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8 Research products, page 1 of 1

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  • Open Access
    Authors: 
    Das, Antariksha;
    Country: Netherlands
    Project: EC | QIA (820445), NSERC

    Contains all the data (Optical coherence measurements; Optical storage expt; Frequency Multiplexing expt; Single-photon storage expt.), along with the post-processing scripts (Matlab files; Origin files) for the experiment reported in Phys. Rev. Lett. 127, 220502

  • Open Access
    Authors: 
    Jonker, P.G.; Bassa, C.G.; Nelemans, G.; Steeghs, D.; Torres M.A.P.; Maccarone, T.J.; Hynes, R.I.; Greiss, S.; Clem, J.; Dieball, A.; +15 more
    Country: Netherlands
    Project: NSF | Black Hole and Neutron St... (0908789), NSERC

    We introduce the Galactic Bulge Survey (GBS) and we provide the Chandra source list for the region that has been observed to date. Among the goals of the GBS are constraining the neutron star (NS) equation of state and the black hole (BH) mass distribution via the identification of eclipsing NS and BH low-mass X-ray binaries (LMXBs). The GBS targets two strips of 6°x1° (12deg2 in total), one above (1°

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamo, Alemu; Snider, James; Lourenço, Ricardo B.; Kurz, Werner A.;
    Country: Netherlands
    Project: NSERC

    This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution — one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada — the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI. The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.

  • Open Access
    Authors: 
    Harro Jongen; Gert-Jan Steeneveld; Jason Beringer; Andreas Christen; Krzysztof Fortuniak; Jinkyu Hong; Je-Woo Hong; Cor M. J. Jacobs; Leena Järvi; Fred Meier; +6 more
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: AKA | Urban green space solutio... (321527), NSERC

    Data contains meteorological and heat flux observations from eddy-covariance towers at 13 sites in different cities.Data is preprocessed for the analysis to infer water storage capacities, which means they are daily aggregates.

  • Open Access
    Authors: 
    Jake Davidson;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: WT , EC | QIA (820445), NSERC

    Data Supporting the attached paper. Contains many .wfm files used to read most data from devices, as well as the associated .m MATLAB scripts written for analysis of each separated data set.Data included is for the purpose of demonstrating an efficient optical quantum memory.

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamu, Alemu; Snider, James; Arabian, Joyce; Kurz, Werner A.; Finkelstein, Sarah;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with total C stored in plants of forested areas in Canada (AGB, BGB and dead plants) in kg/m² and C stock uncertainty. To estimate the C stored in plants of forest areas, we used 47,967 ground measurements of AGB measures and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. Different models were trained using a recursive feature elimination, random forest scheme and a 5-fold cross-validation assessment. The model with higher R² and lowest root mean square error (RMSE) was used for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the maps in kg C m-2.

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamu, Alemu; Arabian, Joyce; Snider, James;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    This dataset contains maps with the spatial distribution of soil carbon concentration (g/kg) in Canada at 6 soil depths: 0cm, 5cm, 15cm, 30cm, 60cm and 100cm. It is being made public to act as supplementary data for the publication 'Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations', published in Geoderma: https://doi.org/10.1016/j.geoderma.2021.115402The study is part of our project that aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. The soil organic carbon concentration maps are intermediate products to generate the soil organic carbon stock map in kg/m2.To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model.

