A Web Based Toolkit for Using Remote Sensing Data

By Chris Roelfsema, posted on June 20th, 2010 in Articles, Biodiversity, Climate, Disasters, Earth Observation, Ecosystems Theme, Oceans, Technology, Water, Weather

Dr. Chris Roelfsema, Prof. Stuart Phinn, Dieter Tracey, Joh Speirs, Michael Hewson and Dr. Kasper Johansen

http://www.gpem.uq.edu.au/cser-rstoolkit

Biophysical Remote Sensing Group
Centre Spatial Environmental Science,
School of Geography, Planning and Environmental Management
University of Queensland, Brisbane, Australia

Contact email: c.roelfsema@uq.edu.au

Maps derived from airborne and satellite imaging systems provide reliable and cost effective information for monitoring, modelling and managing marine, terrestrial and atmospheric environments. As the number of commercial and free airborne and satellite image types increases, along with easier access to public domain and open-source image processing approaches, the choice of which data and processing approach(es) to use is confusing. To enable technicians, scientists and managers to make the most appropriate selection of data and a processing approach for a specific environment and application, an interactive, web-based toolkit was created by the Biophysical Remote Sensing Group at the University of Queensland.

The main aim of the remote sensing toolkit with a conceptualisation of the environments discussed.

The remote sensing toolkit shows managers, scientists and technicians working in marine, terrestrial and atmospheric environments how remote sensing images can be used to map and monitor environmental features.

The toolkit teaches managers, scientists and technicians working in marine, terrestrial and atmospheric environments, how images collected from satellites and aircraft (remote sensing) can be used to map and monitor environmental features or processes, and their change over time.

The toolkit guides users through the process of selecting remotely sensed data to map a specific biophysical variable in a terrestrial, marine or atmospheric environment, and then outlines the processing and resource requirements, and likely costs for implementing such a project. Detailed descriptions are provided for type(s) of image data required, along with the type of processing approach and requisite personnel, hardware and software. The toolkit also provides examples of different mapping applications, and brief explanations of how remote sensing technologies work, as well as references or resource material.

The toolkit is intended to reduce the gap in knowledge between remote sensing scientists/technicians as “the producers” and the wide range of groups using the remote sensing based products “the users.” Developing and improving this understanding is essential to enable remote sensing to be used effectively in terrestrial, marine or atmospheric environments. The toolkit can also be used to educate interested students, scientists, technicians or managers.

The toolkit is not creating a web-based GIS or image processing system to produce maps. Nor does it provide a single solution for specific mapping needs.

As environments, management issues, remote sensing data sources, image processing techniques, and the questions asked by scientist and managers keep changing, the toolkit will be updated. It is meant to function as a heuristic or self-learning and evolving tool, serving as a means for the Biophysical Remote Sensing Group to communicate, share and develop their accumulated knowledge and experience in solving practical problems.

Screenshot from remote sensing toolkit http://ww2.gpem.uq.edu.au/CRSSIS/tools/rstoolkit_new/html/marine/reefs/rf-geo_sh_tb-cl.html

Example screen shot of the marine toolkit where a user can asses what remote sensing option is suitable for mapping coral reef habitats at a specified water depth and/or water clarity rang.

The toolkit is based on the conceptual framework developed from past research by Prof. Stuart Phinn and Dr. Chris Roelfsema. With funding from the Cooperate Research Centre for Coastal Zone, Estuaries & Waterways Management and an Australian Research Council Discovery Grant, Prof. Phinn’s initial framework was translated into a Coastal Remote Sensing web-based toolkit in 2006. This initial toolkit was significantly revised and expanded in 2010 by Dr. Roelfsema and Prof. Phinn and with help of staff and students of the Biophysical Remote Sensing Group and with funding from the World Bank GEF Coral Reef Target Research Program(http://www.gefcoral.org/) and the University of Queensland. The 2010 toolkit covers atmospheric and terrestrial environments, in addition to an expanded marine section, and a section explaining how remote sensing image acquisition, field data acquisition and image processing works. Future work will update the parameters measured and range of sensors, and also develop an interactive learning package with focus on the use of remote sensing in marine, terrestrial and atmospheric environments.

Supporting references:

Roelfsema, C.M (2009) Integrating Field and Remotely Sensed Data for Assessment of Coral Reef and Seagrass Habitats, Doctoral Thesis, School of Geography, Planning and Environmental Management, The University of Queensland.

Phinn, S.R., Roelfsema, C.M. and Stumpf, R. (2010) “Remote sensing: Discerning the promise from the reality”. In: Longstaff, B.J., T.J.B. Carruthers, W.C. Dennison, T.R. Lookingbill, J.M. Hawkey, J.E. Thomas, E.C. Wicks, and J. Woerner (eds) Integrating and applying science: A handbook for effective coastal ecosystem assessment. IAN Press, Cambridge, Maryland. U.S.A., Chapter 15, pp.201-222.

Phinn, S., Stow, D., Franklin, J., Mertes, L. and Michaelsen, J. (2003) Remotely sensed data for ecosystem analyses: “Combining hierarchy theory and scene models”, Environmental Management, 31(3):429-441.

Phinn, S.R., Menges , C. , Hill, G.J.E. and Stanford, M. (2000) “Optimising remotely sensed solutions for monitoring, modelling and managing coastal environments,” Remote Sensing of Environment, 73(2):117-132.

Phinn, S.R. (1998) “A Framework for Selecting Appropriate Remotely Sensed Data Dimensions For Environmental Monitoring and Management”. International Journal of Remote Sensing, 19(17): 3457-3463.

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