Evaluating Multispectral Imagery and Lidar Data for Vegetation Classification: A Comparative Assessment of UASs and Traditional Field Methods to Support Coastal Restoration Monitoring

Abstract

Highlights: What are the main findings? UAS-based wetland vegetation classifications generally aligned with traditional field survey monitoring data, but performance varied by site (LaBranche vs. Spanish Pass, Louisiana), classification method (maximum likelihood vs. random forest), and plot characteristics. Data source (5- vs. 10-band imagery) had little effect, while species detection varied by taxa, with underestimation common for percent cover, and plots with higher taxa richness and canopy complexity had reduced classification performance. What are the implications of the main findings? UASs can complement traditional monitoring surveys for assessing wetland restoration outcomes related to vegetation but is sensitive to methodological and plant community factors. Findings clarify UAS capabilities and limitations, helping to guide appropriate application in coastal restoration monitoring. There is growing interest in uncrewed aircraft system (UAS) technology to supplement coastal restoration monitoring, yet it’s unclear how UAS data products compare to traditional field monitoring data that are fundamental to restoration programs. In this study, wetland vegetation classifications were generated from UAS imagery, lidar data, and supervised methods at restoration sites (LaBranche and Spanish Pass, Louisiana) and compared to traditional field survey data. Analyses examined model factors, method (maximum likelihood and random forest), data source (5- and 10-band imagery plus lidar data), and plot, on classification performance for (1) taxa richness: factors did not affect model comparisons, except for method at Spanish Pass; (2) community assemblage: LaBranche models were more similar to field data, though plot was a factor at both sites and method was a factor at Spanish Pass; (3) species presence identification: LaBranche models performed moderately better, but were species dependent; and (4) percent cover: plot was a factor at both sites, though underestimations were more frequent. Data source did not affect performance, method had variable influence on select metrics, and plots with higher taxa richness or complex canopy structure showed reduced model performance at LaBranche and Spanish Pass, respectively. Capabilities and limitations of UAS technology for wetland vegetation classification are highlighted, offering an understanding of its utility in assessing restoration outcomes related to vegetation.