of debate within glacial geomorphology. To automatically detect ribbed moraines, we extract simple morphometrics from high-resolution digital elevation model data and mask regions where ribbed moraines are unlikely to form. We then test several machine learning algorithms before examining the best performer (K-means clustering) for three study areas of 15 km2 in Norway. Our results demonstrate a balanced accuracy of 65 %–75 % when validating versus ground-truthing. The performance depends on the availability of high-resolution elevation data in Norway that are needed to resolve the spatial scale of the target (10–100 m). We find the method effective at detecting both fields of ribbed moraines, as well as individual ribbed moraines. We propose pathways for the future implementation of this method on a large scale and for increasing the detail of information gained about detected landforms. In conclusion, we demonstrate K-means clustering as a promising method for detecting ribbed moraines, with great potential to reduce the time needed to produce landform maps.

Application of accuracy improvement algorithms for extraction of topographic information and drainage network from DEM using GIS

The extraction of drainage network and watershed information is prerequisite for the study of watershed characteristics like morphometric analysis, which provides a basis for hydrological planning and modeling. The advanced tools of algorithms, Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) data and Geographical Information System (GIS) software are used to extract drainage networks and their watershed boundaries. These tools are complicated to use or produce more errors in the extraction of elevation and drainage networks when applied to flat areas. For removal of errors and to improve the accuracy in preparation of DEM and extraction of drainage network, Burada Kalava River Basin, Andhra Pradesh, India has been taken for application of accuracy improvement algorithms. An automatic generation of drainage network and watershed using digital elevation model results in positional errors due to variations in slope and topography. This study aimed to generate a catchment area and stream network that closely represent the natural stream network and the streams’ real positions. The step-by-step methodology using GRASS-interfaced Quantum GIS algorithms are given for pre-processing of DEM data to improve the positional accuracy before automatic extraction of the stream network and catchment area to resemble the real situation of the watershed. Secondly, efforts are made to analyze the DEM during automatic generation of the stream network and catchment area by assigning various area threshold values, including the application of pour point coordinates in improving the stream network and watershed characteristics. The results are verified and validated with the field information in order to improve the accuracy levels of DEM quality in generation of drainage network and catchment area.

Simulation of lahar flow potensial hazards in seulawah agam volcano eruption-aceh

Abstract Seulawah Agam volcano is an active stratovolcanic volcano located on Sumatra Island. During the decade following the phreatic eruption in 1839 up to now, there have only been a few small-scale increases in activity. The hazards associated with volcanic eruptions can be divided into primary hazards and secondary hazards. One type of significant secondary hazard is lahar flow. The consequences of this volcanic eruption must be overcome because the impact is the same as the main hazard, especially if efforts to overcome it are not taken seriously. Therefore, simulations are needed to understand the lahar flow patterns that can occur due to the eruption of the Seulawah Agam volcano. This lahar flow simulation is based on Digital Elevation Model (DEM) data, namely DEMNAS, and uses the LAHARZ application, which is based on Geographic Information Systems (GIS). The LAHARZ software is designed to calculate potential hazard zones exposed to lahar flows based on volume parameters. Lahar flow simulations were made in hydrological channels with several lahar volume scenarios, such as 1 x 105 m3, 1 x 106 m3, and 5 x 106 m3. The lahar volume estimate used is based on information from historical data on volcanic eruptions whose characteristics are almost the same as the Seulawah Agam volcano. This volume scenario shows differences in the extent of lahar flows and the size of the affected area. The results of this lahar flow simulation can be one of the first steps in efforts to mitigate the hazards of volcanic eruptions in the future.

Positioning of a lunar surface rover on the south pole using LCNS and DEMs

With the renewed interest in the lunar surface exploration, the European Space Agency is supporting, as part of the Moonlight program, the development of a dedicated Lunar Communications and Navigation Service (LCNS) infrastructure. This should enable to achieve real time positioning and autonomous navigation in the cislunar space, including, among others, lunar landing and surface operations. One of the services proposed, as part of the LunaNet framework (NASA, 2022), is called the Lunar Augmented Navigation Service (LANS) and it is based on a GNSS-like concept. However, compared to Earth-based GNSS, the expected number of available satellites in lunar orbit will initially be limited, which triggers the interest of coupling these technologies at the receiver level with other navigation data sources. In the case of surface rovers, use of digital elevation model (DEM) data has shown to be a potential other source of information to support positioning. The purpose of this contribution is to look at the actual navigation performances achievable when using a simple (four satellites constellation) lunar radio navigation constellation combined with a DEM, through a state estimation filter, for different rover dynamic and different DEM accuracy levels. It is shown that a 5 meter real-time horizontal position accuracy can be achieved, for 99.7% of the time, using a precise enough DEM (5 m/pixel) and a 4 satellite-constellation under different rover’s dynamic conditions (i.e. velocities).

