UAVs, Hyperspectral Remote Sensing and Machine learning Revolutionizing Reef Monitoring

Why it matters

Recent advances in Unmanned Areal Systems (UAS) also commonly known as drones or Remotely Piloted Aircraft (RPA) sensed imagery, sensor quality/size and geospatial image processing enable UAS’s to rapidly and continually monitor coral reefs for any determining the type of coral and signs of coral bleaching. This research focuses on  an Unmanned Aerial Vehicle (UAV) remote sensing based methodology to increase the efficiency and accuracy of existing surveillance practices to monitor corals and to detect coral bleaching. The methodology uses a UAV UAS integrated with advanced digital hyperspectral RGB sensors and machine learning algorithms. We evaluate the methodology on a predictive model for Bleaching detection.

 Coral bleaching signature extraction example for Porites Massive 

 

Overview

This project investigates the capabilities of unmanned aerial systems (UAS) and hyperspectral imaging technologies for identification, monitoring and surveying of reef ecosystems. The overall aim of this project is to investigate the capabilities and use of these technologies to support in coral bleaching detection and monitoring to assist in other coral protection and reef sustainability projects. We explore the combination of air-borne RGB and hyperspectral imagery with in-water based data at sixty-four distinct locations with several types of coral under diverse levels of bleaching. We describe the technology used, the sensors, the UAV, the flight operations, the processing workflow of the datasets from each imagery type, the methods for combining multiple air-borne with ground based datasets and we finally present relevant results of correlation between the different processed datasets. The development of a methodology for the collection and analysis of airborne multi and hyperspectral imagery would provide coral reef researchers, scientists and UAV practitioners, with reliable data.

The aim of this project is to predictive models and deep learning combined with high resolution hyperspectral detection technologies to increase surveying efficiency and to develop methodology for aerial coral bleaching detection.

The project objectives are:

  • Modelling region-wide environmental changes to identify criteria for selecting endangered regions.
  • Prioritizing sampling times and areas for optimized detection capabilities.
  • Evaluate utility of high-resolution imagery for object detection under effects of water column spectral scattering.
  • Evaluating capabilities of data collected from Unmanned Ariel Systems (UAS).
  • Modeling data extrapolation for demonstrating capabilities of UAS reef conservation systems.

Real world impact

The development of a methodology for the collection and analysis of airborne multi and hyperspectral imagery would provide coral reef researchers, scientists and UAV practitioners, with reliable data.

Milestones & Achievements

  • Integration of Hyperspectral and rgb camera with UAS on multi-rotor completed.
  • Initial identification of coral reef for case study.
  • Data capture of reef.
  • Images orthorectified and processed for deep learning classification and additional data refinements.
  • Predictive/deep learning model developed.

Partners


Chief Investigators

Team

Other Team Members