Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

Abstract

Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4m (one IKO pixel), 8m (2×2 IKO pixels), 12m (3×3 IKO pixels), and 16m (4×4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4m×4m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8m, and a random or slightly clustered pattern of cover over 9–240m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2×24m IKO pixels, 3×34m IKO pixels, and 4×4m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32m and a random or slightly clustered pattern across 33–240m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4m to 240m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales.