Airborne Lidar Intensity & Geoarchaological Prospection

7/27/2019 Airborne Lidar Intensity & Geoarchaological Prospection

1/14

Airborne Lidar Intensity andGeoarchaeological Prospection in RiverValleyFloors

KEITHCHALLIS1*,CHRISCAREY

1,MARKKINCEY

1ANDANDYJ.HOWARD

2

1 IBM Visual and Spatial Technology Centre, Birmingham Archaeology, University of Birmingham,

Edgbaston, Birmingham, B15 2TT, UK2 Institute of Archaeology and Antiquity, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

ABSTRACT Archaeologicalapplications of airborne lidar topographic data are now well known and documented in the academicliterature.Rather less well explored by archaeologists are the potential of lidar intensity data.In this paper we exploretheapplicationof lidarintensity forgeoarchaeologicalprospectionin river valley floors.Afterbrieflyconsideringthe con-text of archaeological remote sensing in river valleys, we examine some factors influencing the lidar intensity recordand exploreprocessingsteps that mayberequired to effectivelyutilizeintensitydata, beforereviewingthe utilityof inten-sity data for the geoarchaeological assessment of test sites in the Trent Valley of the English Midlands (UK). Resultssuggest that intensity imagery may assist greatly in the interpretation of airborne lidar topographic data and that itsanalysis contributes to a qualitative understanding of landcoverand the burial environment of archaeologicalremains,either culturalor environmental; furthermore insome circumstances it ispossible to identifyanthropogenic archaeolo-gical cropmarks in intensity imagery. It is concluded that the standard methodology employed in using airborne lidarfor archaeological survey should as a matterof course include the collection and analysis of intensity data.Copyright# 2011 John Wiley & Sons,Ltd.

Key words: floodplain; alluvium; geoarchaeology; palaeochannels; lidar intensity

Introduction

The archaeological applications of airborne lidartopographic data are now well known. As well asgeoarchaeological mapping and prospection (Brun-ning and Far-Cox, 2005; Challis, 2005, 2006; Carey et al.,2006; Challis et al., 2006; Jones et al., 2007), publishedapplications include over-arching landscape studies(Barnes, 2003; Bewley et al., 2005; Bofinger et al., 2006;Shell and Roughley, 2004; Powlesland et al., 2006),investigation of the potential for lidar to detect

upstanding archaeological remains beneath the veg-etation canopy (Deveraux et al., 2005; Risbol et al., 2006;Sittler and Schellberg, 2006; Crow etal., 2007; Doneus etal., 2008) and studies of the uses of lidar to contribute tothe compilation and refinement of records of the

historic environment (Holden etal., 2002; Bewley, 2003;Crutchley, 2006; Challis et al., 2008).

Rather less well explored by archaeologists arethe potential applications of lidar intensity data, asecondary output of topographic measurements thatrecords a laser image of the land surface derivedfrom measurements of the amplitude of each reflectedlaser pulse (Figure 1). Thus far applications usingairborne lidar intensity have been restricted to a fewspecialist fields including characterizing forest cano-pies (eg. Donoghue et al., 2007), determination of

the age of lava flows from active volcanoes (Spinettiet al., 2009) and classification of glacial surfaces(Lutz et al., 2003). A comprehensive summary of usesof airborne lidar intensity is given by Hofle and Pfeifer(2007). A number of studies (e.g. Song et al., 2002;Chust et al., 2008; Yoon et al., 2008) suggest that lidarintensity may be more generally useful for determi-nation of land cover and could serve as a usefuladdition to lidar elevation data when interpreting lidarsurvey results.

Archaeological ProspectionArchaeol. Prospect. 18, 113 (2011)Published online 3 February 2011 in Wiley Online Library(wileyonlinelibrary.com/journal/arp) DOI: 10.1002/arp.398

* Correspondence to: K. Challis, IBM Visual and Spatial TechnologyCentre, Birmingham Archaeology, University of Birmingham, Edg-baston, Birmingham, B15 2TT, UK. E-mail: [email protected]

Copyright# 2011 John Wiley & Sons, Ltd. Received 14 April 2010

Revised 10 December 2010


2/14

This paper explores the application of airbornelidar intensity for geoarchaeological prospection inriver valley floors. Our premise is that considerationof routinely collected intensity data may assist ingeoarchaeological interpretation of lidar elevationproducts. After first exploring the context of archae-ological remote sensing in river valleys we assessthe quality of collected lidar data and explore theneed for correction for instrument factors and thenundertake visual interpretation of intensity data. Weexplore a range of geomorphological and archaeolo-

gical features in fluvial environments in several testsites in the Trent Valley of the English Midlands, UK(Figure 2), in order to discern to what extent intensitymight assist in interpretation of lidar survey results.A number of conclusions are drawn about the utility ofairborne lidar intensity data for geoarchaeologicalprospection and the contribution that inclusion ofintensity data in lidar based archaeological studiesof landscape might make.

