UAS-multisensor input imagery and random forest classification products for mapping vegetation species at a tallgrass prairie in Urbana, IL

Three mosaic raster files are provided depicting Uncrewed Aerial Systems (UAS) data collection from RGB, multispectral, and hyperspectral sensors, which were collected at Weaver Park in Urbana, IL. Along with spectral bands, all image products are fused with canopy height models (CHM), where RGB and multispectral products are fused with an SfM-derived (structure from motion) CHM and the hyperspectral product is fused with a LiDAR-derived (Light Detection and Ranging) CHM. A multispectral phenological time series product is also provided, where Normalized Difference Vegetation Index (NDVI) was calculated across six time periods in one growing season and where an additional 15 NDVI-derived metrics were calculated, resulting in a 21-band image product. Differential GPS (dGPS; 2cm resolution) data identifying vegetation species are also provided, which were used at the training and testing datasets for conducting random forest classifications on each image product. Random forest classification models were applied to each of the three UAS-sensor types both with and without CHM fusion, and to the multispectral phenology time series image, resulting in the 7 vegetation maps provided.

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Cite this as

Hall, Emma, Lara, Mark J (2024). Dataset: UAS-multisensor input imagery and random forest classification products for mapping vegetation species at a tallgrass prairie in Urbana, IL. https://doi.org/10.1594/PANGAEA.967351

DOI retrieved: 2024

Additional Info

Field Value
Imported on November 30, 2024
Last update November 30, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.967351
Author Hall, Emma
Given Name Emma
Family Name Hall
More Authors
Lara, Mark J
Source Creation 2024
Publication Year 2024
Subject Areas
Name: LandSurface

Related Identifiers
Title: Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands
Identifier: https://doi.org/10.3390/rs14143453
Type: DOI
Relation: References
Year: 2022
Source: Remote Sensing
Authors: Hall Emma , Lara Mark J .