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154 results for “multispectral”
Multispectral absorbance and fluorescence analysis of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water years 2017 and 2018 (1 Oct 2016 - 30 Sep 2018)
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are multispectral absorbance and fluorescence analyses of organic carbon dissolved in samples of well-mixed river thalweg water. Data include excitation-emission matrices, absorbance spectroscopy, as well as absorbance and fluorometric summary indices calculated at specific wavelengths of excitation and emission. Data are from the 2017 and 2018 water years (1 Oct 2016 to 30 Sep 2018). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA. These data are a correction of a previously published data product (doi:10.6073/pasta/6ba30f4ebb63175a4399c5d0aa6a8698). Inconsistencies between availability of EEMS data, absorbance data, and fluorometric summary metrics have been corrected. P
Multispectral absorbance and fluorescence analysis of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)
The Upper Clark Fork River (UCFR) Long Term Research in Environmental Biology (LTREB) umbrella monitoring project generating these data is conducted separately and complementarily to the 200-million-dollar (USD) superfund project for ecological restoration of the UCFR, associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the UCFR in western Montana includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river’s floodplain closest to contaminant sources. The UCFR LTREB project includes bi-weekly water quality monitoring across the first 200 km of the river and its major tributaries along a gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The monitoring program began in 2017 with funding likely to be extended through 2028. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are multispectral absorbance and fluorescence analyses of organic carbon dissolved in samples of well-mixed river thalweg water. Data include excitation-emission matrices, absorbance spectroscopy, as well as absorbance and fluorometric summary indices calculated at specific wavelengths of excitation and emission. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at 13 project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
Spectral and Chemical Dataset for Ripeness Monitoring in cv. Tempranillo Grapes Using a Multispectral Sensor
<p><strong><span>The dataset consists of 1010 samples of Tempranillo grape berries, offering a comprehensive record of spectral and chemical measurements that serve as a valuable resource for evaluating berry ripeness and sugar content (ºBrix). Each row in the dataset corresponds to a single berry, and the columns include a unique identifier (ID), the date of sampling (spanning 21 different days during the ripening period in 2024), expressed as Day of the Year (DOY) from DOY 210 to DOY 284. The dataset also includes measurements for nine spectral bands which represent the reflectance values recorded by the sensor (F1–F8 and NIR), a dedicated channel to detect ambient light flicker (CLEAR), and the sugar content (</span><span>°Bx</span><span>), ranging from 4.8 to 45 </span><span>°Bx</span><span>, encompassing all maturity stages from early ripeness to over-ripeness. The dataset is structured so that rows correspond to individual berries, and columns represent the measured variables, enabling statistical and machine learning analyses</span></strong></p> <p><strong><span>Center wavelength (λp) (F1: 415 nm, F2: 445 nm, F3: 480nm, F4: 515nm, F5: 555nm, F6: 590nm, F7: 630nm, F8: 680nm)</span></strong></p>
Data for estimating spruce tree health using drone-based RGB and multispectral imagery
<p>The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a> </p> <p>The image data was acquired between 11th and 14th September 2020.</p> <p>RE = Red-Edge M multispectral data<br>RGB = RGB data (Phantom 4 Pro)<br>Altum = Altum multispectral data</p> <p>The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively.</p> <p>The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above.</p> <p>Key for Field Reference:</p> <p>Z = tree height<br>dbh = diameter-at-breast-height (cm)<br>vari = Discoloration (score 0-5)<br>harsu = Defoliation (score 0-4)<br>pihka = Resin flows (score 0-2)<br>runko = Stem/bark structural damage (score 0-2)<br>latvus = Significantly decreased canopy size (score 0-1)</p>
Supplement for Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland
<p>Supplement to Jackisch et al., 2021: Drone-based magnetic and multispectral surveys to develop a 3D model for mineral exploration at Qullissat, Disko Island, Greenland.</p> <p><a href="https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html">https://se.copernicus.org/articles/13/793/2022/se-13-793-2022.html</a></p> <p>Data set contains 3D model in dxf file, additional images, selected handheld spectra.</p> <p>Publication summary:</p> <p>We integrate UAS-based magnetic and remote sensing mineral exploration data with legacy exploration data of a Ni-Cu-PGE prospect on Disko Island, West Greenland. The basalt unit has a complex magnetization, and we use a 3D magnetic vector inversion on the UAS magnetics to estimate magnetic properties and spatial dimensions of the mineralized unit. Our 3D modelling reveals a horizontal sheet and a strong remanent magnetization component. We highlight the advantage of UAS in rugged terrain.</p> <p> </p>
Multispectral Images of the Kiev Folia
<p>Multispectral Images of the Kiev Folia (F. 301, Nr. 328 P), acquired at the Kiev National Library in 2018.</p> <p>Detailled descriptions of image acquisition, processing and usage are found in<em> Images_Kiev-Folia_zenodo_1.0.pdf </em></p>
SD4EO: AI-based synthetic satellite multispectral agricultural textures in Spain (Oct 2017 - Sep 2018)
