A Proactive Approach for ROW VM Using GIS and Remotely ...

The spatial visualization capabilities of geographicinformation systems (GIS) technology provide an efficientand cost-effective method to analyze and display remotelysensed data. By identifying predetermined and/or customland cover classes and associated vegetation densities alongright-of-way (ROW) corridors, resource managers are able todevelop strategies for prioritizing planned maintenance,calculate accessibility to determine equipment needs andrelevant safety protocols, map environmentally sensitiveareas, assist in identifying a wide array of encroachmentissues, and target only the specific locations that require afield inspection. Understanding how to utilize multispectralimagery, automated feature extraction processes, and GISanalysis techniques can lead to an advanced and proactiveROW vegetation management (VM) program.

A Proactive Approachfor ROW VM Using GISand Remotely SensedDataDeborah Sheeler andWilliam Ayersman

Keywords: Aerial Imagery,Distribution, GeographicInformation Systems (GIS), GISAnalysis, Image Analysis, LightDetection and Ranging (LiDAR),Photo Detection and Ranging(PhoDAR), Remote Sensing,Remotely Sensed Data, Rights-of-Way (ROW), Transmission, UtilityCorridors, Vegetation Density,Vegetation Encroachment,Vegetation Management (VM).

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Environmental Concerns in Rights-of-Way Management 12th International Symposium© 2019 Utility Arborist Association. All rights reserved.

INTRODUCTIONRemote sensing has a variety ofapplications for utility forestry andvegetation management (VM) (Pitt et al.1997). The combination of remotelysensed data and geographic informationsystems (GIS) can provide a visualinterpretation of large, complexdatasets. Further, remote sensing anddata analysis enables users to modelenvironments, detect patterns, andidentify trends that allow users to makeinformed decisions (Dixon et al. 1994;DeFries, et al. 1999; Verbesselt et al.2009).

Remote sensing is defined as theacquisition of information about anobject without making physical contactwith the object (NOAA 2018). Varioussensor devices can perform datacapture, such as cameras, radars, andlasers mounted on multiple platformsbased on the ground or on ships,aircrafts, unmanned aerial systems(UAS) (e.g. drones) or satellites. Whenprocessed, analyzed, and interpreted,remote sensing data allows for a widerange of applications in many fields ofstudy (Lefsky et al. 2002).

The nature of the collection processdictates the structural characteristics ofthe data and its usefulness for specificapplications. Aerial imagery, a commonremote sensing product, uses cameras tocapture digital images of the earth’ssurface and spectral characteristics (e.g.,color, pattern, texture) of an object(Goetz et al. 1985). While camerasensors can capture aerial imagery andspectral data, other remotely senseddata products, such as LiDAR (lightdetection and ranging) and PhoDAR(photo detection and ranging), cangenerate 3D point cloud formats forenhanced visualization techniques.LiDAR uses laser light to measuredistances by illuminating a target andanalyzing the reflected light to producemass point cloud datasets. PhoDAR usesoverlapping high resolution images tomeasure distances between objects by

analyzing similar features and patternsto produce point cloud data. All threeproducts can be analyzed to generateland cover layers or determine the sizeof features and objects on the earth’ssurface. When combined, theseproducts can be used to generate notonly two-dimensional (2D) rasterimages, but also 3D models of thelandscape.

As new VM applications arediscovered, using a combination of theseremote sensing methods and technologyyields endless possibilities to assesspotential issues and solve problems inand around rights-of-way (ROWs).These technological capabilities allowfor a complete assessment of ROWcorridors to determine potential risks toutility networks. In an industry wheremitigating risks are a top priority,remote sensing technology not onlyprovides an important analysis tool, butit also can help lead the way to solvingchallenging problems.

GIS Role in VM

GIS usage constitutes a multitude ofentities that range from computerhardware, software, data, and personnelused to interactively capture, manage,analyze, and display geographic data. AGIS serves as a repository of locationinformation and asset details. Location-based tracking allows users a way tovisually identify exactly where assets andvegetation are spatially located andunderstand trends in the data as a resultof current management practices (T&DWorld 2011). Innovative geoprocessingapproaches in GIS enables decision-makers to locate issues and furtheranalyze existing data to determine themost cost-effective approach andefficient work assignments. With a GIS,workers in the field can use mobiledevices to access, view, and updateinformation on a web map in real timeor offline maps without a connectionthat will automatically sync updateswhen the connection returns.

