Morphometric classification and spatial distribution of dolines in southern Slovenia

As the distinctive features of karst terrain, dolines are frequently regarded as diagnostic indicators of karst landscapes. Since the beginning of the karst scientific research in 19th century, dolines have consistently captured the attention of geographers, geologists, and geomorphologists. Geomorphological research provides valuable knowledge about the spatial distribution of dolines, their characteristics, origin and development. These features not only serve as significant structural and geomorphological indicators, but also as indicators of karst evolution. One of the most significant and frequent research approaches in the study of dolines in modern geomorphology involves analyzing their spatial distribution. The main objective of this research is to determine the spatial distribution and density of dolines in the External Dinarides of Bosnia and Herzegovina, and make an analysis of the geological structure and morphometric features of the terrain as influential factors of distribution. A digital database was created based on a 1 : 25,000 topographic map, where points on the map denote the positions of the doline areas. The data on doline positions were manually adjusted for digital analysis, which was performed by using the ArcGIS 10.1 software package. The findings indiate that the morphometric relief parameters have a considerable impact on the distribution of dolines. Higher doline density is observed in the areas characterized by Jurassic and Cretaceous limestones within the karst plains of the middle hypsometric belt.

Valjevo–Mionica karst is a limestone area within the easternmost flanks of the Internal Dinarides in western Serbia. In the spatial extent of 380 km2, it hosts typical karst landforms, primarily dolines, blind valleys, dry valleys and caves. Dolines are present on 75% of the total area, and the exact number of them within the area outline is 5319. The aim of the study is to determine the guidance factors for the spatial distribution of dolines, primarily morphological, while lithological, tectonic and climatic factors are presented at the basic level. Morphological factors in this study are analysed through morphometrical characteristics and calculations, which include the elevation, the mean topographical slope and the landform classification based on geomorphons. Digital elevation models with resolutions 90 m and 30 m are used. Data sources for doline positions were the topographical maps of 1:25,000 scale.Spatial distribution of dolines in the study area is rather uneven. Three zones (clusters) of higher concentration may be distinguished, where 34% of the total study area hosts 72% of dolines. These three zones are the karsts of the villages Lelić, Bačevci and Robaje, divided by deep canyon valleys. Maximum density of dolines, judging by the Kernel density method, is 33 dolines per km2 in the zone of the Stapar village, at the northwestern outskirt of the study area. The main factors influencing the spatial distribution of dolines are the topographical slope and the phases of morphological/hydrographical evolution of the area.

ABSTRACTA method is presented to explicitly incorporate spatial and scale vagueness – double vagueness – into geomorphometric analyses. Known limitations of usual practices include using a single fixed set of crisp thresholds for morphometric classification and the imposition of a single arbitrary number of scales of analysis to the entire digital elevation model (DEM). Among the advantages of the proposed method are: fuzzification of morphometric classification rules, scale-dependent adaptive fuzzy set parametrization and an objective definition of maximum scale of analysis on a cell-by-cell basis. The method was applied to several DEMs ranging from the ocean floor to surface landscapes of both Earth and Mars. The result was evaluated with respect to modal morphometric features and to characteristic scales, suggesting a more robust method for deriving both morphometric classifications and terrain attributes. We argue that the method would be preferable to any single-scale crisp approach, at least in the context of preliminary hands-off morphometric analyses of DEMs.

As an essential geomorphological structures on planetary surface, impact craters can provide significant information in determining the planetary chronology. This paper proposes a novel automatic crater detection algorithm by using digital elevation model (DEM) data. The method includes: 1) pre-processing of the original DEM data, which can eliminate the effect of other landform; 2) iterative crater detections, which can eliminate small objects and analyze roundness. We use the DEMs instead of the imagery data because the DEMs could be unaffected by the solar altitude and atmospheric conditions, etc. Our detection algorithm is evaluated using several test sets of Martian DEM data obtained by the Mars Obiter Laser Altimeter (MOLA) boarded on the Mars Global Surveyor. The experimental results show the high true detection rate and low false detection rate of our algorithm according to the Barlow catalogue.

A fortran-77 program for preliminary extraction of drainage networks based on a DEM