Archaeological remote sensing of rivervalleys

Airborne remote sensing techniques have traditionallybeen employed to great effect in mapping thecultural archaeology and to a lesser extent thegeomorphology of valley floor landscapes. Archae-ologists have largely focused their attention on thecomprehensive mapping of cropmarks and otherfeatures of the archaeological landscape revealed fromaerial photographs (e.g. Whimster, 1989). Aerial photo-graphs have also been employed in mapping geomor-

phology in alluvial landscapes, for example in extensivestudies of the valleys of the Rivers Trent (Baker, 2003)and Thames (Lambrick, 1992). Studies combining aerialphotography, airborne lidar and ground reconnaissanceto produce detailed landform assemblage charactermaps of river valleys have set the benchmark standardfor what may be achieved (e.g. Passmore and Wad-dington, 2006; Howard et al., 2008b).

Mapping of fluvial geomorphology provides acontext for past cultural landscapes and assists inidentifying topographical features of high archaeolo-gical potential (for example relict river channels),isolating areas of past river erosion and valleyfloor reworking where little in the way of archae-ological material might be expected to survive anddelineating areas of (stable) gravel terrace, which oftenform the focus of past human activity, and in generalare higher and dryer than the surrounding floodplain

(cf. Howard and Macklin, 1999). In general fluvialfeatures have a significant topographic element thatmay be readily identified in lidar elevation data(cf. Brown (1997) for geomorphological descriptionsand classification of typical features). In most valleyfloors the greatest potential for organic preservation isassociated with palaeochannels, the position of whichwithin the landscape may be determined from theanalysis of aerial photography, historic maps (Large andPetts, 1996) and of course from lidar (Challis, 2006).

The systematic reconnaissance, mapping and classi-fication of valley floor landscapes has played a

significant role in the strategic management of thegeoarchaeological resource and intimately associatedarchaeological remains in the face of growing impactsfrom aggregate extraction and other developmentpressures (Bishop, 2003), in addition to other issuessuch as future climate change (Howard et al., 2008a).However, one shortfall of the types of study notedabove is that, while they provide maps of the broaddistribution of geoarchaeological features withinalluvial landscapes, they provide no indication ofthe state of preservation of that material. This issignificant since the presence of wet, organic-richsediments may greatly increase the archaeological

value of these valley floor deposits. As we discusslater, it is possible that examination of lidar intensityvalues alongside the elevation record may go someway towards assessing the preservation potentialof sediments since (amongst other factors) intensityis effected by the physical properties (moisturecontent, organic content, etc.) of the reflecting materialas well as its topographic characteristics. While werecognized that other airborne remote sensing tech-niques (e.g. multispectral or thermal), may be equally

Figure 1. Thelidar process showingintensity values are a reflection ofthe amplitude of eachreflected pulse.

Copyright# 2011 John Wiley & Sons, Ltd. Archaeol. Prospect. 18, 113 (2011)DOI: 10.1002/arp

2 K. Challis et al.


3/14

Figure 2. The studyareashowingtheTrentValleywithinthe UK,the extentof lidardata collectionand individualstudy windows.Thisfigureisavailablein colouronlineat wileyonlinelibrary.com/journal/arp


Airborne Lidar Intensity and Geoarchaeological Prospection 3


4/14

or more effective than lidar at detecting geoarchaeo-logical and anthropogenic features and assessingpreservation potential of sediments, we have chosento focus on lidar intensity as a tool since vast quantitiesof such data are routinely collected during airbornelidar surveys by archaeologists and others, but issubsequently archived without examination since itsvalue and potential to date has been unrealized.

Lidar intensity measurements

The physical principle underlying lidar intensity issummarized by Hofle and Pfeifer (2007). Lidarintensity measurements represent the reflected energyfrom a highly focused beam of near infrared radiation(NIR), which provides a concentrated measurement ofan objects reflectivity. Since NIR reflectance varies in

response to a number of earth-surface characteristics,intensity data has the potential to provide a qualitativedescriptor of earth surface materials. There aresignificant variations in intensity measurementsbetween different lidar systems largely due todifferences in receiver properties and type andwavelength of laser used. The present paper considersonly results from an Optech Airborne Laser TerrainMapper, which has a near infrared laser operatingbetween 1047 and 1068 nm (varying by system) and arein common use in the UK by commercial contractorsand government agencies for lidar survey.

