Computer-Aided Civil and Infrastructure Engineering 28 (2013) 68–79

Improved Floodplain Delineation Method UsingHigh-Density LiDAR Data

Sagar S. Deshpande∗

Leonard Jackson Associates, Vienna, VA, USA

Abstract: With the improvements in sensor technolo-gies over the past decade, there has been a significantdecrease in the cost of acquisition and increase in thedensity and accuracy of Light Detection and Ranging(LiDAR) data. Due to its advantages over traditionalsurveying techniques, LiDAR data are widely preferredfor floodplain delineation. But, processing dense LiDARdata is time-consuming and memory intense. Therefore,it is divided into manageable areas/tiles or simplified toraster DEM (Digital Elevation Model) format for fea-ture extraction process such as floodplain delineation.This results in increase in processing time and decreasein accuracy due to loss of true elevation. Furthermore, asfloodplain boundaries are unknown prior to delineation,processing time also increases as LiDAR data over largerextent is processed. Hence, there is a need of improved,automated method that will process only the LiDAR datathat contribute to the floodplain. This article, describesa time-efficient floodplain delineation method that di-vides the LiDAR data into regular tiles and processesonly the tiles that contribute to floodplain. This method isexperimented using LiDAR data saved in ArcGIS “Ter-rain” format at 0.0, 0.1, and 0.3 m pyramid levels. Thesedata are then preprocessed to obtain elevation informa-tion which is used to filter and process only LiDARdata tiles that truly contribute to the floodplain boundary;thus, reducing processing time. Results from two pilot hy-draulic models showed that this method saved 12–34% ofprocessing time compared to the conventional method.

1 INTRODUCTION

Floods are one of the most common, serious, and costlynatural disasters. During past decades, the number offlood disasters occurring worldwide has grown signif-icantly. Climate change combined with growing urban

∗To whom correspondence should be addressed. E-mail:sdeshpande@leonardjackson.net.

areas, have increased the frequency and the severity offlood events (Sole et al., 2008). Due to increased threatsof flooding, both inland and coastal, accurate floodplaindelineation is essential to make decisions regarding con-struction, insurance, and other regulatory practices (No-man et al., 2003).

In the United States, the Federal Emergency Man-agement Agency (FEMA) manages the NationalFlood Insurance Program (NFIP) who produces FloodInsurance Rate Maps (FIRMs). The FIRMs show areasof high flood risk by mapping the 1% annual chanceflood. This information is used in town planning, inpurchasing homeowners insurance, by home buyers,and by existing home owners who need to know howto appropriately protect their property from flooding.For all of these users, accurate floodplain mapping isessential. Light Detection and Ranging (LiDAR) dataare exceedingly accurate; therefore, it is being used forfloodplain delineation.

In this article, a time-efficient method is presented forfloodplain delineation using high density LiDAR data.This method involves: preprocessing of LiDAR datato extract elevation information for each tile and useof this information to filter and process the tiles thattruly contribute to the floodplain. Analysis has beenperformed to study the time saved using Terrain dataat 0, 0.1, and 0.3 m pyramid levels. These levels wereselected so that the accuracy of the topographic data iswithin FEMA specifications. The main objectives are tomake the floodplain delineation process computation-ally efficient by avoiding processing of unnecessary dataand to use the best resolution of LiDAR data to pro-duce accurate floodplain delineation.

2 BACKGROUND INFORMATION ANDLITERATURE REVIEW

In FEMA’s NFIP program, countywide FIRMs are pre-pared that show flood hazard zones for riverine and

C© 2012 Computer-Aided Civil and Infrastructure Engineering.DOI: 10.1111/j.1467-8667.2012.00774.x

Floodplain delineation method using LiDAR data 69

coastal flooding sources. These zones are identified byperforming hydrologic and hydraulic analysis, or coastalanalysis, using atmospheric, topographic, and geospa-tial information. Topography is one of the prime com-ponents which influence the mapping accuracy. FEMArequires that the topographic data accuracy require-ments, regarding map scale and contour intervals, beequivalent to the National Map Accuracy Standard(NMAS) (FEMA, 2003a). FEMA further defines twogeneral categories for vertical accuracy of topographicdata as:

1. Two-Foot Equivalent—Data that have an accu-racy of ±36.5 cm (±1.2 ft) at the 95% confidenceinterval [i.e.: 95% of data points have accuracywith respect to the true ground elevation equal to±36.5 cm (1.2 ft) or smaller].