In recent decades, in the Pre-Carpathian region of Ukraine during the summer period, floods and flood events became more frequent. They were accompanied by significant economic and environmental loss. Especially powerful were the floods of 2008 and 2020, but the floods in 2014 and 2016 also had destructive consequences. Therefore, the study of river channel processes, river stability and assessment of flooded land areas due to floods is an urgent problem. The aim of the study is to propose a methodology for hydrological modeling of sections of riverbeds with complex morphometric and hydrological characteristics. The construction of a digital elevation model (DEM) and the selection of the distance between the cross-sections, as well as the determination of the Manning coefficients, have the greatest impact on the accuracy of the modeling, so these factors should be given maximum weight when calibrating the model. The object of the study was the section of the Dniester River in Ukraine in the place of transition from the foothill part of the channel to the hilly–marshy part with complex meandering and significant shifts of the river. The methodology of hydrological modeling includes three principal components: construction of the DEM, determination of the type of underlying surface and determination of the level of water rise in the riverbed. The research was carried out on the basis of imaging from unmanned aerial vehicles (UAVs). In 2017, the imaging of a section of the Dniester riverbed was carried out in the summer during a period of significant vegetation growth, which affected the accuracy of determining the heights of the model points. According to the results of this imaging, the residual mean square (RMS) for determining the heights of the points exceeded the permissible value of the RMS (0.25–0.3 m) by two times. In 2021, imaging was performed in the autumn period when there was no leaf cover. The RMS of the DEM for 2021 imaging was 0.26 m. According to the results of the survey in 2017 and 2021, orthophotoplans were created, which were used to determine the planned displacements of the river bed and clarify the Manning coefficients, which characterize the roughness of the underlying surface. The value of the water level rise was obtained on the basis of the graph on the date of the maximum rise of the water level on 24 June 2020 according to the hydrometeorological station located near the selected area. The result of the research is hydrological modeling using the HEC-RAS module for a site with complex hydrological and morphometric characteristics on the date of the maximum water rise. It was established that in order to achieve the required accuracy of the DEM, imaging should be carried out in the leafless period of the year, since the accuracy of constructing the DEM decreases by half during the growing season. On the basis of the obtained orthophoto plans, a methodology for determining refined Manning coefficients was developed, which allows taking into account changes in the underlying surface of the channel area. The area of the flooded area was calculated based on the level of water rise during the 2020 flood.

The purpose of this study was to evaluate the performance of Digital Elevation Models (DEMs) in the morphometric characterization of a basin located in a transitional region between the São Francisco Plateau, São Francisco Depression and Espinhaço Range reliefs. For the study, four DEMs were generated by interpolation of the SRTM data and topographic maps, using the Topo To Raster interpolator with and without mapped hydrography support, available in ArcGIS® 9.3 software. Another DEM was obtained from the SRTM original data. From the generated DEMs, the morphometric characteristics of the basin were determined and compared to those obtained from topographic maps, denominated reference (REF), by means of percentage errors. The evaluation was also performed in a qualitative way, comparing the drainage and the basin delineations. In general, the DEMs obtained with the support of the mapped hydrography (SRTM-TRH and CT-TRH) provided the best results, with small errors, mainly for the main morphometric characteristics of the basin, drainage area and main river length, which ranged from 0.38 to 1.12% and 5.28 to 7.07%, respectively. On the other hand, the DEMs generated without the support of the mapped hydrography (SRTM-O, SRTM-TR and CT-TR) presented major errors mainly in determining the drainage area and length of the main river, which varied from 18.1 to 26.6% and 26.7 to 34.4%, respectively. These occurred due to a deviation of the main river in the São Franciscana Depression region, which allows us to conclude on the necessity and importance of evaluating DEMs before their use.

Dolines are small to intermediate enclosed depressions and are the most numerous karst feature in Slovenia. They are circular in plan form and vary in diameter from a few metres to over a kilometre. They are developed in limestone, dolomite, carbonate breccia and conglomerate and occupy different geomorphic settings. They were formed by various processes like dissolution, collapse, suffosion and transformation of caves to surface features by denudation. Publicly accessible lidar data, provided by a nationwide laser scanning project of Slovenia, was used for this study. To catalogue the dolines, we manually label a fraction of the digital elevation model (DEM) with a binary mask indicating if the area is a doline or not. We then train a slightly modified u-net, a type of machine learning algorithm, on the labelled territory. Using the trained algorithm, we infer the binary mask on the entire DEM. We convert the resulting mask into an ESRI Shapefile and manually verify the results. We note that the training and inference are error prone on types of relief that were less common in the training set (e.g., the relatively uncommon collapse dolines). We believe manual verification mitigates most of these errors, so the resulting map is a good basis for the doline study. We have made our georeferenced catalogue of dolines available at https://dolines.org/ (Mihevc & Mihevc 2021). Dolines are found in most of the karst areas, except mountains where they were eroded by glacial action or covered by glacial deposits. We detected 471,192 dolines and divided them into three genetic types. Most abundant are solution dolines (470,325). The average doline is 9 m deep, has a diameter of 42 m and a volume of 14,098 m3. The density of dolines on levelled surfaces can be as high as 500/ per km2. They are absent from the floors of poljes and steeper slopes, and are less abundant on sloping surfaces. We have identified 314 dolines to be of collapse origin. The mean depth of collapse dolines is 49 m, and 20 of them are deeper than 100 m. The mean volume is 1.2 million m3, with the largest having a volume of 11.6 million m3. Most of the collapse dolines can be found close to ponors or springs or corridors where large underground rivers flow. We have detected 553 suffosion dolines formed by suffosion of sediments in blind valleys or on poljes. This basic data set for dolines enables further study and comparison of dolines with the geology and topography of the karst.

