POPULATION LEVEL STORM RESILIENCE MODEL THREATS_SRM Julian Forbes ‐ Laird BA(Hons), MICFor, MRICS, MEWI, M.Arbor.A, Dip.Arb.(RFS) www.flac.uk.com
A method produced for Network Rail Infrastructure Ltd Implemented with Astrium Geo ‐ Information Services
Origins
‐ 23 May 2011 ‐ 03 January 2012 On both dates Route Scotland was effectively closed during high winds due to the large number of windthrown trees External report recommended ‘enhanced tree removal’ ORR: implement recommendation or develop risk ‐ based model FLAC commissioned to research & design a model for identifying storm vulnerability to enable targeted tree removal
First principles
What is a dangerous tree?
Civil engineering protocol for management of faulted structures: If a load ‐ bearing structure has suffered primary failure (e.g. cracking has developed), it has proven unfit for purpose and should be taken out of service prior to suffering secondary failure (i.e. collapse) Being dynamic living organisms, trees’ ability to self ‐ repair complicates the identification of primary failure, but when this is observed, the tree can properly be described as dangerous: No predictable delay prior to secondary failure
THREATS_NR
• A bespoke adaptation of THREATS for the railway environment • The only railway ‐ specific TRA system • Developed by JFL in 2009 for NR to use in its NLTS • >3.4million trees inspected on foot • Just over 24K hazard trees identified (7%) • Tree failures onto the railway reduced by >70% in four years • The vast majority of ongoing failures are third party trees & non ‐ defective windthrow
Hazard tree found by 23m high third party larch, 1.1m stem dia., very severe basal decay ( Ganoderma JFL, 23.IV.09 resinaceum ), weighted towards 2 x 65mph lines, 165m from bridge abutment Primary failure had Failure Score Imminent/Immediate 50 x already occurred! Target Score Very high value 40 x Impact Score Default 10 (STS) 10 = Total 20000 Risk Category 7 ‐ Emergency
WEST LAVINGTON, July 2010
‐ Off ‐ site tree failed onto the railway ( Armillaria ) ‐ Collision in fog with 95mph express, >300 passengers ‐ 1200m stopping distance under emergency braking ‐ Impact occurred <100m from 4m embankment ‐ RAIB investigation into accident also examined NR tree risk management system… ‐ THREATS / THREATS_NR subject to detailed scrutiny ‐ Probably the most detailed & technical review undertaken of a TRA system
Taiwan, April 2011 Derailment of low speed, narrow gauge train: 5 dead, 107 injured
THREATS_SRM
Prior research Lochawe research project, December 2010: stability of trees on slopes: Failure criteria established for tree height v degrees of slope FLAC used LiDAR mapping (DTM/DSM differential) to find veg height & degree of slope Proof of concept was achieved for use of LiDAR for identification of tree failure predisposing factors
SRM rationale Trees fall over in high winds. Right? Wrong… Some trees fall over in high winds To design the storm resilience model, I asked the question: WHY? Why only some…
FLAC investigated >40 tree failure sites to record underlying site ‐ related causal factors… for example: ‐ Topographic exposure ‐ Poor drainage ‐ Steep slope ‐ Width of tree belt ‐ Railway orientation Separately or in combination, the factors derive the SRM failure score
Risk Factor examples Topographic exposure Where the tree or group of trees exceeds by >1/3 the height of other trees or adjacent terrain (within 50m) to windward (relative to the prevailing wind), including where the tree is located on the upper slopes of a cutting such that it exceeds the cutting lip by this amount Railway orientation from 90 ‐ 180 ˚ & from 270 ‐ 360 ˚ This factor seeks to express situations where the railway is flank ‐ on to the prevailing wind. Effectively, this covers railway orientations from NW ‐ SE through South
Key explanation
The objective of the SRM is to identify mechanically similar sites to those where failures are known to have occurred, and to set SR thresholds for them as the basis of network ‐ wide tree management and risk control strategies THREATS_SRM does not seek to model tree risk, so much as site risk : some locations are inherently vulnerable to tree failure due to site ‐ related factors THREATS_SRM provides a framework for identifying & stratifying these sites
• Having identified the factors involved, the next challenge was to map them • NR commissioned a LiDAR, RGB and partial IR survey of the Scotland network • Undertaken by Astrium GIS (formerly InfoTerra), a subsidiary of EADS
Introduction How it works
The model relies on computer ‐ based automatic interrogation of the LiDAR dataset, based on the SRM parameters The LiDAR data interrogation uses three algorithms designed by FLAC: ‐ Approximation of stem position from polygon centroid ‐ Estimated tree count from height & area of canopy ‐ Method for inferring stem diameter at track impact point, based on LiDAR data for tree height
Stem dia. at track
Height / stem diameter ratios A measured dataset of 5000 trees was interrogated to find H/D correlations that could be used to infer stem dia. from tree height (as measured by LiDAR). We found: • 6m to <14m H/D = 31 • 14m to <21m H/D = 36.5 • ≥ 21m H/D = 27 (modeled up to 35m)
Stem dia. at track impact point The formula for this is: • D = Db (1 ‐ H/Ht) • Where: • Db = computed stem diameter • Ht = total height of tree • H = height above ground being considered (= track offset) • D = diameter of tree at height H (= dia. of timber on track)
Worked examples 1. A tree of height ( HT ) 12m has a computed dia. ( Db ) of 372mm. For a 7m track offset ( H ): subtract 7m from 12m to get 5m which is 41.6%, so timber dia. at track ( D ) will be 372 x .416 = 155mm, falling within 150 ‐ 350mm size range for Impact Score 2. 19m tree with a 10m track offset has a computed dia. of 513mm. 19m minus 10m gives 9m or 47.4%, so timber dia. at track will be 513 x .474 = 244mm, also falling within 150 ‐ 350mm size range for Impact Score 3. The same tree as (2) but with a 7m track offset: 19 ‐ 7 = 12 or 63.1%, so dia. at track will be 513 x .631 = 323mm. This trips a mid ‐ point uplift threshold and so would be scored as 350 ‐ 750mm
The survey corridor as seen by LiDAR…
Airborne data corridor – 10cm imagery
Airborne data corridor – 50cm LiDAR
Semi ‐ automatic tree canopy identification
Estimate the trunk position
Estimate the crown extent
Distance to rail
Trunk impact diameter
Distance to bridges
Overall tree risk (THREATS_SRM scoring)
Overall tree risk – by management unit
Outcomes 1.1million polygons identified from the LiDAR dataset Each polygon returns 88 data cells… 96.8 million pieces of data Of 1.1mi polygons, only 1500 found to be in Site Risk Categories 6 & 7, or 0.14% ORR briefed on THREATS_SRM: “Excellent” “Delighted”
Does it work? At worst, the answer lies somewhere between maybe & probably Time – and the next storm – will tell BUT: It’s better than a) nothing and b) felling all the trees AND: Applying the model to subsequent non ‐ defective failures identifies the same site risk factors as causative
Other applications of THREATS & remote sensing… THREATS_EW A risk assessment model for landslip THREATS_WF A risk assessment model for wildfires
Thank you for listening
Recommend
More recommend
