Imagery of Casey and Wilkes was captured by a Sensefly eBee drone on 19 and 20 December 2015, flying at approximately 120 metres above ground level. A mosaic and Digital Surface Model (DSM) of each area was created from the imagery using Pix4D software. The spatial reference of these products is UTM zone 49S, WGS84. Elevations of the DSMs are above the WGS84 ellipsoid. The flights were conducted as a demonstration to test the capability. No ground control points were used to georeference the mosaics and DSMs. The data should not be used for anything other than a demonstration.

This dataset is a high-resolution (60 cm) grass biomass map derived from drone/UAS (unmanned aerial system)-based multispectral remote sensing, calibrated to in situ field data. It was developed for research on prescribed fire behavior responses to vegetation conditions, and vegetation community regrowth-responses post-fire. It addressed a key need for nondestructive grassland biomass measurements, in a use case where directly measuring grass biomass by destructive harvest would have disturbed the intact fuel beds needed for burning in the prescribed fire experiment. Methods Grass biomass map data collection and processing consisted of three steps: (1) drone flights for multispectral image acquisition, (2) clipping and measuring grass biomass in 1-foot square (0.30 m x 0.30 m), spatially referenced plots, and (3) development and application of predictive relationships between in-situ grass biomass and spectral indices from the drone imagery. The data object published here is a grass biomass map from 7 October 2020 (2020-10-07) calculated using the predictive relationship developed in step3. Multispectral drone imagery was collected using a Draganfly Commander unmanned aerial system (UAS) (Draganfly Innovations, Saskatoon, SK CA) flying a Micasense Rededge multispectral camera (AgEagle, Wichita, KS USA). A mapping flight for developing grass biomass calibrations was conducted over an experimental grassland site at UCSB Sedgwick Reserve, Santa Barbara, CA (34.702, -120.036) on September 16, 2020 (2020-09-16). The Rededge Camera was flown at 90 m above ground level in a pre-programmed grid flight with 80% front and side overlap between images. Multispectral imagery was calibrated to reflectance with before- and after-flight reference panel images and processed into geotiff (.TIF) image mosaics in Pix4D software (Pix4D, Prilly, CH). Image mosaics consisted of five wavelength bands: blue (475 ± 20 nm), green (560 ± 20 nm), red (668 ± 10 nm), red-edge (717 ± 10 nm nm) and near-infrared (NIR) (840 nm ± 40). Raw drone imagery produced had a native spatial resolution of 0.06 m (6 cm). Imagery was georeferenced to less than 1 m absolute accuracy with 12 in-scene ground control points (GCPs) surveyed with a Trimble PG200 RTK antenna (Westminster, CO USA). Next, in-situ grass biomass data (or residual dry matter (RDM) at this dry-season time of year) was collected at 25 locations across the experimental grassland site that represented a range of grass cover conditions. Grass sampling occurred on 25 September 2020, the week after drone image acquisition. Sampling locations were referenced by tape measure to 4-foot T-posts visible in the drone imagery. Grass was clipped to mineral soil and massed within standard-sized, 1-foot square sampling frames used for rangeland monitoring in California annual grasslands (Bartolome et al., 2006). As grass sampling was done after the calibration image flight, the drone imagery thus represented grass biomass conditions at clip-plot sampling locations before clipping. This enabled direct comparison of spatially aligned drone reflectance data and grass biomass. No significant meteorological events such as rain or wind, or additional major physical disturbances occurred onsite between drone mapping and grass biomass sampling. After sampling grass biomass in the field, step-wise multiple linear regression was used to develop predictive relationships among spectral indices calculated from the Sep 16 drone imagery and field-measured grass biomass (RDM). Regression analyses were carried out in R software (R Core Team 2023, Vienna, AT). Area-based grass biomass units modeled were pound per acre (lb acre-1) to make the model relevant for Santa Barbara County rangeland managers who track grassland biomass in these units. The strongest relationship found among field-measured grass biomass and spectral indices was the following (Eq. 1) Eq. 1: RDM (lb acre-1) = -52501 - 46600*OSAVI + 72132*TNDVI – 114995*blue + 91390*green where spectral data and indices calculated from drone imagery included: OSAVI (Optimized Soil-Adjusted Vegetation Index) = (NIR-Red)/(NIR+Red+0.16) (Fern et al., 2018; Rondeaux et al., 1996) TNDVI (Transformed Normalized Difference Vegetation Index) = √ (NIR-Red)/(NIR+red) + 0.5 Blue = blue reflectance band Green = green reflectance band