2. Four-Foot Equivalent—Data that have an accu-racy of ±73.2 cm (±2.4 ft) at the 95% confidenceinterval.

The horizontal accuracy requirement of topographicdata is a function of the intended map panel scale.Due to its high vertical accuracy, laser sensor tech-nology in the form of LiDAR has quickly becomethe prime source of topographic information for hy-drologic/hydraulic analysis and floodplain delineation.Due to its advantages over traditional measurementequipment, several researchers have studied use of ter-restrial as well as airborne laser technology in struc-tural monitoring (Lee and Park, 2011; Park et al., 2007;Siringoringo and Fujino, 2009; and Zalama et al., 2011),transportation engineering (Cai and Rasdorf, 2008), andother fields of civil engineering. Although its horizontalaccuracy is less than vertical accuracy (Cai and Rasdorf,2008; Liu et al., 2009), the combined accuracy of LiDARdata exceeds that of the traditional topographic dataused by floodplain managers (Maune, 2001), as well asFEMA’s quality standards. With the improvements insensor technology over the past decade, there has beena significant increase in the resolution and a decreasein the cost of acquisition of LiDAR data (Chen, 2007).LiDAR data can also measure ground elevation in veg-etated areas, therefore increasing the detail when com-pared to an aerial survey (Sasaki et al., 2008). This im-proved detail provides more accurate topography alonga river or stream in vegetated areas. The basic principleof a LiDAR survey is based on laser distance measure-ment using a scanning mirror mechanism. At a samplingrate of 30 kHz or higher, a LiDAR system can producespatial elevation data with an average horizontal spac-ing of 1 m or less (Liu et al., 2009); thus providing verydense elevation data.

Use of dense LiDAR data is time-consuming andmemory intense. Hence, it is processed by several meth-

ods to reduce the frequency of elevation points such asmanual editing, spatial and statistical filtering, and mul-tiple return analysis techniques (Bryant et al., 2002).Rath and Pasche (2004) presented a fast and efficientmethod to reduce huge LiDAR data sets based on slopeclassification for hydrodynamic simulation. Omer et al.(2003) used original data and seven filtered data sets andfound that filtering to 4◦ can be performed without com-promising cross-sectional geometry, hydraulic model re-sults, or floodplain delineation results. Some researchalso studied tiling of the data files and irregular spatialpattern of the LiDAR data. Tiling is a process in whichdata are reorganized and stored in contiguous regular“tiles” (Chen, 2007). Hug et al. (2004) and Chen (2007)have proposed tools to efficiently organize and processLiDAR data using tiles.

Conventionally, LiDAR data have been convertedover a smaller extent to topographic surfaces, suchas a raster Digital Elevation Model (DEM; a grid ofsquares with elevation information), Triangulated Ir-regular Network (TIN), or contours, to facilitate pro-cessing. The raster DEMs of different resolutions canbe created by interpolating point elevation informa-tion from LiDAR data. Colby and Dobson (2010)compared flood modeling results using digital terrainmodel (DTM) of different resolutions and found thathigher resolution terrain is needed to better representfloodplain in low-relief areas whereas higher resolutiondata may be useful in high-relief areas due to steepslopes. Several other studies have also investigated theeffects of topographic data resolution and mapping(Garbrecht and Martz, 1994; Zhang and Montgomery,1994; Molnar and Julien, 2000; Moglen and Hartman,2001; Colby and Dobson, 2010). Interpolation of origi-nal random points to create DEM results in loss of trueelevation values. There is a need to develop improvedautomated processing techniques that preserve the orig-inal random point data (Bryant et al., 2002).

Unlike a raster DEM, a TIN maintains the exact fea-tures of the LiDAR data accurately. TIN is preferableto a DEM when it is critical to preserve the precise lo-cation of narrow or small surface features such as leveesor narrow stream channels (Maune, 2001). The map-ping accuracy of the TIN surface created by using LI-DAR data is much more accurate than alternative map-ping accuracies on an interpolated 2 ft contour surface, a10 ft contour surface, or an NED 1 arc second DEM sur-face (Cohen, 2007). Gesch (2009) also revealed that thehigh vertical accuracy and spatial resolution of LiDARdata improve identification and delineation of vulnera-ble lands.

Although increasingly available, efficient processingand extraction of useful information using LiDAR dataremains a big challenge in several fields (Chen, 2007).

70 Deshpande

Fig. 1. Floodplain delineation procedure.

Danner et al. (2007) presented a four-stage methodwhich involved conversion of terrain data to rasterDEM for extraction of river network and watershed hi-erarchy. This method was able to process over 20 GBof raw data in fewer than 26 hours. Seventy-six percentof this time was spent in the initial DEM construc-tion stage. Wallis et al. (2009) described a parallel al-gorithm to enhance hydrologic terrain preprocessingso that larger data sets can be efficiently computed.Several other studies have been reported which useLiDAR data for flood risk modeling, hydrologic anal-ysis, and hydraulic analysis (Sole et al., 2008; Bryantet al., 2002; Sasaki et al., 2008; Miller and Shrestha, 2004;Tate et al., 2002; Colby and Dobson, 2010). The increasein processing time for LiDAR data is a result of thou-sands of ground elevation points which have been ob-tained in a small area.

