Stular Et Al. - 2012 - Visualization

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Visualization of lidar-derived relief models for detection of archaeological features

Benjamin !Stular a,*, !Ziga Kokalj b,c, Kri!stof O!stir b,c, Laure Nuninger aaUniversity of Franche-Comt, 32 rue Mgevand, F-25030 Besanon, Franceb Scientic Research Centre of the Slovenian Academy of Sciences and Arts, Novi trg 2, SI-1000 Ljubljana, Sloveniac Space-Si e Centre of Excellence for Space Sciences and Technologies, A!sker!ceva 12, SI-1000 Ljubljana, Slovenia

a r t i c l e i n f o

Article history:Received 30 June 2011Received in revised form21 March 2012Accepted 29 May 2012

Keywords:ArchaeologyMethodologyHigh-resolution DEMLidarVisualisation

a b s t r a c t

This paper presents visualisation techniques of high-resolution digital elevation models (DEMs) for visualdetection of archaeological features. The methods commonly used in archaeology are reviewed andimprovements are suggested. One straightforward technique that has so far not been used in archaeologyethe shift method e is presented. The main purpose of this article is to compare and evaluate differentvisualisation methods. Two conclusions have been reached. Where a single method must be chosen e forprinting or producing digital images for non-professionals e the use of sky view factor or slope gradient isendorsed, both presented in greyscale. Otherwise interpreters should choose different techniques ondifferent terrain types: shift onat terrain, sky view factor onmixed terrain, slope gradient on sloped terrainand sky view factor (preferably as a composite image with slope gradient) on rugged terrain.

! 2012 Elsevier Ltd. All rights reserved.

1. Research aims

This paper presents results of a project that investigated variousvisualization techniques that can be applied to record archaeolog-ical features from lidar digital elevation models. The Kobarid regioninwestern Slovenia was scanned on a request of theWalks of Peacein the So!ca Region Foundation for better understanding andmanagement of a vast complex of World War I entrenchments andfortications.While one research aim of the project was to examineperformance of a dedicated lidar survey in an area with a highnumber of previously known archaeological sites in a largelywooded mountainous environment, the other aim of equalimportance was to carry out a theoretical and practical analysis ofa range of visualization methods. The comparison was necessarybecause a number of techniques either fail to adequately revealarchaeological features on rugged and diverse terrain, require toomuch processing, or are difcult to interpret. In order to contrastour ndings with experience of others, we have also conducteda survey among 12 users with medium to high expertise level ininterpretation of lidar data.

2. Background and context

Lidar derived highly detailed digital elevation modelshave proven to be a very useful tool for detection of various

archaeological features, ranging from houses, ramparts, trenches,ditches, fossil elds and terraces, past land division (e.g. Romancenturiation), abandoned quarries and mining areas, burialmounds, ancient roads (Roman, medieval), and other elements ofarchaeological landscape in environments where other surveyingtechniques do not provide satisfactory results. Detection fromairborne lidar data has been successfully applied in at andundulating agricultural regions (e.g. Challis et al., 2008 with earlierreferences; Crutchley, 2009; Buteux and Chapman, 2009; Cornsand Shaw, 2009) as well as forested slopes on hilly or moun-tainous terrain (Sittler, 2004; Devereux et al., 2005; Doneus et al.,2008; !Stular, 2011) and even in a dense jungle (Chase et al.,2011). Data is increasingly becoming available to archaeologists,both from surveys conducted especially for archaeological or otherpurposes (such as ood protection, forest management etc.), butalso from completed or current state-wide aerial laser scanningprojects (e.g. in Austria, the Czech Republic, Denmark, theNetherlands, Slovenia).

Archaeologists specializing in remote sensing are interpretingspatial data with a combination of perception and comprehension(Parcak, 2009). Similarly, a successful interpretation of lidar-derived data must be based on a well-judged use of a suite oftechniques coupled with careful and well-informed interpretation(Challis et al., 2011). The later, i.e. the user determined knowledge-based interpretation, includes complex pattern recognition and theability of the user to recognise, identify and classify complexlandforms based on experience and previous archaeologicalknowledge (e.g. Crutchley, 2006).