Spatial data processing hasadditional capabilities, which cangenerate much needed summarystatistics about circuits and substationsup to management regions. Thistargeted analysis begins the conversationprocess of when, where, and how muchfocus should be given to particular areasof the network. Routine trimming cyclesand line maintenance strategies can bedeveloped using GIS analysis as a basisfor strategic planning. By taking apriority-based approach to managingvegetation, cost savings can beoptimized.

Approaches

For many utility companies, vegetationprograms follow strict managementguidelines that constitute trimming,pruning, or removing potential hazards,whether that be trees or other types ofwoody and non-woody vegetation(Fellers 2017). Transmissioninfrastructure is typically more focusedthan distribution since outages on thosecircuits would affect a much largercontingent of customers. To understandthe extent of work to be completed in agiven year, surveys are conducted eitherby walking, driving, or aerial mapping.These surveys can indicate the numberof removals, linear feet of trimming orpruning, acres of mowing or the squarefootage of herbicide to be applied forherbaceous cover, and smaller woodyvegetation.

Depending on the level of detailrequired, field inspections can add costsand introduce risks to employeesthrough heat stress, insect and sunexposure, or tripping hazards. Toreduce these costs and risks, aerialmapping surveys have become muchmore common in today’s VM practices.Figure 1 compares the expectedproducts and services that are receivablefrom Image Analysis, Field Survey, andLiDAR assessments.

314 Part V: Technology

LiDAR Data and Analysis

One of the most common remotesensing applications for VM involves thecollection and analysis of LiDAR data.As a proactive approach, LiDAR datacan be acquired to detect vegetationheights and assess potential grow-in andfall-in risks to conductors, therebycovering more ground while decreasingthe safety hazards for field inspections.With LiDAR point cloud data, anobject’s height can be shown in a 3Dmodel of vegetation, utility lines, andstructures that are accurately mapped,manmade, and exhibit natural features(NOAA 2013). Given a set of clearancecriteria, these assessments can analyzepoint cloud data to identify locationsand distances of vegetationencroachment along utility corridors.Further analysis can quantify andprioritize clearance work needed alongspecific circuits. Using the dense pointclouds, the number of trees can beestimated for trimming and removal bydelineating individual crowns fordominant and co-dominant trees in thecanopy. One downfall to this assessmentis that understory trees tend to beunderrepresented, where crowns cannotbe identified accurately.

The main constraint to LiDARanalysis is the sheer cost to obtain,process, manage, and store the data.While the data is very useful for workplanning and management, the bulksize of LiDAR data sets puts a strain oncomputer hardware and servers thatother methods may not invoke.Although there are severalconfigurations and forms that LiDARdata can be formatted when workingwith the point cloud data andderivatives, LiDAR is most commonlystored in LAS or LAZ files whenacquired through a third-party vendor.Depending on the project size, thesedata can range from a few gigabytes tomany terabytes. Conversion of thesedata formats to workable geospatialdatasets creates additional storagerequirements that can be hundreds ofgigabytes in size, increasing the need forample storage space.

315A Proactive Approach for ROW VM Using GIS and Remotely Sensed Data

Figure 1. Comparison of Data Collection and Analysis Methods

^Tree heights can be estimated using photogrammetric processes that capture images and projects a3D environment using a significant amount of overlap of adjacent images. Tree height estimationusing images makes it possible to identify fall-in risk.*Tree species can be mapped using hyperspectral imagery if available. Cost to acquire this data wouldgreatly hinder the cost-effective approach to image analysis.

From point cloud data, elevationsare transformed into raster gridsthrough geoprocessing operations.Since LiDAR can penetrate foliage andsimilar obstructions, providing acomplete 3D representation, bare earthdigital elevation model (DEM), anddigital surface model (DSM) can begenerated to capture vegetation andother object heights. Through heightthresholding, the separation of tall andshort vegetation is possible, providingthe ability to distinguish trees at risk ofcontacting the conductors. Aftervegetation has been processed intoappropriate risk classifications, eachvegetation class can be converted intopolygons for additional analysis.

Imagery Data and Analysis

Another method of vegetationassessment with remotely sensed datarevolves around multi-spectral imagerycollection and analysis. Depending onthe imagery utilized, imageinterpretation can not only identify andclassify land cover types across a largearea of land, but also estimate objectheights. The key word here is estimate.

