Detection of Ancient Egyptian Archaeolog

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Detection of ancient Egyptian archaeological sites usingsatellite remote sensing and digital image processing

Robert K. Corrie*a

a

Institute of Archaeology, University of Oxford, Oxford OX1 2PG, United Kingdom

ABSTRACT

Satellite remote sensing is playing an increasingly important role in the detection and documentation of archaeologicalsites. Surveying an area from the ground using traditional methods often presents challenges due to the time and costsinvolved. In contrast, the multispectral synoptic approach afforded by the satellite sensor makes it possible to covermuch larger areas in greater spectral detail and more cost effectively. This is especially the case for larger scale regionalsurveys, which are helping to contribute to a better understanding of ancient Egyptian settlement patterns. This study presents an overview of satellite remote sensing data products, methodologies, and image processing techniques fordetecting lost or undiscovered archaeological sites with reference to Egypt and the Near East. Key regions of theelectromagnetic spectrum useful for site detection are discussed, including the visible near-infrared (VNIR), shortwave

infrared (SWIR), thermal infrared (TIR), and microwave (radar). The potential of using Google Earth as both a data provider and a visualization tool is also examined. Finally, a case study is presented for detecting tell sites in Egypt usingLandsat ETM+, ASTER, and Google Earth imagery. The results indicated that principal components analysis (PCA) wassuccessfully able to detect and differentiate tell sites from modern settlements in Egypt’s northwestern Nile Delta region.

Keywords: Archaeological site detection, remote sensing, satellite imaging, tell, spectral signature, ASTER, Egypt

1.

INTRODUCTION

Remote sensing is used in numerous applications ranging from medical imaging to radio astronomy. It can be broadlydefined as the collection of information about an object, area, or phenomenon, without direct physical contact, and whereelectromagnetic energy usually acts as the communications link between the sensor and the study area. As defined in this paper, remote sensing is discussed from an Earth observation perspective and can include both airborne and spacebornemultispectral and hyperspectral imaging, Radio Detection And Ranging (RADAR), and Light Detection And Ranging(LIDAR). Ground based geophysical survey techniques, such as resistivity, magnetometry, and subsurface imagingradar, although often used in archaeological survey, are normally considered in the realm of geophysics and are notdiscussed further here.

There are many advantages to conducting a remote sensing survey when compared to a ground campaign. Primarily,remotely sensed data can be safely collected from a distance, which otherwise may be impossible due to rugged orwaterlogged terrain, regional political instability, or as a result of a site being located on privately held or military ownedland. A remote sensing survey may also prove more economical and efficient, not normally being affected by the usualtime, personnel, and budget constraints. Furthermore, remote sensing offers a non-invasive technique for thereconnaissance and recording of cultural heritage that ensures the preservation of sites for future generations.

The aim of this paper is to provide an initial approach to the techniques and methodologies used in remote sensing fordetecting ancient Egyptian archaeological sites and features. It is addressed primarily to Egyptian archaeologists and

Egyptologists, although the information may also prove relevant to Near Eastern specialists working on similar aridenvironments. It is assumed the reader has an elementary knowledge of the principles and concepts used in remotesensing. More specifically, some understanding of electromagnetic energy and its interaction with matter, passive andactive remote sensing systems, sensor resolution, and elementary image processing are essential. Some of these conceptsare discussed in this paper. For a more in-depth treatment the reader is directed to Lillesand et al.1

*[email protected] ; phone +44 (0)1865 279120; fax +44 (0)1865 790819

Earth Resources and Environmental Remote Sensing/GIS Applications II,edited by Ulrich Michel, Daniel L. Civco, Proc. of SPIE Vol. 8181, 81811B · © 2011 SPIE

CCC code: 0277-786X/11/$18 · doi: 10.1117/12.898230

Proc. of SPIE Vol. 8181 81811B-1

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The exploration and cataloging of ancient Egyptian sites from the ground began with the French invasion of Egypt under Napoleon Bonaparte in 1798 – 1801. This initial work was followed by several further exploratory surveys, including aPrussian-led expedition under the German Egyptologist Karl Richard Lepsius2 in 1842 – 1845, and British led surveysunder Francis L. Griffith and Flinders Petrie in 1891 – 1910 and 1883 – 1927 respectively. These and similar campaignsset out to establish a comprehensive record of the most visible archaeological sites. However, many sites werecompletely missed or badly interpreted due to the limited perspective offered by a ground-based approach.

Prior to the Space Age, archaeological remote sensing in Egypt had its origins in aerial photography using camera filmsensitive to the visible and near-infrared wavelengths3. The value of these and other aerial surveys from around the worldwere well recognized, given their advantage of providing unrestricted and unbiased views of archaeological featuresfrom the air. However, limited spectral capability, in addition to poor geographical coverage, were limiting factorsreducing their full effectiveness. The use of high-resolution orbital imaging for site detection became fully realized withthe declassification of American (Corona) and Russian (KVR-1000) spy satellite photography, and with the launch ofcommercial satellites beginning with IKONOS in 1999. In 1996, Japanese researchers at the Tokai University Researchand Information Center (TRIC) and the Institute of Egyptology at Waseda University, were the first to detect an ancientEgyptian site using satellite remote sensing. The discovery was made in the “pyramid zone” on the Nile’s left west bank,in an area where it was believed several pyramids lay undiscovered. The site was named “Dahshur North” and afterexcavation was determined to be a mudbrick tomb of the New Kingdom with a suggested date of the late 18 th to early19th dynasties (ca. 1330 – 1250 BCE). It is the first New Kingdom monument to be found in this region, where most ofthe surrounding archaeology is from the Old and Middle kingdoms4, 5, 6. There are several pyramids in this area that are

still unaccounted for, including those of Neferkare (7th

– 8th

dynasty), and Ity (9th

– 10th

dynasty). In 2008, a group atCairo University may have rediscovered the missing pyramid of Menkauhor (5 th dynasty), which had been originallydiscovered by Lepsius in 1842, but later had been lost under shifting sands7.

Important archaeological finds are continuing to be uncovered using satellite imaging. As recently as 2006, RobertSchiestl, an archaeologist with the German Institute of Archaeology, Cairo, detected the remains of two previouslyunknown 13th dynasty pyramids in the South Saqqara region west of the river Nile8 - identified as sites SAK S 3 andSAK S 7 (Figure 1). The two other 13 th Dynasty pyramids – the Khendjer pyramid (SAK S 5) and the “Unfinished pyramid” (SAK S 6) were discovered in the early 19th Century (Lepsius) and later excavated by Gustave Jéquier 9 in1929 – 1931. Jéquier’s excavation ramps can still be seen next to the sites. Schiestl’s recent discovery demonstrates theutility of using satellite remote sensing as a tool for site detection, even in areas thought to contain no new finds.

Figure 1. SAK S 3 and SAK S7: recent pyramid discoveries in South Saqqara. QuickBird image courtesy of Google Earth.




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2.

