Unmanned Aerial Vehicles (UAVs) for Monitoring Macroalgal Biodiversity: Comparison of RGB and Multispectral Imaging Sensors for Biodiversity Assessments

Leigh Tait,Hannah Charan-Dixon,David Schiel,Ian Hawes,John Pirker,Jochen Bind

doi:10.3390/rs11192332

Abstract

Developments in the capabilities and affordability of unmanned aerial vehicles (UAVs) have led to an explosion in their use for a range of ecological and agricultural remote sensing applications. However, the ubiquity of visible light cameras aboard readily available UAVs may be limiting the application of these devices for fine-scale, high taxonomic resolution monitoring. Here we compare the use of RGB and multispectral cameras deployed aboard UAVs for assessing intertidal and shallow subtidal marine macroalgae to a high taxonomic resolution. Our results show that the diverse spectral profiles of marine macroalgae naturally lend themselves to remote sensing and habitat classification. Furthermore, we show that biodiversity assessments, particularly in shallow subtidal habitats, are enhanced using six-band discrete wavelength multispectral sensors (81% accuracy, Cohen’s Kappa) compared to three-band broad channel RGB sensors (79% accuracy, Cohen’s Kappa) for 10 habitat classes. Combining broad band RGB signals and narrow band multispectral sensing further improved the accuracy of classification with a combined accuracy of 90% (Cohen’s Kappa). Despite notable improvements in accuracy with multispectral imaging, RGB sensors were highly capable of broad habitat classification and rivaled multispectral sensors for classifying intertidal habitats. High spatial scale monitoring of turbid exposed rocky reefs presents a unique set of challenges, but the limitations of more traditional methods can be overcome by targeting ideal conditions with UAVs.

Highlights

Kelp and macroalgal ecosystems cover a large proportion of the earth’s coastlines [1], support diverse and productive ecosystems [2], and are some of the most prolific carbon fixers on the planet [3]
Pixel resolution can be problematic for habitat classification, where spectral signatures of multiple vegetation types are dampened [17]
Exposed coastal rocky reef ecosystems are challenging habitats to monitor for several reasons: (a) they are ecologically highly variable in space and time; (b) they are often exposed to swell, being exposed for short periods and sometimes difficult to access due to coastal topography; and (c) rocky reefs are typically a narrow interface between land and sea, making them difficult to sample in situ over large distances

Summary

Introduction

Kelp and macroalgal ecosystems cover a large proportion of the earth’s coastlines [1], support diverse and productive ecosystems [2], and are some of the most prolific carbon fixers on the planet [3] These habitats on rocky shores are a key component of temperate marine ecosystems, providing a variety of ecosystem services, producing biomass for consumers within and far outside of kelp beds, and contributing to carbon sequestration in deep sediments [4]. Satellite imagery is greatly affected by meteorological conditions, such as cloud and aerosol interference, surface glare, and poor synchrony with tides. For many applications these limitations are absorbed by the high frequency of satellite passes. Autonomous unmanned aerial vehicles (UAVs, commonly referred to as “drones”) deployed to coincide with meteorological and oceanographic conditions provide a potential alternative, allowing the targeting of key meteorological variables and performing moderate-scale mapping of intertidal and shallow subtidal kelp and macroalgae beds [20]

Methods

Findings

Discussion

Conclusion