Airborne LiDAR andphotogrammetry are both viablemethods for capturing point clouds for3D modelling of manmade hardstructures and vegetation. Althoughboth methods produce point clouds, themanner of capturing data differs inmany ways, resulting in point cloudswith differing characteristics (Schwind2018). Photogrammetric detection andranging (PhoDAR) is a remote sensing3D capture technology which usesphotogrammetry to generate true-colorpoint clouds by processing high-resolution imagery and interpolatingknown object locations within multipleoverlapping photos. The potential valuein this method is the powerful 3Dvisualization capabilities that areacquired at a lower cost and allow forfaster processing turnaround time.Combined with previously acquiredLiDAR data, imagery analysis can beideal for evaluating potential vegetationencroachment locations.

In cases where photogrammetry isunable to generate accurate results,another imagery analysis method utilizesa 2D top-down, multi-spectralorthoimagery as a useful remotelysensed data source. While determiningexact vegetation heights is not possiblewith this approach, you can complete aland cover classification and a healthassessment of nearby vegetation toidentify locations with higher densitiesof vegetation encroachment, in additionto locations of possible fall-in risks withnearby vegetation that shows signs ofstress to help prioritize work alongcircuits. In order to provide the bestextractions, the imagery would require ared and infrared band for distinguishingvegetation from other types of landcover. For VM, land cover data extractedfrom image interpretation can also beanalyzed to identify locations of low-lying and tall vegetation types (e.g.,grassland or tree canopy), which helpsresource managers prioritize pruningcycles and chemical applications foreffective VM.

With multiple high-resolutionimagery sources attainable for little-to-no cost, this method has the ability to bereplicated on a much quicker cycle at adrastically cheaper cost than LiDARacquisition and processing. Somefeatures of LiDAR cannot be duplicatedwith imagery analysis, but imageryassessment is still very much a viable

option given the costs to complete a fullsystem snapshot using the most currentimagery available.

Common approaches for extractingvegetation with this method can be frommachine learning training samples orvegetation indices (VI). Standard VIsinclude the Normalized DifferenceVegetation Index (NDVI) (Tucker 1979;Running et al. 1995), EnhancedVegetation Index (EVI) (Matsushita etal. 2007), and Soil-Adjusted VegetationIndex (SAVI) (Huete 1988; Epting et al.2005), which are numerical indicatorsthat use the visible and near-infraredbands of the electromagnetic spectrumand are adopted to analyze remotesensing measurements and assesswhether the target being observedcontains live green vegetation or not.The most frequently used index, NDVI,is a ratio (using red and near-infraredbands) ranging from -1 to 1 withvegetation being a positive value—normally greater than 0.3 (Julien et al.2006).

For each multi-spectral image tile, atraining data set or vegetation index canbe calculated to determine the locationof vegetation and non-vegetation covertypes. After segmenting the vegetationdata, tree canopy can be converted fromindividual pixels into grouped polygonsto determine coverage within thedistribution buffer (Figure 2). Polygondata are edited to correct any


Figure 2. Processing steps to achieve tree canopy polygons for vegetation density analysis

misclassifications that occur during theinitial automated extractions.Depending on the quality of imagerysources, multiple rounds of qualitycontrol and assurance checks should beperformed in order to make the data asaccurate as possible.

Geospatial Processing

Regardless of the data extraction andcollection process, geospatial post-processing is needed to define criticalelements of any vegetation monitoringprogram. To determine quantifiableestimates of potential tree canopyencroachment, tree canopy density canbe generated in two ways: 1) by full spanor circuit using the percent of existingarea of vegetation polygons within theutility corridor or 2) determiningspecific vegetation locations byconverting the vegetation polygons intolinear vegetation segments or points ofinterests through an automatedtransformation processes. In the case oflinear trimming, converting polygons toa polyline format is a way to establish thelength of the encroaching vegetationsegments (Figure 3). Attributes can betransferred from the distribution linedata to the vegetation segment data foradditional queries and data summaries.This common metric, vegetation density,is represented by the percentage of thedistribution line that has encroachmentwithin the buffered distance. Thesedensity values make it possible tosummarize data by feeder or circuit tohelp estimate and prioritize work byidentifying locations along feeders withhigher vegetation. By incorporatingcustomer outage data, a proactiveapproach to analyzing and predictingcircuits that could have a higherlikelihood of multiple outages leading toincreased customer minutes ofinterruption (CMI) and system averageinterruption duration index (SAIDI)metrics is a step in the right direction.

