Increasing human population, economical development, and industrialization of our society lead to disturbance of natural ecosystems. To prevent long-term damage of the environment, the affected ecosystems need to be rehabilitated to a sustainable form after industrial operations cease. Environment rehabilitation is a long-term process that is complex and costly as it includes restoration of soil, water bodies, and reintroduction of plant, insect, and animal species. We developed a wireless sensor network for evidence-based rehabilitation of disturbed natural ecosystems. Drawing on our case study of an open-cut surface mine in rural Australia, we show that microclimate data can provide insights into the efficiency of specific rehabilitation processes, disentangle the impact of microclimate on the rehabilitation success, and provide early indicators into potential rehabilitation problems. We worked with ecologists to provide domain-based advice on addressing a range of rehabilitation problems and developed a system that periodically generates reports on the rehabilitation status of areas of interest. The report incorporates data from expert surveys from the field and microclimate data from sensors, and provides recommendations to improve rehabilitation in under-performing areas. Such evidence-based assessment of rehabilitation is an important step towards ensuring compliance with set rehabilitation objectives, potentially leading to both more successful and less costly environment rehabilitation.
Major factors influencing ecosystem condition are resource inputs, such as climate and soil, and internal factors, such as species distribution, shading, or decomposition rates. Climate sensing network collects information about resource inputs while BioCondition surveys provide estimates on the internal factors. Limited historical data on climate and species distributions are available from the government.
Overview of our system. Microclimate sensor data is combined with BioCondition surveys using our analytics and visualization packages to generate rehabilitation status reports.
Sensing Node and Base Station
We found that bio-condition scores, shown below correlate well with soil moisture readings from our sensor nodes, confirming the utility of sensor network data as an indicator of ecosystem condition.
For more details on this work, please refer to the following publication:
B. Kusy, C. Richter, S. Bhandari, R. Jurdak, M. Ngugi, G. Nelder, “Evidence-based Landscape Rehabilitation through Microclimate Sensing,” In proceedings of the Twelfth Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Seattle, USA, June 2015.