THE ELECTROMAGNETIC SPECTRUM:WINDOWS OF OPPORTUNITY

Regions of the electromagnetic (EM) spectrum present windows of opportunity. Our eyes are sensitive to a narrow bandof visible light that makes up one small part of a much larger spectrum. The regions of energy beyond the visible, whiledetectable only by the multispectral satellite sensor, can provide useful information to an archaeological study. Fordifferent earth surface materials, the amount of emitted and reflected radiation varies as a function of wavelength,

resulting in a unique spectral signature. Spectral signatures for different earth surface materials are well known and have been compiled into spectral libraries, many of which are available online10. Four regions of the EM spectrum are of potential interest to satellite remote sensing, and include the visible and near-infrared (VNIR), shortwave infrared(SWIR), thermal infrared (TIR), and microwave (radar). Each region is sensitive to different physical properties and can provide different information about the same area of coverage.

The majority of archaeological remote sensing work in the Middle East, including Egypt, has been limited to detecting

sites using the visible range of the EM spectrum, i.e., blue (0.4 – 0.5 µm), green (0.5 – 0.6 µm), and red (0.6 – 0.7 µm),often neglecting a satellite’s full multispectral capabilities. This has usually involved simply enlarging high-resolutionimages by zooming into areas of interest to find sites and performing simple image processing procedures11,12,13.However, surveying large areas of ground using the highest resolution sensors can be prohibitively expensive.Researchers have found that using a combination of lower resolution imagery, such as Landsat ETM+ and ASTER 14,15,in combination with higher resolution products, such as Corona, for confirmation of site locations, can offer a more cost

effective solution. Declassified spy satellite photography from the American Corona16

and Russian KVR missions17

,offer a valuable resource for archaeologists, especially in the Middle East where data availability and imagery costs may be a problem. Collected in the 1960’s and 1970’s using black and white film, and delivered as a single panchromaticchannel, Corona can be used in the same way as an aerial photograph and holds enormous potential as a source ofarchival material for visualizing archaeological sites and landscapes that have changed over time. Imagery from theCorona KH-4B series camera (operated between 1967 – 1972) holds the most potential for work in Egypt due to its highspatial resolution (~1.8m), wide geographic coverage of Egypt, and low cost. When used in combination with other data products, it has been instrumental in identifying such features as hollow ways, canals, and archaeological sites in Iraq 18,and the mapping of ancient tell sites in Egypt19.

The NIR region (~ 0.7 – 1.2 µm) provides useful spectral information for areas rich in vegetation cover, such as the NileDelta, floodplain, and desert oases, due to its sensitivity to vegetation (biomass) and health (vigor). It has been shownthat for regions covered by more than 75% vegetation the two most promising EM regions for detecting archaeological

features are at the chlorophyll peak of 0.56 µm, and the red edge at 0.67 – 0.72 µm20. Healthy vegetation reflectsstrongly in the near-infrared when compared to the visible wavelengths, making this region useful for detectingarchaeological features, such as crop or soil marks. Crop marks appear due to the differential growth rates of vegetationcover and can be indicative of a surface or subsurface archaeological site. The NIR has also been used to increase imagecontrast in the pyramid zone to locate new pyramids, and in the ancient city of Tanis for the highlighting of mudbrickstructures21.

While the VNIR bands are sensitive to vegetation cover, the SWIR and TIR channels (~1.6 µm – 3 µm and ~3 – 20 µmrespectively) are more sensitive to soil, mineral, and rock geology18, and can help distinguish materials that may bespectrally similar in the VNIR channels. Exploiting the channels of higher spectral resolution sensors, such as ASTER,may help to improve the spectral signature collection for archaeological features.

The TIR is often used in geological remote sensing for the identification and mapping of various rock types and rockforming minerals and is particularly sensitive to silicate composition22. It can also be a valuable tool for detecting

archaeological sites, where differential heating may be an indicator of archaeological features within a site. This is because archaeological materials at or below ground level can affect the temperature of their surroundings (i.e., differentmaterials cool at different rates). Although most TIR data is of coarse spatial resolution, it may be useful for detectinglineaments such as roadways, canals, and walls, in addition to larger scale features. Most thermal imagers make use of

the 8 – 14 µm range, with a secondary window occurring at 3 – 5 µm (atmospheric windows). Other regions cannot beused due to atmospheric absorption caused by water and atmospheric gases, such as carbon dioxide. Many remotesensing systems include at least one thermal band, which are typically displayed in grayscale, where bright areasrepresent warm regions, and dark areas represent cooler regions. ASTER is unique in having five thermal infrared




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channels - covering 8 to 12 µm at a spatial resolution of 90 m (in contrast, Landsat ETM+ has only one thermal band at60 m). At a resolution of 90 m, ASTER TIR is not ideal for locating buried structures, but its increased spectralcapabilities should prove useful in locating archaeological features on a larger scale.

Unlike optical sensors, which passively receive energy reflected from or emitted by an object, Radio Detection andRanging (RADAR) provides its own energy source by actively transmitting pulses of microwave energy that arereflected off a distant surface or object. Radar has a side looking geometry, illuminating the ground scene at an obliqueangle and enhancing variations in both roughness and texture23. Radar offers several advantages to optical remotesensing in that it can penetrate through both cloud and rain, in addition to operating during both day and night,independent of illumination from the sun. A major benefit of radar at longer wavelengths is its ability in aridenvironments to penetrate up to 2 meters of sand in optimal conditions, i.e., extremely dry, fine-grained, clay-free sand 24.This can be very useful for detecting subsurface archaeological structures in desert regions and for locating palaeochannels, also known as “radar rivers” (see Section 4).

3.

ACQUIRING SATELLITE DATA

Resolution is a key concept to understand in order to choose the most appropriate imagery for a given application. Thereare four types of resolution often referred to in remote sensing: spatial, spectral, radiometric, and temporal. The spatialresolution of a sensor is the area represented on the ground by a single pixel in the image. The spectral resolution refersto the number and width of the spectral bands (or channels) recorded in the EM spectrum (generally the higher thespectral resolution of a sensor, the greater the number of wavelengths the instrument is sensitive to). Finally, theradiometric and temporal resolutions refer to the number of gray levels used to represent an image (i.e., 256 forLandsat 7), and the revisit period of the satellite over the same area (i.e., 16 days for Landsat 7) respectively.

There are a number of remote sensing products available that are useful in archaeological applications. Most offer goodlevels of spatial and spectral detail, however there is a trade-off in resolution due to the design limitations of thetechnology: a sensor offering high spatial resolution will often be limited to low or medium spectral resolution and viceversa. For example, a high spatial resolution sensor will provide high-resolution capability in the VNIR (perhaps sub1 m), but will provide no capability in the SWIR or TIR. ASTER comes closest, offering adequate spatial resolution(depending on what you want to image), with 14 spectral channels, including three in the VNIR (15 m), six in the SWIR(30 m), and five in the TIR (90 m). This is adequate for larger scale mapping, but it is somewhat limited for sitedetection purposes and would have to be complemented with a higher resolution instrument, such as Corona, or one ofthe higher cost commercial sensors. ASTER is a resource that has been underutilized by the archaeological community

and holds potential for future work in the spectral discrimination of ancient and modern materials due to its high spectralcapability.