In general, floodplain delineation for tens of kilo-meters of river requires processing millions of LiDARpoints. Moreover, floodplain delineation involves sev-eral iterations to obtain a hydraulically correct modeland continuous floodplain boundary using preliminaryfloodplain. These iterations require even more timeand are computationally intense. Due to these time-consuming processes, it is important to create an ef-ficient tool which can delineate floodplain for usingLiDAR.

3 DEFINITION OF RIVERINE FLOODPLAINDELINEATION

Riverine floodplain delineation is a process of identi-fying floodplain boundaries for a river. This processcan be performed either manually, using topographicmaps, or also can be performed using digital data savedon computer. Floodplain delineation performed usingcomputers is more accurate and time efficient than themanual method and errors due to misinterpretations areavoided.

Typically, to perform floodplain delineation usingcomputers, topographic data are converted to TIN(Figure 1a) or raster DEM. Then, flood water eleva-tions, simulated by a hydraulic model or any othersimilar source are interpolated to create a water sur-face. Figure 1b shows a TIN water surface overlainon TIN topographic data. The line of intersection ofthese two surfaces defines the floodplain boundary.Figure 1c identifies the area below the water surface asfloodplain.

4 DATA SET AND SOFTWARE DESCRIPTION

To demonstrate the methodology, LiDAR data cov-ering Oswego County, NY were used (Figure 2).

Floodplain delineation method using LiDAR data 71

Fig. 2. LiDAR data in ArcGIS Terrain format covering Oswego County, NY along with the locations of water surfaces ofModels 1 and 2 (a) and area enlarged in (b).

These data were collected in June 2008 and consistof 843 million elevation points with an average spacingof 1.2 m (4 ft). These data require 77.3 and 30.4 GB ofdisk space in LAS format and ArcGIS Terrain format,respectively. The horizontal datum referenced was theNorth American Datum of 1983 (NAD83), and the ver-tical datum referenced was the North American Ver-tical Datum of 1988 (NAVD88). The vertical accuracyof this LiDAR data is 20 cm (0.66 ft) (LiDAR QualityAssurance (QA) Report, Oswego County, NY: DatedJune 26, 2009) and thus, satisfies the FEMA standardsfor topographic data.

Two riverine hydraulic models, shown in Figure 2as Models 1 and 2, were used to study time efficiencyof the new method. These models are approximately27 and 34.8 km in length. It is assumed that the hy-draulic simulation of the two models is complete andthe water surfaces obtained from these models areused in the new floodplain delineation process. Ar-cGIS version 9.3 was used to store and process theLiDAR data in the ArcGIS-specific Terrain data for-mat. A customized tool was developed in Visual Stu-dio 2008 Express using the tools available in ArcGISto implement the new procedure of floodplain delin-eation. The computer used for processing, analysis, andvisualization of the data had 2 GB RAM and IntelCore 2 Duo (T5500@1.66GHz) central processing unit(CPU).

5 FLOODPLAIN DELINEATION WORKFLOWUSING HIGH DENSITY LIDAR

Figure 3 shows the entire workflow of the new flood-plain delineation method. This workflow is described indetail in following subsections.

5.1 Hydraulic model setup in GIS environment

Importing and setting up a hydraulic model is a rou-tine procedure; therefore, it is briefly described in thissubsection. As described earlier, several hydraulic mod-eling softwares are available. Among these programs,Hydrologic Engineering Centers River Analysis System(HEC-RAS) hydraulic model developed by the U.S.Army Corps of Engineers (USACE) is the most com-mon. It is designed to perform one-dimensional hy-draulic calculations for a full network of natural andconstructed channels (http://www.hec.usace.army.mil/software/hec-ras/hecras-features.html). HEC-GeoRASis another program, developed by the USACE, for anArcGIS environment which can be used to transfer datafrom ArcGIS to HEC-RAS for modeling simulations.Once the hydraulic modeling is complete, the outputfrom HEC-RAS, in the form of georeferenced cross-sections with flood water elevations, can be importedinto ArcGIS using HEC-GeoRAS for floodplain delin-eation. Flood water surfaces are created by interpolat-ing the flood water elevations at each cross-section.

72 Deshpande

Fig. 3. Schematic diagram showing hydraulic model setup in GIS environment, preprocessing LiDAR data, and use ofpreprocessing data in the new floodplain delineation method.

Likewise, an existing historic hydraulic model is alsoa source of flood water elevations. Mostly, such modelis available only in nondigital format as it was devel-oped in 1980s or 1990s using Hydraulic EngineeringCenter’s HEC-2 program. The cross-sections used inthis model are digitized in a GIS environment from ahistoric map. Flood water elevations from the historichydraulic model are manually assigned to these cross-sections. Virtual water surfaces are created by interpo-lating the flood water elevations at each digitized cross-section. This process is called redelineation (FEMA,2003b). Yang et al. (2005) also presents another GIS-based approach for delineating and displaying data gen-erated by HEC-2 numerical model.

In this study, Model 1 was imported using historicdata and Model 2 was obtained by importing a HEC-RAS model. The flood water surfaces for both models(Figure 4) were then created as TINs by linear inter-polation of the flood water elevations along the cross-sections.