* Corresponding author. Tel.: 386 1 4706 387; fax: 386 1 4257 757.E-mail address: [email protected] (B. !Stular).

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Journal of Archaeological Science

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Journal of Archaeological Science 39 (2012) 3354e3360


Despite this, potential of lidar data is far from being fullyexploited. Most archaeological applications of lidar have beenbased on visual interpretation of the relief model. In this case, thevisibility of potential archaeological features depends to a largeextent on the chosen visualization.

Most aerial lidar elevation data are published in the form ofanalytically shaded relief. Since, on the one hand, vast majority ofmodern scientic publications are limited to greyscale print and, onthe other hand, viewers perceive shaded relief as the most intuitiveand natural representation of topography (Imhof, 2007), thisseems perfectly acceptable. However, using shaded relief asa primary tool for visual interpretation can have signicant draw-backs on the number of nds and quality of interpretation(e.g. Challis et al., 2008; Devereux et al., 2008; Hesse, 2010; Kokaljet al., 2011). Regardless of this, only a few visualization techniquesare routinely used by archaeologists.

The next chapters present a range of visualization techniquesthat can aid the detection and interpretation process. They havebeen selected based on experience and literature survey. Sometechniques are well already known to the archaeological audience,others not so.

3. Materials and methods employed

3.1. The study area

Kobarid (Italian: Caporetto; German: Karfreit) region in theupper So!ca/Isonzo valley in western Slovenia has been chosen asa study area because it has known archaeological sites in variablestate of preservation and exploration, and is set in a very diverselandscape, ranging from low lying and relatively at valleys to thesurrounding mountains exceeding the height of 2000 m (Fig. 1).The region is on the border between the Mediterranean and theAlps. The valley oors are warmer and wetter than average Alpinevalleys due to the proximity of the Adriatic Sea. However, theclimate dramatically shifts to colder in the mountains. Because ofits natural position, the area has been strategically importantthroughout history as a transportation route from northern Italyacross the Alps. Archaeological sites vary from burial mounds andhilltop enclosures to a large extent of World War I trenches andreinforcements (!Stular, 2011).

3.2. Data acquisition and processing

Laser scanning of the 57 km2 area was commissioned, per-formed, and processed with a clear focus on archaeological

purposes (Table 1). The survey was conducted in early March, theperiod of dormant vegetation when the trees was not yet in leaf,crop heights were minimal, the fallen leaves from the autumn hadalready compacted and the ground was without snow covere revealing the bare ground to the maximum degree. Filtering ofthe lidar point cloud and generation of a digital elevation modelwas performed with Repetitive Interpolation algorithm (REIN)(Kobler et al., 2007). Algorithm settings were optimised to removeonly the vegetation cover but leave remains of past human activi-ties as intact as possible. The lter therefore preserved buildings,walls, dikes and trenches as well as retained some spruce trees,especially young, where the laser beam did not reach the ground.

3.3. Lidar DEM visualization techniques

We have grouped the visualization methods into four groups(Table 2; Fig. 2).

3.3.1. Relief shading and relief illumination methodsThe relief shading method, sometimes referred to as hill-

shading, has a respectable tradition in cartographic representa-tions of terrain (Imhof, 2007; Slocum et al., 2004). Computergenerated shaded relief maps emphasise structures that areobliquely illuminated, but hide those that are illuminated along theperpendicular axis. This issue has been addressed extensively andsome solutions have been proposed, e.g. enhancing shaded reliefwith planimetric and prole curvature (Kennelly, 2008), multidi-rectional oblique-weighted (MDOW) shaded relief (Mark, 1992),Lambertian reection shaded relief (Oren and Nayar, 1994), prin-cipal component analysis (PCA) of hill shadings from multipledirections (Devereux et al., 2008) and sky view factor (Kokalj et al.,2011).

However, because some of these techniques were developed toemphasize high frequency information (changes of morphology)on a medium resolution DEM, their application to high-resolutionDEMs is not straightforward. Applying the planimetric and prolecurvature modications to our dataset, for example, over-emphasized noise and thus obscured archaeological features.