The Landsat series of satellites have been the workhorses of remote sensing since the 1970s, producing three generationsof sensors: the Multispectral Scanner (MSS), the Thematic Mapper (TM), and the Enhanced Thematic Mapper (ETM+).Since April 21, 2008, all archived scenes from all generations of Landsat satellites can be downloaded free of charge forall users. The latest Landsat sensor – the ETM+ onboard Landsat 7 offers general-purpose capabilities - 7 bands in theVNIR and SWIR at medium resolution (30 m). It includes a panchromatic band at 15 m spatial resolution that can beused to pan-sharpen the multispectral bands to create a 15 m dataset without losing the color information from theoriginal multispectral channels (see Section 5).

There is a large choice of commercial high-resolution imagery available, but it is expensive and is normally charged persquare kilometer (rather than per satellite scene). Currently the highest spatial resolution commercially available imageryis provided by GeoEye-1 and WorldView-2, of which both satellites are capable of sub half-meter resolutions (correct asof July 2011). WorldView-2’s extra spectral bands in the VNIR offer further capability. GeoEye’s next generationsatellite – GeoEye-2, scheduled for launch in early 2013, will be capable of imaging objects as small as 0.25 m. Inaccordance to US government regulations, its imagery will most likely be re-sampled to 0.50 m, as is the case withcurrent commercial high-resolution imagery. In contrast to multispectral sensors, which record data in a few tens ofchannels, hyperspectral instruments record data in hundreds or even thousands of discrete channels. NASA’s Hyperionoffers 220 different spectral bands from VNIR to SWIR at medium spatial resolution (30 m). Table 1 lists somecommonly used sensors with their technical specifications.




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SATELLITE SENSORSUBSYSTEM withnumber of bands

SPATIALRESOLUTON (m)

COST (US $)

Landsat 7 ETM+

VNIR (4)SWIR (2)TIR (1)

PAN (1)

303060

15

Free as of April 21, 2008

ASTERVNIR (3)SWIR (6)TIR (5)

153090

Free for coverage in the US.Coverage outside the US is

charged at ~ $US 80

SPOT-5PAN (1)VNIR (4)SWIR (1)

2.5 - 51020

$1,700 for old archive imagery,and $7,800 for 2.5 m pan data

(3,600 km2 scene)

Corona 4B Visible (1) 1.8 $30 per scanned frame

KVR-1000 Visible (1) 2 - 3 $1,000 – 4,000 per frame

IKONOSVNIR (4)PAN (1)

41

From $10 per km2 forarchived imagery

GeoEye-1VNIR (4)

PAN (1)

1.65

0.5

From $12.50 per km2 for

archived imagery

QuickBird-2VNIR (4)PAN (1)

2.40.6


WorldView-2VNIR (8)PAN (1)

1.840.5


Hyperion VNIR-SWIR (220) 30 Free as of August 5, 2009

Shuttle Imaging Radar(SIR)

Synthetic ApertureRadar (SAR)

30 Free

PALSARL Band SyntheticAperture Radar

10-100¥52,500 (3 different scan

modes available)

ASTER GDEM DEM20 vertical;

30 horizontalFree

SRTM DEM15 vertical;

30 horizontalFree

Table 1. Satellite sensor specifications.

Remotely sensed imagery can be purchased and downloaded from the following satellite imagery providers:

1. The Global Land Cover Facility (GLCF) of the University of Maryland: http://glcf.umiacs.umd.edu/.2. The Land Processes Distributed Archive Center (LP DAAC) of the United States Geological Survey (USGS):

https://lpdaac.usgs.gov/.

3. The Warehouse Inventory Search Tool (WIST) of the USGS: https://lpdaac.usgs.gov/lpdaac/get_data/wist/.

Declassified high-resolution photography can be ordered from:

1. Corona: http://eros.usgs.gov/#/Find_Data.2. KVR-1000: http://www.sovinformsputnik.com/.

The cost of acquiring large amounts of commercial high-resolution satellite imagery for regional survey purposes can be prohibitive. Although data costs have come down, pricing is still beyond the reach of most archaeological budgets. Forexample, the cost of the WorldView-2 Basic Bundle, consisting of the full complement of spectral bands(1 panchromatic and 8 multispectral) costs over US$8,000 (correct as of July 2010) for imagery that is currently archivedand does not require the re-tasking of the satellite. In the past, this has meant that researchers have had to rely on lowerresolution imagery, i.e., Landsat ETM+ and similar datasets, and order higher resolution data only when essential.




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The use of high-resolution imagery delivered through the Google Earth (GE) application (http://earth.google.com/) iscausing a mini-revolution in the way that scientists, professionals, and private individuals are collecting and usingsatellite data of the Earth. Imagery from an external Google server is streamed over a broadband connection to a remoteuser anywhere in the world. The wide coverage of the Earth with high-resolution satellite imagery via GE can be veryuseful for archaeological prospection25. For the first time both professional and amateur archaeologists have free accessto large amounts of high-resolution satellite data. The imagery is provided at various spatial resolutions with the majority being one to three years old. Currently most land areas of the Earth are made available as base imagery in GE at 15 m,using pan-sharpened Landsat ETM+ data. The base imagery is gradually being replaced with higher resolution data suchas 2.5 m SPOT and 0.6 m (60 cm) QuickBird-2 (QB-2), in addition to aerial photography and imagery from othersources. Currently about 70% of Egypt is covered by the QB-2 satellite (http://www.digitalglobe.com/) at a spatialresolution of 60 cm. Areas that do not yet have QB-2 coverage are covered by the French satellites - Système Probatoired'Observation de la Terre (SPOT, http://www.spotimage.com/) at resolutions ranging from 2.5 m to 10 m.

GE comes with several useful features, including the ability to use historical (multi-temporal) imagery to view regions ofthe Earth at earlier dates. This can be useful for several reasons. Firstly, if many archived images of the same area areavailable they can give an idea of how the landscape has changed over time. For example, historical imagery can be usedin change detection studies for modeling the effects of urban expansion on a landscape. Secondly, as discussed earlier,the Delta and floodplain can exhibit unique vegetation patterns, such as crop marks, that may assist in detectingarchaeological remains. Depending on the time of year that the satellite image was acquired, the crop mark may not bevisible. GE allows imagery to be examined at different dates to find a more appropriate image. A second useful feature

of GE is the ability to change the angle of the sun and the surface lighting. The time of day that the scene was acquiredcan be an important factor when examining archaeological features. For example, tell sites can often be spotted based ontheir characteristic shape and shadowing, which is dependent on the position of the sun. Digital Elevation Models(DEMs) – such as NASA’s SRTM or ASTER GDEM, can be added as a separate layer to GE to assist in theidentification and visualization of tells and mound-type burial structures by giving a more life-like three-dimensional perspective. Previous workers have used DEMs combined with mathematical modeling to assist in the identification oftell sites in the Near East26, 27.