5.2 LiDAR data preprocessing

Generally, raw LiDAR data are converted to softwarespecific formats which are capable of storing and re-

trieving a huge amount of data sets. This facilitates theuse of tools which are available in the software to pro-cess the data. The subsections which follow describe theimporting and preprocessing steps of LiDAR data intoan ArcGIS environment.

5.2.1 Importing LiDAR data into an ArcGIS environ-ment. ArcGIS’s Terrain datum is a relatively new for-mat, which was introduced after the release of its 9.2version. It is a TIN-based platform that can produceaccurate surfaces quickly by efficiently managing point-based data; such as LiDAR in a geodatabase. This dataset is also capable of managing, editing, and produc-ing accurate TINs and also allows creating TIN pyra-mid levels based on Z-tolerance. The lowest pyramidlevel (0.0 m) stores all of the elevation points. At ahigher pyramid level, points are eliminated from theTerrain, based on the Z-tolerance value; thus, reduc-ing the number of points. For example, data points areeliminated from the Terrain based on 0.1 m pyramidlevel (Z-tolerance) to produce surface that is within a0.1 m vertical accuracy relative to the 0.0 m pyramid sur-face. To explore the benefits of this data set using thisnew method, the LiDAR data for Oswego County, NYwere converted to Terrain format. Tools available in

Floodplain delineation method using LiDAR data 73

Fig. 4. Water surface and cross-section layout of Models 1 and 2.

ArcGIS 9.3 were used to convert the LiDAR data fromOswego County, NY to the Terrain format (Figure 2a).LiDAR elevation points classified as “bare earth” wereused to produce the Terrain. Three pyramids: 0.0 m(base level), 0.1 m (0.5 ft), and 0.3 m (1 ft) were cre-ated to study the performance efficiency and changein floodplain area at different levels using the newmethod. These pyramids were selected so that the ac-curacy of the topographic data at each pyramid levelwould be within the two-foot equivalent vertical accu-racy of ±36.5 cm (±1.2 ft) defined by FEMA.

5.2.2 Creation of tiles to divide the Terrain data into aregular grid pattern. The Terrain data have to be con-verted to TIN or raster DEM format to perform anyanalysis. In this new method, Terrain data are convertedto TIN because it is more accurate than raster DEM.Converting the entire Terrain data, which contain mil-lions of points, to a TIN data set will required hugememory and would slow down the entire process. Therealistic limit for a TIN format in ArcGIS is about 10million nodes (Atkinson, 2010). Furthermore, convert-ing only the area of Terrain that overlaps the water sur-face of Models 1 and 2 is also memory intense and ex-ceeds the node limits of a TIN. Therefore, it is requiredto convert a manageable portion of the entire Terrain toperform any analysis. A regular tile pattern covering theentire extent of the Terrain data is then created whichdivides the LiDAR data. Based on the average spacingof the LiDAR data, tiles of size 410 m × 410 m are cre-ated. Figure 2b shows an enlarged area of the Terraindata overlain by the tiles.

5.2.3 Preprocessing Terrain data. The purpose of pre-processing the Terrain data is to obtain the elevationinformation within each tile. Terrain data are clipped tothe extents of each overlapping tile as a TIN surface.A tool named “Terrain to TIN,” available in ArcGIS,is used to obtain a TIN surface for the extent of eachtile. Minimum and maximum elevation values are ob-tained from each clipped TIN data and are then savedas attributes of each tile. This step cannot be performedmanually because thousands of tiles cover the entireLiDAR data set. Hence, a tool is developed to im-plement this step. After extracting the elevation in-formation, the clipped TINs are deleted, because theyare duplicates of the Terrain and also require largerdisk space. The elevation parameters obtained by pre-processing are then used to filter only those gridswhich truly contribute to the floodplain. This process isdescribed in the next subsection.

5.3 New floodplain delineation method

As described earlier, a floodplain boundary is a linewhere the dry ground intersects the water surface.Figure 5 shows a schematic representation of three pos-sible scenarios that can occur between a triangle ofground surface TIN and floodwater surface in the flood-plain delineation process.

Figure 5a represents the first scenario where the en-tire water surface is above the ground surface andFigure 5b represents the second scenario where thewater surface is entirely below the ground surface. It

74 Deshpande

Fig. 5. The three scenarios that can occur between ground surface TIN and water surface.

should be noted that these scenarios represent all tri-angles of ground surface TIN that are entirely insideor outside the boundaries of the floodplain. In the firstscenario, the resultant floodplain is represented by thewater surface area, as the ground data are below wa-ter whereas in the second scenario, the area coveredby the water surface will not contribute to the flood-plain boundary. Thus, such triangles are not processedto identify floodplain boundaries as they are entirely be-low or above the water surface.

