Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

Falco, Nicola; M. Wainwright, Haruko; Chadwick, K. Dana; Dafflon, Baptiste; J. Enquist, Brian; Unhlemann, Sebastian; Breckheimer, Ian; Lamb, Jack; Chen, Jiancong; Tuvshintugs, Orgil; Balde, Abdoulaye; H. Williams, Ken; Brodie, Eoin

doi:10.15485/1602034

Title: Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

Dataset
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Abstract

The package contains the following datasets: - Classification vegetation map: it contains the classification map (PNG, geotiff), which is derived from hyperspectral and LiDAR airborne data acquired by the NEON AOP in June 2018 using a machine learning approach, and the class code (csv) showing the corresponding vegetation class to pixel values. - Classification_reference_data (csv): it contains reference data used in the machine learning procedure to predict vegetation and non-vegetation classes; - LiDAR_derived_products (geotiff): elevation, slope, curvature, Topographic Wetness Index (TWI), Topographic Position Index (TPI), solar insolation, canopy height model (CMD). The topographical metrics were smoothed with a 5x5 pixel window; - Vegetation_indices (geotiff): Normalized Difference Vegetation Index, Normalized Difference Nitrogen Index, Normalized Difference Water Index; - Cloud_shadow_urban_masks (geotiffs): Masks of clouds and shadows that were applied to the mapping; - Covariates_10mGrid_vegetationClasses_topographicMetrics_soilProperties (csv): 10-m gridded data that integrate all the previous datasets as well as geophysical data. Geotiffs can be visualized with any GIF software or library able to handle geotiff images. CSV can be open with any software able to handle comma-separated values files.

Authors:

; M. Wainwright, Haruko; Chadwick, K. Dana; Dafflon, Baptiste; J. Enquist, Brian; Unhlemann, Sebastian; Breckheimer, Ian; Lamb, Jack; Chen, Jiancong; Tuvshintugs, Orgil; Balde, Abdoulaye; H. Williams, Ken; Brodie, Eoin

Lawrence Berkeley National Laboratory
Stanford University
University of Arizona
RMBL
Arizona State University

Publication Date:: Mon Jan 01 04:00:00 UTC 2024

DOE Contract Number:: AC02-05CH11231

Research Org.:: Watershed Function SFA

Sponsoring Org.:: ESS-DIVE; U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; Airborne remote sensing; Canopy height; EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION INDEX > NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI); EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER; EARTH SCIENCE > LAND SURFACE > SOILS; EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION > DIGITAL ELEVATION/TERRAIN MODEL (DEM); Hyperspectral imaging; Modelling results; NEON AOP; NEON Campaign 2018; Soil electrical conductivity; Vegetation classification; Vegetation classification map; Vegetation indices; watershed

OSTI Identifier:: 1602034

DOI:: https://doi.org/10.15485/1602034

Citation Formats


                    Falco, Nicola, M. Wainwright, Haruko, Chadwick, K. Dana, Dafflon, Baptiste, J. Enquist, Brian, Unhlemann, Sebastian, Breckheimer, Ian, Lamb, Jack, Chen, Jiancong, Tuvshintugs, Orgil, Balde, Abdoulaye, H. Williams, Ken, and Brodie, Eoin. Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018.  United States: N. p., 2024. 
        Web.  doi:10.15485/1602034.

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                    Falco, Nicola, M. Wainwright, Haruko, Chadwick, K. Dana, Dafflon, Baptiste, J. Enquist, Brian, Unhlemann, Sebastian, Breckheimer, Ian, Lamb, Jack, Chen, Jiancong, Tuvshintugs, Orgil, Balde, Abdoulaye, H. Williams, Ken, & Brodie, Eoin. Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018.  United States.  doi:https://doi.org/10.15485/1602034

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                    Falco, Nicola, M. Wainwright, Haruko, Chadwick, K. Dana, Dafflon, Baptiste, J. Enquist, Brian, Unhlemann, Sebastian, Breckheimer, Ian, Lamb, Jack, Chen, Jiancong, Tuvshintugs, Orgil, Balde, Abdoulaye, H. Williams, Ken, and Brodie, Eoin. 2024.  
"Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018".  United States.  doi:https://doi.org/10.15485/1602034.  https://www.osti.gov/servlets/purl/1602034. Pub date:Mon Jan 01 04:00:00 UTC 2024

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@article{osti_1602034,

  title        = {Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018},

  author       = {Falco, Nicola and M. Wainwright, Haruko and Chadwick, K. Dana and Dafflon, Baptiste and J. Enquist, Brian and Unhlemann, Sebastian and Breckheimer, Ian and Lamb, Jack and Chen, Jiancong and Tuvshintugs, Orgil and Balde, Abdoulaye and H. Williams, Ken and Brodie, Eoin},

  abstractNote = {The package contains the following datasets: - Classification vegetation map: it contains the classification map (PNG, geotiff), which is derived from hyperspectral and LiDAR airborne data acquired by the NEON AOP in June 2018 using a machine learning approach, and the class code (csv) showing the corresponding vegetation class to pixel values. - Classification_reference_data (csv): it contains reference data used in the machine learning procedure to predict vegetation and non-vegetation classes; - LiDAR_derived_products (geotiff): elevation, slope, curvature, Topographic Wetness Index (TWI), Topographic Position Index (TPI), solar insolation, canopy height model (CMD). The topographical metrics were smoothed with a 5x5 pixel window; - Vegetation_indices (geotiff): Normalized Difference Vegetation Index, Normalized Difference Nitrogen Index, Normalized Difference Water Index; - Cloud_shadow_urban_masks (geotiffs): Masks of clouds and shadows that were applied to the mapping; - Covariates_10mGrid_vegetationClasses_topographicMetrics_soilProperties (csv): 10-m gridded data that integrate all the previous datasets as well as geophysical data. Geotiffs can be visualized with any GIF software or library able to handle geotiff images. CSV can be open with any software able to handle comma-separated values files.},

  doi          = {10.15485/1602034},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Jan 01 04:00:00 UTC 2024},

  month        = {Mon Jan 01 04:00:00 UTC 2024}

}

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DOI: https://doi.org/10.15485/1602034

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