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25 results for “Height estimation”
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)
<p>This repository contains:</p> <ul> <li>a folder called "_toReproduceResults" containing data, script and methodology to reproduce most of the work described in the research manuscript,</li> <li>the main output of the research work: 24 folders (each of them corresponding to a French city), containing building geometry footprints and their corresponding building height as well as averaged building height value aggregated at rectangular grid cell (100 m by 100 m). The footprint geometries comes from the OpenStreetMap project and the building height has been estimated using a RandomForest algorithm using as independent variables indicators describing the building size and shape and the building environment. The data has been produced using the GeoClimate Software (version 0.0.1).</li> </ul> <p>A more detailed description of the content can be used in the file "Metadata.csv".</p>
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></p><p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p><p><strong>Citation:</strong></p><p>Use of these data require citation of this dataset:</p><p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></p><p>Original research article:</p><p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data.</p> <p>See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong></p> <p>Use of these data require citation of this dataset:</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869</p> <p>Original research article:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p>
A simple method to estimate capture height biases at landbird banding stations: opportunities and limitations
<p>Mist-nets are one of the most important tools for the capture of wild birds in ornithological research. The probability of capturing birds may vary by net height, which may drive capture biases. Such biases are rarely estimated, likely because of the relatively high cost and effort associated with constructing and operating elevated mist-net rigs where multiple mist-nets are stacked above one another. Therefore, a low-cost and -effort method to collect capture height data may allow broader investigation and better accounting of potential bias in existing banding protocols. Here, we investigate whether recording net panel of capture (with net panels indicating capture height, e.g., "upper panel") in ground-level mist-nets provides sufficient information to estimate capture height biases and compare these estimations to those obtained with traditional elevated mist-net rigs. Of the 29 taxa analyzed, we detected elevated capture biases for 11 (37.9%) and ground-level capture biases for seven (24.1%). When compared to estimates derived from elevated mist-net rigs at the same study site, we found high agreement with ground-level biases (75.0%) and low agreement with elevated biases (23.1%). These results suggest panel height of ground-level nets is a reliable method to estimate ground-level biases; however, scale of sampling may influence elevated biases, particularly for species that center their activity at the mid-story. Recording panel height may be quickly integrated into a station's processing protocols and broader application may improve our understanding of these biases.</p>
Digisonde Data files used for JGR-Space Physics paper "A simplified method of true height analysis to estimate the real height of sporadic E layers"
<p>paper submitted for publication in JGR-Space Physics.</p> <p>Digisonde Data files used for analysis</p> <p> </p> <p>A simplified method of true height analysis to estimate the real height of sporadic E layers</p> <p> </p> <p>Christos Haldoupis</p> <p>Department of Physics, University of Crete, Heraklion, Greece</p> <p>Haris Haralambous</p> <p>Frederick University and Frederick Research Center, Nicosia, Cyprus</p> <p>Chris Meek</p> <p>Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, SK, Canada</p>
Geodetic height changes at proposed IHRF sites estimated from GRACEGRACE-FO data
<p>This data provide information about geodetic heights change used to obtain all results presented in the article "<span>Yadeta S.M., </span><strong><span>Godah W.</span></strong><span>, Szelachowska M., Fotopoulos G., (2023): </span><span> <em>Assessment of temporal variations of orthometric/normal heights at proposed International Height Reference Frame sites using GRACE/GRACE-FO</em>. Survey Review. </span><span><a href="https://doi.org/10.1080/00396265.2023.2293367"><span>https://doi.org/10.1080/00396265.2023.2293367</span></a></span>" </p>
Supplementary Materials: Use of Sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest
<p>Supplementary materials for the paper:</p> <p>Use of Sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest<br> Paper DOI: <a href="https://doi.org/10.1016/j.ecoinf.2022.101680">https://doi.org/10.1016/j.ecoinf.2022.101680</a></p> <p>This release is the one used for the definitive version of the article.</p>
Seismic analysis of the detachment and impact phases of a rockfall and application for estimating rockfall volume and free-fall height
<p>Digital Elevation models of the Mount Granier and Mount Saint-Eynard.</p> <p>Mount Saint-Eynard DEMs were carried out using an Optech Ilris-LR laser scanner.</p> <p>Mount Granier DEMs were carried out by photogrammetry.</p> <p> </p>
Satellite and LiDAR imagery for canopy height and NDVI estimation for mangroves in Puerto Rico
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Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
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A simple method to estimate capture height biases at landbird banding stations: opportunities and limitations
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Global canopy top height estimates from GEDI LIDAR waveforms for 2019
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.</p> <p>The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>contains more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research article. These citations are as follows:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852</p> <p> </p>
Preprocessed adult height GWAS summary statistics and estimated LD matrices
<p>This dataset contains preprocessed adult height genome-wide association study (GWAS) summary statistics and estimated linkage disequilibrium (LD) matrices, which were created and analyzed in the following publication.</p> <p>Zhu, Xiang; Stephens, Matthew. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann. Appl. Stat. 11 (2017), no. 3, 1561--1592. doi:10.1214/17-AOAS1046. https://projecteuclid.org/euclid.aoas/1507168840</p> <p>For more information on this dataset, please see https://stephenslab.github.io/rss/Height2014.</p>
Dataset used in the paper 'Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca'
<p>The dataset here includes GLD360 lightning data and LF data analyzed in the paper "Virtual reflection height of nighttime equatorial ionosphere estimated with low-frequency magnetic sferics measured in Malacca".</p>
Assessing a Height Artificial Intelligence Algorithm to Estimate Height of Children
ClinicalTrials.gov study NCT06578338. IPD Sharing: NO. Countries: 1. Publications: 2.
Estimation of Effective Dose 95 (ED95) of Intrathecal Isobaric 2-chloroprocaine (2-CP) Based on the Height (cm) of a Patient Undergoing Ambulatory Knee Arthroscopy
ClinicalTrials.gov study NCT03882489. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Canopy height distributions and estimated above-ground biomass across a tropical rain forest landscape in Costa Rica, 1992-2018
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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.