Figure 5c represents the third scenario in which thewater surface intersects the ground surface TIN. Suchtriangles are processed to identify the line of intersec-tion, which will delineate the floodplain boundary. Thissituation would occur when the elevation of water sur-face is between the minimum and maximum elevationsof the overlapping topographic data.

Considering the three different scenarios discussedearlier, the computational time can be reduced only ifthe topographic data and the water surface data, thatsatisfy the third scenario, are processed. These trian-gles can be identified if the minimum and maximumvalue of the topographic data is available by preprocess-ing. However, it is not time-efficient to preprocess andcheck millions of triangles in a Terrain data set whichis based on the above three scenarios. Therefore, theabove three scenarios are generalized to filter the tilesthat contribute to the floodplain.

Figure 6a shows the tiles that overlap the water sur-face of Model 1 and its vicinity. To reduce the numberof tiles, only the tiles that spatially overlap the watersurface are selected (Figure 6b). Processing each over-lapping tile to obtain the floodplain boundary is a time-consuming task because it involves clipping the Terrain

data to TIN format, which covers thousands of pointsand then performing floodplain delineation by intersect-ing the TIN format with the virtual water surface. Thus,the tiles that truly contribute to the floodplain boundaryare filtered by implementing all three of the criteriondescribed in the previous subsection. A step-by-step ap-proach, as described below, is implemented for each tilethat overlaps the water surface:

1. Clip the flood water surface TIN to the extentsof the overlapping tile. (Note: It is faster to clipthe flood water surface TIN as it has few elevationpoints within a tile.)

2. Extract the minimum (WSmin) and maximum(WSmax) elevation value for the clipped water sur-face TIN.

3. Retrieve the minimum (TOPOmin) and maxi-mum (TOPOmax) elevation values saved as at-tributes to the tile during the preprocessing stage.These values represent the maximum and mini-mum elevation for the topographic data within thetile.

4. Perform the following checks to identify if the tilewould contribute to the floodplain boundary:

i. Case 1: (WSmax) < (TOPOmin) = True. In thiscase, the ground surface within this tile is entirelyfree from flooding and doesn’t contribute to thefloodplain. Therefore, LiDAR data are not pro-cessed to perform floodplain delineation.

ii. Case 2: (WSmin) > (TOPOmax) = True. In thiscase, the ground surface within the tile is en-tirely covered with water and, therefore, this en-tire tile contributes to the floodplain. Because it

Floodplain delineation method using LiDAR data 75

Fig. 6. Water surface of Model 1 and the overlapping tiles.

is entirely covered with water, there is no flood-plain boundary within this tile and its LiDARdata are not processed to perform floodplaindelineation.

iii. Case 3: In this case, it can be observed that por-tions of the ground data intersect the water sur-face. The Terrain data within this tile are pro-cessed to identify the areas within the tile that areabove and the areas that are below the water sur-face. A tool named “TIN difference” available inArcGIS is used to identify these areas and per-form floodplain delineation.

By examining these three scenarios, and performingthe appropriate checks, the tiles which directly con-tribute to the floodplain boundary can be identified. Thetime required to clip the Terrain data and to performfloodplain delineation is eliminated for the tiles that donot contribute to the floodplain boundary which signifi-cantly reduces processing time.

To determine the time saved by implementing thismethod, floodplain delineation was also performed forModels 1 and 2 at three pyramid levels using the con-ventional methodology. The three scenarios used in thenew method were not implemented and, as a result, allthe tiles that overlapped the water surface were pro-cessed in the conventional method to obtain the flood-plain boundary.

The processing time required for floodplain de-lineation and floodplain area, at different pyramid

Table 1Time required to preprocess the Terrain data

Pyramid level (m)

Preprocessing results 0 0.1 0.3

Time required (hours) 40.4 34.6 17.1Percent of time required

compared tobase-level pyramid

100% 86% 42%

Average number ofpoints within each tile

73,793 36,500 4,384

Percent of average pointdensity compared tobase-level pyramid

100% 49.50% 5.90%

levels for Models 1 and 2, are presented in the nextsection.

6 RESULTS AND DISCUSSIONS

As described previously, the LiDAR data are convertedto Terrain data at three pyramid levels: 0.0, 0.1, and0.3 m and preprocessing is done based on the overlap-ping 24,252 tiles that cover the entire Oswego County,NY. Table 1 summarizes the time required to prepro-cess the Terrain data.

The results show that higher preprocessing time wasrequired for the lower level pyramids. It took around

76 Deshpande

Fig. 7. Tiles identified based on the three cases.

40 hours to preprocess the Terrain at 0.0 m pyramid;whereas only 35 and 17 hours were required to pro-cess the Terrain at 0.1 and 0.3 m pyramids, respectively.The average density of data points at 0.1 and 0.3 mpyramids was 50 and 6%, respectively, of the pointdensity at 0.0 m pyramid. The percentage decrease inthe average number of points is significant from 0.0 to0.1 m and to 0.3 m pyramids, compared to percent-age decrease in the processing time. Thus, the reduc-tion in the time is primarily due to the decrease in theaverage point density within each tile, and is also af-fected by the time required to clip the data and save theattributes.

