The Project
The Narrabri Gas Project is a major gas extraction and transfer initiative designed to supply natural gas to homes, businesses, and industries across NSW. To ensure ongoing compliance with environmental approval conditions, annual biodiversity and rehabilitation monitoring is required across the project footprint. e2m was engaged to develop and implement both the Biodiversity Monitoring Program (BMP) and Rehabilitation Monitoring Program (RMP) to meet these obligations.
Our Services
e2m designed and executed a comprehensive monitoring framework tailored to the project’s scale and regulatory requirements. In the first monitoring round, we established a network of representative monitoring sites across the project area, ensuring long-term consistency and data integrity. To manage the large and complex datasets generated, e2m developed a suite of innovative, AI-driven tools and automated workflows to streamline data processing and analysis.
Outcomes
e2m successfully implemented both the BMP and RMP, delivering a robust, repeatable monitoring framework to meet regulatory expectations. The team developed a range of innovative approaches to data processing and analysis focused around automation and the use of artificial intelligence (AI).
We used an AI based object detection model to automatically split camera trap images that contained animals from those that were empty or contained vehicles or humans. This method allowed a reduction in time spent processing images while also increasing the quality of labelled images through the avoidance of human labelling errors. It is also a highly repeatable method that can be reliably used year-on-year.
We used another machine learning model to automatically process photographs taken for the quantification of foliage cover. This model worked by segmenting photographs into a range of categories with the output being the proportion of the image identified within each category. The use of this model near eliminated the inherent inconsistencies that arise from human processing and allows for precision, repeatability and comparable results between monitoring surveys.
We also produced scripted data analysis code in Statistical Software R for the robust analysis of all monitoring data. This code was specific for each dataset and can be used for all subsequent monitoring surveys to ensure results are comparable.
These innovations not only enhanced data quality and reporting speed but also positioned the project for long-term compliance success. e2m’s work demonstrates the power of combining ecological expertise with advanced technology to deliver scalable, high-integrity monitoring solutions.