Hourly land surface temperature retrieval over the Tibetan Plateau using Geo-LightGBM framework: Fusion of Himawari-8 satellite, ERA5 and site observations

The Tibetan Plateau (TP) is highly sensitive to even minor fluctuations in land surface temperature (LST), which can result in permafrost melting and degradation of alpine grasslands, leading to serious ecological consequences. Therefore, it is crucial to have high-temporal-resolution and seamless hourly estimating and monitoring of LST for a better understanding of climate change on the TP. Here, we employed Himawari-8 satellite, Digital Elevation Model (DEM), ERA5 reanalysis and meteorological station observations data to develop a new LightGBM framework (called Geo-LightGBM) for estimating LST on the TP, and then analyzed the spatiotemporal variations of those LST. Geo-LightGBM demonstrated excellent LST estimation accuracy, with an R2 (coefficient of determination) of 0.971, RMSE (root-mean-square error) of 2.479 °C, and MAE (mean absolute error) of 1.510 °C. The estimated LST values for the year 2020 agreed well with observed values, with remarkable differences in hourly LST variations. Meanwhile, the estimated LST was more accurate than that from FY-4A. Spatially, there were two high LST centers, located in the Yarlung Zangbo River Basin and the Qaidam Basin, and a low LST center located in the central TP. The SHAP (SHapley Additive exPlanations) and correlation analyses revealed DSCS (the mean ground downward shortwave radiation under clear-sky conditions) to be the most importantly input variable for estimating LST. Spatiotemporal dummy variables (e.g., longitude, latitude, DEM) were also found to be crucial for model accuracy improvement. Our findings indicate the potential for constructing a high-precision and seamless 24-h LST real-time retrieval and monitoring platform for the TP by combining satellite and China's independently developed CLDAS (China Land Data Assimilation System) data in future.

Assessing petroleum contamination in parts of the Niger Delta based on a sub-catchment delineated field assessment

The Niger Delta in Nigeria is a complex and heavily contaminated area with over 150,000 interconnected contaminated sites. This intricate issue is compounded by the region’s strong hydrological processes and high-energy environment, necessitating a science-based approach for effective contamination assessment and management. This study introduces the concept of sub-catchment contamination assessment and management, providing an overarching perspective rather than addressing each site individually. A description of the sub-catchment delineation process using the digital elevation model data from an impacted area within the Delta is provided. Additionally, the contamination status from the delineated sub-catchment is reported. Sediment, surface water and groundwater samples from the sub-catchment were analyzed for total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs), respectively. Surface sediment TPH concentrations ranged from 129 to 20,600 mg/kg, with subsurface (2-m depth) concentrations from 15.5 to 729 mg/kg. PAHs in surface and subsurface sediment reached 9.55 mg/kg and 0.46 mg/kg, respectively. Surface water exhibited TPH concentrations from 10 to 620 mg/L, while PAHs ranged from below detection limits to 1 mg/L. Groundwater TPH concentrations spanned 3 to 473 mg/L, with total PAHs varying from below detection limits to 0.28 mg/L. These elevated TPH and PAH levels indicate extensive petroleum contamination in the investigated sediment and water environment. Along with severe impacts on large areas of mangroves and wetlands, comparison of TPH and PAH concentrations with sediment and water quality criteria found 54 to 100% of stations demonstrated exceedances, suggesting adverse biological effects on aquatic and sediment biota are likely occurring.

Geochemical Pathways Defined by Predictive Regolith-Landform Models Using TanDEM-X Data in the Tanami Region, Australia

The Granites-Tanami Orogen (GTO) is a significant auriferous province located in the poorly exposed southwestern part of the North Australian Craton. This paper looks at the data sources that are suited to studying regolith-landform in areas such as the Tanami Region. One of the most useful datasets for regional regolith-landform mapping is a digital elevation model (DEM). The DEM data that were reviewed demonstrated the utility of TanDEM over other sources, and quantitative results focused on these data for identifying regolith-landform patterns that would aid in geochemical sampling and other land use studies. This paper will demonstrate that neither the classical approach of mapping boundaries based on visual estimates of breaks in slope, nor the alternative approach of producing maps and models derived from algorithms, can be used in isolation. Visual estimates of many boundaries are complex, unreliable and time consuming, and algorithmic mapping is driven by parameters than can produce an infinite set of models. This paper shows that simple landform visualisation is often the most powerful tool for map and model creation, supported by geomorphometric analysis and remotely sensed spectral imagery. The use of TanDEM data is shown to produce the best regolith-landform maps and models of the Tanami Region, which can then improve mineral exploration success.