In Figure 7, tiles classified using the three scenariosthat overlap the water surface of Models 1 and 2 areshown. It can be seen that using the new method, onlythe nonshaded tiles are processed.

Figure 8 shows the floodplain obtained for the twomodels using the 0.0 m level pyramid. Floodplains de-lineated using 0.1 and 0.3 m pyramid Terrain were notidentical to those delineated using 0.0 m pyramid andshowed variations along the floodplain boundaries.

Floodplain delineation was also performed by usingthe conventional method, which did not use the prepro-cessed information from the three pyramid levels. Iden-tical floodplains were obtained by the new method andthe conventional method at each pyramid level. Resultsinvolving the floodplain areas at three pyramid levels,

processing time, and other factors for Models 1 and 2are tabulated in Tables 2 and 3.

The following points are based on the results listed inTables 2 and 3:

1. All tiles that overlap the water surface wereprocessed in conventional floodplain delineationmethod; whereas only the short-listed tiles wereprocessed using the new method. The new methodwas able to reduce the number of processed tilesby 43 and 17% for Models 1 and 2, respectively.

2. Large numbers of Case 2 tiles were observedfor both models. Overall floodplain width ofModel 2 was narrower than Model 1. Therefore,a large number of Case 1 tiles were observed forModel 1.

3. Using this method, 29, 32, and 34% of time weresaved for Model 1 and 12, 13, and 17% of timewere saved for Model 2 using the three levels ofpyramid Terrain, respectively. This shows that thetime saved increases as the density of data pointsdecreases.

4. Processing time of 122 and 45 minutes was re-quired for Models 1 and 2, respectively, by con-ventional method using Terrain at 0.0 m pyramid.A time saving of 82 and 76% was observed for thetwo models, respectively, using the new method at0.3 m pyramid.

Floodplain delineation method using LiDAR data 77

Fig. 8. Floodplain boundary at pyramid level 0.0 m.

5. The change in area of the floodplain obtained byusing 0.1 m pyramid Terrain was less than 1%. Us-ing the 0.3 m pyramid Terrain, the change was lessthan 3%. Close visual inspection showed that thefloodplain width slightly increased in some areaswhereas it decreased in other areas, therefore com-pensating for the change in area as a result of usingthe higher pyramid Terrains.

6. Spot check along the entire floodplain boundaryobtained using 0.1 m pyramid Terrain showed lessthan 5 ft variations to that obtained using 0.0m pyramid Terrain. But, discrepancy more than10 ft was observed at several locations betweenfloodplain boundary obtained using 0.3 m pyramidTerrain. Particularly, in low-relief terrain, flood-plain delineated using the 0.0 m pyramid Ter-rain represented minor topographic variations ac-curately compared to floodplain from other twoTerrains. When visually compared to the orthoim-agery, the floodplain boundaries obtained at 0.0and 0.1 m pyramids were more appropriate thanthat obtained at 0.3 m pyramid.

It was observed that the following factors affected thenumber of skipped tiles:

1. Floodplain width: The count of tiles that were en-tirely within the floodplain boundary, depended onthe width of the floodplain. There were a higher

number of these types of tiles in Model 1 comparedto Model 2.

2. Cross-section extent: The number of tiles entirelyoutside the floodplain depends on the extent of thehydraulic model cross-sections. The cross-sectionswere extended to enclose the floodplain bound-ary between them because the expected floodplainboundary between them was not known. There-fore, for very narrow floodplains, the number ofsuch tiles could increase significantly.

7 CONCLUSIONS

Over the past decade, density of LiDAR data has in-creased due to development in sensor technology. Thiscalls for development of task-specific and time-efficientmethods for feature extraction. Over the past severalyears, methods to filter LiDAR data point density havebeen researched. This article presents a time-efficientfloodplain delineation method using high-density Li-DAR data. This method preprocesses LiDAR data di-vided into regular tiles to obtain elevation information.This elevation information is then used to filter and pro-cess only the tiles that contribute to the floodplain, thusreducing the processing time. Furthermore, this methodcan be applied to filtered as well as raw data to saveadditional processing time. Results based on two pi-lot studies showed that this method can improve time

78 Deshpande

Table 2Comparison of parameters obtained for floodplain

delineation of Model 1 using conventional method and thenew method

Pyramid level (m)

Processing results 0 0.1 0.3

Tiles processed byconventionalmethod

454 454 454

Tiles processed bynew method

259 259 259

Percent reduction innumber of tiles

43% 43% 43%

Time required byconventionalmethod (minutes)

122 84 33

Time required bynew method(minutes)

86 57 22

Percent ofprocessing timesaved by newmethod comparedwith conventionalmethod

29% 32% 34%

Percent ofprocessing timesaved by the newmethod comparedwith 0.0 mpyramid

NA 34% 75%

Floodplain area 19,696,854.24 19,696,854.24 19,870,925(sq. meters)

Percent change infloodplain area

NA <10E-12% –0.88%

efficiency compared to the conventional method. Thus,this method can save significant time for studies involv-ing hundreds of kilometers of floodplain delineation.The novelty of the presented method is that it en-ables use of best resolution LiDAR data efficiently. Thismethod saves time not only by avoiding manual creationof raster surfaces but also by filtering and processingonly LiDAR tiles that contribute to floodplain. How-ever, the time saved depends on hardware and softwareconfiguration.