Vegetation Accessibility

Accessibility can be described as theability to gain access to tree work bymeans of a bucket truck or othermechanical device. Using streetcenterline data, variable buffer distancefor each road classification isimplemented from the street (Figure 4).Any identified vegetation work thatoccurs inside this buffer can beconsidered accessible to a bucket truckor other type of mechanical equipmentused for trimming, pruning, or removal.All other vegetation can be consideredas not accessible. If a canopy trimsegment is deemed non-accessible, anywork associated with that section of treecanopy would need to be completed

manually; for instance, by using a treeclimber. In terms of budgeting andplanning for tree work in a fiscal year,this metric can assist by estimating thelinear footage of manual trimming vs.mechanical trimming, as well asdetermine safety requirement for thework to be completed. Metrics can besummarized for each feeder, substation,or management area.

Data Accuracy

Data accuracy is of the utmostimportance in determining linearfootage of tree encroachment andaccessibility. Random check points arecreated along the utility lines to test fortree canopy encroachment accuracy. In


Figure 3. Transformation of Tree Canopy Polygons to Linear Trim Segments

Figure 4. Accessible and inaccessible vegetation segments as defined by proximity to road networks

order to provide a fully comprehensiveaccuracy result, testing of omission andcommission errors needs to beconducted along the entire segment.Sampling only encroachment segmentswould not give an accuracy result that isfully comprehensive.

In general, LiDAR leads to moreaccurate mapping of vegetation heightsfor encroachment issues given thenature of how the data are mapped. Theability to use 3D rather than a 2D imagecreates an environment where it’spossible to visually segment trees fromother vegetation. Although this is themain limitation to using aerial orsatellite imagery, referencing knownspectral characteristics and relativespectral response (RSR) from differentfeatures will also provide better accuracyby determining which bands will workbest for the application (Barsi et al.2014).

Determining accuracy is twofold:spatial placement and length.Identifying the vegetation location is thefirst step, but to accurately assess andbudget for future work, the appropriateamount of vegetation length needs to beaccounted for. Testing for placement ismore straightforward using imagery, butlength precision requires some fieldtesting. For past assessments, fieldmeasurements have been used tovalidate the methods of imageryextraction as a way to assess vegetationencroachment. Not only was spatialplacement accuracy well above 90percent, but the length was also within adistance of two meters for mostvegetation encroachment data.

Common misclassifications includetree encroachment that occurred inheavily shaded areas on the aerialimagery and overextension of theLiDAR that shows tree canopy in non-vegetated areas on high resolution aerialimagery due to the span angle. Parallaxin some images tends to be morepronounced, which can lead tomisclassification due to “leaning” treecanopy into the encroachment area.

DISCUSSION

Management Implications

Improving Reliability Metrics

Energy companies face many risk factorsfor system disruptions ranging fromequipment failures to vegetation,weather, and wildlife (Dokic et al. 2016).When these outages occur, utilities forgorevenues and have to bear the costs offixing the outage quickly. This leads tocommercial customers without a meansto conduct business and residentialcustomers to have complications at theirresidence. Within the past decade,blackouts caused by vegetation-inducedoutages have cost utilities billions ofdollars (Campbell 2012; Dokic et al.2016). While these outages can occur onany part of the system, the distributionnetwork is considered to be moresusceptible to outages due to the sheerexposure of the conductors toimpending threats. To address this issue,the North American Electric ReliabilityCorporation (NERC) has begunregulating utilities to establish andenforce reliability standards. Failure toachieve reliability standards come withenforced penalties that can range from$1,000 to $1 million per incident, withsome fines being assessed daily (NERC2018).

Numerous utility companies openlyacknowledge that one of the leadingcauses of power outages are the result ofvegetation-related infractions (Doostanet al. 2018). By assessing vegetationdensity per circuit and prioritizingcircuits by their amount of exposure tovegetation, energy companies have theability to reduce future vegetationoutages. Government-monitored and -regulated metrics, such as CMI, SAIDI,and SAIFI, are also potentially decreasedas a result of this increased focus tovegetation-based failures (Combs 2017).For instance, SAIDI is measured in unitsof time in the course of a year, mostly inminutes. SAIDI can be reduced byremoving vegetation from the worst

performing circuits or by targetingcircuits with high volumes of vegetationencroachment. In theory, this has thepotential to reduce CMI, the numeratorof SAIDI.

Work Planning

Spatial visualization capabilities of GIStechnology provide an effective methodof analyzing and displaying remotelysensed data to aid in developing workplanning strategies. By determining thedensity of vegetation along distributioncorridors, GIS analysis can helpschedule and prioritize trimming cyclesalong circuits surrounded by the mostvegetation, calculate vegetation distancefrom roads and circuit lines in order todetermine equipment needs (e.g.,bucket trucks), analyze required safetyprotocols, and provide field personnelthe most efficient routes to locationsidentified for further inspection.