MDOW shaded relief has less contrast than a normal, single-source illuminated hill shaded relief. This is because it is createdby aweighted combination of four shadings with evenly distributedillumination sources that give detail in shades and overexposedareas of normal hill shading.

Lambertian reectance is a relief rendering technique, widelyused in computer graphics. Relief surfaces are treated as a Lam-bertian reector that assumes an ideal surface reecting all thelight that strikes it. In this way the surface appears equally brightfrom all viewing directions (Oren and Nayar, 1994; Slocum et al.,2004).

Principal components analysis (PCA) summarizes the informationfrom several (e.g. 16) relief images, shaded with evenly distributedillumination sources (Devereux et al., 2008). The PCA e especiallya combinationof therst andsecondprincipal components, ora falsecolour composite image of the rst three e simplies interpretationof multiple shading data, but it does not provide consistent resultswith different datasets (Kokalj et al., 2011).

The shortcomings of relief shading are successfully addressed bydiffuse illumination relief shading models, such as a uniform skyillumination (Kennelly and Stewart, 2006) and sky view factor(SVF; Zak!sek et al., 2011). Both have been successfully applied inarchaeology (!Stular, 2011; Kokalj et al., 2011), with the latter havinga signicant advantage because it is much faster to calculate andaccessible as a free tool (ZRC SAZU, 2010). Sky view factor isa measure for the portion of the sky visible from a certain point. Theportion of the visible sky limited by the relief horizon correspondsFig. 1. Kobarid case study area.

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to the relief illumination, e.g. a ridge is more illuminated than thebottom of a steep valley. Openness (Yokoyama et al., 2002) isa similar method e it estimates the mean horizon elevation anglee but the results are more difcult to interpret.

3.3.2. Colour cast modelsUse of colour cast schemes is a well-accepted visualisation

method in archaeology (e.g. van Zijverden and Laan, 2004; Corenet al., 2005; Budja and Mleku!z, 2010), but it comes as no surprisethat the examples cited are those of coastal or ood plains. Onmostother terrain types height variations exhibited by archaeologicalfeatures pale in comparison to topographic height variations. Thisproblem can be addressed with the use of the bipolar differentia-tion technique, also known as relative height-coding, wherea colour cast scheme is repeated at the chosen interval, e.g. 1 m.Therefore, anomalies within intervals can be visually assessed(Podobnikar, 2009), especially with a correct colour casting scheme(cf. Moreland, 2009). Although this technique does reveal smalldifferences on a at landscape and the bands can be interpreted ascontours on a steep and diverse terrain (Kokalj et al., 2011),archaeological features are impossible to interpret on ruggedterrain.

3.3.3. DEM manipulation methodsModern geographic information system (GIS) software packages

offer a rich variety of terrain and morphometric analyses. Mosthave a long scholarly tradition and some have been around longerthan desktop GIS software. Not all of them represent the topog-raphy directly. Two methods that provide good results are pre-sented below: slope gradient and trend removal.

Slope gradient is a rst derivative of the DEM and is aspectindependent. It has been successfully used, for example, for forestroad system identication (White et al., 2010) and its use inarchaeology has been reported by Doneus and Briese (2011, 67). Byapplying the inverted greyscale scheme (steep slopes are darker)

the nal image retains relief representation. It is very straightfor-ward to interpret and works especially well when combined withhill shading.

Trend removal is a procedure that separates local small-scalefeatures from large-scale landscape forms. It is based ona presumption that small-scale variations in relief, archaeologicalfeatures among them, are obscured in visualizations by variation ofterrain. The procedure rst calculates a trend DEM and thensubtracts it from the original DEM, producing a local relief model(LRM). The trend can be assessed by generalizing a detailed DEM.Because processing is straightforward, generalization is usuallydone with a low pass convolution lter, such as average or median,or by resampling a DEM to a lower resolution. A better option isa Gaussian lter that produces a smoother transition betweenfeatures, but it is computationally more demanding (Reitbergeret al., 2008; Wagner et al., 2008).