Digital elevation model‐based lineament analysis of the Zindapir anticline, Sulaiman fold‐and‐thrust belt, Pakistan

The N‐S oriented Zindapir anticline is an eastward verging mega‐structure (~130 km along and ~40 km across the strike) in the eastern Sulaiman fold‐and‐thrust belt on the western margin of the Indian Plate in Pakistan. In this study, we carried out a lineament analysis of the anticline based on shuttle radar topographic mission (SRTM) images and digital elevation model (DEM) data to demonstrate the application of this technique for modelling of the fractured reservoirs. We used shaded relief images, variations in the vertical exaggeration, sun and azimuth angles to enhance the DEM data and structures in the area. Stereographic projections indicated that the anticline is an upright, asymmetrical, gentle, doubly plunging and east‐vergent fold. The synthesized lineament map of the fractures indicates a total of 402 fractures across the Zindapir anticline. A total of 204 and 198 fractures were observed in the eastern and western limbs, respectively. Statistical analysis portrayed that the lineaments of the majority of fractures followed northwest‐southeast (NW‐SE) and west‐northwest—east‐southeast (WNW‐ESE) trends. These are oblique or transverse to the fold axis and could be interpreted as shear and tensional fractures related to drag and compression along the western boundary of the Indian Plate. Our study provides an example of lineament analysis for structural characterization and modelling of fractured reservoirs in a collision‐mountain setting.

Analysis of vertical differentiation of vegetation in Taishan World Heritage site based on cloud model

While the forests on Mount Taishan are predominantly man-made, there is a notable vertical variation in vegetation. This study employs the method of cloud model, quantifying uncertainty (fuzziness and randomness) of things. Utilizing digital elevation model (DEM) and vegetation distribution data, we constructed elevation cloud models for Mount Taishan’s deciduous broad-leaved, temperate coniferous, and mixed coniferous-broadleaved forests. Using three numerical features of the cloud model—Expectation (EX), Entropy (EN), and Hyper-entropy (HE)—we quantitatively analyzed the macro regularity and local heterogeneity of Mount Taishan’s forests vertical distribution from the perspective of uncertainty theory. The results indicate: (1) The EX of the core zone elevation of deciduous broad-leaved forest is 716.65 m, temperate coniferous forest is 1053.51 m, and mixed coniferous-broadleaved forest is 1384.09 m. The variation range of the core zone distribution height is smaller in the mixed coniferous-broadleaved forest (EN: 53.74 m) compared to deciduous broad-leaved forest (EN: 99.63 m) and temperate coniferous forest (EN: 121.70 m). (2) The fuzziness and randomness of the distribution height of the lower extension zones of deciduous broad-leaved forest and temperate coniferous forest (EN: 75.15 m, 184.56 m; HE: 24.09 m, 63.54 m) are greater than those of the upper extension zones (EN: 44.75 m, 42.49 m; HE: 14.48 m, 13.23 m). (3) The distribution fuzziness and randomness within temperate coniferous forests exceed those of deciduous broad-leaved forests. Within the core zones, the uncertainty regarding the vertical distribution of vegetation across different aspects remains consistent, which retains the characteristic of man-made forests. However, in transition areas, there is significant disparity, reflecting the adaptive relationship between vegetation and its environment to some extent. In the upper and lower extension zones of deciduous broad-leaved forests, the EX values for the vertical distribution height of mixed coniferous and broad-leaved forests differ significantly from those of deciduous broad-leaved forests (the difference is 22.82–39.15 m), yet closely resemble those of temperate coniferous forests (the difference is 4.79–7.94 m). This suggests a trend wherein deciduous broad-leaved tree species exhibit a proclivity to encroach upon coniferous forest habitats. The elevation cloud model of vertical vegetation zones provides a novel perspective and method for the detailed analysis of Mount Taishan’s vegetation vertical differentiation.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

