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97 results for “remote assessment”
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Datasets of Radwin 2023 Great Salt Lake Remote Sensing Historical Assessment
<p>Included are the culminated datasets for an article in review to be published with the Utah Geological Association. This data helps an investigator reproduce or utilize the data. There are datasets for both Landsat and Sentinel, for both the North and South arms of the Great Salt Lake. Additionally, there are datasets documenting the NDWI threshold used for each Landsat image, the outlier images not used in analyses, NDWI error assessment, and calculation of stats/facts.</p> <p>Paper abstract:</p> <p>The Great Salt Lake has been rapidly shrinking since the highstand of the mid-1980s, creating cause for concern in recent decades as the lake has reached historic lows. Many investigators have assessed the evolution of lake elevation, geochemistry, anthropogenic impacts, and links to climate and atmospheric processes; however, the use of remote sensing to study the evolution of the lake has been significantly limited. Harnessing recent advancements in cloud-processing, specifically Google Earth Engine cloud computing, this study utilizes over 600 Landsat TM/OLI and Sentinel MSI satellite images from 1984-2023 to present time-series analyses of remotely sensed Great Salt Lake water area, exposed lakebed area, surface cover types, and chlorophyll-a analyses paired with modelled estimates for water and exposed lakebed area. Results show that since the highstand of 1986-1987, the water area has declined by 45% (~3,000 km<sup>2</sup>) and the exposed lakebed area has increased to ~3,500 km<sup>2</sup> from ~500 km<sup>2</sup>. The area of unconsolidated sediments not protected by vegetation or halite crusts has risen to ~2,400 km<sup>2</sup>. Significant halite crusts are observed in the North Arm, having a max extent of ~150 km<sup>2</sup> between 2002 and 2003, while only small extents of halite crusts are observed for the South Arm. Vegetation is more prevalent in the Bear River Bay and South Arm, with surface area increases over 400% since 1990. Gypsum is widely observed independent of halite crusts. The results highlight multiple instances of land-use/water-management that led to observable changes in water/exposed lakebed area and halite crust extent. This study demonstrates the important benefits of maintaining a lake elevation above ~4,194 ft to maximize lake and halite crust area, which would help mitigate possible dust events and maintain broad lake extent.</p> <p> </p> <p>Files should be self-explanatory based on filename, where BRB means Bear River Bay. Note there are two video files animating the evolution of the North and South Arms of the Great Salt Lake using satellite imagery from 1984 to 2023. </p> <p> </p> <p>Visit https://github.com/radwinskis for details on code used for this study.</p> <p>Please contact me at markradwin@gmail.com with any questions.</p> <p> </p>
SUPPLEMENTARY DATA TO: Using a citizen science approach to assess nanoplastics pollution in remote high-altitude glaciers
<p>This is the repository of the supplementary data, and it contains the following files: </p> <p>Raw data files as the original output of TD-PTR-ToF-MS for all the samples, all the blanks, all the spikes and all the calibration runs (.h5 files in three zip arcives)</p> <p>Polymer library files (a zip archive including csv files.</p> <p>A data analysis file including raw data, blank subtraction and LOD correction of all measurements (xlsx file).</p> <p>A fingerprinting result file for each plastic type (xlsx file)</p> <p>A data analysis file after plastic fingerprinting (xlsx file). </p> <p> </p> <p> </p>
HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru
<p>The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2021 Data
<p>This is a remote sensing dataset collected in 2021 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 10 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2021.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2021_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p> <p> </p>
Tree mortality in an agricultural landscape of Southwestern Panama assessed using remote sensing and field data
Open the record for dataset details and reuse information.
ASSESSING THE CHLOROPHYLL-A VARIABILITY IN THE GULF OF GUINEA USING REMOTE SENSING DATA.