The method suggested by Hesse (2010) improves the difcultyof abrupt transition from a steep slope to a at terrain, but it doesnot eliminate it entirely. In such cases the algorithm still producesan impression of a rampart (Fig. 3).

Another, much faster method of trend removal is to shift(transpose) a DEM and subtract it from the original DEM. The gridcan bemoved in several directions (e.g. four: east, north, north-eastand south-east), by one or more cells. Using 16 difference DEMs issuggested for geomorphological applications (Jones et al., 2007).This method can be optimised by:

- calculating height differences from shifts in north and eastdirections, each for one and two cells,

- producing a single local relief model by calculating a principalcomponents analysis from the height difference DEMs.

However, we achieved better results without any perceived dataloss (see Section 4) in sample data with a single 2-cell shift in thenorth direction.

3.3.4. Image processing ltersA number of image processing local lters can be applied to

a DEM in order to detect high frequency variation (e.g. Laplacian,Sobels, Roberts lters, unsharpen mask). The simplest is the Lap-lacian lter, specically designed for local edge detection, i.e.emphasizing the sharp anomalies. This lter produces a similarpattern to the slope images since the Laplacian convolution isa discrete approximation of the rst derivative (e.g. Wood, 1996;Vyas, 2008). Slope curvature (e.g. Dragut and Blaschke, 2006)produces similar results as far as visualization is concerned(cf. Kennelly, 2008). The problem of applying Laplacian ltering tolidar derived DEM for use in archaeology is in that it enhances both

Table 1Lidar scanning parameters of the Kobarid region (Slovenia).

Scanner type Riegl LMS-Q560Platform HelicopterDate 4th and 16th March 2007Swath width [m] 60Flying height [m] 450Pulse repetition rate [kHz] 100Average last and only returns per m2 on a

combined dataset11.2

GPS error [m] 0.05e0.08Spatial resolution of the nal elevation model [m] 0.5

Table 2Software and settings used to generate the various visualisations.

Software Settings

DTM Repetitive Interpolation (REIN) 15 iterations on a 23% sampleRelief shading ESRI ArcMap 9.3 315" Sun azimuth, 45" Sun elevationRelief shading (Lambertian) Goldensoftware Surfer 9.0 315" Sun azimuth, 45" Sun elevationRelief shading (MDOW) ESRI ArcMap 9.3 and DEM Surface Tools 315" Sun azimuth, 45" Sun elevation, Maximum Hilshade Levels 256,

Apply Hypsometric Shading - NO, Ramp over observed range of values - YESRelief shading (PCA 1st) ESRI ArcMap 9.3 PCA of 16 shadings with 45" Sun elevation.Relief shading (PCA 1st and 2nd) ESRI ArcMap 9.3 PCA of 16 shadings with 45" Sun elevationSlope gradient ESRI ArcMap 9.3 Slope gradient in degreesPCA of 4 shifts ESRI ArcMap 9.3 PCA of Shift 2 cells nort, south, east, westShift ESRI ArcMap 9.3 2 cell NorthTrend removal (Gaussian) SAGA GIS 2 standard deviations, 2.5 m search radiusSky View Factor ZRC SAZU SVF Standalone 10 m search radius, 8 directionsLaplacian lter SAGA GIS Standard deviation (percent of radius) 50, radius 3, search mode square

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archaeological features and noise (Stal et al., 2010). Also, problemssimilar to trend removal methods can be anticipated. The edgesharpness and thus the type of the archaeological featuresenhanced can be adjusted via standard deviation and radius, but nosingle setting will address all features in the dataset equally.

4. Results and discussion

The discussion on the visualisation techniques of the lidarderived data so far has mostly focused on the comprehensionrather than on perception; this is understandable since compre-hension is arguably the more important part of the archaeologicalinterpretation. The conclusion of the recent review on the subjecthas been, that any technique used succeeds or fails largely asa result of the scale and form of the observed archaeologicalfeatures and that it is not likely, that a single technique will revealall archaeological detail in any landscape (Challis et al., 2011).