There are a number of disadvantages to using GE as a primary imagery provider: 1) It provides no multispectralcapability, other than in the visible part of the EM spectrum. The channels in the near, shortwave, or thermal infrared thatare normally included in a commercial release have been truncated. This limits the user to applications in the visibleregion only; 2) The visible channels have been merged into a single band and displayed in “true color”. This forces theuser to examine the imagery under a specific band combination; 3) Imagery from GE (or Google Maps) can only besaved as a jpeg file and at a limited resolution – up to 4,800 pixels for the professional version, compared to 1,000 pixelsfor the free version. Once saved, the image needs to be imported (with geo-referencing) into a remote sensing or a GIS package for further processing; 4) The spatial and spectral resolution of the imagery has been degraded due to the use ofcompression algorithms for reducing the file size when streaming the imagery over the internet.

Although GE is very good at examining imagery at very high resolution in the visible bands, it cannot substitute for thefull spectral resolution of imagery offered by commercial providers. Unlocked commercial imagery includes many more bands that can be easily manipulated with digital image processing operations.

GE is freely available from http://www.google.com/earth/index.html. The product is currently offered in two versions:GE and GE Pro. GE is free to download, while GE Pro is a commercial product providing extra features, including theability to save and print at higher resolutions, import vector files, and calculate 3D measurements. Competing productsoffering similar functionality include: NASA’s World Wind (http://worldwind.arc.nasa.gov/), USGS EarthExplorer(http://earthexplorer.usgs.gov/), ArcGIS Explorer (http://www.esri.com/software/arcgis/explorer/), and SkylineGlobe

(http://www.skylinesoft.com/). These products are all proprietary, except for World Wind, which is open-source onlyusing data that is freely available in the public domain.




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4.

STRATEGIES FOR LOCATING SITES

In order to detect new sites there needs to be an understanding of how a site is defined, what types of sites may exist,where to look for them, and whether satellite imaging can be used to detect them.

The definition of ‘site’ differs widely among archaeologists, however it is generally understood to represent a locationthat preserves evidence of past human activity. In Egypt this includes the pyramids, temples, and tombs, through to

scatters of ceramics spread across the surface of a site, such as a tell. A tell is an ancient settlement site composed ofmudbrick and forming a large mound created over time by multiple levels of occupation. Not all sites will be visible onsatellite imagery, depending on their size, stratigraphic position, state of preservation, spectral characteristics, contrastdifferences with the surrounding landscape, and the sensor type being used. Many sites may also be undetectable becausethey are too deeply buried under the foundations of modern sites, or they may be hidden by shifting Nile sands. Inaddition, the choice of remote sensing survey and image processing methodology may differ depending on thegeographic location of the site. For example, what may work in the Delta may not necessarily work in the Nile floodplainor in the arid environments of the Western or Eastern Desert.

It is unknown how many archaeological sites remain undiscovered in Egypt. Regional survey work is helping to answerthis question. According to Sarah Parcak, an archaeologist at the University of Alabama, Birmingham, “only 1/100 th ofone percent of archaeological sites in Egypt have been discovered”24, whereas Zahi Hawass, former secretary general ofEgypt’s Supreme Council of Antiquities, has estimated it to be in the region of 30 percent28. Ultimately, it is impossibleto know the total number of sites remaining to be found, however this number likely falls somewhere in between. Thetargets for spaceborne detection include habitation sites (tells, towns, and cities), and mortuary architecture (collapsed orunfinished pyramids of the Old and Middle Kingdoms, private mastaba tombs, temples, chapels, cemeteries, and smallerstructures). A “mastaba” is an Arabic word for a mud bench, and was a type of prestige tomb consisting of an aboveground rectangular structure with a flat roof, outward sloping sides, and with one or more burial chambers beneath.

Fundamental to any remote sensing survey is the development of a research strategy that maximizes the chances ofsearching in high probability locations. Egypt is a very large country covering well over one million square kilometers,much of it consisting of the sparsely inhabited and inhospitable sands of the Sahara Desert. It is also one of the mostdensely populated countries in the world, with an estimated 80 million people inhabiting the banks of the River Nile in aspace of just forty thousand square kilometers. It is therefore important to design algorithms carefully based on a set ofsearch criteria. One criterion may be to search regions close to sources of water. Due to its arid and inhospitableenvironment, the most precious resource in Egypt has always been the River Nile. Populated areas often grow up aroundthe edges of water sources – rivers, tributaries, and oases. It is therefore logical to target areas where there was a ready

supply of water nearby. A Geographical Information System (GIS) can be used to define buffer zones of a predetermineddistance around water sources, such as the Nile Valley, the Nile Delta, and around the desert oases. In addition, areas ofvegetation are associated with water and may be indicative of an archaeological site. Satellite imagery can be searchedfor sources of vegetation using the Normalized Difference Vegetation Index (NDVI). The NDVI is a spectral ratio of thedifference between IR and red intensities of light to the total light being reflected, and provides a good sensitivity tochanges in vegetation cover.

Until as recently as the 1960s, the Nile overflowed its banks annually. This event was predictable, leaving an averagedeposit of about 1 mm of silt a year, and preventing the lowland regions from becoming permanently settled 29.Archaeological sites would have been constructed above these levels on areas of raised land to avoid excessive waterdamage. A future study to simulate the flooding of the Nile using a DEM would be useful to visualize the spatial extentof sites and their interrelationship to each other. To complicate matters the Nile is not stationary. Like all water bodies, ithas evolved by gradually changing its shape and position over time. Data from borehole survey, satellite imagery, andhistorical maps suggest that the Delta head was further south in antiquity and that the Nile itself has been migratingeastwards at a rate of up to 9km every one thousand years30,31,32. The majorities of studies do not consider the migrationof the Nile over time, and draw its position in antiquity close to its present day location. However, its shifting movementshould be taken into account as this can have an impact on where to search for sites.

The use of remote sensing to identify radar rivers and hollow ways can provide further clues to the location of pastsettlement sites. Results from the first Shuttle Imaging Radar (SIR-A) mission onboard the November, 1981, flight of theSpace Shuttle Columbia provided the first regional scale subsurface images of previously unknown valleys and palaeo-channels buried beneath the Selima Sand Sheet of the eastern Sahara Desert of Egypt and the Sudan33,34,35,. These




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features, named “radar rivers”, are detectable using microwaves (radar), but are invisible from the ground andundetectable using optical sensors, such as Landsat. The presence of many present day oases, including the Kharga,Dakhla, Fayyum, and Siwa of the Western Desert can be attributed to these palaeo-drainage networks buried deep underthe sand. Many of these oases served as the crossroads for caravan routes transporting luxury items, including ivory,spices, in addition to animals and slaves. It is hoped that a future study to map the full extent of radar rivers will lead tothe discovery of further ancient water sources and their associated archaeological sites.