The relationship in Eq. 1 represented a significant predictive relationship for dry season grass biomass (RDM) from multispectral drone image spectral data and indices (r2 = 0.501, F = 7.23 (4,21), p < 0.001; root mean square error (RMSE): 556 lb acre-1 on 21 D.F.).

The grass biomass map published herein was produced from drone imagery flown on 2020-10-07. After image collection and processing according to step (1) above, the October 7 imagery was histogram-normalized to the 2020-09-16 imagery to account for differences in sun angle and illumination between dates. From Step 3, Eq. 1 was then applied to the 2020-10-07 imagery to produce a dry grass biomass/RDM biomass map. Unfortunately, there was not time to ground-truth the biomass map with independent grass clip plots before the prescribed fire experiment that this map supported. References

Bartolome, J., Frost, W., & McDougald, N. (2006). Guidelines for residual dry matter on coastal and foothill rangelands in California. Rangeland Management Series, 092, 1–6. Fern, R. R., Foxley, E. A., Bruno, A., & Morrison, M. L. (2018). Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecological Indicators, 94, 16–21. https://doi.org/10.1016/j.ecolind.2018.06.029 Gholami Baghi, N., & Oldeland, J. (2019). Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran? International Journal of Remote Sensing, 40(22), 8223–8235. https://doi.org/10.1080/01431161.2019.1606958 Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of Soil-Adjusted Vegetation Indices. Remote Sensing of Environment, 55, 95–107.

This is drone product set V from Chapter II of Alexandra's dissertation.The orthomosaic and DSM show the Bellamy downstream mill area in Dover, NH, USA. The imagery was collected on August 16, 2019 using a DJI Phantom 3 Professional drone. Ground control points (GCPs) were used for georeferencing the imagery during SfM processing, mostly surveyed with a Topcon Hiper Lite+ and a handful of GCPs were surveyed with a total station where satellite signal was poor. Automated drone flight paths were used to collect nadir and angled aerial images with sufficient image overlap for SfM processing in Agisoft PhotoScan Professional. The PhotoScan-generated report includes additional details on the SfM processing workflow and on the drone products.Please see Alexandra's dissertation (University of New Hampshire) for more information on the drone products and project.These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This dataset consists of unprocessed images and orthomosaic imagery of a barley field in Bozeman, Montana, collected throughout the growing season from emergence to maturity. The orthomosaics were used to develop an open-source workflow for extracting quantitative values from individual plots for downstream analysis of plant traits. This field exemplifies a challenge for plot extraction, as plots were planted with no border rows or alleys. Methods

UAV Imagery Collection:

Data was collected using a Mavic 2 Pro drone with the integrated Hasselblad L1D-20C RGB camera at an altitude of 90 feet (27.4 m). Flights were conducted over a barley field located west of Bozeman Montana (45.676415, -111.149092). DJI GS Pro software was used on an iPad mini to create an automated flight path for imagery capture.  Images were collected while hovering to minimize blurring and captured with 70% overlap along the flight path and 70% overlap between flight passes. Weather permitting, flights were timed as close to 10:00 am or 2:00 pm as possible.

Date Number of Images Time of Flight Notes

June 16 37 10.38

June 21 49 11:27 Increased number of passes for better stitching of edge plots.

June 24 49 10:45

July 01 49 10:16

July 12 59 09:51

One-the-fly flight plan due to hardware issues.