In order to shed additional light on the subject we focus on theperception of the data, largely because we feel that this aspect hasbeen neglected in the past. The perception is highly inuenced bythe contrast between the adjacent cells that enables the recognitionof the repeating patterns. This is especially important when dealingwith the topographical features of low magnitude that are very

common in archaeology. These features will remain of low contrastunless the scale of features is addressed using specic visualizationmethod.

We have focused on testing to what degree contrast is inu-enced by the choice of the visualisation method. To comparecontrast between adjacent cells analytical methods have been used.This method, we argue, offers an important step towards betterunderstanding and quantication of effectiveness of visualisationmethods on the perception of the lidar derived data in archaeology(Table 3).

In accordance with similar studies our analysis revealed that nosingle visualization method outperforms the rest in all types ofterrain. One important result is that all alterations of relief shadingresult in the deterioration of contrast. The interpreters not willingto give up the intuitive look afforded by relief shading will have touse two or more images shaded from different directions.

The Laplacian lter exhibits the highest contrast on average butalso the highest noise and is not useful as a general visualizationtechnique. Trend removal exhibits second highest contrast overalland an acceptable level of noise; however, it also exhibits intro-duced artefacts not accounted for by our data assessment method.Slope gradient and SVF both exhibit good overall performance withlow level of noise. It is thus clear that while some DEM

Fig. 2. Visualisation methods described in text on the rugged terrain with explanation of archaeological features presented on a relief shading image.



manipulations enhance the result in specic circumstances (Lap-lacian lter on a slope and onmixed terrain, shift and trend removalon at terrain) the best overall performers are SVF and slopegradient. These also retain some of the intuitive natural repre-sentation of the relief morphology (Fig. 3).

Because the issue at hand is visual interpretation by a humaninterpreter, these quantitative results must be contrasted witha subjective comparison. We asked 12 users with medium to highexpertise level e i.e. are involved in a project or have alreadynished one or more lidar-related projects respectively e abouttheir preferred visualisation methods. The results revealed that

most of the users are currently using relief shading complementedwith colour cast, slope gradient or 3D visualization. Half of themwere willing to try the PCA transformation, trend removal or SVF.Among the latter there was no particular preference (Fig. 4).

None of the users explicitly stated that the preference towardsrelief shading is related to the fact, that the latter is a direct analogueof the kind of sunlight illuminated landscape which most inter-preters of archaeological aerial imagery are familiar with. However,we believe that the results imply exactly this. The intuitive look ofthe relief shading technique, therefore, substantially aids thearchaeological interpretation despite its obvious shortcomings.

Table 3The comparison of the visualisation methods. The median value of ve different standard deviations is a proxy for contrast; higher values represent higher contrast. Thestandard deviation of standard deviations represents introduced noise; higher values mean more noise has been introduced. The best results e highest contrast, but dis-regarding default relief shading and techniques with disproportionate high noise e are in marked in bold.

TERRAIN: Rugged Sloped Mixed Flat

Contrast Noise Contrast Noise Contrast Noise Contrast Noise

DTM (cm) 11.7 3.8 54.8 1.9 7.2 2.9 7.0 2.3DTM 0.0 0.3 0.6 0.0 0.0 0.0 0.0 0.0Relief shading (315"/45") 46.9 3.4 36.8 11.2 20.8 6.2 8.0 2.3Relief shading (Lambertian) 12.5 1.8 25.1 4.7 17.9 5.6 12.6 4.9Relief shading (MDOW) 6.1 5.1 17.8 5.5 13.2 2.4 3.5 3.0Relief shading (PCA 1st) 25.4 2.9 16.9 6.5 12.1 5.0 5.5 1.9Relief shading (PCA 1st and 2nd) 20.3 1.1 4.0 2.6 7.5 0.8 5.5 1.9Slope gradient 42.4 5.1 31.7 3.8 17.0 5.3 5.3 3.5PCA of 4 shifts 13.5 4.3 31.1 9.4 12.5 1.7 20.5 8.0Shift (2 cell North) 13.6 3.6 33.6 8.9 13.7 1.8 20.8 8.3Trend removal (Gaussian) 2.3 0.2 40.4 3.6 22.2 4.0 50.6 7.4Sky View Factor 30.9 4.2 8.1 3.3 23.7 4.7 6.0 2.8Laplacian lter 40.8 29.6 66.5 21.7 29.6 15.3 20.7 22.0