In addition to using microwave remote sensing for locating radar rivers, VNIR imagery has been successfully used toidentify ancient roads and trading routes (hollow ways). First identified by Wilkinson and Tucker in the North Jazira,Iraq 36, hollow ways represent shallow linear depressions in the landscape resulting from a millennia of human andanimal foot traffic. They can be identified through satellite imagery from their soil, moisture and vegetationcharacteristics18. Although there have been many studies of hollow ways in the Near East, e.g., Iraq, Iran, Syria 18,37,38,there has been no definitive study of them in Egypt. The lack of moisture in a desert environment may not be conduciveto their preservation. However, the locations of many ancient trading routes in Egypt are well known. For example, theDarb el-Arbain, part of which is now paved and still in use today, was a trading route dating from the Old Kingdom,connecting Middle Egypt and northwestern Sudan and passing through the Kharga Oasis. Further studies to detect suchfeatures in Egypt would be informative. However, the dating of desert tracks and hollow ways in aerial photography andsatellite imagery is problematic. This is because an identified lineament may theoretically be of any age. It may be possible to determine its relative age through the principle of superposition (cross cutting relationships). More precisedating may be achieved through an examination of any surface ceramic remains and associated epigraphic materials39.

Desert road archaeology has raised awareness of the significance of caravan routes and desert oasis settlements whereevidence for some of the earliest writing in Egypt’s history has been found. The Theban Desert Road Survey is one such project examining desert routes, and recording the earliest examples of epigraphic materials40.

Archaeological sites in Egypt are often found buried beneath or situated adjacent to modern developments. This canmake site detection challenging. An important step toward solving part of this problem is to use spectral analysis toseparate ancient and modern building materials. The two principal building materials used in ancient Egypt were sun- baked mudbrick (acquired from the flood plain and used for the construction of houses and town walls), and stone(predominately used in the construction of temples and tombs). Amongst surviving monuments, sandstone is the most prevalent, however limestone and granite is also found. Creating databases of the spectral signatures of these materials isexpected to substantially improve detection methods. An even greater problem often encountered is from local farmerswho harvest the mudbrick from tell sites (mudbricks are high in nitrate and phosphates), to be used as a crop fertilizer(known as sebbakh). Tell Mudbricks are also being recycled for use in road and building construction. If recycled intomodern structures, ancient and modern spectral signatures will be identical and impossible to separate.

The nature of the archaeological landscape needs to be understood before a remote sensing survey can be carried out.Egypt consists of two very diverse landscapes: the water-rich Nile Valley (Delta and floodplain) and oases; and the drysandy barren desert (Western and Eastern Deserts, and the Sinai Peninsula). Both environments require the use of verydifferent tools and techniques. It is essential to determine the spatial and spectral characteristics of the target. There alsoneeds to exist sufficient contrast between it and the surrounding matrix for it to be differentiated 13. These will have animpact on the choice of appropriate sensor. For example, radar does not penetrate into the subsurface in moisture-richregions due to increased reflectivity, and so it would make a poor choice for subsurface detection in the floodplain andDelta regions. Finally, a remote sensing survey should never be used in total isolation. It is vital when possible to followup with a ground-based confirmation of imagery results through a surface survey, followed by, if necessary, geophysics,coring, and ideally excavation.

5.

SEARCHING FOR ANOMALIES IN THE IMAGERYThe primary objective of remote sensing is to detect changes in contrast between the area of interest and its surroundingmatrix. This contrast difference is named an “anomaly” until it can be identified, either directly through further image processing, or most usually through a campaign of ground-based investigation (also known as ground truthing). Groundtruthing provides a way to compare what is shown in the imagery to what is physically located on the ground. However,it should be understood that remote sensing has its limitations. Often what is presented in the imagery is not alwaysconfirmed when examined from the ground.




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Digital image processing enhances the imagery to make it more interpretable. There are many types of imageryenhancement techniques, including radiometric (modifying values of individual pixels), spectral (transforming pixelvalues on a multiband basis), and spatial (modifying pixel values based on the values of surrounding (neighborhood) pixels). Examples of these techniques include the contrast stretch, principal components analysis, and convolutionfiltering. In this section, I highlight some of the methods for displaying and enhancing imagery that have beensuccessfully used in previous site detection studies. These include simple band combinations for imagery visualization, pan-sharpening to increase multispectral resolution, and principal components analysis for reducing spectral redundancydue to inter-band correlation. For a more in depth analysis of digital image processing the reader is directed to Lillesandet al.1 and Short41.

Image processing software can only display three bands of data at a time (where a single band is sent to each of the red,green, and blue color guns of the display). However, multispectral satellite imagery consists of multiple bands of data,where each band is sensitive to a different region of the EM spectrum (for example Landsat ETM+ has 8 bands, ASTERhas 14, and Hyperion has 220). Choosing to display a different band combination can highlight different attributes of thelandscape, such as vegetation patterns, geological features, and archaeological anomalies. A choice has to be made aboutwhich three bands to use to best display the particular features of interest. Popular band combinations using LandsatTM/ETM+ imagery include: 3, 2, 1 (R, G, B); 4, 3, 2 (NIR, R, G); and 7, 3, 2 (SWIR, R, G). The band combination of3, 2, 1 is also known as “true color” as it displays the scene using the same red, green, and blue wavelengths of light thatour eyes are sensitive to, helping to create a more natural looking image. The combination 4, 3, 2 is often used inarchaeology for displaying crop marks and other surface vegetation patterns. Vegetation appears red in this combination

due to the near-infrared channel being sent to the red color gun. This is because healthy vegetation reflects strongly inthe near-infrared wavelengths. The combination 7, 3, 2 is often used in geological interpretation of rocks and mineraltypes, which may also be useful for archaeological interpretation. Non-true color images are often referred to as “false-color composites” (FCC) because the band combinations give an artificial representation of the landscape. Other imagery products will have similar band combinations depending on their band allocations.

Many remote sensing imagery products include a higher resolution panchromatic (grayscale) band in addition to lowerresolution multispectral (color) bands (e.g., Landsat ETM+, SPOT-5, IKONOS, and QuickBird). Pan-sharpening can beused to increase the spatial resolution of the multispectral bands by merging their color information with the geometricinformation from the panchromatic band, resulting in high-resolution multispectral imagery. For example, the 15 m panchromatic band from Landsat ETM+ can be merged with the six 30 m multispectral bands, resulting in sixmultispectral (color) bands each at 15 m spatial resolution. In addition to combining bands from a single satellite, it isalso possible to combine bands from two different satellites (e.g., the 5 m pan band from SPOT-5 with the 30 mmultispectral bands from Landsat ETM+). There are a number of algorithms available including intensity hue saturation(IHS), Brovey transform, principal component analysis, and wavelet-based sharpening methods. A quantitative studycomparing different algorithms has shown that the wavelet based method offers better results most of the time42.

