Use of an Unmanned Aerial Vehicle (UAV) to Document Vegetation Coverage Rate in Managed Grasslands Following a Historic River Flood

Abstract

Abstract Our objective was to document the vegetation coverage rate of grasslands impacted by a historic flood event that occurred May and June 2019 within the Arkansas river valley. Primary flooding within the observation area lasted 21 d with a maximum water depth of 3m above flood stage. Three cooperator fields were monitored. Aerial images were captured at 7, 14, 33, and 48 d following water recession. Geo-referenced images were captured at 76.2 m above ground level using an UAV fitted with an integrated camera and a secondary camera with a near infrared filter. Images were stitched and aligned prior to vegetation indexing. Two vegetation indices were calculated: green leaf index (GLI, using the integrated camera color bands) and normalized difference vegetative index (NDVI, using the secondary camera color bands). Image pixels were classified as green vegetation or non-vegetation based on a threshold for each processed image’s indices. The estimated proportion green vegetation was modeled as a dependent variable with day (post water recession) and index method as fixed covariates. A simplex regression model (simplexreg package for R) was chosen for analyzing longitudinal proportion data. Both day (P < 0.001) and index method (P = 0.002) were significant with NDVI predicting less vegetation than GLI. The estimated daily coverage rate was 0.02 ± 0.003. Although fields achieved 90% coverage in 45 d, each cooperator experienced different outcomes in plant community. A d 48 field inventory indicated 38 to 77% undesirable species. The field with the greatest percentage undesirable species was covered with barnyardgrass. Assessment of residual statistics suggests NDVI likely under-estimated vegetation cover of this field on d 48. At inventory, this grass’ growth appeared very erect, with sufficient plant spacing that soil may have influenced NDVI more than GLI. These results demonstrate green vegetation coverage can be estimated without specialized UAV cameras. The UAV was helpful in that it accounted for the entire field at high resolution and images could be captured on cloudy days. Documented flood impacts will be useful in future events.