July 15 49 9:09

July 19 48 11:20

July 25 49 14:04

July 27 49 14:07

August 5 48 10:20

August 8 48 14:44

OpenDroneMap was used to stitch images together and create an orthomosaic of each flight. Parameters were default except for the following arguments:

min-num-features: 4000, max-concurrency: 6, skip-3dmodel: TRUE, fast-orthophoto: TRUE, crop: 0, texturing-outlier-removal-type: gauss_damping, orthophoto-resolution: 0.125, orthophoto-compression: NONE

The minimum number of features defines the number of tie points needed to stitch each pair of images. ‘min-num-features’ was lowered from the default 8000 to 4000 to ease processing time and memory load. ‘max-concurrency’ allocates CPU cores to the stitching project. ‘skip-3Dmodel’ and ‘fast-orthophoto’ keep the stitching procedure from creating undesired files like a 3D model and digital elevation model (DEM). ‘crop’ and ‘orthophoto-compression’ maintain the imagery quality, so nothing was cropped or down sampled. ‘texturing-outlier-removal’ defines how moving objects are processed and the option ‘gauss-damping’ was chosen because it is a less aggressive approach that prioritizes images that do not include the moving object. In this image set, there were no moving objects. ‘orthophoto- resolution’ defines the final resolution of the image. A value of 0.125 was selected for this dataset as a conservative estimate of the true resolution collected by the sensor.

Field Operations:

The field was planted on April 26th, 2022, with spring barley from the S2MET population. Aggregated by Neyhart et. al. 2019, the S2MET barley population provides a representation of high-performance barley from around the United States, selected to be grown across many environments to study genotype-by-environment interactions. Lines were planted in an augmented block design including 12 blocks and four control varieties planted across all blocks. These control varieties were selected as common high-performing barley lines in the Montana region: Odyssey, Lavina, Merit 57, and Hockett. All other lines were planted once. Planting was conducted with a 6-row planter, planting two 3-row plots simultaneously in a North-South orientation. In total, 23-24 plots were planted per block, for a total of 282 plots. After emergence alleys were cut East-West to distinguish plots more easily. 

Data Processing:

This dataset was used to develop an analysis workflow using QGIS and R. After stitching, imagery was loaded into QGIS. First, each image was georeferenced to the flight on June 16th using the 6 ground control points laid out over the extent of the field. Further, each band was calibrated relative to the June 16th flight image using the reflectance calibration pad (Micasense, panel serial number RP02-1622081-SC).

Once georeferenced and calibrated, plants were extracted from each image using the excess greenness index threshold (2 * Green) – Red – Blue). Next, plots were defined through a user-defined line grid overlay that was then translated into a polygon shapefile. This overlay was used to extract digital number statistics in each band, for every plot, on each flight date.

References:

Neyhart, J.L., Sweeney, D., Sorrells, M., Kapp, C., Kephart, K.D., Sherman, J., Stockinger, E.J., Fisk, S., Hayes, P., Daba, S., Mohammadi, M., Hughes, N., Lukens, L., Barrios, P.G., Gutiérrez, L. and Smith, K.P. (2019), Registration of the S2MET Barley Mapping Population for Multi-Environment Genomewide Selection. Journal of Plant Registrations, 13: 270-280. https://doi.org/10.3198/jpr2018.06.0037crmp