A problem often encountered when analyzing satellite imagery is deciding how to extract the most useful informationfrom a multidimensional dataset. This is because there is a tendency for multispectral data to be highly correlated whenthe spectral bands are close together (if two bands are correlated, they share some of the same information). For example,ETM+ bands 1 and 2 are highly correlated, whereas bands 1 and 4 are less so because they are spectrally further apart.Principal components analysis (PCA) and similar techniques (such as the decorrelation stretch and canonical analysis)may be used to reduce the redundancy and dimensionality of a dataset by transforming the bands into a smaller numberof non-correlated components, which when displayed are often more interpretable than the original source data. PCA is a popular tool for analyzing data consisting of multiple bands (for example, ASTER or Hyperion data). Applying PCA produces the same number of output components as bands in the original data. The first principal component (PC1)contains the maximum amount of variation, with each successive component displaying less variation. For example, in

Landsat ETM+ data, usually the first two components together account for over 90% of the total variation in the originaldata, while the first three account for almost all of the variation. If the remaining PCs contain no useful information, theymay be safely discarded. However, even higher order PCs can sometimes contain valuable information. Either thecomponents can be viewed individually in grayscale or a FCC can be generated by sending three components to each ofthe red, green, and blue color guns of the display.

Other image processing tools often used in site detection include shape detection algorithms43,44, object-basedclassification schemes, and various spatial filters (for example the Laplacian filter).




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6.

CASE STUDY: IDENTIFYING TELL SITES USING SATELLITE IMAGERY

Thousands of ancient settlement mounds, or “tells”, can be found scattered throughout the landscape of the Egyptian Nile Delta and floodplain. The Arabic word tell (kom is also used in Egypt), is the remnant of an ancient town or city thatformed over many centuries or millennia of occupation. They are common throughout much of the Near East (Syria,Iraq, Iran, Turkey, Egypt), Balkan Europe, and northwestern India. In Egypt, they range in size from a few tens of squaremeters up to many square kilometers and date from the Pharaonic through to the Roman periods and beyond. Tells used

to be a commonly found feature throughout Egypt, however due to rapid urban expansion many sites have been leveledto make way for agricultural fields, in addition to being buried under the structures of modern developments. They arealso under threat from local farmers, who use the mudbrick as a fertilizer (known as sebbakh). Tell sites represent someof the first emerging communities and further study of them can provide valuable insights into ancient socialorganization and the evolution of early settlement systems. However, after some 200 years of Egyptology there remainsno comprehensive record detailing their locations and antiquity. Of those known only a small proportion have beensurveyed and an even smaller number excavated. A regionally based remote sensing study will provide the first stepstowards a more extensive documentation of these ancient monuments.

Although the Delta has not been extensively surveyed, many tell sites have been mapped, unlike regions of the NileValley in Upper Egypt where few examples survive. A number of resources relating to tell sites have been madeavailable online, including the Egypt Exploration Society’s (EES) Delta Survey Project(http://www.deltasurvey.ees.ac.uk/), and Uppsala University’s Ancient Near Eastern sites database

(http://www.anst.uu.se/olofpede/Links). The Delta Survey Project, led by Jeffrey Spencer of the British Museum,describes over 700 tell sites, providing information on the site name, location (in latitude and longitude), size, surfacefeatures, relevant dating information, and links to external references. The Uppsala University database (based inSweden) has compiled a further 2,500 archaeological sites across the whole of the Near East, including tell sites inEgypt. Both databases can be downloaded as KML files and added as separate layers to Google Earth base imagery (theKeyhole Markup Language (KML) is a Google Earth file format for storing geographic features, such as points, lines,and polygons, images, and models). Several researchers are currently working on tell sites in the Delta, including PennyWilson (Durham University, http://www.dur.ac.uk/archaeology/staff/?id=168), who is involved with a number of projects, including ongoing survey work at the royal city of Sais (Sa el-Hagar,http://www.dur.ac.uk/penelope.wilson/sais.html), and the Western Nile Delta Regional Survey(http://www.dur.ac.uk/penelope.wilson/Delta/Survey.html). Additional work is being carried out by Joanne Rowland(Freie Universität, Berlin), based at Quesna in the Central Delta, and Neil Spencer (British Museum), based at KomFirin. Previous efforts to map tells using spaceborne data have focused on their spatial morphologies using SRTM data inTurkey, Syria, and Iraq 26,27,45-48, or their spectral characteristics in Middle Egypt and the eastern Delta, where Sarah

Parcak successfully used a combination of unsupervised classification and principal components analysis techniques19,24.

This section describes a case study for the detection of tell sites in Egypt’s northwestern Nile Delta using Landsat ETM+,ASTER, and Google Earth QuickBird imagery. The objectives of this work were to determine if remote sensing coulddetect previously known sites and if new sites could be identified for future investigations. KML files from both the EESDelta Survey and from the Uppsala University (UU) Catalog were added as separate layers to Google Earth base imagery(marked in yellow and red respectively in Figure 2). The white box in the northwestern region of the Nile Delta indicates

the extent of the 50 x 40 km study area, located at approximately (30°58’30” – 31°21’10”) N to (30°22’12” –

30°55’44”) E. The study area contains twenty-four known tell sites, centered on the northern region of the Rosetta branch of the Nile. It should be noted that the two catalogs do not represent a comprehensive listing of all known tellsites in the Nile Delta, but provide a reasonable sampling of sites to be used in this case study. This case studycomplements Sarah Parcak’s work, who first suggested that PCA can be a useful technique for the detection of tell sitesin the eastern Nile Delta19. The work presented here extends Parcak’s approach to the northwestern Delta, using PCA

and a thresholding technique, and sets out a comprehensive and well-illustrated methodology for tell site detection.




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Figure 2. The Nile Delta with boxed study area. Tell sites are shown in yellow (EES) and red (UU). Imagery Google Earth.

A single Landsat 7 ETM+ scene (Path 177, Row 38), acquired on January 1, 2003, was downloaded from the LandProcesses Distributed Active Archive Center (LP DAAC, https://lpdaac.usgs.gov/), using the NASA WarehouseInventory Search Tool (WIST). December through February are the wettest months of the year in Egypt and provideoptimal conditions for tell site detection. An increased moisture content in the mudbrick has been shown to be a keyfactor in locating archaeological sites19. Therefore, a January acquisition date was considered ideal. Although the chosenscene had a relatively high cloud content (about 9%), the clouds were isolated to regions over open ocean to the north ofthe study area, and did not impede the image processing. Landsat ETM+ imagery includes eight separate bands,including four in the VNIR (bands 1 – 4), two in the SWIR (bands 5 and 7), one in the TIR (band 6), and one high-resolution panchromatic band (band 8). All bands except for the thermal (which was discarded) were imported fromGeoTIFF (.tif) format into ERDAS IMAGINE’s native file format (.img). The six bands (1, 2, 3, 4, 5, and 7) with aspatial resolution of 30 m were layer stacked as a single multilayer .img file and each channel visually checked forsensor errors prior to further processing.