  • Open Access
    Authors: 
    Gonsamo, Alemu; Sothe, Camile; Snider, James; Finkelstein, Sarah; Arabian, Joyce; Kurz, Werner;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    *** Carbon storage and distribution in terrestrial ecosystems of Canada *** Authors: C. Sothe,1* A. Gonsamo,1 J. Arabian,2 W. A. Kurz,3 S. A. Finkelstein,4 J. Snider2 1School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada. 2World Wildlife Fund Canada, Toronto, Ontario, Canada. 3Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada. 4Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada. Corresponding author: Camile Sothe (sothec@mcmaster.ca) ***General Introduction*** This dataset contains the updated version of maps with the spatial distribution of soil carbon stock in Canada and associated uncertainties. It is being made public to act as supplementary data for the publication 'Large soil carbon storage in terrestrial ecosystems of Canada'. The maps were produced in the Remote Sensing Lab, McMaster University, between January 2020 and October 2021. This research project was made possible by a grant from the World Wildlife Fund (WWF)- Canada ***Purpose of the project*** This project aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. ***Methods*** To generate the soil carbon stock map, we used 39,323 ground samples of soil organic carbon concentration (g/kg) distributed in 6,533 sites, 11,068 ground samples of bulk density (kg/dm3) distributed in 2,157 sites, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model. The uncertainty map was generated using the random forest quantile regression approach difference between 95th and 5th quantiles (90% confidence interval) of soil organic carbon and bulk density predictions. ***Description of the data*** -250m spatial resolution -WGS-84 projection -Area= 8.4 million km² -Units= kg/m² -0-30cm and 0-1m soil depth -water and ice/snow areas were masked based on the 2015 Land Cover of Canada (https://open.canada.ca/data/en/dataset/4e615eae-b90c-420b-adee-2ca35896caf6) -SOC stock in permafrost areas was discounted according to ice abundance using the 'Ground ice map of Canada' (O'Neill et al., 2020 - https://doi.org/10.4095/326885) -shallow soils were discounted using the rooting depth fraction from the National Soil Database (http://sis.agr.gc.ca/cansis/nsdb/slc/index.html) This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with the soil organic carbon (SOC) in kg/m² for entire Canada in 30cm and 1m depth, and the uncertainty in SOC predictions. The SOC stock map was produced using 39,323 ground samples of soil organic carbon concentration (g/kg) distributed in 6,533 sites, 11,068 ground samples of bulk density (kg/dm3) distributed in 2,157 sites, long-term climate data, remote sensing observations and a machine learning model. The soil samples containing the x and y coordinates, depth and SOC (in g/kg) information were overlaid with the stacked covariates (soil forming factors) to compose the regression matrix. Random forest models were trained using a recursive feature elimination scheme and a cross-validation assessment. The best model was used for spatial prediction of SOC over Canada in intermediate depths between 0 and 1 m (0cm, 5cm, 15cm, 30cm, 60cm, 100cm). Afterwards, the SOC stock of each depth increment was computed using SOC concentration and bulk density maps, and corrected with coarse fragment information. The depth increments have been added to compose the 0-30cm and 0-1m depth intervals multiplied by rooting depths fraction to discount shallow soils. Water and ice/snow areas were removed using a mask based on the Land Cover of Canada map. Ground ice in permafrost areas was discounted according to ice abundance using the ground ice map of Canada. The SOC stock uncertainty map is the difference between the first and third quantiles of a quantile regression forest approach of SOC concentration and bulk density prediction (90% confidence interval).

search
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
8 Research products, page 1 of 1
  • Open Access
    Authors: 
    Das, Antariksha;
    Country: Netherlands
    Project: EC | QIA (820445), NSERC

    Contains all the data (Optical coherence measurements; Optical storage expt; Frequency Multiplexing expt; Single-photon storage expt.), along with the post-processing scripts (Matlab files; Origin files) for the experiment reported in Phys. Rev. Lett. 127, 220502

  • Open Access
    Authors: 
    Jonker, P.G.; Bassa, C.G.; Nelemans, G.; Steeghs, D.; Torres M.A.P.; Maccarone, T.J.; Hynes, R.I.; Greiss, S.; Clem, J.; Dieball, A.; +15 more
    Country: Netherlands
    Project: NSF | Black Hole and Neutron St... (0908789), NSERC

    We introduce the Galactic Bulge Survey (GBS) and we provide the Chandra source list for the region that has been observed to date. Among the goals of the GBS are constraining the neutron star (NS) equation of state and the black hole (BH) mass distribution via the identification of eclipsing NS and BH low-mass X-ray binaries (LMXBs). The GBS targets two strips of 6°x1° (12deg2 in total), one above (1°

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamo, Alemu; Snider, James; Lourenço, Ricardo B.; Kurz, Werner A.;
    Country: Netherlands
    Project: NSERC

    This dataset contains two canopy height maps from forested ecosystems of Canada at 250m spatial resolution — one using information from the spaceborne LiDAR GEDI, and the other from ICESat-2. GEDI and ICESat-2 are particular in acquiring canopy height information in Canada — the former providing more accurate information of vegetation, yet not reaching full coverage in Canada, whilst the latter is not specifically designed to provide vegetation information but has a global coverage. We created wall-to-wall maps using ATL08 LiDAR product from the ICESat-2 satellite, and GEDI L2A from GEDI. The data were download for the mid growing season (June and August 2020). Points were filtered regarding solar background noise and atmospheric scattering, totaling 208,554 points from ICESat-2, and 1,249,354 points for GEDI after filtering and point thinning. These points were associated with 14 ancillary variables primarily corresponding to structure information, such as seasonal Sentinel-1 VV and VH polarization, seasonal Sentinel-2 red and NIR bands, and annual PALSAR-2 HH and HV polarization. Afterwards, the random forest algorithm was used to extrapolate LiDAR observations and develop regression models with the abovementioned spatially continuous variables. GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m in relation to ALS data used for validation, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI.

  • Open Access
    Authors: 
    Harro Jongen; Gert-Jan Steeneveld; Jason Beringer; Andreas Christen; Krzysztof Fortuniak; Jinkyu Hong; Je-Woo Hong; Cor M. J. Jacobs; Leena Järvi; Fred Meier; +6 more
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: AKA | Urban green space solutio... (321527), NSERC

    Data contains meteorological and heat flux observations from eddy-covariance towers at 13 sites in different cities.Data is preprocessed for the analysis to infer water storage capacities, which means they are daily aggregates.