Fig. 3. Results of the assessment for sloped, mixed and at terrain (rows). Explanation of archaeological features are presented on relief shading image followed by measuredprole, visualisation method with highest calculated contrast and visualisation method chosen subjectively (columns).



In the authors experience the colour casted DEM, preferablycombined with some kind of trend removal is the best technique touse on a at terrain, SVF followed by Gaussian trend removal ona mixed terrain, slope gradient again followed by Gaussian trendremoval on a sloping terrain and nally, SVF and slope gradient ona rugged terrain. If pressed to select a single visualisation methode for example to produce printed maps or images for non-GIS-enabled analysis e the authors would opt for SVF.

Regardless of the terrain the topographical features of lowmagnitude need special consideration. Features observed on mixedand at terrain in the above case studies, for example, are 0.15 and0.25 m high respectively. In view of the data characteristics thesefeatures are approaching the minimum magnitude that can beobserved. The two techniques that consider the scale of features areSVF and trend removal. The rst performs better on mixed terrain,while the second is better to visualize features on at terrain. Trendremoval is also a close second on the mixed terrain and can thus beviewed as the most suitable technique for the detection of lowmagnitude features. This comes as a no surprise, since this tech-nique has been designed specically to enhance such features(Hesse, 2010).

5. Conclusion

The use of lidar derived data as an archaeological remotesensing technique is methodologically maturing. The archaeolo-gists are most often involved in the analysis after the point clouddata have already been processed. Therefore this paper is focusedon DEM visualisation techniques, arguably the most important partof the process typically performed by archaeologists.

We have reviewed the methods used in archaeology andproposed improvements on trend removal. In addition, we havepresented a technique that has so far not been used in archaeologye the shift method. We have also tested the use of Laplacian lterfor edge enhancement, useful on the data with little noise. Oursuggested improvements (Gaussian trend removal) are imple-mented in the open source software System for Automated Geo-scientic Analyses (www.saga-gis.org), and SVF code is publiclyavailable (ZRC SAZU, 2010). For a growing body of archaeologiststhat are able to obtain lidar derived DEMs but do not specialize inGIS analysis this can be decisive in choosing the appropriatemethods.

\The main purpose of the article was to evaluate differentvisualisation methods of a lidar derived DEM for archaeologicalvisual detection and interpretation. Where a single method has tobe chosen we endorse the use of SVF or, as a visually les pleasingalternative, slope gradient, both presented in greyscale. Otherwiseinterpreters should choose different techniques on differentterrain types. Based on the results presented here, the mostappropriate are:

- shift method or trend removal in combination with colour caston at terrain,

- SVF on mixed terrain,- slope gradient or trend removal on sloped terrain,- SVF (blended with slope gradient) on rugged terrain and- trend removal for low magnitude features.

The authors urge the interpreters to experiment with visuali-zation techniques that best suit their needs. With some additionaltraining the need for the "natural" look and feel of the relief shadingcan easily be overcome and the full potential of the lidar deriveddata for the archaeological interpretation can be harnessed.

Acknowledgements

The research was supported by grants from the Walks of Peacein the So!ca Region Foundation, the Scientic Research Centre of theSlovenian Academy of Sciences and Arts, the Slovenian ResearchAgency, and University of Franche-Comt. The funding sourcesinuenced the collection of data but had no involvement in studydesign or in the analysis and interpretation of data.

The authors wish to thank the participants of the Visualizationworkshop at the Training and Research in the ArchaeologicalInterpretation of Lidar (TRAIL) international workshop, for takingpart in survey. We are grateful to Rachel Opitz, University ofArkansas, for discussion and for helping us with the revision of thetext. We also want to thank the anonymous reviewers for theircomments and suggestions.

