DOI: 10.25349/d93328
title: High-resolution residual dry matter (RDM) map for a California oak savanna/annual grassland derived from drone multispectral remote sensing imagery and in-situ grass biomass data

description: 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.
description: 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 <em>before</em> 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<sup>-1</sup>) 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) <em>Eq.
 1:  </em>RDM (lb acre<sup>-1</sup>) = -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] (Gholami Baghi &
 Oldeland, 2019) Blue = blue reflectance
 band Green = green reflectance
 band   The
 relationship in <em>Eq. 1 </em>represented a significant
 predictive relationship for dry season grass biomass (RDM) from
 multispectral drone image spectral data and indices
 (r<sup>2</sup> = 0.501, F = 7.23 (4,21), p &lt; 0.001;
 root mean square error (RMSE): 556 lb acre<sup>-1</sup> 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.  <strong>References</strong> Bartolome, J., Frost, W., & McDougald, N. (2006). Guidelines for residual dry matter on coastal and foothill rangelands in California. <em>Rangeland Management Series</em>, <em>092</em>, 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. <em>Ecological Indicators</em>, <em>94</em>, 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? <em>International Journal of Remote Sensing</em>, <em>40</em>(22), 8223–8235. https://doi.org/10.1080/01431161.2019.1606958 Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of Soil-Adjusted Vegetation Indices. <em>Remote Sensing of Environment</em>, <em>55</em>, 95–107.
description: The biomass map is published as a .TIF file (GEOTIF). Its spatial
 reference information is the following: Datum WGS84, Projection UTM 10 N
 (EPSG: 32610). Its spatial resolution is 60 cm, aggregated 10x from the 6
 cm resolution, to facilitate free and open file-sharing on Dryad. The file
 is open and viewable with open-source geospatial software including QGIS,
 R, and Python.  Note that this raw form of the map has some negative
 biomass values (“flyers”) which the analyst can choose to mask out as
 not-applicable (NA) or assign a 0-value to, depending on their
 use.