Factors influencing the intensity record may bedivided broadly into: (i) system variables; (ii) targetvariables; and (iii) processing procedures. Systemvariables include those such as the distance betweenthe lidar system and the target (the range controlledlargely by the altitude of the survey aircraft, but alsoinfluenced by topographic variation and the scan angleof individual lidar pulses), the peak pulse power of thelaser system, which may vary as a factor of pulsefrequency, beam divergence, laser footprint size (aproduct of range and beam divergence) and angle ofincidence. Target variables include the cross-sectionalarea of the target within the laser footprint, target

reflectivity, and surface roughness. Processing pro-cedures include variables introduced to the intensitydata by factors such as the interpolation techniqueapplied to convert the point cloud into a regular grid.

A number of studies have considered some or all ofthese factors and attempted to mitigate for them bypost-survey processing of intensity data. Many systemvariables are not routinely possible to correct for.Luzum et al. (2004) have explored normalization ofintensity measurements from regions of high relief for

path length by calculating survey aircraft altitude andlaser scan angle. While it is relatively easy to correct forrange based on survey aircraft altitude, correction forscan angle is more problematic. Coren and Sterzai(2006) report that in Optech instruments angles belowca. 158 from nadir have little effect on intensity values,although since Optech instruments may scan up to 258off-nadir (Optech, 2003) some data can be affected bysuch instrument-induced variations. Significantly, Yoonet al. (2008) found that there was little variation inintensity due to path length on variant reflectors such asvegetation and concluded that this was due to theheterogeneous character of returns from such materialscompared with invariant reflectors such as man-madesurfaces. Finally, Kaasalainen et al. (2009) have attemptedradiometric calibration of intensity data against targets ofknown reflectance in order to allow comparison andmathematical modelling of multiple survey flights.

The impact of atmospheric attenuation on thetransmission and reflection of the laser pulse mayalso affect results, but is difficult to account for,requiring detailed observations of atmospheric con-ditions at the time of survey as well as complexcomputational modelling and so is not routinelycorrected for. For the present study, since noatmospheric data were available and because thesurvey covered a relatively small area for which only asingle data set is used, atmospheric effects have beenassumed to be uniform and negligible.

Assessing variation in intensity data

Lidar data for the study area were acquired byInfoterra Global Ltd on 26 July 2007 using an OptechALTM 2033 Lidar flying at an average altitude of 914 mand recording two returns (first and last) at amaximum scan angle of 20-. Data were supplied asa x,y,z,i point cloud in ASCII format for first and lastpulse returns processed to WGS84 datum withellipsoidal elevation values. The point clouds wereprocessed using Applied Imagerys Quick TerrainModeller software to generate first and last pulse

digital surface models at 1 m spatial resolution and 8-bit greyscale images derived from first and last pulseintensity values. Aircraft recorded intensity valueswere histogram stretched to the full 8-bit dynamicrange (values 0255) but were otherwise unaltered.The digital surface model (DSM) and intensity imageswere reprojected to British National Grid and ellipsoidalelevation values converted to Ordnance Datum usingErdas Imagine 9.1. In the analysis described hereinimage based visual analysis of intensity makes use of


4 K. Challis et al.


5/14

data reprojected to the British National Grid. Sincereprojection alters original values through a process ofinterpolation, statistical analysis of intensity made useof data in the original WGS84 datum with no additionalprocessing steps employed on the point cloud.

The DSM and intensity data for the entire studyarea are shown in Figure 3. It is immediately apparentthat while topographic variation effectively highlightsgeomorphological features of archaeological signifi-cance (palaeochannels, terrace/floodplain boundary,etc.) these same variations are apparent to a differingdegree in the corresponding intensity data. In someinstances palaeochannels in particular are indicatedby areas of low intensity returns (Figure 4) althoughthis is not uniformly the case and examples areexamined in more detail later. Our initial assessmentof variation in intensity suggested that factors beyondelevation and surface material affect returned values.

Sediment moisture content seems the most likelycause of these variations, since experimental workhas demonstrated the susceptibility of intensity to theimpact of target moisture (cf. Kaasalainen et al., 2009).Our own field investigation to test this hypothesis isreported upon elsewhere (Challis et al., 2011).

