data for MS

description: The purpose of this project is to use high resolution color infra red digital imagery during peak vegetative growth in order to develop a cover map for the Mississippi River flood plain. This information will be used to identify changes within the basin over the past 10 years (time since the last systemic imagery was collected and analyzed by the Service and USGS), strategically guide biological programs in support of natural resource conservation, and assist decision makers from the FWS, COE, USGS, states, and NGO's et al in making science-based decisions in the collaborative management of the resources within the Mississippi River Basin.; abstract: The purpose of this project is to use high resolution color infra red digital imagery during peak vegetative growth in order to develop a cover map for the Mississippi River flood plain. This information will be used to identify changes within the basin over the past 10 years (time since the last systemic imagery was collected and analyzed by the Service and USGS), strategically guide biological programs in support of natural resource conservation, and assist decision makers from the FWS, COE, USGS, states, and NGO's et al in making science-based decisions in the collaborative management of the resources within the Mississippi River Basin.

Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10−22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10−8).Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.

Appendix for Master's thesis. Additional graphs for the ones exemplary printed in the thesis

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected as part of the Hyperspectral Infrared Imager (HyspIRI) mission's preparatory airborne campaign during four flights aboard a DOE B-200 and a NASA ER-2 aircraft over California and New Mexico, U.S., 2008-10-20 to 2008-10-29. A focus of this data collection was the USDA Jornada Experimental Range (Jornada) in southern New Mexico. To complement the programs of ground measurements, JORNEX (JORNada EXperiment) began in 1995 to collect remotely sensed data from aircraft and satellite platforms to provide spatial and temporal data on physical and biological states of the Jornada rangeland. JORNEX uses remote sensing techniques to study arid rangeland and the responses of vegetation to changing hydrologic fluxes and atmospheric driving forces. This deployment was coordinated by NASA's Dryden Flight Research Center (DRFC), renamed Armstrong Flight Research Center in 2014, located in Edwards, California, and the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 30-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.

An orthoimage is remotely-sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthoimagery combines the image characteristics of a photograph with the geometric qualities of a map. The Landsat Mosaic orthoimagery database contains Landsat Thematic Mapper imagery for the conterminous United States. The more than 700 Landsat scenes have been resampled to a 1-arc-second (approximately 30-meter) sample interval in a geographic coordinate system using the North American Horizontal Datum of 1983. Three bands have been selected from the eight spectral bands available for each frame. These are bands 4 (near-infrared), 3 (red), and 2 (green), typically displayed as red, green, and blue, respectively. The image is a full-resolution (spectral and spatial), 24-bit color-infrared composite that simulates color infrared film as a "false color composite". NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This work has been published in the Nature Scientific Data. Suggested citation: Rajib et al. The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. Nature Scientific Data 8, 271 (2021). https://doi.org/10.1038/s41597-021-01048-w

Here, we present the first-available dataset that quantifies land use change along the floodplains of the Mississippi River Basin (MRB) covering 60 years (1941-2000) at 250-m resolution. The MRB is the fourth largest river basin in the world (3.3 million sq km) comprising 41% of the United States and draining into the Gulf of Mexico, an area with an annually expanding and contracting hypoxic zone resulting from basin-wide over-enrichment of nutrients. The basin represents one of the most engineered systems in the world, and includes complex web of dams, levees, floodplains, and dikes. This new dataset reveals the heterogenous spatial extent of land use transformations in MRB floodplains. The domination transition of floodplains has been from natural ecosystems (e.g. wetlands or forests) to agricultural use. A steady increase in developed land use within the MRB floodplains was also evident.

To maximize the reuse of this dataset, our contributions also include four unique products: (i) a Google Earth Engine interactive map visualization interface: https://gishub.org/mrb-floodplain (ii) a Google-based Python code that runs in any internet browser: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing (iii) an online tutorial with visualizations facilitating classroom application of the code: https://serc.carleton.edu/hydromodules/steps/241489.html (iv) an instructional video showing how to run the code and partially reproduce the floodplain land use change dataset: https://youtu.be/wH0gif_y15A

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 254 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

This dataset is an educational toy dataset for object detection with remote sensing data. The dataset contains aerial imagery of the city of Stuttgart at 20cm ground sample distance together with bounding box labels indicating cars (single class) present in these images. Labels are present in the COCO format.

The dataset is split into three parts:* a training split (1000 images),* a validation split (190 images) and* a test split (190 images).

