In the past, the management of their tree stock was approached on an ad hoc basis, primarily addressed during major works or as a reactive response to specific issues.
Tree inspections weren’t routinely carried out for the 500,000 highway trees, and residents and road users’ expectations were low. In recent times, however, with rising interest in environmental measures, pressured budgets, increased legislation and a better-informed society, tree management became a far more complex challenge.
We implemented our ‘Software, Advisory and Surveying’ approach. We are working through a programme to survey 71,000 trees in year one, with plans to increase this by 34,000 each year, improving processes and increasing the tree information to start building a picture of the tree stock.
We integrated risk assessments with open-source rainfall data from the Environment Agency to build each asset’s history and provided a platform to collect and present this data in clear ways, with Tree SMART. We explored new technology including automation to discover the most efficient methodology for each asset, including understanding trees that may not be Staffordshire County Council-managed trees, but had an impact on the highway network.
We used LiDAR surveying to create the initial inventory of 71,000 trees within falling distance of highways for the first phase of surveying. This included all B roads, town centres and priority hotspots.
We continue to work with Staffordshire to help streamline their tree management processes. This service will greatly increase knowledge of the highway tree stock, allowing a risk-based approach to be taken for maintenance and monitoring.
This in turn introduces great cost savings and influences future tree-planting plans, increasing social value and offering environmental benefits, including helping with the journey to carbon net zero. Awareness of the tree stock allows evidence-based cases to be built for funding and cost-effective programming to be carried out.
KaarbonTech's support propels Staffordshire Highways in effective data-driven asset management, including highway trees."