Processing intensity data

Lidar intensity values are affected by the rangebetween the laser and target, such that measuredintensity declines by the second power of the range(Luzum et al., 2004). In areas of high relief this fall-offhas a significant effect on measured intensity values.The maximum relief variation in the present study isapproximately 55 m, which is sufficient to causesome variation in intensity for targets of similarreflectivity, although within individual study win-dows relief variation is rarely above 25 m. The rangevariation due to scan angle for an aircraft flyingat 914 m is approximately 58 m; again this is likely tohave some impact on measured intensity values. Inthis study we have examined only the effects ofcorrecting for range effects due to relief, sincecorrections due to scan angle require information on

the time lag for each returned pulse, which was notavailable.

In order to examine the impact of topographiceffects on intensity, data were normalized to range byapplying a procedure adapted from Luzum et al.(2004). Intensity data were processed using the

Figure 3. The study area showingthe lidar-derived last pulse digital surface model (DSM) and correspondingintensitydata.




6/14

following formula:

Ni Sirange2

mean elevation2

where Si is the sampled raw intensity, range is thedistance between the instrument and the ground(calculated by subtracting elevation from the recordedaverage altitude of the survey aircraft) and meanelevation is the arithmetic mean of the lidar-derivedelevation for the DSM window under study. Thenormalization routine was varied by substituting astandard range of 600 m (cf. Luzum et al., 2004) andusing the per pixel elevation for each intensity returnextracted from the DSM, as a substitute for mean

elevation in order to compare effectiveness of differentmethods.

Normalization methodsThe results of normalization of intensity data usingeach method are shown in Figure 5 with a statisticalsummary in Table 1. Note that in each case data havebeen standardized to fit an 8-bit range for visualdisplay and so have a minimum value of 0 and amaximum of 255. Normalization using any of themethods tried introduces subtle changes to the data. Inthe data illustrated in Figure 5 the range variation isapproximately 42 m for ground returns. Normaliza-

Figure 4. Atypicalpalaeochannelfeatureshowinglidar topographyand intensitydata.The scatter plotshows a sample ofintensityvaluesfor atrans-ect across the palaeochannel, within-channel values are highlightedand are consistently lower than for the surrounding area of similarland cover.The two profiles show elevation and intensity values along the transect.Note that the elevation variation across the palaeochannel is in the orderof 0.5 m.This figure is available in colouronlineat wileyonlinelibrary.com/journal/arp


6 K. Challis et al.


7/14

tion to DSM elevation values has the most significantimpact, in particular improving clarity and distinc-tiveness within areas of woodland cover (as notedby Donoghue et al., 2007) but elsewhere overcompen-sates for the elevation-derived range effect onintensity. There is little to choose between normal-ization using average DSM value and an arbitrarystandard elevation, as one might expect, as they simply

substitute different single standard values. Overall theimpact of normalization is to skew normalized datatowards the lower end of the range and increasethe internal variation.

Impact of normalization

In Figure 6 and Table 2 we illustrate the impactof normalization to range on intensity data for atypical area of floodplain (top), a substantial archae-ological earthwork (centre), and an area of archae-

ological cropmarks (bottom). In each case the figureshows from left to right the lidar elevation model,raw intensity, normalized intensity and a differenceraster based on subtracting raw from normalizedintensity. It is worth noting that the cropmarks, ofa Romano-British villa complex at Cromwell, Nottin-ghamshire (SK 801625), are faintly evident in theintensity data, in spite of the fact that data were

collected outside of the main cropmark season andin less than ideal conditions (July 2007 havingbeen unseasonably wet), although there is no corre-sponding elevation component (i.e. no significantvariation in crop height). This suggests that routineexamination of intensity data might be expected to addto the cropmark record and could provide a valueadded benefit to archaeological lidar surveys. Detec-tion of cropmarks in NIR intensity data is dependenton the same physical variation in crop colour thatis recorded by conventional aerial photography,although cropmark detection is improved in the NIR(cf. Challis et al., 2009). One consequence of this is

that it may be advantageous to schedule lidar surveysof cropmark-producing landscapes for the optimumpoint in the cropmark season.

Results (Figure 6) suggest that there is littlesignificant visual improvement in the clarity ofintensity data due to normalization for range inthese study areas. In general normalization increasesthe variation in values within the data, but withno significant visual improvement. Variation betweenpre- and post-normalized intensity data were

Figure 5. Comparisonof rangenormalizationmethods. (A)Normalizedtoaveragedigitalsurfacemodel (DSM)elevation.(B) Normalizedto standardrange of 600 m (C)Normalized to perpixelelevation extractedfrom DSM.Ineach casegreyscale imagesof post-processedintensityandassociatedhistograms of image values are shown.

Table 1. Statistics fornormalizedand standardintensity values forthe three normalization methodsshown in Figure 5.

