Remote Sensing Research Unit

The Meraka Institute’s Remote Sensing Research Unit (RSRU) was created in 2005 to elevate the impact of the CSIR’s remote sensing activities on the advancement of science, technology and environmental management.

The mission of the RSRU is to conduct basic and applied remote sensing research for the advancement of scientific knowledge about the environment. This will be achieved by developing world-class remote sensing scientists and collaborating with other stakeholders to generate RS products that can assist in understanding and managing our environment.

Remote sensing (RS) refers to the science of identifying, observing and measuring objects from a distance. It involves the measurement of different wavelength radiations, reflected or emitted by objects by which they may be identified, classified and monitored. In our case, we are mostly involved in earth surface properties observed from satellites.

We work closely with universities to improve remote sensing training and post-graduate supervision. We collaborate with various other CSIR business units and centres (e.g. the units for Built Environment and Natural Resources and Environment and the Satellite Application Centre) on multiple research projects, ranging from automated feature extraction in the urban environment to regional active fire mapping and land degradation monitoring.

Our research group has a balanced composition of remote sensing researchers with natural resources backgrounds and computer scientists interested in digital image processing and time-series analysis. As a young and dynamic group, we strive to become leaders in our field and improve the overall level of RS in South Africa by advancing RS as a science.

With the inception of the South African Space Agency (SASA), the establishment of Earth Observation as one of SASA’s science themes and the launch of our own SumbandilaSat in May 2007, there are plenty of opportunities for RS research.

We are currently focusing on four main science themes:

• Time-series analysis of AVHRR, MODIS and SPOT data
• Automated Feature Extraction
• Fire: active fire, burn scar and fire risk mapping and modelling
• Interoperability of sensors