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22 results for “Sinkholes”
Figure 1 in First evidence of parasitation of a Bosmina (Cladocera) by a water mite larva in a karst sinkhole, in Quintana Roo (Yucatán Peninsula, México)
Figure 1 Water mite larva (Unionicola sp.) attached to a water flea (Bosmina tubicen). The scale bar indicates 50 μm.
Figure 2 A in First evidence of parasitation of a Bosmina (Cladocera) by a water mite larva in a karst sinkhole, in Quintana Roo (Yucatán Peninsula, México)
Figure 2 A – Lateral view of the Unionicola larva, frontal view on the Bosmina. B – Close up of perforations made by pedipalps and chelicerae of the water mite in the valve of the water flea. Scale bars indicate 50 μm.
An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes
<p>The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).</p> <p> </p>
Dataset Railway sinkhole (leveling, GPR, TLS)
<p>Dataset of the paper titled: Sinkhole Subsidence Monitoring Combining Terrestrial Laser Scanner and High-Precision Leveling</p>
Musfur sinkhole (Qatar)
Musfur sinkhole is also know as Dhal Al Misfir (دحل المسفر). Coordinates: 25.175175728759516, 51.21170898510756 Source: Objaverse 1.0 / Sketchfab
Rapid Karstification Process with Evaporite-Driven Sinkholes in Southern Kohat Basin, NW Pakistan
<p>This file consists of datasets (GIS files, InSAR Velocities, Electrical resistivity data, and profiles) used in our study. </p>
Figure 5 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 5. The canonical correspondence analysis between liverwort community distribution and environmental factors: humidity, temperature and light levels.
Figure 3 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 3. Variation in abundance of species, genera and families recorded in liverwort communities at different depths.
Figure 2 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 2. The variation in diversity and the number of species, genera and families recorded in liverwort communities at different depths.
Figure 1 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 1. Diagram of Monkey-Ear Sinkhole.
Figure 4 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 4. The vertical distribution of liverwort communities at different depths.
Terrestrial and aerial photos, GCPs and derived point clouds of a sinkhole in Northern Thuringia
<p>Aiming at comparing the results of using either terrestrial or aerial photos for structure from motion photogrammetry of a sinkhole in Northern Thuringia we took these photos in 2017, 2018 and 2019 and surveyed ground control points. Therefore, the data allows both, the comparison of the results of using terrestrial or aerial photos for the 3D reconstruction, and the mulit-year monitoring of geomorphic changes within the sinkhole.</p>
Figure 5 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 5 Stenoninereislackeyi (Hartman, 1958) comb. n. A–F non-type specimen (USNM 45699) A whole specimen, dorsal view B left jaw, dorsal view C Chaetiger 2, right parapodium, anterior view D Chaetiger 6, right parapodium, anterior view E Chaetiger 17, right parapodium, anterior view F Chaetiger 23, right parapodium, anterior view G Chaetiger 29, right parapodium, anterior view. Scale bars: 1 mm (A); 50 µm (B); 0.1 mm (C–G).
Figure 2 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 2 Stenoninereismartini Wesenberg-Lund, 1958 A, C–F syntypes (USNM 29726) B non-type (USNM 61623) A whole specimens, dorsal view B whole specimen, dorsal view C close-up of prostomium, dorsal view D chaetiger 6, left parapodium, anterior view (dorsal cirrostyle incomplete) E chaetiger 13, left parapodium, anterior view F chaetiger 18, left parapodium, anterior view. Scale bars: 0.5 mm (A–B); 0.1 mm (C–F).
