Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning

Abstract

Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…Due to the resolution that the LiDAR point cloud is capable of generating, individual trees can be identified, and thus tree metrics can be directly computed. In (Vizireanu et al, 2020;Neuville et al, 2021), DBH is estimated based only on LiDAR retrieved data, other forest attributes estimated by LiDAR cloud points are canopy cover (Cai et al, 2021), which can be derived through the density of vegetation points, this metric is also used to predict biomass near rivers (Resop et al, 2021), and with the purpose of determining crown fuels (Suwardhi et al, 2022). Morphological features derived from LiDAR point cloud can be key factors to determine and differentiate between alive trees and snags or deciduous and evergreen trees, this study is done by Stitt et al (2022).…”

“…Other vegetative problems are investigated using linear regression models. Resop et al (2021) studied the correlation between vegetation metrics, the distance from water sources, and seasonal variation; the results show that there is no correlation between the distance to the water stream and canopy height and vegetation density. Using multi-spectral VIs, regression models have been used to predict biomass in the tidal marsh; the best VI was ExG however the correlation index only reached 0.376 (Morgan et al, 2021).…”

Consumer-grade UAV solid-state LiDAR accurately quantifies topography in a vegetated fluvial environment

“…More recently, UAV LiDAR including topographic-bathymetric systems have been demonstrated across several fluvial environments and applications (e.g. Resop et al, 2019;Mandlburger et al, 2020;Islam et al, 2021;Resop et al, 2021). Despite these pertinent examples, the growth trajectory of UAV LiDAR surveys remains significantly slower than the comparable rate for UAV SfM photogrammetry when it was in its geomorphic application infancy (Babbel et al, 2019;Pereira et al, 2021), due to the relatively high entry cost of LiDAR sensors and associated large payload UAV platforms that are required.…”

Consumer‐grade UAV solid‐state LiDAR accurately quantifies topography in a vegetated fluvial environment

“…More recently, UAV LiDAR including topographic–bathymetric systems has been demonstrated across several fluvial environments and applications (e.g. Islam et al, 2021; Mandlburger et al, 2020; Resop, Lehmann, & Cully Hession, 2019; Resop, Lehmann, & Hession, 2021). Despite these pertinent examples, the growth trajectory of UAV LiDAR surveys remains significantly slower than that of UAV SfM photogrammetry when it was in its geomorphic application infancy (Babbel et al, 2019; Pereira et al, 2021), due to the relatively high entry cost of LiDAR sensors and associated large payload UAV platforms required.…”