<h3>Introduction</h3> <p>The report begins by highlighting the importance of oceans in influencing the Earth’s climate and supporting marine life. It focuses on phytoplankton, which are crucial for the marine food web and global carbon cycle. The study aims to evaluate the variability of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Gulf of Guinea using satellite remote sensing data.</p> <h3>Materials and Methods</h3> <ul> <li><strong>Study Site</strong>: The Gulf of Guinea, located on the eastern edge of the Atlantic Ocean, bordered by several West African countries.</li> <li><strong>Data</strong>: Monthly Chl-a and SST data from the Aqua-MODIS satellite, covering the period from 2020 to 2022.</li> <li><strong>Methods</strong>: Analysis of satellite images using Python programming to evaluate spatiotemporal variability and conduct time series analysis.</li> </ul> <h3>Results and Discussion</h3> <ul> <li><strong>Chlorophyll-a Variability</strong>: The study found significant spatial and temporal variability in Chl-a concentrations, with higher values near the coastline due to nutrient inputs from rivers and coastal upwelling.</li> <li><strong>Sea Surface Temperature Variability</strong>: SST showed relatively uniform spatial distribution but notable seasonal and interannual variability, influenced by climatic phenomena like the West African Monsoon.</li> <li><strong>Interannual and Monthly Climatology Variability</strong>: The report discusses the seasonal patterns and the influence of environmental factors on Chl-a and SST.</li> </ul> <h3>Conclusion</h3> <p>The study concludes that Chl-a concentrations are higher near the coast due to nutrient inputs and coastal upwelling, while SST shows a consistent seasonal cycle. These findings provide insights into the dynamic nature of marine productivity in the Gulf of Guinea and the influence of environmental factors on phytoplankton biomass.</p>
Data from: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the processed and extracted metrics from the Dutch land cover, country wide airborne laser scanning and Sentinel-1 and 2 datasets used as input predictor variables in the species distribution modelling step. The study area within the Netherlands comprised five Dutch provinces (Groningen, Drenthe, Overijssel, Gelderland, and Flevoland) for which both ALS and Sentinel data were available for the same year. The land cover metrics were derived using the Dutch land cover map from 2018 (LGN2018 or LGN8). The country-wide LiDAR point clouds were derived from the third Dutch national ALS flight campaign (AHN3, Actueel Hoogtebestand Nederland). The AHN3 dataset is openly accessible data available from (<a href="https://ahn.arcgisonline.nl/ahnviewer/">https://ahn.arcgisonline.nl/ahnviewer/</a>). The Sentinel datasets were processed using Google Earth Engine. </p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- Derived data 2020 & 2021
<p>This is derived data from a remote sensing experiment performed in 2020 and 2021. This repository contains .csv and .R files that can be used to replicate the analysis presented here:</p> <p><a href="https://zenodo.org/record/6859791#.Y-K6ky-B1z8">https://zenodo.org/record/6859791#.Y-K6ky-B1z8</a></p> <p>If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2020 Data
<p>This is a remote sensing dataset collected in 2020 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 5 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2020.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2020_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Remote Assessment of Physical Function
ClinicalTrials.gov study NCT04339959. IPD Sharing: NO. Countries: 1. Publications: 1.
Geriatric Oncology Care in Brazil: Remote Geriatric Assessment-Driven Interventions With Supportive Care
ClinicalTrials.gov study NCT07084454. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Data from: A neighborhood approach for using remotely sensed data to estimate current ranges for conservation assessments
Open the record for dataset details and reuse information.
Assessing Challenges to Virtual Education (FACVE) in Emergency Remote Teaching Situations
Open the record for dataset details and reuse information.