This method is implemented in ArcGIS but the con-cept can be extended to other data processing softwarefor feature extraction. During this investigation, sev-eral other implementations of the above method wereidentified for future studies which not only use LiDARdata, but also other spatial data sets. Depending on thefeature extracted method, appropriate parameters canbe extracted during the preprocessing which then can

Table 3Comparison of parameters obtained for floodplain

delineation of Model 2 using conventional method and thenew method

Pyramid level (m)

Processing results 0 0.1 0.3

Tiles processed byconventionalmethod

145 145 145

Tiles processed bynew method

107 107 107

Percent reduction innumber of tiles

26% 26% 26%

Time required byconventionalmethod (minutes)

45 28 13

Time required by newmethod (minutes)

39 24 11

Percent of processingtime saved by newmethod comparedwith conventionalmethod

12% 13% 17%

Percent of processingtime saved by thenew methodcompared with 0.0m pyramid

NA 38% 72%

Floodplain area 2,834,140 2,820,364 2,757,287(sq. meters)

Percent change infloodplain area

NA 0.50% 2.71%

reduce the feature extraction time. Coastal floodplaindelineation, shoreline extraction, and tsunami mappingare some of the study topics that can be implementedusing this method. It was also observed that the prepro-cessing step is time-consuming which calls for develop-ment of efficient preprocessing methods.

REFERENCES

Atkinson, E. (2010), Processing Terrain data in the riverproximity, in Arc Hydro River Scoping Workshop, De-cember 1–3, 2010. Available at: http://www.ce.utexas.edu/prof/maidment/esri/ArcHydroRiver/Workshop.htm,accessed March 2, 2011.

Bryant, P., Campos-Marquetti, R. & Daniel, C. (2002),LiDAR derived elevation data to support floodplain mod-eling, in Proceedings of the ISPRS Technical CommissionII Symposium 2002, XXXIV(2), Xi’an, August 20–23, 2002.ISSN 1682-1750.

Cai, H. & Rasdorf, W. (2008), Modeling road centerlinesand predicting lengths in 3-D using LIDAR point cloudand planimetric road centerline data, Computer-Aided Civiland Infrastructure Engineering, 23(3), 157–73.

Floodplain delineation method using LiDAR data 79

Chen, Q. (2007), Airborne LiDAR data processing and infor-mation extraction, Photogrammetric Engineering and Re-mote Sensing, 73(2), 109–12.

Cohen, C. (2007), The Impact of Surface Data Accuracy onFloodplain Mapping, CE 394K.3 GIS in Water Resources,Final Report Fall 2007, University of Texas. Availableat: http://www.crwr.utexas.edu/gis/gishydro08/Introduction/TermProjects/Cohen.htm, accessed March 2, 2011.

Colby, J. D. & Dobson, J. G. (2010), Flood modeling in thecoastal plains and mountains: analysis of terrain resolution,Natural Hazards Review, 11(1), 2010, 19–28.

Danner, A., Molhave, T., Yi, K., Agarwal, P.K., Arge, L.& Mitasova, H. (2007), TerraStream: from elevation datato watershed hierarchies, in Proceedings of the 15th An-nual ACM International Symposium on Advances in Geo-graphic Information Systems, GIS ’07, Seattle, Washington,November 7–9, 2007.

FEMA. (2003a), Appendix A: Guidance for aerial map-ping and surveying, in Guidance and Specifications forFlood Hazard Mapping Partners, available at: http://www.fema.gov/library/viewRecord.do?id=2206, accessedMarch 2, 2011.

FEMA. (2003b), Appendix C: Guidance for riverine flood-ing analyses and mapping, in Guidance and Specifica-tions for Flood Hazard Mapping Partners, available at:http://www.fema.gov/library/viewRecord.do?id = 2206, ac-cessed March 2, 2011.

Garbrecht, J. & Martz, L. W. (1994), Grid size dependency ofparameters extracted from digital elevation models. Com-puters and Geosciences, 20(1), 85–7.

Gesch, D. B. (2009), Analysis of LiDAR elevation data forimproved identification and delineation of lands vulnerableto sea-level rise, Journal of Coastal Research, 53, 49–58.

Hug, C., Krzystek, P. & Fuchs, W. (2004), AdvancedLiDAR data processing with Lastools, Presented at theXXth ISPRS Congress, July 2004, Istanbul, Turkey, 12–23.

