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.
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.
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•First deep segmentation investigation for karst depression detection in Brazil.•Copernicus Digital Elevation Model with 30-m resolution achieved superior accuracy.•Feature Pyramid Network was more accurate model in detecting karst depressions.•Combining digital elevation model with morphometric attributes improves accuracy.•Successful quantification using semantic-to-instance segmentation conversion.
Dolines are the characteristic landforms in karstic landscapes, and their morphometric characteristics are commonly used for surface karst research. More than 140,000 dolines were identified using ...1:25,000 scale topographic maps (TOPO-Maps) in the West and Central Taurus, Türkiye’s most important karst terrain. However, the accuracy of these TOPO-Maps in terms of real morphometric characteristics of dolines in the Taurus Mountains is unknown. For this reason, in this study, dolines were automatically delineated from high-resolution digital elevation models (DEMs) produced by unmanned aerial vehicles (UAV-DEMs), and their morphometric characteristics were compared with the results obtained from 1:25,000 scale TOPO-Maps. UAV-DEMs with resolution below meter level provide a more accurate representation of the real Earth surface and thus landforms can be examined in more detail. In this study, six locations covering a total of 8.03 km
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were modeled with UAV. According to the results, the accuracy of the TOPO-Maps in terms of doline numbers was 0.24 (24%)–0.98 (98%), and this rate increased as the average area of the dolines increased. Doline density was always high with UAV-DEM data, and doline density had an average 99% increase when compared with TOPO-Map data. Due to UAV-DEMs providing the opportunity to calculate more morphometric characteristics, in this study, we provide information for the first time about the depth, slope, and volume characteristics of dolines in the Taurus Mountains. The mean depth, slope, and volume of solution dolines were 31 m, 45°, and 207,931 m
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in the Taurus Mountains, respectively.
The Taurus Mountain is one of the most important karstic region of the world and dolines are characteristics landforms of this area. However, the number and distribution of doline are unknown in the ...study area. The aims of this study are to explain the total number of dolines, distribution of doline density, effects of slope conditions and the change of doline orientation in the Taurus Mountains. According to the 1/25000 scale topographic maps utilized in this study, a total of 140,070 dolines were determined in a 13,189 km
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area on eleven high karstic plateaus bordered by steep slopes and deep gorges. These plateaus are substantially affected by highly-faulted and jointed systems and about 80% of each plateau is covered with neritic limestone. The dolines are located at an elevation between 10 and 2870 m. Average elevation of all dolines is 1842 m. 90% of dolines are located between 1300 and 2270 m and only 5% of dolines found under 1330 m. According to this results, the densest doline zone corresponds to the alpine and periglacial zone above the treeline. Doline density reaches > 100 doline/km
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on Mt. Anamas and the Seyran, Geyik and Akdağ ranges as well as the Taşeli plateau. Maximum density (187 doline/km
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) is found on the Akdağ Mountains. However, 66% of the study area is characterized by low density, 29.9% with moderate density, 3.4% with high density and 0.7% with very high density. The highest doline densities are seen on gentle slopes (15°–25°/km
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) and steep slopes (> 35°/km
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) are limited doline distribution. According to the rose diagram formed by the azimuths of the long axis of the dolines at the Central Taurus, two direction are dominant in doline orientations (NW–SE and NE–SW). However, dominant directions are NE-SW at eastern, NE–SW and NW–SE at central and NW-SE at western part of the Central Taurus. According to this elongations, doline orientations are formed an arc which is formed by tectonic evolution of the Central Taurus.
•Multi-methodological approach to a unique archive for the Holocene.•Micromorphological descriptions of complex polygenic soils and sediments.•The pedosediments are characterized and compared to ...international taxonomies.•Changes in soil properties are driven by parent material, relief, and land use.•These are isolated and their influence is discussed in detail.
