Project

About the project

Description

The Kimberley region is vast and remote, as well as difficult and expensive to access in order to carry out field work and collect monitoring observations. Remote sensing technologies can provide cost effective methods to gather historical and baseline monitoring data at metre to kilometre resolution, both at synoptic scales for regional management applications and in near real time to guide operational decision making.

Archives of remotely sensed data extend back more than 25 years, providing a valuable record of changing environmental conditions. These technologies can contribute to improving asset inventories in marine parks. Also, through providing observations that can be used to challenge models (e.g. through model-data-assimilation approaches), they can increase the reliability of the deterministic models (hydrodynamics, biogeochemical) and probabilistic models required to develop adaptive management related approaches to manage this unique environment.

Aims

  • Provide a review of remote sensing work and procedures with respect to the development of remote sensing methods to support improved and cost effective monitoring and management of the Kimberley region.
  • Quantify the reliability of remotely sensed turbidity products for use in  the Kimberley Region.

Methods

  • Undertake a review of management requirements for remotely sensed data along with the technical and operational constraints of available remote sensing technology.
  • Collect reference datasets of in-water constituents to understand uncertainty in the products.
  • Computer analysis and modelling to develop a regionally-tuned algorithm for TSS

Outcomes

  • A good understanding by management agencies of the applicability and reliability of remote sensed data as a management and monitoring tool, including knowledge of how to access data.
  • An improved understanding of the application and limitations of remotely sensed data for monitoring and managing the Kimberley region.
  • Improved access to remotely sensed data in appropriate formats
  • Increased uptake of remotely sensed data, at appropriate cost and with relevant information content
  • A good understanding of which remotely sensed products may be related to environmental indicators of condition
  • A good understanding by management agencies of the appropriate space and time scales required to monitor various environmental values (e.g. condition, change, classification) and the appropriateness of remotely sensed data for these tasks: i.e. assessing whether the information from remotely sensed data is “fit‐for‐purpose” and the suitability of surrogate measurements

Research Articles

Cherukuru N, Dekker A, Hardman-Mountford N, Clementson L, Thompson P. (2019) Bio-optical variability in multiple water masses across a tropical shelf: Implications for ocean colour remote sensing models. Estuarine, Coastal and Shelf Science, 219, 223-230. doi.org/10.1016/j.ecss.2019.02.015

Dorji P, Fearns P (2017) Impact of the spatial resolution of satellite remote sensing sensors in the quantification of total suspended sediment concentration: A case study in turbid waters of Northern Western Australia. PLoS ONE doi.org/10.1371/journal.pone.0175042

Dorji P, Fearns P. A. (2016) Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors. Remote Sensing. 2016; 8(10):810 doi:10.3390/rs8100810

Dorji P, Fearns P, Broomhall M (2016) A Semi-Analytic Model for Estimating Total Suspended Sediment Concentration in Turbid Coastal Waters of Northern Western Australia Using MODIS-Aqua 250 m Data. Remote Sensing. 2016; 8(7):556 doi:10.3390/rs8070556

Media

Presentations

WAMSI Project 1.4 Remote Sensing (2017 WAMSI Research Conference)

WAMSI Project 1.4 Remote Sensing – Peter Fearns (Parks and Wildlife Lunch and Learn session)

WAMSI Project 1.4 Remote Sensing – Jim Greenwood (Parks and Wildlife Lunch and Learn session)

Remote Sensing (2015 WAMSI Research Conference)

Details

Program: Kimberley Marine Research

Completed: December 2017

Location: Kimberley Region

Project Leader: David Antoine, Curtin University

Email: david.antoine@curtin.edu.au

Publications

Summary

Final Report

Technical Report

Water Quality Report

Data Report