Lee, H. M. & Park, H. S. (2011), Gauge-free stress estima-tion of a beam-like structure based on terrestrial laser scan-ning, Computer-Aided Civil and Infrastructure Engineering,26(8), 647–58.

Liu, J-K., Li, R., Deshpande, S., Niu, X. & Shih, T-Y. (2009),Estimation of blufflines using topographic LiDAR data andorthoimages, Journal of Photogrammetric Engineering andRemote Sensing, 75(1), 69–79.

Maune, D. F. (2001), Digital Elevations Model Technologiesand Applications: The DEM Users Manual, American So-ciety of Photogrammetry and Remote Sensing, Bethesda,MD.

Miller, S. N. & Shrestha, S. R. (2004), Semi-automated extrac-tion and validation of channel morphology from LiDARand IFSAR Terrain data, in ASPRS Annual ConferenceProceedings, May 23–28, 2004, Denver, CO.

Moglen, G. E. & Hartman, G. L. (2001), Resolution effects onhydrologic modeling parameters and peak discharge, Jour-nal of Hydrologic Engineering, 6(6), 490–97.

Molnar, D. K. & Julien, P. Y. (2000), Grid size effects on sur-face runoff modeling, Journal of Hydrologic Engineering,5(1), 8–16.

Noman, N. S., Nelson, E. J. & Zundel, A. K. (2003), Improvedprocess for floodplain delineation from digital terrain mod-els, Journal of Water Resources Planning and Management,129(5), 427–36.

Omer, C. R., Nelson, E. J. & Zundel, A. K. (2003), Impactof varied data resolution on hydraulic modeling and flood-plain delineation, Journal of the American Water ResourcesAssociation, 39(2), 467–75.

Park, H. S., Lee, H. M., Adeli, H. & Lee, I. (2007), A new ap-proach for health monitoring of structures: terrestrial laserscanning, Computer-Aided Civil and Infrastructure Engi-neering, 22(1), 19–30.

Rath, S. & Pasche, E. (2004). Hydrodynamic floodplain mod-eling based on high-resolution Lidar measurements, in P.Liong & Babovic (eds.), 6th International Conference onHydroinformatics 2004, World Scientific Publishing Com-pany, ISBN: 981-238-787-0.

Sasaki, H., Mukoyama, S., Nagata, N. & Namikawa, K.(2008), Digital terrain analysis using airborne laser scanner(LIDAR) data for natural hazard assessment, in The In-ternational Archives of the Photogrammetry, Remote Sens-ing and Spatial Information Sciences, XXXVII(B8), 1225,Beijing, 2008.

Siringoringo, D. M. & Fujino, Y. (2009), Non-contact op-erational modal analysis of structural members by laserDoppler Vibrometer, Computer-Aided Civil and Infrastruc-ture Engineering, 24(4), 249–65.

Sole, A., Giosa, L., Nole, L., Medina, V. & Bateman, A.(2008), Flood risk modelling with LiDAR technology, inD. Proverbs, C. A. Brebbia, & E. Penning-Rowsell (eds.),Flood Recovery, Innovation and Response I. Transaction:Ecology and the Environment volume 118. Transactions ofthe Wessex Institute, WIT Press, Ashurst, Southhampton,UK, ISBN: 978-1-84564-132-0.

Tate, E. C., Maidment, D. R., Olivera, F. & Anderson, D. J.(2002), Creating a terrain model for floodplain mapping,Journal of Hydrologic Engineering, 7(2), 100–08.

Wallis, C., Wallace, R., Tarboton, D. G., Watson, D. W.,Schreuders, K. A. T. & Tesfa, T. K. (2009), Hydrologicterrain processing using parallel computing, in 18th WorldIMACS Congress and MODSIM09 International Congresson Modeling and Simulation, Cairns, Australia, July 13–17,2009.

Yang, J., Townsend, R. D. & Daneshfar, B. (2005), A GIS-based approach to river network floodplain delineation, inC. A. Brebbia & J. S. Antunes do Carmo (eds.), River BasinManagement III. Transaction: Ecology and the Environ-ment volume 83. Transactions of the Wessex Institute, WITPress, Ashurst, Southhampton, UK, ISBN: 1-84564-023-3.

Zalama, E., Garcıa-Bermejo, J. G., Fernandez, J. L. & Med-ina, R. (2011), An effective texture mapping approach for3D models obtained from laser scanner data to buildingdocumentation, Computer-Aided Civil and InfrastructureEngineering, 26(5), 381–92.

Zhang, W. & Montgomery, D. R. (1994), Digital eleva-tion model grid size, landscape representation, and hydro-logic simulations, Water Resources Research, 30(4), 1019–28, doi:10.1029/93WR03553.

Copyright of Computer-Aided Civil & Infrastructure Engineering is the property of Wiley-Blackwell and its

content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's

express written permission. However, users may print, download, or email articles for individual use.