The raw uncalibrated digital numbers (DNs) were converted to exoatmospheric reflectance (a dimensionless value),

representing the ratio of upwelling to downwelling radiation, measured on a scale between 0 and 1 (or 0 – 100 %).Converting to reflectance has many advantages, including the ability to compare spectral information from one satellitescene to another. Although not strictly necessary when working with a single scene, this pre-processing step is requiredwhen quantitatively comparing values between scenes of a multi-scene, multi-temporal dataset. Converting DNs toreflectance is a two-step calculation. The DNs are first converted to radiance at sensor values (spectral radiance), with

units of W m-2 sr -1 µm-1. This requires the gain and bias parameters of the sensor, which can be found in the header of thedownloaded metadata file. The second step involves converting spectral radiance to exoatmospheric (apparent)reflectance, which requires scene-specific information, such as the solar zenith angle, and the mean Earth-Sun distance in




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astronomical units. Further information, including the conversion equations for the above calculations can be found inthe Landsat 7 Science Data Users Handbook 49.

The multispectral imagery was pan-sharpened with the high-resolution panchromatic band to 15 m using a principalcomponent algorithm with nearest neighbor resampling. Improving the spatial resolution of a dataset may not improvethe ability to classify finer levels of spectral detail, but it does allow for the mapping of smaller features, thus helping toaddress the mixed-pixel problem. The results of the pan-sharpening are shown in Figure 3, where the city of Fuwa

(31°12’20” N, 30°33’24” E) is used as an example (displayed in a band combination of 4, 3, 2). The image is visiblysharper, containing more definition in both the urban and vegetated areas.

Figure 3. Landsat ETM+ FCC (4, 3, 2) at 30 m prior to multi-resolution merge (left); same scene post 15 m PC merge (right).

Color composites were constructed in an effort to determine the combination of bands that would best enhance the tellsites in the imagery. In addition to displaying the usual band combinations, i.e., 3, 2, 1 (true color) and 4, 3, 2 (false coloremphasizing the NIR in red), the Optimum Index Factor (OIF) was also calculated. The OIF determines the bandcombination that displays the maximum variability in a scene, and is ranked from lowest to highest, with the highest

score indicating the combination that displays the highest variability. Landsat ETM+ has a total of 20 three-bandcombinations (there are 20 ways of combining a total of 6 bands in groups of 3). To calculate the OIF, the correlationmatrix and the standard deviations for each channel were entered into an Excel worksheet containing the OIF equation,to produce a ranked list of OIF combinations. The results indicated that band combination 3, 4, 7 ranked highest for the pan-sharpened imagery, while 1, 4, 7, was the highest for the unsharpened imagery. It should be noted that bothcombinations include spectral information from all three regions of the visible, NIR, and the SWIR. It has beensuggested that using channels from each of these three regions should result in a more interpretable image 50. The OIFonly indicates the choice of bands for display, leaving the analyst to decide the color to which each band can be assigned.It was decided to use both the 3, 4, 7 (R, NIR, SWIR) band combination suggested by the OIF calculation, in addition toa combination of 4, 3, 2 (NIR, R, G) for loading up scenes for display and classification purposes. Further details of thecalculation and specifics of the OIF equation can be found in Chavez et al.51,52

To successfully detect tell sites, the spectral characteristics of “tell earth” needs to be identified and differentiated fromother landscape materials. Difficulties in detection are compounded by several factors. Modern developments often arose

adjacent to tell sites, or were even built over the top of them. In addition, tell mudbricks were often recycled to be usedas modern building materials. In an effort to gain a better understanding of the spectral characteristics of tell sites, tenspectral profiles were collected for each of four different ground cover types: water, modern urban centers, agriculturalcrops, and tell sites (Figure 4). Healthy vegetated areas (red) show the typical profile of low reflectance in the visible(bands 1-3) and SWIR (bands 5 and 6), and high reflectance in the NIR (band 4). Water (blue) exhibits high absorptionthroughout the spectrum as would be expected, producing minimal reflectance values. Modern towns and cities (black)and tell earth (yellow) share similar spectral characteristics, with a modest decrease in the visible, an increase in the NIR,and a decrease towards the end of the SWIR.




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However, the reflectance of tell sites when compared to modern sites show highervalues throughout all wavelengths,especially in the NIR and SWIR, indicatingthe potential for spectral separation in thisregion of the spectrum.

Unsupervised and supervised classificationswere run on both the standard 30 m and15 m pan-sharpened Landsat imagery. Theunsupervised classification was performedusing the ISODATA algorithm, initiallywith 14 classes, which was later expandedto 20, and then 50. A maximum iteration of15 was set, with a convergence threshold of95%. The unsupervised classification clearlyidentified both modern and ancient sites(shown as black clusters in Figure 5), inmarked contrast with agricultural fields(red). However modern settlements and tell

sites were mixed between a number of thesame classes and the technique provedunsuccessful, regardless of the number ofclasses, or if pan-sharpened imagery wasused.

Similarly, a supervised approach wascarried out using a Maximum Likelihoodclassifier. Five classes were identified forclassification: water, modern towns / cities,roads, tells, and vegetation. Signatures werecollected from training areas representativeof each of the classes, including from 15 tellsites evenly distributed throughout thescene. The results of the supervisedclassification were similar to theunsupervised, and the classifier was unableto spectrally separate tell sites from modernurban centers. The increased spectralcapability of the ASTER sensor with its 14channels may help to separate these classesin a future study.

PCA was run on the 30 m Landsat reflectance data to decorrelate the bands. This was achieved by transforming the sixcorrelated bands into six uncorrelated grayscale components (PC1 – PC6). The first three components, PC1 – PC3,accounted for approximately 99.4% of the total variance of the original dataset (79.5%, 18.8%, and 1.1% respectively).However, they were unable to separate modern settlements from ancient tell sites. It was the fourth component, PC4

(accounting for only 0.35% of the total variance), that was able to best differentiate tell sites from the modern sites(shown in Figure 6 after a two standard deviation stretch). PC4 clearly identifies nineteen of the twenty-four previouslyknown tell sites (circled in green in Figure 6), in addition to eight previously unknown sites (circled in red). Theremaining five undetected sites, were either too small to detect or were buried beneath urban sprawl. The remaining twocomponents, PC5 and PC6, accounted for the remaining 0.25% of the variance, but did not contain any usefulinformation. Band numbers 7 and 5 (SWIR) from the original six-band dataset were found to be contributing the most toPC4 (called the factor loadings). The aforementioned variances and factor loadings were obtained from the eigenvectormatrix and eigenvalues, which were calculated during the PCA operation.

Figure 4. Spectral profiles for several landscape features. Tell sites are displayedin yellow and have consistently higher spectral values in the NIR and SWIR.