References

Budja, M., Mleku!z, D., 2010. Lake or oodplain? Mid-Holocene settlement patternsand the landscape dynamic of the I!zica oodplain (Ljubljana Marshes,Slovenia). Holocene 20, 1269e1275.

Buteux, S., Chapman, H., 2009. Where rivers meet: the archaeology of Catholme andthe Trent-Tame conuence. In: CBA Research Report 161. Council for BritishArchaeology, York, p. 200.

Challis, K., Kokalj, Z., Kincey, M., Moscrop, D., Howard, A.J., 2008. Airborne lidar andhistoric environment records. Antiquity 82, 1055e1064.

Challis, K., Forlin, P., Kincey, M., 2011. A generic toolkit for the visualization ofarchaeological features on airborne LiDAR elevation data. Archaeological Pro-spection 18 (4), 279e289.

Chase, A.F., Chase, D.Z., Weishampel, J.F., Drake, J.B., Shrestha, R.L., Slatton, K.C.,Awe, J.J., Carter, W.E., 2011. Airborne LiDAR, archaeology, and the ancient Mayalandscape at Caracol, Belize. Journal of Archaeological Science 38, 387e398.

Coren, F., Visintini, D., Prearo, G., Sterzai, P., 2005. Integrating LiDAR IntensityMeasures and Hyperspectral Data for Extracting of Cultural Heritage. WorkshopItaly-Canada 2005, Padova 17e18 maggio 2005.

Corns, A., Shaw, R., 2009. High resolution 3-dimensional documentation ofarchaeological monuments & landscapes using airborne LiDAR. Journal ofCultural Heritage 10, 72e77.

Crutchley, S., 2006. Light detection and ranging (lidar) in the Witham valley, Lin-colnshire: an assessment of new remote sensing techniques. ArchaeologicalProspection 13, 251e257.

Crutchley, S., 2009. Ancient and modern: combining different remote sensingtechniques to interpret historic landscapes. Journal of Cultural Heritage 10S,e65ee71.

Devereux, B.J., Amable, G.S., Crow, P., Cliff, A.D., 2005. The potential of airborne lidarfor the detection of archaeological features under woodland canopies. Antiquity79, 648e660.

Devereux, B.J., Amable, G.S., Crow, P., 2008. Visualisation of LiDAR terrain models forarchaeological feature detection. Antiquity 82, 470e479.

Fig. 4. Results of survey (n 12) on preferred visualisation methods amongst expe-rienced users. The graph is organized so that the most commonly used methods are onthe left and the most desired for the future on the right.



Doneus, M., Briese, C., 2011. Airborne Laser Scanning in forested areas e potentialand limitations of an archaeological prospection technique. In: Cowley, C.D.(Ed.), Remote Sensing for Archaeological Heritage Management. EuropaeArchaeologia Consilium, Brussel, pp. 59e76.

Doneus, M., Briese, C., Fera, M., Janner, M., 2008. Archaeological prospection offorested areas using full-waveform airborne laser scanning. Journal ofArchaeological Science 35, 882e893.

Dragut, L., Blaschke, T., 2006. Automated classication of landform elements usingobject-based image analysis. Geomorphology 81, 330e344.

Hesse, R., 2010. LiDAR-derived Local Relief Models e a new tool for archaeologicalprospection. Archaeological Prospection 17, 67e72.

Imhof, E., 2007. Cartographic Relief Presentation. ESRI Press, Redlands.Jones, A.F., Brewer, P.A., Johnstone, E., Macklin, M.G., 2007. High-resolution inter-

pretative geomorphological mapping of river valley environments usingairborne LiDAR data. Earth Surface Processes and Landforms 32, 1574e1592.

Kennelly, P.J., Stewart, J.A., 2006. A uniform sky illumination model to enhanceshading of terrain and Urban areas. Cartography and Geographic InformationScience 33, 21e36.

Kennelly, P.J., 2008. Terrain maps displaying hill-shading with curvature. Geomor-phology 102, 567e577.