<p>This dataset has been created as part of the deliverables for ESA’s <a title="https://eo4society.esa.int/projects/sd4eo/" href="https://eo4society.esa.int/projects/sd4eo/" target="_blank" rel="noopener">SD4EO project.</a> It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types:</p> <ul> <li> Barley.</li> <li> Wheat.</li> <li> Other grain leguminous.</li> <li> Peas.</li> <li> Fallow & Bare soil.</li> <li> Vetch.</li> <li> Alfalfa.</li> <li> Sunflower.</li> <li> Oats.</li> </ul> <p>The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset.</p> <p>The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season.</p> <p>In <em>this</em> dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area.</p> <p>Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data.</p> <p>File names are structured as follows:</p> <ul> <li>Prefix "HO" indicating the synthesis method</li> <li>The crop type name: <ul> <li>Barley</li> <li>Wheat</li> <li>OtherGrainLeguminous</li> <li>Peas</li> <li>FallowAndBareSoil</li> <li>Vetch</li> <li>Alfalfa</li> <li>Sunflower</li> <li>Oats</li> </ul> </li> <li>Year/Month/01 (representing the start of the month period)</li> <li>Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)</li> <li>Number of the synthesis variant</li> </ul> <p>The generation parameters for all images include:</p> <ul> <li>Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)</li> <li>4 levels of depth in the Steerable pyramid</li> <li>6 orientations in the Steerable pyramid</li> <li>14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.</li> <li>30 iterations</li> </ul> <p>A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses).</p> <p>Thus, the following files can be considered partially failed syntheses:</p> <ul> <li>HO_Alfalfa_20180801_768_1.nc</li> <li>HO_FallowAndBareSoil_20180101_768_3.nc</li> <li>HO_OtherGrainLeguminous_20171201_256_4.nc</li> <li>HO_Vetch_20180301_384_3.nc</li> </ul> <p>Files are encoded in the standardized net4CDF format [<a href="https://unidata.github.io/netcdf4-python/">link</a>], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period.</p> <p>The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size:</p> <ul> <li>'B02' 10m (Sentinel-2)</li> <li>'B03' 10m (Sentinel-2)</li> <li>'B04' 10m (Sentinel-2)</li> <li>'B08' 10m (Sentinel-2)</li> <li>'B05' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B06' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B07' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B11' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B12' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'B8A' originally 20m, resampled to 10m (Sentinel-2)</li> <li>'VH' also resampled to 10m (Sentinel-1)</li> </ul> <p>The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid.</p> <p>Additionally, the following metadata are stored as xarray attributes:</p> <ul> <li>"long_name": corresponding to the crop type name</li> <li>"date": the period of the original data used as the base image for synthesis</li> <li>"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia</li> <li>"synthetic_method": corresponds to the high-order constrained method</li> <li>"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast</li> </ul> <p>A total of:</p> <p><strong> 9</strong> types of crops x <strong>12</strong> months x <strong>6</strong> variants = <strong>648</strong> synthetized multispectral textures</p> <p>occupying <strong>34.5</strong>GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license.</p> <p>The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.</p> <p> </p>
Snow-Cloud Validation Masks for Multispectral Satellite Data.
<p>Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.</p>
Codes and test datasets developed for Mapping paleolacustrine deposits with a UAV-borne multispectral camera: Implications for future drone mapping on Mars.
<p>NASA’s Ingenuity Mars Helicopter has ushered in a new era in planetary exploration by utilizing Unmanned Aerial Vehicles (UAVs) to enhance our understanding of planetary surfaces. This project evaluates the potential of UAVs for mapping Martian environments, using Lake Natron, Tanzania, as an analog for Martian paleolakes.</p> <p>During two field seasons (January and July 2023), we employed a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera and a TerraSpec Halo VNIR-SWIR spectrometer to capture high-resolution imagery and spectral data. Almost all image processing and analysis were performed using Python scripting, except for image mosaic and Digital Elevation Model (DEM) generation.</p> <p>We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. </p> <p>In this repository, we share all the code developed during our study. Processing steps include,<br>1. DN to radiance conversion<br>2. Panel radiance extraction<br>3. Calculate reflectance factors using DLS data<br>4. Calculate reflectance at MicaSense band<br>5. Convert radiance to reflectance using 1 point empirical line method (1p ELM)<br>6. Convert radiance to reflectance using 2 point empirical line method (2p ELM)<br>7. Atmospheric correction using 6SV method<br>8. Convert radiance to reflectance using DLS data<br>9. Calculate Band indices<br>10. Weighted Kmean clustering<br>11. Finding the optimal number of clusters using the elbow method<br>12. Cmean clustering</p> <p>We also included sample image data used in the study. Feel free to contact us for more information/data.</p>
Multispectral Spectral Imaging dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm </strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <ul> <li>Images captured using a <a href="https://photography.phaseone.com/xf-camera-system/">PhaseOne XF Multispectral Camera System.</a> <ul> <li>Image filenames are arranged as postacards_postcardmsi-<strong>Postcard</strong>- <strong>(Wavelength No.)(Filter)</strong>_*sequence order number*_R.tif where wavelength number is the nominal central illumination wavelength in nm (365, 385, 410, 420, 450, 480, 510, 550, 600, 630, 640, 660, 740, 850, 940), Filter is the colour of the long-pass filter (N - no filter, I - Infrared filter, G - Green filter, R - Red filter) and sequence order number is a count from 0001 denoting the order in which the image was acquired)</li> <li>Complementary flats for each of the object images, used typically to process even illumination distribution, captured of white, flat, smooth, non-chemically processed imaging standard flat paper with the same naming convention as above. </li> </ul> </li> <li>postcard_postcardmsi-Postcard.json - Metadata read out collected from MS camera system</li> <li>Truecolour RGB reference image</li> </ul>
Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629
<p>In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
UAV multispectral imagery dataset over a vineyard affected by Botrytis in 'Tomiño', Pontevedra, Spain. It includes GPS location of vine trunks, diseases and GCP points.