  • Open Access
    Authors: 
    Jake Davidson;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: WT , EC | QIA (820445), NSERC

    Data Supporting the attached paper. Contains many .wfm files used to read most data from devices, as well as the associated .m MATLAB scripts written for analysis of each separated data set.Data included is for the purpose of demonstrating an efficient optical quantum memory.

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamu, Alemu; Snider, James; Arabian, Joyce; Kurz, Werner A.; Finkelstein, Sarah;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with total C stored in plants of forested areas in Canada (AGB, BGB and dead plants) in kg/m² and C stock uncertainty. To estimate the C stored in plants of forest areas, we used 47,967 ground measurements of AGB measures and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. Different models were trained using a recursive feature elimination, random forest scheme and a 5-fold cross-validation assessment. The model with higher R² and lowest root mean square error (RMSE) was used for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the maps in kg C m-2.

  • Open Access
    Authors: 
    Sothe, Camile; Gonsamu, Alemu; Arabian, Joyce; Snider, James;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    This dataset contains maps with the spatial distribution of soil carbon concentration (g/kg) in Canada at 6 soil depths: 0cm, 5cm, 15cm, 30cm, 60cm and 100cm. It is being made public to act as supplementary data for the publication 'Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations', published in Geoderma: https://doi.org/10.1016/j.geoderma.2021.115402The study is part of our project that aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. The soil organic carbon concentration maps are intermediate products to generate the soil organic carbon stock map in kg/m2.To generate the soil carbon concentration maps, we used 6,533 ground soil samples, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model.

  • Open Access
    Authors: 
    Gonsamo, Alemu; Sothe, Camile; Snider, James; Finkelstein, Sarah; Arabian, Joyce; Kurz, Werner;
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: NSERC

    *** Carbon storage and distribution in terrestrial ecosystems of Canada *** Authors: C. Sothe,1* A. Gonsamo,1 J. Arabian,2 W. A. Kurz,3 S. A. Finkelstein,4 J. Snider2 1School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada. 2World Wildlife Fund Canada, Toronto, Ontario, Canada. 3Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada. 4Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada. Corresponding author: Camile Sothe (sothec@mcmaster.ca) ***General Introduction*** This dataset contains the updated version of maps with the spatial distribution of soil carbon stock in Canada and associated uncertainties. It is being made public to act as supplementary data for the publication 'Large soil carbon storage in terrestrial ecosystems of Canada'. The maps were produced in the Remote Sensing Lab, McMaster University, between January 2020 and October 2021. This research project was made possible by a grant from the World Wildlife Fund (WWF)- Canada ***Purpose of the project*** This project aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. ***Methods*** To generate the soil carbon stock map, we used 39,323 ground samples of soil organic carbon concentration (g/kg) distributed in 6,533 sites, 11,068 ground samples of bulk density (kg/dm3) distributed in 2,157 sites, long-term climate data, multisource remote sensing data, topographic information, soil type, depth, and a 3D random forest regression model. The uncertainty map was generated using the random forest quantile regression approach difference between 95th and 5th quantiles (90% confidence interval) of soil organic carbon and bulk density predictions. ***Description of the data*** -250m spatial resolution -WGS-84 projection -Area= 8.4 million km² -Units= kg/m² -0-30cm and 0-1m soil depth -water and ice/snow areas were masked based on the 2015 Land Cover of Canada (https://open.canada.ca/data/en/dataset/4e615eae-b90c-420b-adee-2ca35896caf6) -SOC stock in permafrost areas was discounted according to ice abundance using the 'Ground ice map of Canada' (O'Neill et al., 2020 - https://doi.org/10.4095/326885) -shallow soils were discounted using the rooting depth fraction from the National Soil Database (http://sis.agr.gc.ca/cansis/nsdb/slc/index.html) This project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with the soil organic carbon (SOC) in kg/m² for entire Canada in 30cm and 1m depth, and the uncertainty in SOC predictions. The SOC stock map was produced using 39,323 ground samples of soil organic carbon concentration (g/kg) distributed in 6,533 sites, 11,068 ground samples of bulk density (kg/dm3) distributed in 2,157 sites, long-term climate data, remote sensing observations and a machine learning model. The soil samples containing the x and y coordinates, depth and SOC (in g/kg) information were overlaid with the stacked covariates (soil forming factors) to compose the regression matrix. Random forest models were trained using a recursive feature elimination scheme and a cross-validation assessment. The best model was used for spatial prediction of SOC over Canada in intermediate depths between 0 and 1 m (0cm, 5cm, 15cm, 30cm, 60cm, 100cm). Afterwards, the SOC stock of each depth increment was computed using SOC concentration and bulk density maps, and corrected with coarse fragment information. The depth increments have been added to compose the 0-30cm and 0-1m depth intervals multiplied by rooting depths fraction to discount shallow soils. Water and ice/snow areas were removed using a mask based on the Land Cover of Canada map. Ground ice in permafrost areas was discounted according to ice abundance using the ground ice map of Canada. The SOC stock uncertainty map is the difference between the first and third quantiles of a quantile regression forest approach of SOC concentration and bulk density prediction (90% confidence interval).