Georeferencing of Satellite Images with Geocoded Image Features

Abstract. Currently, using digital orthophoto map (DOM) and digital elevation model (DEM) as reference to achieve geometric positioning of newly acquired satellite images has become a popular photogrammetric approach. However, this method relies on DOM and DEM data which requires a lot of storage space in practical applications. In addition, for geometric positioning of satellite images, only sparse image feature points are needed as control points. Consequently, for the sake of convenience, the compression of control data emerges as a necessity with significant practical implications. This paper investigates a "cloud control" photogrammetry method based on geocoded image features. The method extracts SIFT feature points from DOMs, and obtains their ground coordinates, then constructs geocoded image feature library instead of DOM and DEM data as control, thus realizing the compression of control data. Experiments conducted on the Tianhui-1, Ziyuan-3 and Gaofen-2 satellite images demonstrate that the proposed method can achieve high-precision geometric positioning of satellite images and greatly reduce the size of the control data. Specifically, with the reduction of the reference data from 180~1248 MB 2 m DOM and 30 m DEM to 5~10 MB geocoded image features, the geopositional accuracies of the test Tianhui-1, Ziyuan-3 and Gaofen-2 images are improved from 3.12 pixels to 1.74 pixels, 3.69 pixels to 1.09 pixels, and 150.93 pixels to 2.67 pixels, respectively.

Prioritization of soil erosion-prone sub-watersheds using geomorphometric and statistical-based weighted sum priority approach in the middle Omo-Gibe River basin, Southern Ethiopia

ABSTRACT Soil erosion, a major factor contributing to land degradation, poses significant environmental challenges across sub-Saharan Africa, including Ethiopia. This study identifies erosion-prone sub-watersheds in southern Ethiopia's middle Omo-Gibe River basin using digital elevation model data, analyzed using a geomorphometric and statistically weighted sum priority approach in a Geographic Information System environment. Additionally, hypsometric analysis, lithology, land use/cover analysis, and field observations were integrated to enhance methodological reliability in assessing soil erosion patterns and subsequent land degradation in the basin. Results highlight sub-watersheds with varying degrees of erosion sensitivity, emphasizing morphometric parameters and landscape characteristics. Furthermore, hypsometric analysis confirms a moderate to high susceptibility of sub-watersheds to soil erosion. Moreover, half of the river basin consists of shrubland, encompassing scrubs, range lands, and small wooded areas. These are less efficient in mitigating erosion compared to forests, especially in the face of significant summer rainfall and ensuing runoff. Statistical prioritization identified 19 sub-watersheds, covering 43% of the basin's total area, as highly prone to soil erosion and land degradation. The study's findings are valuable for Ethiopian watershed management decision-makers, enabling informed resource allocation and targeted interventions to control soil erosion and protect water resources by utilizing the outputs of this study.

The correlation between autumnal landscape colors and environmental factors and the analysis of ecological service functions in subtropical urban scenic forests

Background and objectivesUrban scenic forests are a main component of forest parks, and a quantitative study of the landscape color of urban scenic forests can provide a scientific reference for the planning of forest parks and the design of scenic forest management measures. The purpose of the study is to find the quantitative correlation between autumn landscape color and ecological service functions and to obtain the important influencing factors.Materials and methodsThis study focuses on Purple Mountain National Forest Park in Nanjing as a case study area and uses forest resource survey data from Purple Mountain National Forest Park in 2020, autumn landscape color photograph data from Purple Mountain National Forest Park in 2020, and digital elevation model data as the main information sources. The correlation between two ecological functions of above-ground biomass (AGB), tree species diversity (TSD), and influencing factors (including color factors, stand factors, and terrain factors) were both analyzed by Pearson correlation analysis. Then, multiple linear regression (MLR) and random forest (RF) methods were used to perform the quantitative relationship between the functions.ResultsThe results show that, in the established quantitative models of AGB, with TSD as the dependent variable, the correlation coefficients of the MLR model are both above 0.784, while the correlation coefficients of the RF model are all above 0.872. Moreover, the brightness value of the main color (BRI), the number of yellow-green blocks (NYG), and the number of yellow blocks (NY) have important effects on the two ecological service functions.ConclusionIn conclusion, there are complex non-linear relationships between the ecological service functions of AGB, TSD, and influencing factors, and the landscape color can reflect the ecological function of the scenic forest to some extent. In addition, stand factors and color factors have important effects on the ecological function of AGB. Color factors and terrain factors have important effects on the ecological function of TSD. BRI, NYG, and NY have important effects on the two ecological functions. Finally, this quantitative method has universal applicability in the temperate zone, warm temperate zone, and subtropical zone of China.