Mean Standarddeviation

Unscaled 98.59 32.19Normalized to digitalsurface model

124.10 53.23

Normalized toaverage elevation

127.16 60.93

Normalized tostandard range

127.17 60.95




8/14

Figure 6. Comparisonof normalizedand standardintensitydata fora typicalsectionof floodplaingeomorphology(top),a substantialarchaeologicalearthwork (centre) and an area of archaeological cropmarks (bottom). In each case the figure shows from left to right the lidar elevation model,rawintensity, normalizedintensity and a differencerasterbased on subtractingrawfromnormalized intensity.Image bottom rightshowsthe archae-ological cropmarks as plotted by English Heritage superimposed on the intensitydata.


8 K. Challis et al.


9/14

examined by generating a difference raster producedby subtracting pre-normalized values from normal-ized. Difference images display the impact of normal-ization at a landscape scale; in particular they clearlyvisualize the effect of topography on intensityvariation, the end result being a useful fusion ofthe three-dimensional topographic and intensity-

derived image aspects of lidar data and seem toslightly improve definition of variations in intensityby highlighting areas of maximum change.

Is normalization required in areas of low relief?

Our work suggests that normalization of intensitydata for range variation has little impact on intensityvalues in areas of low relief where there is littleconsequent variation in range. This is undoubtedlydue to the negligible range effect in areas of littleelevation variation, the expected relationship between

range and intensity being largely absent and probablyfurther suppressed by the heterogeneous vegetationcover (cf. Yoon et al., 2008).

Lidar intensity and alluvialgeoarchaeology

In this final section last pulse ground lidar elevationand normalized intensity data for two locales(Figure 7), which are representative of the entire studyarea, are examined in order to assess the utility of thesedata to inform a series of empirical statements about

the character of floodplain and terrace topographyand sediments and their archaeological potential.Such generic information may be particularly valuablein providing a first level assessment of the archae-ological potential of valley floor landscapes andcould provide a framework for the rapid assessmentof large catchment areas.

Figure 7A shows an area around South Muskhamin Nottinghamshire (SK 788577), which has beenmapped by the British Geological Survey (BGS) as

Holme Pierrepont Sand and Gravel and which formsareas of upstanding river terrace (BGS Sheet 113,Ollerton). The terrace is bisected from south to northby a sinuous major palaeochannel of the River Trent.Mapping of superficial geology indicates that thechannel is infilled with fine grained alluvium,however, close examination of the lidar elevation datashows that the channel is both more extensive andmorphologically more complex than existing mappingsuggest. Shallow, sinuous depressions within theterrace surface are also evident in the lidar elevationdata, which might indicate areas where superficialcolluvial/alluvial deposits might enhance preser-vation of buried archaeological material. Lidar inten-sity data for this area show some difference in cropcolour and growth on the terrace to the east of thepalaeochannels, suggesting variations in the compo-sition of the underlying terrace; otherwise the intensity

data contribute little to the qualitative understandingof the burial environment.

Figure 7B shows an area of predominantly finegrained alluvium, deposited by overbank flooding,adjacent to the present channel of the River Trentbetween Kelham and Averham in Nottinghamshire(SK 778546). Lidar elevation data clearly distinguishesthe lower lying areas of floodplain alluvium andmarginally higher islands of terrace (composed ofHolme Pierrepont Sand and Gravel). A number ofsinuous depressions interpreted as palaeochannelsare visible across the terrace surface and their

dimensions suggest that they may represent formerchannels of the Trent, which in this area may haveformed part of an anastomosing river system (Knightand Howard, 2004). The level of detail recorded bylidar elevation data is substantially greater than thatpresent on the existing mapping of superficial geology(BGS Sheets 126, Nottingham and 127, Grantham) andrepresents an appropriate base map for geomorpho-logical mapping and identification of areas of potentialdifferential preservation preparatory for archaeologi-cal field investigation. For example, areas of terracemight be identified as being of greater potential forsettlement-related activates, terrace margins as areas

of high preservation potential as alluvium depositedby overbank flooding may blanket cultural deposits,and palaeochannels as prime locations for recoveryof organic sediments and environmental archaeologi-cal remains (cf. Howard and Macklin, 1999). Theintensity data for this area adds considerably toqualitative understanding of the burial environment.Variations within intensity values on the floodplainindicate cropmarks mirroring several of the sinuouspalaeochannels. To the east of the central terrace

Table 2. Statistics fornormalizedand standardintensityvaluesforthe earthworkstudyarea shownin Figure 6.

