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9 results for “Inland water bodies”
LakeSST: Lake Skin Surface Temperatures in French inland water bodies
<p>The data set LakeSST contains skin surface temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1.2 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the data set makes it an ideal resource for studies on the temporal evolution of lake surface temperatures and for geographical studies of temperature patterns.</p>
Water Body Checklists 2019: Seto Inland Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Seto Inland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Seto Inland Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Seto Inland Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
GLOBMAP SWF: a global annual surface water cover frequency dataset since 2000 for change analysis of inland water bodies
<p>The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes and trends of inland water bodies due to their high seasonal variation and abrupt change. We generated a global annual surface water cover frequency dataset (GLOBMAP SWF) from the MODIS land surface reflectance products to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water observations directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water body and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show reasonable performances on frozen water, saline lake, bright surface and cloud-frequent regions. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water body extent with consideration of its seasonal variation.</p> <p>The GLOBMAP SWF dataset is provided in Version 1.0 (https://zenodo.org/record/6462883#.YxC16HZBw2w). Here we provide the number of MOD09A1 (MODIS 8-day composite land surface reflectance) clear-sky snow/ice-free observations (<em>N<sub>Clear</sub></em>) data as a quality dataset of GLOBMAP SWF product. The clear-sky observation refers to the valid MOD09A1 observation that not covered with clouds and snow/ice. The more available clear-sky observations, the more reliable the estimated SWF.</p> <p>The <em>N<sub>Clear </sub></em>dataset is provided by 296 1200 km × 1200 km tiles at annual temporal and 500 m spatial resolutions in the sinusoidal projection with Geotiff format for each year during 2000-2020. The file is named as "GLOBMAPClearCount. AYYYY001.hHHvVV.V01.tif", where “YYYY” refers to the year of the file, and “HH” and “VV” explains the number of tiles that are the same with MODIS standard tile. The valid range is 0-46, scale factor is 1.0. The <em>N<sub>Clear </sub></em>of permanent water (land obervation count of 46), permanent snow/ice and terrain shadows are set to 50.</p>
Dataset used in "Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle". https://doi.org/10.5194/hess-2017-625.
<p>Dataset used in</p> <p>Bathymetry observations of inland water bodies using a tethered single-beam sonar controlled by an Unmanned Aerial Vehicle</p> <p>Filippo Bandini<sup>1</sup>, Daniel Olesen<sup>2</sup>, Jakob Jakobsen<sup>2</sup>, Cecile Marie Margaretha Kittel<sup>1</sup>, Sheng Wang<sup>1</sup>, Monica Garcia<sup>1</sup>, and Peter Bauer-Gottwein<sup>1</sup></p> <ul> <li><sup>1</sup>Department of Environmental Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark</li> <li><sup>2</sup>National Space Institute, Technical University of Denmark, Kgs. Lyngby, 2800, Denmark</li> </ul> <p><strong>Hydrol. Earth Syst. Sci.</strong></p> <p><strong>https://doi.org/10.5194/hess-2017-625</strong></p> <p> </p> <p>The dataset contains</p> <p>-data/observations that were used to obtain the figures shown in the paper. Data have .mat extension (Binary data container format used by MATLAB; may include arrays, variables, functions, and other types of data;)</p> <p>-scripts to compute statistics and plot data, with .m extension (contain MATLAB code, either in the form of a script or a function)</p> <p>-shape files (shp — shape format; the feature geometry itself, .shx — shape index format, .dbf — attribute format, .prj — projection format; .sbn and .sbx — spatial index of the features, .cpg — used to specify the code page, .<em>qpj</em> QGIS projection file) or raster files (.geotiff) to reproduce the map contents reported in the referenced paper.</p> <p>The repository is subdivided into directories containing the dataset shown in the paper. These directories are named with the figures and/or tables numbers of the referenced paper. </p>
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
<p>Climate warming will impact the sustainability of arid and semi-arid zone environments so we need to understand the influence of changes in arid lands on vertebrate populations. However, biomonitoring and biodiversity assessment in arid environments can be prohibitively time-consuming, expensive, and logistically challenging due to their often remote and inhospitable nature. Sampling of environmental DNA (eDNA) coupled with high-throughput sequencing is an emerging biodiversity assessment method. Here we explore the application of eDNA metabarcoding and various sampling approaches to estimate vertebrate richness and assemblage at human-constructed and natural water sources in a semi-arid region of Western Australia. Three sampling methods: sediment samples, filtering through a membrane with a pump, and membrane sweeping in the water body, were compared using two eDNA metabarcoding assays, 12S-V5 and 16smam, for 120 eDNA samples collected from four gnammas (gnamma: Australian Indigenous Noongar language term – granite rock pools) and four cattle troughs in the Great Western Woodlands, Western Australia. We detected higher vertebrate richness in samples from cattle troughs and found differences between assemblages detected in gnammas (more birds and amphibians) and cattle troughs (more mammals, including feral taxa). Total vertebrate richness was not different between swept and filtered samples, but all sampling methods yielded different assemblages. Our findings indicate that eDNA surveys in arid lands will benefit from collecting multiple samples at multiple water sources to avoid underestimating vertebrate richness. The high concentration of eDNA in small, isolated water bodies permits the use of sweep sampling which simplifies sample collection, processing, and storage, particularly when assessing vertebrate biodiversity across large spatial scales.</p>
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
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FIGURE 1 in Rotifers from inland water bodies of continental Ecuador and Galápagos Islands An updated checklist
FIGURE 1. Map of Ecuador showing number of recorded rotifer families, genera and species in each region.
FIGURE 1 in An annotated checklist of the main representatives of meiobenthos from inland water bodies of Central and Southern Vietnam. I. Roundworms (Nematoda)
FIGURE 1. The study region and schematic map of location of the studied water bodies. I–III – Đǻk Lǻk, Khánh Hòa and Đỗng Nai provinces, respectively. Numbers 1–71 represent the sites listed in the first column of the Table 1.
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