Estimating fuel moisture in grasslands using UAV-mounted infrared and visible light sensors: MicaSense remote sensing data

This data publication consists of the complete set of raw images, orthomosaics created from these images, and ground control points (GCPs) from two unmanned aerial vehicle (UAV) flights over the Mazama Meadows site near Olympia, Washington on 09/22/2020. There are single-band images in five wavelengths - red, green, blue, red edge, and near infrared collected using a MicaSense RedEdge camera from the flights at 9:34am and 2:56pm. The drone flew at 3 meters per second at a height of 16 meters and the images were recorded once per second.
Computational fluid dynamics (CFD) fire behavior models have the potential to substantially improve decision support systems for wildland fire management in the United States, but satisfactory fuel inputs and training datasets are impediments to model development. These data were collected to help address the need for continuous fuels maps of fuel moisture across burn areas. Together with the short-wave infrared (SWIR) data (https://doi.org/10.2737/RDS-2021-0072) and the field moisture data (https://doi.org/10.2737/RDS-2021-0070), these data were used in a study to investigate creating a spatially-explicit moisture input for fire models. A regression model was developed using the imagery data to predict the moisture collected in the field.
Original publication date was 09/23/2021. Minor metadata updates were made on 10/05/2021 and 10/20/2021.