Mean Standarddeviation

Earthwork Standard 80.59 39.44Normalized 127.45 63.56

Cropmark Standard 88.1 44.54Normalized 127.27 62.68

Channel Standard 120.28 60.93Normalized 126.13 63.73




10/14

island, itself indicated by an area of differential colour(showing as lower intensity return probably indicatingparched crop), several areas of very low-intensityreturns probably indicate highly saturated ground

and perhaps in several cases shallow standingwater. These low-lying areas correspond to severalvery shallow topographic depressions indicated inelevation data and suggest channel features or perhaps

Figure 7. Representative areas of terrace (A) and floodplain (B). In each case lidar topographyand range normalized intensity are shown. Figureannotations indicatethe principlefeatures discussed in the text.


10 K. Challis et al.


11/14

simply depressions that may contain saturated sedi-ments with superior preservation of organic remains.Other linear cropmarks, probably buried field drains,are also apparent on the intensity data.

Conclusions

Examination of lidar intensity imagery from a varietyof archaeological and geomorphological settingsindicates that these data do contain information notpresent in the corresponding elevation data. Empiricalinterpretation, based on a common understanding ofthe character of soils, sediments and vegetation in thearea under examination, allows the use of intensityimages to add qualitative information to the interpret-ation of a landscape area. In effect the intensity imageis subject to the same knowledge-based interpretation

as might be used to extract information from aconventional aerial photograph.

Since intensity data are (or can be) routinelycollected during a lidar flight aimed primarily atgathering topographic data, it is suggested that theexamination of these data are routinely incorporatedin the archaeological interpretation of existing lidardata, and that their collection always forms part ofthe parameters of a an airborne lidar survey commis-sioned for archaeological purposes.

We have found little evidence of variation inintensity values due to instrument factors in our data.

Normalization of intensity values for range based on avariety of parameters made no significant difference tothe visual quality of data and we conclude that in areasof low relief variation where the survey aircraft hasflown at a uniform altitude, range normalization isof no benefit for archaeological analysis of lidarintensity data.

It is unfortunate that the largest provider of lidardata in Great Britain, The Environment Agency, donot routinely include intensity data as part of theirproduct, and indeed in most cases such data arearchived and accessed only at considerable additionaleffort and expense. While commercial survey con-

tractors do provide intensity data as part of theirproduct, there is no evidence from published accountsthat such data are accessed or examined by archae-ologists commissioning lidar surveys. In this paper,we have demonstrated that lidar is a significant toolfor geoarchaeological prospection and that examin-ation of intensity data assists in the interpretationof elevation products, the qualitative assessment oflandscape character and can, in appropriate circum-stances, record anthropogenic archaeological crop-

marks. For these reasons we propose that collectionand systematic examination of lidar intensity datashould routinely form part of the protocol of lidar-based aerial reconnaissance conducted by archaeolo-gists, a practice that we believe to have demonstratedin this paper has the potential to dramatically increaseboth the quantity and quality of information providedby such surveys.

Acknowledgements

Research was funded by DEFRA through the Aggre-gates Levy Sustainability Fund distributed by EnglishHeritage (PN 4782). The authors gratefully acknowl-edge the English Heritage Project officers, Dr IngridWard and Buzz Busby for their support and theRegional Science Advisor for the East Midlands DrJim Williams for his encouragement in the instigationof this research. We would also like to thank thefarmers of the lower Trent Valley for generously pro-viding access to their land.

References

Baker S. 2003. The palaeochannel record in the TrentValley UK: contributions towards cultural heritagemanagement. Internet Archaeology 20: http://intarch.ac.uk/journal/20/baker_toc.html.

Barnes I. 2003. Aerial remote-sensing techniques used inthe management of archaeological monuments on the

British armys Salisbury Plain training area, Wiltshire,UK. Archaeological Prospection 10: 8390.

Bewley R. 2003. Aerial survey for archaeology. The Photo-grammetric Record 18(104): 273292.

Bewley RH, Crutchley SP, Shell CA. 2005. New light on anancient landscape: lidar survey in the StonehengeWorld Heritage Site. Antiquity 79(305): 636647.

Bishop M. 2003. s and agenda in archaeological researchand management: a case study from the Trent Valley,UK. In Alluvial Archaeology in Europe, Howard AJ,Macklin MG, Passmore DG (eds). Balkema: Lisse;123131.

Bofinger J, Kurz S, Schmidt S. 2006. Ancient Maps modern data sets: different investigative techniquesin the landscapes of the Early Iron Age Princely Hill

Fort Heuneburg, Baden-Wurttemberg. In From Space toPlace, Campana S, Forte M. (eds). Proceedings of the2nd International Workshop on Remote Sensing inArchaeology, CNR, Rome, Italy, 4-7 December. BritishArchaeological Reports, International Series 1568: 8792.