<p>This dataset contains a set of ground data and four flights captured on grape harvest over a vineyard affected by Botrytis cinerea. UAV flights took place on 16 September 2021, at 30 m height and using different angles (0, 30, 45 degrees). Pictures were taking using a Micasense RedEdge 3 sensor and were calibrated using the provided Micasense reflectance panel. The flight path was programmed to fly in autonomously, following manufacturer’s instructions (DJI). The dataset includes a shapefile with the GPS location of vine trunks, bunches affected by Botrytis and GCP points.</p>
Optimising multispectral active fluorescence to distinguish the photosynthetic variability of cyanobacteria and algae
<p>Dataset underlying the following paper:</p> <p>Courtecuisse, E.; Marchetti, E.; Oxborough, K.; Hunter, P.D.; Spyrakos, E.; Tilstone, G.H.; Simis, S.G.H. Optimising Multispectral <br> Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae. Sensors 2023, 23</p> <p>This study assesses the ability of a new active fluorometer, the LabSTAF, to diagnostically assess the physiology of freshwater cyanobacteria in a reservoir exhibiting annual blooms. Specifically, we analyse the correlation of relative cyanobacteria abundance with photosynthetic parameters derived from fluorescence light curves (FLCs) obtained using several combinations of excitation wavebands, photosystem II (PSII) excitation spectra and the emission ratio of 730 over 685 nm (Fo(730/685)) using obtained with excitation protocols with varying degrees of sensitivity to cyanobacteria and algae. FLCs captured obtained with blue excitation (B) and green–orange–red (GOR) excitation wavebands capture physiology parameters of algae and cyanobacteria, respectively. The green–orange (GO) protocol, expected to have the best diagnostic properties for cyanobacteria, did not guarantee PSII saturation. PSII excitation spectra showed distinct response from cyanobacteria and algae, depending on spectral optimisation of the light dose. Fo(730/685), obtained using a combination of GOR excitation wavebands, Fo(GOR, 730/685), showed a significant correlation with the relative abundance of cyanobacteria (linear regression, p-value < 0.01, adjusted R2 = 0.42). We recommend using, in parallel, Fo(GOR, 730/685), PSII excitation spectra (appropriately optimised for cyanobacteria versus algae), and physiological parameters derived from the FLCs obtained with GOR and B protocols to assess the physiology of cyanobacteria and to ultimately predict their growth. Higher intensity LEDs (G and O) should be considered to reach PSII saturation to further increase diagnostic sensitivity to the cyanobacteria component of the community.</p>
Multispectral Images of the Euchologium Sinaiticum, Pars Nova (Codex Sin. Slav. NF 1)
<p>Multispectral Images of the Euchologium Sinaiticum, Pars Nova (Codex Sin. Slav. NF 1), acquired in St. Catherine's Monastery, Egypt, in 2007. Detailled descriptions of image acquisition and usage are found in<em> NF1_Images_Documentation.pdf</em>.</p>
Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.
<p>This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."</p>
Synthetic multispectral & multitime data
<p>The synthetic image dataset 'mixed_4obj_time.tif' comprises four colour channels and 60 timepoints.</p> <p>It can be used to demonstrate spectral mixing/unmixing over time.</p>
Raw data for "Fluorescence crosstalk reduction by modulated excitation-synchronous acquisition for multispectral analysis in high-throughput droplet microfluidics."
<p>Raw data to quantify the crosstalk reduction and signal resolution improvement by MESA used in Figure 3 and 4.</p> <p><br> </p>
Generating Imperviousness Maps from Multispectral Sentinel-2 Satellite Imagery
<p>This dataset contains a list of Sentinel-2 tiles covering Italy for the year 2017. For each tile, a corresponding ground truth GeoTIFF is present which contains a clip of the soil consumption provided by ISPRA (<a href="https://www.isprambiente.gov.it">https://www.isprambiente.gov.it</a>).</p> <p>Dataset can be used to train a Machine Learning model to extract imperviousness maps using Sentinel-2 satellite images.</p> <p>More details can be found reading the paper: </p> <p>Giacco, G., Marrone, S., Langella, G., & Sansone, C. (2022). ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery. <em>Future Internet</em>, <em>14</em>(10), 278.</p>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</p>
Data supplementing the paper: "Teachability Of Multispectral Optoacoustic Tomography"
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Allen Brain Atlas
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OpenNeuro
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