Part 4 – How do you Cost Effectively Collect Representative Data over a Large Area?
No matter what the basis for assessing rehabilitation success, the cost-effective collection of representative data is a key requirement – as it is for any monitoring program. In the final article in this series, Astron’s Principal Scientist Katina Strelein discusses how the rapidly advancing technology of remote sensing may be able to help overcome some of the limitations associated with conventional rehabilitation monitoring methodologies.
Remote Sensing – What is it?
Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. It utilises data captured by sensors mounted on satellites, Unmanned Aerial Vehicles (UAV) or manned aircraft. Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas. Photographic cameras are the most commonly used passive sensors. Active collection emits energy in order to scan objects and areas whereupon a sensor measures the radiation that is reflected or backscattered from the target. Radar and LiDAR are examples of active sensors.
Landscape Scale Monitoring- Gathering Data Representative of the Whole Ecosystem
Monitoring at a much larger scale using remote sensing removes some of the issues associated with whether data from point based monitoring methods such quadrats or transects is representative of the performance of the area of interest (and how many transects/quadrats are required for the data to be scientifically valid). Conducting monitoring at the landscape scale encompassing all of a rehabilitated landform is a census instead of a sub-sample. This means that for the variables measured, the performance of the whole landform is understood and any areas of poorer performance can be identified. The scale of monitoring also means that the heterogeneity within the rehabilitation can be compared to natural heterogeneity in the analogue vegetation. This can be useful for determining if patches of low or no vegetation cover in rehabilitation indicate failure or are consistent with the surrounding vegetation.
If data can be captured at regular intervals for a reasonable cost (and potentially at times when access is restricted for ground-based monitoring), remote sensing can also be used to capture temporal variation in variables. Historic data may also be available which can be accessed to improve understanding of the site history and long term variability.
Remote sensed data can be used to help assess rehabilitation performance in the three key areas of landform geometry, landform stability and vegetation. It is particularly useful for monitoring variables such as landform stability which are difficult to monitor effectively using transects or quadrats.
However, there are still some variables, such as species diversity, which currently cannot be assessed using remote sensed data. Remote sensing approaches to rehabilitation monitoring also require a high degree of technical expertise – both in data capture and analysis – to ensure monitoring results are valid and repeatable.
The use of remote sensing for environmental monitoring is a rapidly advancing technology that allows monitoring of rehabilitation and the surrounding environment at a previously unachievable scale cost effectively. The additional information acquired by monitoring at the landscape scale allows for increased understanding of the surrounding environment for setting appropriate completion criteria and also the performance of rehabilitation for demonstrating trends towards meeting completion criteria.