Figure 5. Unsupervised classification showing towns/tells in black.




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Figure 6. Landsat ETM+ PC4 grayscale image after a two standard deviation stretch showing tell sites as high brightness values.Sites previously known from an earlier survey are circled in green, while newly discovered sites are circled in red.

To exploit the increased spectral resolution of ASTER in the SWIR, two scenes were downloaded from LP DAAC. TheSWIR was resampled to 15 m to match the resolution of the VNIR dataset before layer stacking (TIR was discarded).

The DNs were converted first to radiance and then to reflectance values prior to mosaicking, using a procedure reported by Smith53 and Milder 54. After PCA was applied to the mosaicked ASTER scenes, it was found that PC5 was the mostappropriate component for emphasizing tell sites (high brightness values), in marked contrast to the low brightnessvalues of the surrounding landscape. ASTER PC5 is shown in Figure 7 after a two standard deviation stretch.

Figure 7. ASTER PC5 grayscale image after a two standard deviation stretch. Tell sites appear as high brightness values.




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The Landsat ETM+ PC4 image was subset to examine an area in closer detail. Limits were set for detecting pixel valuesgreater than a given threshold, which was performed using the ERDAS IMAGINE model maker tool using an inputraster (the ETM+ PC4 image), a process (function), and an output raster. The function consisted of the formula:

EITHER 0 IF ( $n1_PCAinputimage < -0.020 ) OR 1 OTHERWISE

This function replaces the input raster with a binary image, where values below the threshold are replaced with a 0, and

values above the threshold are replaced with a 1. Thresholding makes it possible to determine which pixels most likelyrepresent tell earth. A threshold of -0.020 was found optimal after a process of trial and error. The CLUMP andELIMINATE GIS functions were used to remove small groups of pixels within the binary output image. The CLUMPfunction works by performing a contiguity analysis on the input raster (using 8 connecting neighbors), to identifyclumps, or contiguous groups of pixels, which are then recoded into a single class. Finally, the ELIMINATE functionwas applied to the clump thematic, to remove clumps of pixels of a specified size. The resultant image is displayed inFigure 8, where the output from the threshold model has been overlaid in red on the Landsat scene (displayed in 3, 2, 1).Modern urban regions appear unchanged (light gray), whereas regions likely to be tell sites are indicated in red.

Figure 8. Landsat ETM+ true color composite (3, 2, 1) with PC4 overlay displayed in red.

Figure 9A shows an enlarged view of the above Landsat image and is provided with several additional annotations.Circled locations represent regions of brightness in PC4 (Figure 6), and correspond well with the locations of tell sites.Previously mapped sites from both the EES and UU are circled in green, whereas newly detected sites are circled in red.In addition to detecting nineteen known tell sites, eight new sites were identified, including one of circular shape with

approximate radius of 110 m, located at 31°18’31” N, 30°48’58” E (Figure 9B). Tell sites adjacent to modern towns orcities are well differentiated and are easily identifiable using the thresholding approach, such as kom el-Haddadi, located

at 31°19’58” N, 30°47’19” E (Figure 9C), and kom el-Khariba, located at 31°19’42” N, 30°50’55” E (Figure 9D). Tellsites (both known and unknown) were confirmed using high resolution QuickBird imagery from Google Earth. However,five previously known sites were not detected using this approach. After examining their locations using Google Earth, it became apparent that those tells had been leveled and buried beneath modern sites. Too little remained on the surface to

be detected using the spatial resolution of the satellite sensor. This technique created a small number of false positives, ascan be seen in the southeast corner of Figure 9A (indicated by uncircled red areas). Some of these regions may representtell sites, however they have not been confirmed with any degree of confidence. Future ground observations of theseregions will be able to clarify their origins. Adjustment of the threshold levels for the detection method will reduce false positives, however this may also reduce the number of pixels representing real tell sites.




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F i g u r e 9 .

L a n d s a t E T M + t r u e c o l o r c o m p o s i t e ( 3 , 2 ,

1 ) s u b s e t w i t h P C 4 t h r e s h o l d o v e r l a y i n r e

d .

P r e v i o u s l y k n o w n a n d n e w l y d e t e c t e d t e l l s i t e s a r e

c i r c l e d i n g r e e n a n d r e d r e s p e c t i v e l y ( A ) ; a n e w l y d e t e c t e d t e l l s i t e ( B ) ; t h e t e l l s i t e o f k o m

e l - H a d d a d i ( C ) , a n d t e l l s i t e k o m e l - K h a r i

b a ( D ) .




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7.

CONCLUSIONS

Satellite remote sensing is revolutionizing the way new archaeological sites are being detected and recorded. This ismade possible through advances in sensor design with the introduction of instruments offering higher spatial and spectralresolutions. Remote sensing offers many advantages when compared to more traditional survey approaches, including providing a safe, cost effective, and synoptic view of the landscape. Data obtained via satellite can be viewed andanalyzed from an image processing lab from anywhere in the world, making possible international and multidisciplinary

collaborations. Most importantly, remote sensing offers a non-intrusive method for archaeological reconnaissance, bymapping and conserving sites for future generations of archaeologists. The practical component of this paper presented astrategy for the detection of ancient settlement mounds, or “tells”, in Egypt’s northwestern Delta. PCA was applied to both the VNIR and SWIR channels of Landsat ETM+ and ASTER imagery, and the results indicate that it can be auseful technique for detecting tell sites in this region of the Nile Delta. It was the higher-order components that containedthe patterns of variability able to separate the spectral characteristics of tell sites. These features were not present in anyother lower-order component or individual ETM+ or ASTER channel. Furthermore, the increased spectral resolution inthe SWIR bands provided by ASTER yielded greater contrast in its principle components, when compared to usingLandsat imagery. This work constitutes an important first step for mapping the spatial distribution of these importantmonuments, which in turn will help to provide a window into urban land distribution and settlement patterns at a time ofthe first emerging communities. Many of these sites are at risk from urban, industrial, and agricultural expansion, inaddition to looting and vandalism. It is important to document these sites before they completely disappear from thearchaeological record. However, it should be made clear that the results from these investigations have not yet been

independently verified from the ground, and any newly detected sites should remain as anomalies until a groundcampaign can prove otherwise.

ACKNOWLEDGEMENTS

The author wishes to thank Keble College, Oxford, and the Keble Association, for a generous award in support of thiswork. This work was also supported by an international society for optics and photonics (SPIE) conference travel award.Thanks are also due to the Department of Earth Sciences, University of Hong Kong, for providing work facilities duringthe writing of this paper. ASTER data was generously supplied by NASA through a program for approved educationaluse. ASTER data are distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at theU.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (https://lpdaac.usgs.gov/).Finally, the author also wants to thank John Baines of the Oriental Institute, Oxford, and Gary Lock of the Institute ofArchaeology, Oxford, for their helpful comments and suggestions in the review of this paper.

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