Dataset associated with the manuscript " Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics"
<p>Raw dataset associated with the manuscript " Locally developed models improve the accuracy of remotely assessed metrics as a rapid tool to classify sandy beach morphodynamics"</p>
Bird observation data for: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the bird observation data used in Koma, Z., Seijmonsbergen, A.C., Grootes, M.W., Nattino, F., Groot, J., Sierdsema, H., Foppen, R. & Kissling, W.D. (2022): Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds. <em>Diversity and Distributions</em> 28: 685–699.</p> <p>The content of this directory is shared under Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0, see https://creativecommons.org/licenses/by-nc-nd/4.0/). For accessing the bird occurrence data for further use then reproducing this article you can contact with Henk Sierdsema (Henk.Sierdsema@sovon.nl) and Ruud Foppen (Ruud.Foppen@sovon.nl) for further information.</p>
Quantitative Assessment of the Impact of Future Land Use Changes on Flood Risk Using Remote Sensing, Machine Learning, and a Hydraulic Model
<p> </p> <p>The RF Machine learning code </p> <p>Topological, geomorphology, geology, metrological information of the Tajan watershed.</p> <p>Land use land cover images of the Tajan watershed</p> <p>River, transportation roads, villages map </p> <p>Global damage function datasets.</p>
Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling
<p>This directory contains files related to the MSc graduation research project of Luc van Dijk of the Department of Physical Geography, Utrecht University. The project is titled "<em>Assessing Eolian Snow Redistribution in Din-Gad Catchment, Central Himalaya, using Remote Sensing and Modelling</em>" and was completed on April 14, 2023. Below is a description of the files in this directory.</p> <p><strong>Satellite_imagery.zip</strong><br> Folder containing all the pre-processed satellite images, that were exported from Google Earth Engine. Additional to the standard image bands, the bands 'NDSI', 'SC' and 'ASI' are present. These describe the Normalized Difference Snow Index, the Snow Cover and the Avalanche Susceptibility Index, respectively. The Google Earth Engine pre-processing script can be found here: https://code.earthengine.google.com/b7fe3ca48de410c9a3fff842f88a1e7f</p> <p><strong>SPHY_output_SnowStorage.zip</strong><br> Folder containing the SPHY output maps (variable: SnowStorage in mm) of the study domain.</p> <p><strong>SRCM.py</strong><br> The python-based Snow Redistribution Classification Model.</p> <p><strong>SRCM_output.zip</strong><br> Folder containing the SRCM output files. Each file has 8 bands: (1) snowmelt, (2) snow removal by avalanching, (3) eolian snow removal, (4) unexplained snow removal, (5) snowfall, (6) snow deposition by avalanching, (7) eolian snow deposition, (8) unexplained snow deposition.</p> <p><strong>SRCM_output_aggregated_wind_heatmaps.zip</strong><br> Folder containing the results of SRCM_output.zip, but only bands 3 and 7 and aggregated per month.</p> <p><strong>WindNinja_output_resampled.zip</strong><br> Folder containing the wind fields that were downscaled from ERA5-Land data using WindNinja. The wind fields were converted from vector (speed, direction), to 2-band raster layers (speed, direction) and resampled from 100 m to 30 m resolution.</p>
UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions
<p>Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.</p> <p><strong>UrbAlytics</strong> is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/"><strong>AI4Copernicus</strong></a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/"><strong>Latitudo 40</strong></a> and <a href="https://www.landsrl.com/land-research-lab"><strong>LAND Research Lab®</strong></a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation. </p> <p><strong>The dataset</strong></p> <p>This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of <strong>Milan</strong> and <strong>Naples</strong>, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions.<br> For each city, the dataset contains the following layers for the analysis period 2018-2022.</p> <p><strong> HEATWAVE POTENTIAL RISK (HPR)</strong></p> <p>Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.</p> <p><strong> MICROCLIMATIC PERFORMANCE INDEX (MPI)</strong></p> <p>The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance. </p> <p><strong> PARK COOL ISLANDS (PCI)</strong></p> <p>Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. <strong> </strong>The layer distinguishes between major and minor Park Cool Islands. <em>Major PCI</em> includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer<strong>. </strong><em>Minor PCI</em> includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a></p>
Assessing Functional Capacity in Directly and Remotely Monitored Home-based Settings: A Protocol for a Multinational Validation Study in Individuals With Chronic Respiratory Diseases
ClinicalTrials.gov study NCT06447831. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.