Change-mapping of estuarine intertidal seagrass (Zostera muelleri) using multispectral imagery flown by remotely piloted aircraft (RPA) at Wharekawa Harbour, New Zealand

Abstract

This research assesses a survey method for measuring the change in density of intertidal estuarine seagrass (Zostera muelleri) at Wharekawa Harbour, New Zealand, using an autonomous remotely piloted aircraft fitted with a narrowband multispectral camera. Image classification was modelled using the random forest classifier trained with ground observation data sourced from 63 photographed quadrat stations upon three parallel transect lines. Seagrass coverage in the georeferenced and rectified ground photography was estimated by visual interpretation on a three-tier (low, medium and high) density scale, then exact leaf area additionally calculated from digitised seagrass leaf coverage visible in rectified ground photography. Three replicate aerial surveys across four months were conducted to compare predicted change in seagrass density with actual measured change, during austral summer growth and autumn decline. Classification of the resulting image mosaic (2.5 cm pixel size) achieved up to 90–93% overall accuracy across multiple surveys when attributing density class, 93–96% accuracy for prediction of seagrass presence, and 81–91% in terms of detection of seagrass on the ground. Change-maps allow regions of growth and decline to be visualised. Correlation (r) between actual and predicted change for 48 independent test grid squares was 0.89 and 0.61 for the summer and autumn change periods respectively. Rapid visual interpretation of classification end-member classes yielded change measurement equivalent to that of accurately measured seagrass leaf area. The research demonstrates that RPA survey using a multispectral camera is viable for monitoring change in seagrass condition.