Sinkholes in the karstified Middle Keuper gypsum of Middle Franconia, Germany reveal numerous archeological findings and contain unique pedosedimentary records of landscape development and human-environment interaction during the Holocene. Mudstone-derived high clay contents result in a unique set of sedimentary and pedogenic properties of these archives in that all units exhibit vertic properties. Since the horizons have a polygenic history, detailed field and micromorphological observations, together with laboratory analyses (grain-size, color, iron-pedochemistry) are the basis for a classification of the pedosediments. 14C dating provides a chronological framework for geomorphological stability phases and thus, identified soil-forming processes. The first anthropogenic colluvial sediments of the studied archive date to the Early Bronze Age and accumulated in an initial karst depression. Water saturation caused iron reduction of these early infillings. In the pedosediments above, oxidation and iron release are largely related to changes in the parent material. Here, the soil material contains coarse silt indicating an admixture of nearby periglacial slope deposits. Furthermore, fossil root traces point to a longer time with stable conditions, which we interpret as absence of humans or at least an extended period of fallow land. The superimposed sediments from the Urnfield period are more inhomogeneous and consist of material from the eroded agricultural soils of the surrounding. The characteristics of these pedosediments point to intensified human land use. Pedogenic processes such as iron release, reduction of iron, swelling-shrinking, and short-distance translocation of clay intensively overprinted all soil sediments. In this context, micromorphology proved to be an essential tool for the characterization of polygenesis in terms of soil-forming as well as redepositional processes that lead to the formation of these complex archives. The present study reflects a multi-methodological approach to a rather disregarded type of polygenic terrestrial records. It constitutes an example for the investigation of clayey pedosediments in karstic environments under moderate climate in the frame of a prehistoric geoarcheological setting.
The Moul El Bergui area has been known since the 1980s for its high agricultural potential and its permanent irrigation systems. Recently, this area has suffered significant material damages due to ...collapse dolines that still threaten the population, infrastructures, and agricultural lands. Accordingly, this study was carried out aiming at identifying the mechanism and triggering factors of their occurrence through field inventory and geophysical survey. In the first phase of this study, a collapse dolines inventory was established using geomorphological approach. From the nine inventoried collapse dolines, five have occurred before 1962 and four have occurred after this date. They were found to be aligned following the main tectonic structures characterizing the study area. Moreover, the comparison of aerial photographs revealed that their diameters have expanded from 1962 to 2010. In the second phase, six electrical resistivity tomography profiles (ERT) were performed between the identified collapse dolines. The ERT profiles highlighted the different hydrogeological karst components and proved the presence of underground cavities as well as fractured zones. The obtained results demonstrate that the collapse dolines around the study area occur as a result of various associated processes, including dissolution of carbonated bedrock and presence of fractures and/or faults that facilitate the infiltration of meteorological and irrigation waters. Therefore, the heavy rainfall, consolidated dunes barriers, and irrigation may be considered as the main triggering and aggravating factors to collapse dolines occurrence. For this reason, using collapse dolines as outlets for any irrigation canal or floodwaters will necessarily lead to a faster and significant erosion process. In this case, catastrophic collapse and significant subsidence may occur in areas subject to water flows from irrigated plains.
•Collapse dolines occurrence was assessed using electrical resistivity tomography.•Collapse dolines were aligned following the main tectonic structures.•ERT profiles proved the presence of underground cavities and fractured zones.•Results indicated that hydrologic and geologic factors promote ground collapsing.•Using collapse dolines as outlets for excess water accelerated the erosion process.
Dolines are important features strongly influencing the outcomes of groundwater vulnerability maps, subsidence risk and land use studies. Their relationship with subsurface features like epikarst, ...stresses the importance of doline mapping for environmental and hydrological management strategies. Current methodologies to map dolines from elevation models apply morphometric attributes on depressions, including a depth threshold, to filter depressed areas and to define dolines. However, the use of a single threshold tends to overlook dolines located in already depressed areas. In this work a new geographic information systems (GIS)-based methodology is proposed to identify karst depressions within digital elevation models, applying a multidepth threshold approach. The method statistically classifies depression intervals to identify dolines at variable depths. The method was tested in the Yucatan karst, displaying a final accuracy of 63% after testing different parameters. The results are affected by false positives due to the impossibility of verifying by imagery 190 possible dolines in areas of dense vegetation. Nevertheless, out of 655 estimated dolines, 464 match those located by imagery giving sensitivity and precision values of 85% and 71%, respectively. Comparing this methodology against single threshold outcomes, improvement is evident in doline mapping. Notwithstanding, its application and performance with lower and higher resolution elevation models must be investigated.
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