For example, when acquiring a newservice territory, not much may beknown about the current vegetationliabilities and maintenance needs. Usingthe methodology and approachdescribed in this paper can open upmanagement knowledge of whether ornot to send field personnel for site visits.The time it would take for remotesensing to capture hundreds, if notthousands, of line miles could provide areturn on investment (ROI) rather thanhaving to pay for travel expendituresand labor costs to assess the currentstatus of the entire system—not tomention less exposure to environmentaland structural field hazards.

Safety and Equipment Needs

Safety within the utility industry isthe top priority above all else. By honingthe development of best managementpractices (BMPs) for prevention andeducation, utility groups havesuccessfully reduced the numbers ofincidents and deaths (OSHA).

As a proactive approach to assessingfield conditions and potential safetyhazards, analyzing remotely sensed data


prior to traditional field inspections canpreemptively reduce the number ofaccidents each year. Depending on thedate of acquisition, remotely sensed datais essentially a snapshot in time.Although it may not represent existingground conditions at the time of fieldinspections, it can—at the very least—reduce exposure to hazardous fieldconditions by identifying and preparingfor potential hazards at specificlocations. Whether for planningpurposes or work requirements, fieldinspectors can precisely navigate tospecific locations to determineequipment needs and relevant safetyprotocols.

CONCLUSIONSThe methods discussed in this paper areways that utility companies canproactively plan and manage theirvegetation work. LiDAR analysis istheoretically a more accurate method interms of vegetation heightidentification, but the costs torepeatedly collect, process, and analyzethe data can be expensive. The secondmethod, imagery analysis, provides acost-effective means to assess vegetationencroachment on a regular updatecycle. Both methods have their benefitsand drawbacks, but ultimately, it is up tothe utility company to decide on themethod that works for theirorganization and the return oninvestment. Ideally, LiDAR would becollected at some point to establish abaseline of current conditions of utilitystructures for engineering purposes withsupplemental imagery analysis beingused to update vegetation data as newimagery becomes available.

With a focus on improvingreliability, products from theseassessments will provide importantinformation for prioritizing lineinspections, as well as determiningpriority circuits for focused VMprescriptions, which greatly assist withcost projections and budgeting. Inaddition, knowing the density ofencroachment along the distribution ortransmission lines allows utility

companies to prioritize circuits withhigher density, while forecasting low-lying vegetation removal that mayrequire potential herbicide applications.Reporting linear kilometers (or miles)of tree canopy and acreages ofherbaceous cover will assist in makinginformed decisions when determiningrequired equipment and budgets forROW VM.

Making safety a top priority andreducing risks should always be at theforefront of any discussion. Ultimately,the use of remotely sensed data toprioritize field work can reduceexposure to risks as well as provide aneffective approach to capturing a system-wide snapshot of potential vegetationissues.

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AUTHOR PROFILESDeborah SheelerDeborah Sheeler is currently theProduction Manager for Geospatial andSoftware Support Services with DaveyResource Group, Inc (DRG). She hasmore than 20 years of professionalexperience applying advancedgeospatial technology at all scales forutility VM (UVM), asset management,and environmental consulting servicesacross North America. Her primaryresponsibilities include managing thedevelopment of innovative GIS andremote sensing solutions and softwaresupport for both internal operationsand external clients. Prior to joiningDRG, Sheeler was a graduate/teachingassistant at Kent State University, whereshe holds a Master of Arts degree ingeography with a concentration in GISand natural hazards research. She alsoholds a Bachelor of Science degree inGeography from the University ofCentral Missouri with a minor in EarthScience.

William AyersmanWilliam Ayersman is the GeospatialServices Coordinator with DRG. He is ageospatial analyst with extensiveexperience applying spatial analysis andpredictive modeling to natural resourceissues. His daily responsibilities involvegeospatial project coordination for allutility forestry, remote sensing andimage analysis projects, LiDAR analysis,database and project management, andthe creation and design of predictiveand suitability models. Ayersman plays akey role in the development of DRG’sinnovative GIS tools and solutions,focusing on the urban canopy effects ofstormwater, watersheds, and ecosystemcost/benefits analysis. Prior to joiningDRG, Ayersman worked as a GIS Analystfor the Natural Resource AnalysisCenter in Morgantown, West Virginia.He holds a Master of Science degree inForestry and a Bachelor of Sciencedegree in Forest Management fromWest Virginia University.


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