Infrastructural hazards from trees include impact damage and associated service disruption from storm-related windthrow. Most of the trees which fail under severe weather conditions do so not because of ill health or due to extreme mean windspeeds, but instead due to subtle combinations of precise micro-environmental circumstances.
These circumstances articulate a tree’s “storm resilience”.
Our collaboration with FLAC has enabled us to model the storm resilience of tree populations at national scale, in turn facilitating targeted, risk-based control.
The implementation of the first model, THREATS_SRM, was so successful in reducing infrastructure downtime and associated costs, that the UK’s Network Rail commissioned successor models, POLESTORM and FAILSAFE, for nationwide rollout. POLESTORM is significantly more sophisticated than the original model which was, in itself, unique. FAILSAFE reduces the risk of death and injury from tree failure onto mass transit systems.
Our tree and vegetation-related natural hazard management systems are designed to run within a computer-based, automatic data extraction and analysis architecture. They can be used for any infrastructure owner or operator to assess infrastructural hazards, as well as supporting vegetation management.
Wildfires are a widespread, common and highly destructive phenomenon, annually consuming millions of hectares of vegetation. Whilst in many cases wildfires are an essential part of a natural ecosphere, in others they pose a serious hazard to life and property, and even to aspects of the ecosphere itself.
Understanding likely wildfire behaviour enhances community and infrastructure protection and is key to reducing its human impacts. Existing computer-based behavioural models require an input identifying the “fuel complex” in any given area. The quality of this input data can vary enormously, weakening the accuracy of the model.
Our response to this situation is PHYTOPYR: intelligent, high-resolution fuel mapping to inform and enhance existing wildfire risk models, delivered by remote sensing at regional scale, resolvable to a sub-10m grid.
Using the Zephyr High-Altitude Pseudo-Satellite (HAPS), PHYTOPYR, which is currently under development, can provide detailed vegetation survey information as the critical model input. This information can be augmented and integrated with high-resolution LiDAR-based DTM/ DSM data, where available, providing unparalleled accuracy in fuel complex mapping.
PHYTOPYR bridges the gap between empirical models and fuel classification systems, overcoming the need for manual, often broad-brush vegetation assessments, which frequently fail to reflect real-world conditions at meaningful scale.
The integration of PHYTOPYR data with preferred fire behaviour modelling results in a step-change improvement in baseline knowledge: this delivers a commensurate reduction in non-machine inputs and flip-side increase in model performance.
Equally important to community and infrastructure protection are early warning of fire initiation and real-time fire progress monitoring.
To support this stage of the wildfire control process, we have conceived HiSTARS, a technology package which uses the OPAZ sensor mounted on Zephyr.
HiSTARS can direct the Zephyr to likely fire initiation points, loiter to watch for early signs of ignition, provide locational information for fire control teams, and supply persistent, real-time high-resolution video in a variety of spectra, giving critical update information of fire progress and the nature of material being combusted.
HiSTARS also enables rapid and flexible updating of ecosphere, community and infrastructure risk assessment, to guide emergency coordinators’ decisions on intervention/ evacuation.
Together, PHYTOPYR and HiSTARS combine into a unique tool system for large-area wildfire protection.
For more information on Zephyr, please visit:
Wherever natural terrain has been modified into graded slopes to accommodate transport routes, this gives rise to the possibility of collapse, either of the modified earthwork (especially those reliant on legacy engineering), or of the adjacent natural landform.
Modern mass transit systems and highway networks frequently traverse and bisect landscapes through cuttings and along embankments. To assist in safeguarding this infrastructure, we have developed the CRISES model to seek out inter-relational landslide predisposing factors, focussed towards topography altered by engineering works.
Based on a multi-layered, remotely-sensed survey and mapping exercise, CRISES models these factors to identify locations of potentially inadequate slope shear strength.
CRISES considers the presence and combination of landslide predisposing factors, the value of the transport infrastructure at risk, and the likely consequences for this infrastructure of a landslide. The methodology’s inbuilt scoring system then differentiates slopes into seven levels of overall risk.
This approach gives safety managers unparalleled information to aid decisions on slope reinforcement intervention.
As with our other natural hazard management systems, CRISES is specifically designed to operate at national scale. We believe that CRISES offers a revolutionary, safe and highly cost-efficient means of evaluating landslide risk throughout transport networks.