Brunning R, Farr-Cox F. 2005. The River Siger rediscov-ered: lidar survey and relict landscape on the SomersetClaylands. Archaeology and the Severn Estuary 16: 715.

Brown AG. 1997. Alluvial Geoarchaeology: FloodplainArchaeology and Environmental Change. Cambridge Uni-versity Press: Cambridge.




12/14

Carey C, Brown AG, Challis K, Howard AJ, Cooper L.2006. Predictive modelling of multi-period geoarchaeo-logical resources at a riverconfluence: a case study fromthe Trent-Soar, UK. Archaeological Prospection 13(4):241250.

Challis K. 2005. Airborne LiDAR: a tool for geoarchaeo-logical prospection in riverine landscapes. In Archae-ological Heritage Management in Riverine Landscapes,Stoepker H (ed). Rapporten Archeologische Monumenten-zorg 126: 1124.

Challis K. 2006. Airborne laser altimetry in alluviatedlandscapes. Archaeological Prospection 13(2): 103127.

Challis K, Howard AJ, Kincey M, Moscrop D, Carey CJ,Hill T, Smith DN, Gearey BR, Thompson A. 2006. UsingAirborne Lidar Intensity to Predict the Organic Pres-ervation of Waterlogged Deposits. In From Space toPlace, Campana S, Forte M. (eds). Proceedings of the2nd International Workshop on Remote Sensing inArchaeology, CNR, Rome, Italy, 4-7 December. BritishArchaeological Reports, International Series 1568: 9398.

Challis K, Kokalj Z, Kincey M, Moscrop D, Howard AJ.2008. Airborne lidar and historic environment records.

Antiquity 82(318): 10551064.Challis K, Kincey M, Howard AJ. 2009. Airborne geoarch-

aeological remote sensing of valley floors using Dae-dalus ATM and CASI. Archaeological Prospection 16(1):1733.

Challis K, Carey C, Kincey M, Howard AJ. 2011. Asses-sing the preservation potential of temperate lowlandalluvial sediments using airborne lidar intensity. Jour-nal of Archaeological Science 38: 301313.

Chust G, Galparsoro I, Borja A, Franco J, Uriarte A. 2008.Coastal and estuarine habitat mapping, using LIDARheight and intensity and multi-spectral imagery. Estu-arine, Coastal and Shelf Science 78: 633643.

Coren P, Sterzai P. 2006. Radiometric correction of laserscanning. International Journal of Remote Sensing 27:30973104.

Crow P, Benham S, Devereux BJ, Amable GS. 2007. Wood-land vegetation and its implications for archaeologicalsurvey using Lidar. Forestry 80(3): 241252.

Crutchley S. 2006. Light detection and ranging (lidar) inthe Witham Valley, Lincolnshire: an assessment of newremote sensing techniques. Archaeological Prospection.13(4): 251257.

Devereux BJ, Amable GS, Crow P, Cliff AD. 2005. Thepotential of airborne lidar for detection of archaeologi-cal features under woodland canopies. Antiquity79(305): 648660.

Doneus M, Briese C, Fera M, Janner M. 2008. Archae-

ological prospection of forested areas using full-wave-form airborne laser scanning. Journal of ArchaeologicalScience 25: 882893.

Donoghue DNM, Watt PJ, Cox NJ, Wilson J. 2007. Remotesensing of species mixtures in conifer plantations usingLiDAR height and intensity data. Remote Sensing ofEnvironment 110: 509522.

Holden N, Horne P, Bewley RH. 2002. High resolutiondigital airborne mapping and Archaeology. In Aerial

Archaeology: Developing Future Practice, Bewley R,Raczkowski W (eds). NATO Series 1, Vol. 337, IOSPress: Amsterdam; 173180.

Hofle B, Pfeifer N. 2007. Correction of laser scanningintensity data: Data and model-driven approaches.

ISPRS Journal of Photogrammetry and Remote Sensing62: 415433.

Howard AJ, Macklin MG. 1999. A generic geomorpholo-gical approach to archaeological interpretation andprospection in British river valleys: a guide for archae-ologists investigating Holocene landscapes. Antiquity73: 527541.

Howard AJ, Challis KC, Holden J, Kincey M, PassmoreDG. 2008a. The impact of climate change on archae-ological resources in Britain: a catchment scale assess-ment. Climatic Change 91: 405422.

Howard AJ, Brown AG, Carey CJ, Challis K, Cooper LP,Kincey M, Toms P. 2008b. Archaeological resourcemodelling in temperate river valleys: a case study fromthe Trent Valley, UK. Antiquity 82(318): 10401054.