Figure 1 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 1 Morphology of Stenoninereis species A non-type of S.lackeyi comb. n. (USNM 53273) B, I syntypes of S.elisae sp. nov. (USNM 55366) C–F non-type of S.elisae sp. nov. (USNM 55360) G non-type of S.martiniWesenberg-Lund 1958 (USNM 61623) H holotype of S.tecolutlensis de León-González & Solís-Weiss, 1997 (USNM 174870) A anterior end, dorsal view B chaetiger 6, left parapodium (solid and dashed red lines: vessels; solid and dashed light blue lines: unknown structures, likely nerves) C shaft of notopodial sesquigomph spinigers, chaetiger 20 D shaft of neuropodial supra-acicular sesquigomph spiniger, same chaetiger E shaft of neuropodial sub-acicular heterogomph spiniger, chaetiger 20 F Shaft of neuropodial sub-acicular heterogomph falciger, chaetiger 20 G–I anterior ends, dorsal view. Abbreviations: AC, anterior cirri; An, antennae; Cp, cirrophore; Cs, cirrostyle; DC, dorsal cirrus; Es, esophagus; NeL, neuroacicular ligule; NoD, notopodial dorsal ligule; NoV, notopodial ventral ligule; Ph, pharynx; Pp, palpophore; Ps, palpostyle; VC, ventral cirrus; ¿?, unknown structures, likely nerves by their position. Scale bars: 0.2 mm (A, G–I); 0.1 mm (B); 5 µm (C–F).
Figure 4 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 4 Stenoninereislackeyi (Hartman, 1958) comb. n. A–J paratype (AHF-POLY-806) A whole specimen, dorsal view B anterior end, dorsal view C posterior end, dorsal view D notopodial sesquigomph spiniger, chaetiger 27 E supra-acicular sesquigomph spinigers, chaetiger 27 F sub-acicular heterogomph spiniger, chaetiger 27 G sub-acicular heterogomph falcigers (uppermost one at the left), chaetiger 27 H chaetiger 7, right parapodium, anterior view I chaetiger 19, right parapodium, anterior view J chaetiger 26, right parapodium, anterior view. Scale bars: 1 mm (A); 0.25 mm (B–C); 10 µm (D–G) 0.1 mm (H–J).
Figure 3 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 3 Stenoninereismartini Wesenberg-Lund, 1958 A–J non-type specimens (USNM 61623) A chaetiger 2, right parapodium, anterior view B chaetiger 9, right parapodium, anterior view C chaetiger 21, right parapodium, anterior view D chaetiger 27, right parapodium, anterior view E chaetiger 28, left parapodium, anterior view F notopodial sesquigomph spinigers, chaetiger 28 G supra-acicular sesquigomph spinigers, chaetiger 28 H sub-acicular heterogomph spiniger, chaetiger 28 I sub-acicular heterogomph spiniger, chaetiger 28 J left jaw, dorsal view. Scale bars: 50 µm (A, D); 0.1 mm (B–C); 10 µm (F–I); 50 µm (J).
Figure 6 from: Conde-Vela VM (2019) Sinkhole and brackish water nereidid polychaetes: Revision of Stenoninereis Wesenberg-Lund, 1958 (Annelida). Subterranean Biology 30: 95-115. https://doi.org/10.3897/subtbiol.30.36273
Figure 6 Stenoninereiselisae sp. nov. A–M Syntypes (USNM 55366) A whole specimen, dorsal view B whole specimens, dorsal view C anterior end, dorsal view D Posterior end, dorsal view E chaetiger 6, right parapodium, anterior view F chaetiger 16, right parapodium, anterior view G chaetiger 18, right parapodium, anterior view H chaetiger 24, right parapodium, anterior view I subacicular heterogomph spinigers, chaetiger 18 J–L subacicular heterogomph falcigers, chaetiger 18 M notopodial homogomph spiniger, chaetiger 49. Scale bars: 0.5 mm (A–B); 0.25 mm (C); 0.2 mm (E–H); 10 µm (I–L); 30 µm (M).
Karst Sinkhole Detecting and Mapping Using Airborne LiDAR
<p>Corresponding data set for Tran-SET Project No. 18GTUNM01. Abstract of the final report is stated below for reference:</p> <p>"The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training.</p> <p>The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise."</p>
Mangrove sinkholes (cenotes) of the Yucatan Peninsula, a global hotspot of carbon sequestration
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