Prediction of Peak Discharge Using the SCS Curve Number Method in the Manikin Watershed

Surface runoff is a crucial hydrological variable in the analysis of water infrastructure planning. A reliable method for predicting surface runoff resulting from rainfall in an ungauged watershed is the SCS CN method. This research aims to represent the effectiveness of the Curve Number (CN) method in calculating peak discharge in the Manikin Watershed. The data used for this analysis includes rainfall data from three rain stations, each with a 25-year dataset, water level data of 11 years, digital elevation model (DEM) data, land use maps, and hydrogeological maps. The SCS curve number method is the most commonly used method for the estimation of peak discharge in a watershed. The calculated flood discharge values for the Manikin River Basin, with return periods of 5, 10, 20, 25, 50, 100, 500, and 1000 years, are as follows: 60.32 m3/s, 84.74 m3/s, 111.98 m3/s, 121.41 m3/s, 153.25 m3/s, 188.79 m3/s, 287.24 m3/s, and 337.30 m3/s, respectively. The reliability testing of the method in the Manikin Watershed was determined by a Nash–Sutcliffe efficiencies (NSE) value of 0.93 and root mean square error (RMSE) value of 19.70. As a result, the Curve Number Method proves to be highly reliable in representing the peak discharge in the Manikin Watershed.

Contractional strains and maximum displacement-length ratios of lunar wrinkle ridges in four Maria of basalt

Compressional stresses from the basalt basins on the lunar are responsible for the formation of wrinkle ridges in lunar mare basalts. According to the wide angle multispectral camera (WAC) mosaic image, we selected 62, 75, 73, and 58 single wrinkle ridges in Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis, respectively, for this paper. Several topographic profiles near the midpoint of each wrinkle ridge are generated to measure the maximum displacement (Dmax) and height of the wrinkle ridges using digital elevation model (LOLA) data. After that, we create 2D plots of displacement-length (L) for the ridge population in the four Maria and compare the results. A linear fit method derives the Dmax/L ratios (γ) from the D-L data. We calculated the contractional strains in each mare area based on the cap D sub m a. x over cap L data. Moreover, each mare's gravity pattern, mare thickness, and formation age are also presented. The ridges in Mare Imbrium and Mare Serenitatis have a higher γ value (1.83 × 10−2 and 1.98 × 10−2) than the ridges in Mare Fecunditatis and Mare Tranquillitatis, which have γ values of (1.67 × 10−2 and 1.75 × 10−2). Finally, the contractional strains (ε) in Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis are estimated to be 0.23 %, 0.41 %, 0.39 %, and 0.18 % (considering 25° is the fault plane dip θ), respectively. The maximum values of the free-air gravity anomalies in Mare Fecunditatis range from −30 to 250 mGal, while minimum gravity anomalies in Mare Serenitatis range from −80 to 140 mGal. Mare Imbrium, Mare Serenitatis, Mare Fecunditatis, and Mare Tranquillitatis have an average thickness of 300 m, 910 m, 652 m, and 760 m, respectively. Furthermore, the Mare Imbrium ridge group is older than the Lunar Wrinkle Ridges in Mare Serenitatis. Mare Tranquillitatis ridge group formation takes longer than Mare Imbrium ridge group formation. Therefore, we believe that it has thicker basaltic units, a longer wrinkle ridge formation time, and higher gravity anomaly than the Mare Imbrium and Mare Serenitatis basins, even though the formation of the Mare Tranquillitatis and Mare Fecunditatis basins occurred earlier.

Modeling remote-sensing and geophysical data to delineate the favorable mineralization localities at Semna area, Central Eastern Desert, Egypt

Semna area is located within the Golden Triangle district in the Central Eastern Desert of Egypt. The study maintains using multisource datasets of remote sensing, aero-spectrometry, aero-magnetic, and field investigations for unraveling the ambiguities associated with the alteration zones at the targeted area. Three remote sensing mapping techniques including; constrained energy minimization, linear spectral unmixing, and mineral indices, were adopted to delineate the alteration zones. The γ-ray spectrometry data of K (%), K/eTh, K/eU, and F-parameter enabled mapping the potassium-enriched localities as indication on possible potassic alteration zones. The surface and subsurface linear structural features were delineated from the digital elevation model and aero-magnetic data, respectively. The outcomes of all the implemented datasets were integrated using GIS overlay modeling, producing an integrated mineralization favorability/potentiality map. Eighteen localities with high potential for mineralization were highlighted. A field study was conducted to the investigated area during which, several alteration indicators were observed, including quartz veins, iron oxides staining, kaolinite, malachite, carbonate, and muscovite (sericite) alteration indicator minerals.

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.