Taiwan's geological setting, characterized by rapid tectonic uplift and among the world's most intense precipitation patterns and recurring extreme rainfall events, offers a natural laboratory for studying sediment flux and erosion rates in mountain river basins. The availability of open-source satellite-derived digital elevation models (DEMs) provides an invaluable opportunity to evaluate their suitability for constraining sediment flux in these dynamic environments. The Laonong River, one of Taiwan's prominent and vulnerable watersheds, has been selected as a representative study area due to its history of past and ongoing landslides, making it ideal for understanding erosion processes and sediment transport dynamics. This study assesses erosion rates in the Laonong River Basin over the past two decades using satellite-derived DEMs from diverse optical and radar sources. By evaluating the suitability of underutilized global DEMs, including ASTER GDEM, NASADEM, SRTM, ALOS World 3D DEM (AW3D30), Copernicus DEM, FAB DEM, and TanDEM-X EDEM, and benchmarking them against a high-accuracy LiDAR DEM, we aim to enhance our understanding of the erosional processes. Accuracy assessments are conducted in stable areas through spatial domain analysis, utilizing comprehensive metrics, including RMSE, bias, and standard deviation, to quantify discrepancies and ensure rigorous error evaluation. Additionally, metadata analyses identify voids and artifacts filled from external sources, while Fourier analysis is applied to detect and mitigate vertical biases, enabling a robust examination of DEM suitability in this complex terrain.Our findings revealed that while Copernicus DEM, FAB DEM, and TanDEM-X EDEM exhibited good vertical accuracy in the spatial domain, their reliance on external DEMs for void-filling rendered them unsuitable for multitemporal analysis. Similarly, ASTER GDEM was excluded due to its high standard deviation, significant negative bias, and prolonged acquisition period, averaging over 13 years. As confirmed through Fourier analysis and in the spatial domain against LiDAR DEM, AW3D30 demonstrated excellent vertical accuracy and minimal vertical bias. NASADEM, being the successor of SRTM, was preferred over its predecessor due to lower vertical bias and minimal external void-filling. Consequently, NASADEM and AW3D30 were identified as the most reliable DEMs for capturing topographic changes across different decades in the Laonong River Basin. Horizontal co-registration was refined to sub-pixel accuracy using the Nuth and Kääb method, while Fourier analysis was employed for vertical alignment, effectively minimizing biases across DEMs acquired at different time points. Spectral analysis identified long-wavelength topographic features crucial for correcting offsets and enhancing the accuracy of DEM differencing. Our results estimate that about 119 Mm3 of sediment volume has been transported out of the system over 20 years, as calculated from NASADEM and LiDAR DEM. We documented the spatial pattern of erosion and deposition across the whole Laonong River basin in the DEMs of Differences (DoD) maps, and the results were validated from the Google Earth imageries. These findings highlight the capability of underutilized satellite-derived DEMs in capturing sediment erosion rates over multiple decades, demonstrating their utility in environments where erosional signals are dominant over the inherent noise in the dataset.

How (Not) to Lie with Spatial Statistics

The aim of the study is to analyze the Morphometric characteristics  of discharge Bastora,represented characteristics cadastral,Formal,topographic,and characteristics of the water drainage network, study relied on( GIS)and digital elevation model( DEM) as a tool to set up a network  rivers draining map and classified according to the way Strahler to mortabha  river basin and draw some morphometric characteristics .the study has revealed the importance of spatial data lind metadadt and its role in morphometric studies and characteristics  of the analysis.

Clinical sites utilizing magnetic resonance imaging (MRI)-only simulation for prostate radiotherapy planning typically use fiducial markers for pretreatment patient positioning and alignment. Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI. In this study, a deep learning-based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy-seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple-Echo T1 -VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image-to-image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F1 classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers. Ninety-six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F1 classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were <1mm. We have developed a deep learning-based approach to automatically detect fiducial markers in MRI-only simulation images in a clinically representative patient cohort. The automatic detection algorithm-predicted markers can allow for patient setup with similar accuracy to independent human observers.

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

Abstract. Digital Terrain Models (DTMs) are still one of the most emerging products derived from remote sensing technologies such as LiDAR. A DTM usually is defined as the current digital representation of the earth´s surface. However, due to natural events and human influences the earth's surface is constantly changing. Therefore, the ability to analyse DTM data over time allows to understand and quantify changes, which are crucial for environmental monitoring, disaster management and urban planning. In this paper we introduce a GeoDBMS-based approach to manage, sustainably provide, analyse and visualize spatio-temporal DTM data. First, we review current approaches for the management and temporal modelling of DTM data. Then we present an event-based time-stamping spatio-temporal data model that meets special requirements for the management and analysis of DTMs to monitor spatial changes within the DTM in time. Based on this model, we present methods for the management and analysis of spatio-temporal DTMs. Thereby the model is extended by an appropriate server-infrastructure and a graphical user interface that enables to query, analyze, visualize and export time series of DTMs. Data consistency and accuracy for DTMs are also considered. A use case is conducted based on spatio-temporal DTM datasets of South-West Germany, demonstrating the suitability of the approach. Furthermore, geodetic applications and new DTM research directions are shown. Finally, conclusions are drawn from the paper and an outlook is presented describing our future research based on new use cases, including the analysis of DTMs and Digital Surface Models (DSMs) in the United Arab Emirates.

Alluvial dolines in the central Ebro basin, Spain: a spatial and developmental hazard analysis