Quantifying Vegetation and Landscape Metrics with Hyperspatial Unmanned Aircraft System Imagery in a Coastal Oligohaline Marsh

Abstract

Billions of dollars are projected to be spent on restoration projects along the northern Gulf Coast which will require efficient monitoring at both landscape and project-specific scales. Recent developments in unmanned aircraft systems (UAS) have sparked interest in the ability of these “drones” to capture hyperspatial imagery (pixel resolution < 10 cm) that resolves individual species and produces accurate data for monitoring programs in coastal landscapes. We present a case study conducted at Coastwide Reference Monitoring System (CRMS) station 0392, a Spartina patens–dominated, oligohaline coastal marsh in Terrebonne Parish, Louisiana. Results demonstrate the ability of UAS technology to collect hyperspatial, multispectral aerial images in a coastal wetland, and to produce very-high-resolution orthomosaics and digital elevation models. We then used object-based image analysis (OBIA) techniques to (1) delineate the land–water interface, (2) classify composition by dominant species, and (3) quantify average plant height by species. Model results were validated with traditional on-the-ground CRMS vegetation surveys. Results suggest that OBIA methods can overcome the spectral variability of hyperspatial datasets, quantify uncertainties in conventional techniques, and provide improved estimates of wetland vegetation cover and species composition. These methods scale conventional plot-level coverage values to data-rich landscape-level models and provide useful tools to monitor restoration performance, landscape changes, and ecosystem services in coastal wetland systems.