Monitoring ecological characteristics of a tallgrass prairie using an unmanned aerial vehicle

Abstract

Site‐specific conditions, climate, and management decisions all dictate the establishment and composition of desired plant communities within grassland restorations. The uncertainty, complexity, and large size of grassland restorations necessitate monitoring plant communities across spatial and temporal scales. Remote sensing with unmanned aerial vehicles (UAVs) may provide a tool to monitor restored plant communities at various scales, but many potential applications are still unknown. In a tallgrass prairie restoration located in Franklin Grove, IL, we used UAV‐based multispectral imagery to assess the ability of spectral indices to predict ecological characteristics (plant community, plant traits, soil properties) in the summer of 2017. Using 19 sites, we calculated the moments of 26 vegetation indices and four spectral bands (green, red, red edge, near infrared). Models based on each moment and a model with all moments were estimated using ridge regression with model training based on a subset of 15 sites. Each tested for significant error reduction against a null model. We predicted mean graminoid cover, mean dead aboveground biomass, mean dry mass, and mean soil K with significant reductions in cross‐validated root mean square error. Averaged coefficients determined from cross‐validation of ridge regression models were used to develop a final predictive model of the four successfully predicted ecological characteristics. Graminoid cover and soil potassium were successfully predicted in one of the sites while the other two were not successfully predicted in any site. This study provides a path toward a new level of ease and precision in monitoring community dynamics of restored grasslands.

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

“…Treeline ecotone vegetation can, however, vary over short distances and be very fragmented (Ullerud et al., 2016), and UAV imagery with ultra‐high resolution can have certain benefits over other platforms with lower resolution. The use of UAVs for biodiversity monitoring and detection of land cover change can thus potentially be a better solution than using imagery from spaceborne or manned airborne platforms taken at unfavourable times (Blackburn et al., 2021; Getzin et al., 2012; Woellner & Wagner, 2019).…”

Digital whole-community phenotyping to assess morphological and physiological features of plant communities in the field

“…Such methods are used for the classification of land-cover types or to predict different aspects of diversity. Additionally, they are used in ecosystem monitoring to create habitat maps or capture characteristics of plant communities (Blackburn et al, 2021; Cruzan et al, 2016). While there are many different types of sensors to be used with satellites and UAVs to record different aspects of plant diversity, cover, and status (Barbedo, 2019), they only provide a two-dimensional representation of the vegetation.…”

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field

“…They provide large-scale spatial and temporal data at a resolution that allows for the classification of land-cover types or the prediction of different aspects of diversity ( Zhu and Woodcock, 2014 ; Wachendorf et al., 2018 ; Cavender-Bares et al., 2020 ; Sun et al., 2021 ). Additionally, they are used in ecosystem monitoring to create habitat maps or capture characteristics of plant communities ( Cruzan et al., 2016 ; Blackburn et al., 2021 ). At present there are many different types of sensors to be used with satellites and UAVs to record different aspects of plant diversity, cover, and status ( Barbedo, 2019 ), and the possibilities for three-dimensional measurements also increased in recent years ( Lepczyk et al., 2021 ).…”