This data publication contains active-fire electro-optical (EO) videos and georeferenced individual video frames collected as part of a prescribed fire research campaign conducted at the Camp Swift Military Base in Bastrop County, Texas on January 15, 2014. The Camp Swift Fire Experiment 2014 consisted of three fires ignited in burn blocks of dimensions 100 meters (m) by 100 m on January 15, 2014. Fires were ignited on relatively flat areas of grass vegetation in moderate winds. Unmanned aerial vehicle EO active-fire video was recorded during each of the three fires. The video and image stills contained within this data package were collected with a Cloud Cap Goodrich TASE200 EO/Infrared (IR) unit mounted on an MLB Company SuperBat III unmanned aerial vehicle (UAV). All active-fire videos were collected at an oblique angle. Still images from the active-fire video were exported and georeferenced using a set of ground-control points around each burn block.The objective of the research burns was to create a dataset comprised of ground based and remote sensing measurements. This dataset represents active-fire aerial videos and individual georeferenced video frames of the burn blocks acquired with an Electro-Optical (EO) sensor mounted on an unmanned aerial vehicle (UAV). The data contained with this package were collected to assess the possibility of recording active-fire information using video recorded in the visible spectrum. Active-fire imagery was also acquired at Camp Swift to assess the ability of the sensors to provide a better interpretation of ground measurements. For example, if it is not known if a particular set of instruments were in flanking versus heading versus merging fire lines, it is uncertain how to use the measured data for validation since the predicted global behavior might be wrong.A summary of the Camp Swift project can be found in the full data download (\Supplements\ CampSwiftFireExperiment2014_Project_Overview.pdf). A United States Forest Service ArcGIS Online interactive website is also developed to describe the study and examine the integrated data quality for the Camp Swift effort (see cross reference below). Finally, a document detailing the procedures used to set up the burn blocks can be found in the full data download (\Supplements\CampSwiftFireExperiment2014_BurnBlockDesign.pdf).

This is drone product set N from Chapter II of Alexandra's dissertation.The orthomosaic and DSM show the Bellamy beaver dam area in Dover, NH, USA. The imagery was collected on August 23, 2019 using a DJI Phantom 3 Professional drone. Ground control points were used for georeferencing the imagery during SfM processing, surveyed with a Topcon Hiper Lite+. Automated drone flight paths were used to collect nadir and angled aerial images with sufficient image overlap for SfM processing in Agisoft PhotoScan Professional. The PhotoScan-generated report includes additional details on the SfM processing workflow and on the drone products.Please see Alexandra's dissertation (University of New Hampshire) for more information on the drone products and project.These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This is drone product set C from Chapter II of Alexandra's dissertation.The orthomosaic and DSM show East Branch Piscataqua River in Falmouth, ME, USA. The imagery was collected on October 16, 2017 using a DJI Phantom 3 Professional drone. Ground control points were used for georeferencing the imagery during SfM processing, but note that the elevation values in the DSM are only useful for relative topography across the site. The Topcon Hiper Lite+ recorded inaccurately negative elevation values which limits the dataset's compatibility with other datasets and the DSM will likely need to be corrected for relative compatibility. The XY locations are more reliable based on checks against alignment with base maps in ArcGIS. The drone PS' relative accuracy was deemed acceptable for the purposes in Chapter II. Automated drone flight paths were used to collect nadir and angled aerial images with sufficient image overlap for SfM processing in Agisoft PhotoScan Professional. The PhotoScan-generated report includes additional details on the SfM processing workflow and on the drone products.Please see Alexandra's dissertation (University of New Hampshire) for more information on the drone products and project.These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

This is drone product set A from Chapter II of Alexandra's dissertation. The orthomosaic and DSM show Town Brook in Plymouth, MA, USA. The imagery was collected on September 28, 2017 using a DJI Phantom 3 Professional drone. Ground control points were used for georeferencing the imagery during SfM processing, but note that the elevation values in the DSM are only useful for relative topography across the site. The Topcon Hiper Lite+ recorded inaccurately negative elevation values which limits the dataset's compatibility with other datasets and the DSM will likely need to be corrected for relative compatibility. The XY locations are more reliable based on checks against alignment with base maps in ArcGIS. The drone PS' relative accuracy was deemed acceptable for the purposes in Chapter II. Automated drone flight paths were used to collect nadir and angled aerial video, from which timed stills were extracted for SfM processing in Agisoft PhotoScan Professional. The PhotoScan-generated report includes additional details on the SfM processing workflow and on the drone products. Please see Alexandra's dissertation (University of New Hampshire) for more information on the drone products and project.These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.