Each image features a height and width of 128 pixels each, contains RGB bands and is stored in the png format. Image data were cropped from aerial imagery of the city of Stuttgart taken in 2021, which are available through Stadtmessungsamt Stuttgart as open data under the CC BY 4.0 license: https://opendata.stuttgart.de/dataset/luftbilder-2021

This dataset has been labeled by Yilsey Terea Benavides Miranda, Khem Raj Devkota, David Michael Udoh and Gökhan Yücesan as part of the Master of Photogrammetry and Geoinformatics course "Remote Sensing Studio" in the 2024 summer term at the Stuttgart University of Applied Sciences.

This is the dataset presented in the paper The Mountain Habitats Segmentation and Change Detection Dataset accepted for publication in the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, USA, January 6-9, 2015. The full-sized images and masks along with the accompanying files and results can be downloaded here. The size of the dataset is about 2.1 GB.

The dataset is released under the Creative Commons Attribution-Non Commercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/legalcode).

The dataset documentation is hosted on GitHub at the following address: http://github.com/fjean/mhscd-dataset-doc. Direct download links to the latest revision of the documentation are provided below:

• PDF format: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.pdf
• Text format: http://github.com/fjean/mhscd-dataset-doc/raw/master/mhscd-dataset-doc.rst

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

These datasets contain all input and output data used for the paper 'Satellite altimetry reveals intensifying global river water level variability'. Detailed descriptions of the datasets and their attributes can be found in the accompanying technical documentation. The code used to generate these datasets is available in our GitHub repository at https://github.com/Fangchq/Satellite-rivers/tree/master.

This dataset is about: Master track from POLAR 6 flight P6_246_HAMAG_2024_2402120201 in 1 sec resolution (zipped, 514 KB).

These files contain classified topo/bathy lidar data. Data are classified as 1 (valid non-ground topographic data), 2 (valid ground topographic data), and 29 (valid bathymetric data). Classes 1 and 2 are defined in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. These data were collected by the Coastal Zone Mapping and Imaging Lidar (...

The data content of remote sensing (RS) images of sea surface temperature (SST) and normalized water-leaving radiance (nLw), for the year 1998, with respect to the River Rhine plume, is investigated. Questions that this study tries to answer are: is it possible to identify the plume from the available RS images, and under which conditions is this possible? How much information on the plumes behaviour can be derived from these images? Does or can this information contribute to our general knowledge of the plume? The images provide a spatial resolution of I km2 and a temporal resolution of I or 2 images per day per sensor for nLw and SST, respectively (in the case of a cloudless atmosphere). In the presence of clouds, no signal is detected for the area of surface water underneath the clouds. Two hypotheses are set up to explain how the RS images can be used to trace the plume. In the hypotheses links are established between salinity gradients that delimit the plume and SST and nLw respectively. The results are based on these hypotheses. From the available images, 9 SST images in spring provide detailed information on the stratified plume and allow for derivation of indirect information on sub-surface processes. In winter the temperature gradients as visible on SST imagery seem to indicate the broad plume patterns. From the nLw images it was not possible to identify the boundaries of the plume. However it is expected that the nLw images are an excellent source for monitoring suspended particulate matter (SPM) in the North Sea. The general conclusion of this study is that the RS data used in this project provide a valuable source of information, with respect to the Dutch coastal zone, in addition to the currently available measurement techniques and computer models. The SST imagery turns out to be particularly useful for tracing stratification, whereas nLw imagery seems to be an excellent source for monitoring SPM in the North Sea. For detailed monitoring of the DCZ and the plume, increased spatial and temporal resolutions are required.

All used raw remote-sensing data, processed data and result data of the Master thesis project of Lorenz Fischer (M.Sc. Geosiences program).
Thesis titel: Detecting common seal haul-out sites from remote-sensing data, in the Lower Saxon Wadden Sea National Park, Germany.

Written at the University of Tuebingen (Faculty of Science, Department of Geosciences, Work group Environmental System Analysis and Work group Geoinformatics).
In cooperation with the German Aerospace Center (DLR) (German remote-sensing data center (DFD), Department of Geo-Riscs and Zivil Security (GZS), Work group Natural Hazards) and the Lower Saxon Wadden Sea National Park Authority.

This dataset is about: Master tracks in different resolutions from POLAR 6 flight P6_246_HAMAG_2024_2402180501. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.967666 for more information.

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map