Jones AF, Brewer PA, Johnstone E, Macklin MG. 2007.High-resolution interpretative geomorphological map-ping of river environments using airborne LiDAR data.Earth Surface Processes and Landforms 31: 15741592.

Kaasalainen S, Hyyppa H, Kukko A, Litkey P, Ahokas E,Hyyppa J, Lehner H, Jaakkola A, Suomalainen J, Aku-

jarvi A, Kaasalainen M, Pyysalo U. 2009. Radiometriccalibration of LIDAR intensity with commercially avail-able reference targets. IEEE Transactions on Geoscienceand Remote Sensing 47: 588.

Knight D, Howard AJ. 2004. Trent Valley Landscapes.Heritage Marketing and Publications: Kings Lynn.

Lambrick G. 1992. Alluvial archaeology of the Holocenein the Upper Thames basin 1971-1991 : a review. In

Alluvial Archaeology in Britain, Needham S, MacklinMG (eds). London; The Society of Antiquaries: 209225.

Large ARG, Petts GE. 1996. Historical channelfloodplaindynamics along the River Trent: Implications for riverrehabilitation. Applied Geography 16(3): 19120.

Lutz E, Geist TH, Stotter J. 2003. Investigations of airbornelaser scanning signal intensity on glacial surfaces utilizing comprehensive laser geometry modellingand orthophoto surface modelling (a case study: Svar-tisheibreen, Norway). Proceedings of the ISPRS Workshopon 3-D reconstruction from airborne laserscanner and

INSAR data, Dresden; 143148.Luzum B, Starek M, Slatton KC. 2004. Normalizing ALSM

Intensities. GEM Center, Report No. Rep_2004-07-001:University of Florida.

Optech. 2003. ALTM 2033 Airborne Laser Terrain Mapper.Optech: Toronto.

Passmore D, Waddington C, Van Der Schriek T. 2006.Enhancing the evaluation and management of rivervalley archaeology; geoarchaeology in the TillTweed

catchment, northern England. Archaeological Prospection13(4): 269281.Powlesland D, Lyall J, Hopkinson G, Donoghue D, Beck

M, Harte A, Stott D. 2006. Beneath the sand remotesensing, archaeology, aggregates and sustainability: acase study from Heslerton, the Vale of Pickering, NorthYorkshire, UK.Archaeological Prospection 13(4): 291299.

Risbol O, Kristian Gjertsen A, Skare K. 2006. Airbornelaser scanning of cultural remains in forests: somepreliminary results from a Norwegian project. In FromSpace to Place, Campana S, Forte M. (eds). Proceedingsof the 2nd International Workshop on Remote Sensing


12 K. Challis et al.


13/14

in Archaeology, CNR, Rome, Italy, 4-7 December. Brit-ish Archaeological Reports, International Series 1568:107112.

Shell CA, Roughley CF. 2004. Exploring the Loughcrewlandscape: a new approach with airborne lidar. Archae-ology Ireland 18(2 68): 2023.

Sittler B, Schellberg S. 2006. The potential of lidar isassessing elements of cultural heritage hidden underforest canopies or overgrown vegetation: Possibilitiesand limits in detecting microrelief structures for archae-ological surveys In From Space to Place, Campana S,Forte M. (eds). Proceedings of the 2nd InternationalWorkshop on Remote Sensing in Archaeology, CNR,Rome, Italy, 4-7 December. British ArchaeologicalReports, International Series 1568: 117122.

Song JH, Han SH, Yu K, Kim Y. 2002. Assessing thePossibility of Land-cover Classification Using Lidar Inten-sity Data, IAPRS, 9-13 September, Graz; 34, 4.

Spinetti C, Mazzarini F, Casacchia R, Colini L, Neri M,Behncke B, Salvatori R, Buongiorno M, Pareschi M.2009. Spectral properties of volcanic materials fromhyperspectral field and satellite data compared withLiDAR data at Mt. Etna. International Journal of AppliedEarth Observation and Geoinformation 11(2): 142155.

Whimster R. 1989. The Emerging Past. Air Photography andthe Buried Landscape. Royal Commission on the Histori-cal Monuments of England: London.

Yoon J, Shin I, Lee KS. 2008. Land cover characteristics ofairborne LiDAR intensity data: a case study. IEEEGeoscience and Remote Sensing Letters 5: 801805.




14/14

Airborne Lidar Intensity & Geoarchaological Prospection

Documents

Transcript of Airborne Lidar Intensity & Geoarchaological Prospection