Kobler, A., Pfeifer, N., Ogrinc, P., Todorovski, L., O!stir, K., D!zeroski, S., 2007. Repetitiveinterpolation: a robust algorithm for DTM generation from Aerial Laser ScannerData in forested terrain. Remote Sensing of Environment 108, 9e23.

Kokalj, Z., Zak!sek, K., Kri!stof, O., 2011. Application of sky-view factor for the visu-alization of historic landscape features in lidar-derived relief models. Antiquity85, 263e273.

Mark, R., 1992. Multidirectional, Oblique-weighted, Shaded-relief Image of theIsland of Hawaii. U.S. Geological Survey.

Moreland, K., 2009. Diverging color maps for scientic visualization. In: Bebis, G.,Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Pajarola, R., Lindstrom, P.,Hinkenjann, A., Encarnao, M., Silva, C., Coming, D. (Eds.), Advances in VisualComputing. Springer, Berlin, Heidelberg, pp. 92e103.

Oren, M., Nayar, S.K., 1994. Generalization of Lamberts reectance model. LectureNotes in Computer Science 801, 269e280.

Parcak, S.H., 2009. Satellite Remote Sensing for Archaeology. Routledge, London,New York.

Podobnikar, T., 2009. Methods for visual quality assessment of a digital terrainmodel. SAPIENS 1, 1e10.

Reitberger, J., Krzystek, P., Stilla, U., 2008. Analysis of full waveform LIDAR data forthe classication of deciduous and coniferous trees. International Journal ofRemote Sensing 29, 1407e1431.

Sittler, B., 2004. Revealing historical landscapes by using airborne laser-scanning:a 3D-Modell of ridge and furrow in forests near Rastatt (Germany). Interna-tional Archives of the Photogrammetry, Remote Sensing and Spatial Informa-tion Sciences 36 (8), 258e261.

Slocum, T.A., McMaster, R.B., Kessler, F.C., Howard, H.H., 2004. ThematicCartography and Geographic Visualization. Prentice-Hall, Upper Saddle River,528 pp.

Stal, C., Bourgeois, J., De Maeyer, P., De Mulder, G., De Wulf, A., Goossens, R.,Nuttens, T., Stichelbaut, B., 2010. Kemmelberg (Belgium) case study: compar-ison of DTM analysis methods for the detection of relicts from the First WorldWar. In: Reuter, R. (Ed.), 30th EARSeL Symposium: Remote Sensing for Science,Education and Culture. EARSeL.

!Stular, B., 2011. The use of lidar-derived relief models in archaeological topographye The Kobarid region (Slovenia) case study. Arheolo!ski vestnik 62, 393e432.

van Zijverden, W.K., Laan, W.N.H., 2004. In: Brner, W. (Ed.), Archaeologie undcomputer Workshop 9. Forschunggesellschaft Wiener Stadtrchaologie.

Vyas, A. (Ed.), 2008. Remote Sensing and GIS: Reader. CEPT University, Ahmedabad.Wagner, W., Hollaus, M., Briese, C., Ducic, V., 2008. 3D vegetation mapping using

small-footprint full-waveform airborne laser scanners. International Journal ofRemote Sensing 29, 1433e1452.

White, R.A., Dietterick, B.C., Mastin, T., Strohman, R., 2010. Forest roads mappedusing LiDAR in steep forested terrain. Remote Sensing 2, 1120e1141.

Wood, J.D., 1996. The Geomorphological Characterisation of Digital ElevationModels. University of Leicester, Leicester.

Yokoyama, R., Shirasawa, M., Pike, R.J., 2002. Visualizing topography by openness:a new application of image processing to digital elevation models. Photo-grammetric Engineering and Remote Sensing 68, 257e266.

Zak!sek, K., O!stir, K., Kokalj, !Z., 2011. Sky-view factor as a relief visualization tech-nique. Remote Sensing 3, 398e415.

ZRC SAZU, 2010. IAPS ZRC SAZU. Institute of Anthropological and Spatial StudiesZRC SAZU. Available at: http://iaps.zrc-sazu.si/en/svf#v.


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