CHACHAPOYAN ARCHAEOLOGICAL SITE LOCATION WITH SATELLITE MAGERY

Peter Bangarth Deparîment of Anthropology

Submitted in partial fulfilment of the requirements for the degree of

Master of Arts

Faculty of Graduate Studies The University of Western Ontario

London, Ontano September 1998

0 Peter Bangarth 1998

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The Chachapoyan people lived in the northem Andes corn approximately IWO

years ago to their conquest by the Incas 500 years ago. Their location at a rare east-west

passage through the Andes controls a possible route for migration and tram-Andean

exchange of goods. Ethnohistoric accounts of üieir social organization suggest M e r

understanding oftheir culture is important for the study of cultural complexity. Site

location is critical to questions of social organization and migration, but the remoteness

and niggedness of the region has prevented any comprehensive mapping or study of the

Chac hapoyans.

While terrestrial survey is extremely difficult, remote sensing of the region by

satellite provides a technology to overcome the terrain. This study develops a method for

detecting known archaeological sites fiom their spectrai responses in Landsat Thematic

Mapper imagery. Radanat hagery provides support for location of known sites. Possi-

ble new sites are suggested by their similarity to known sites. Colour images to provide

maps for exploration of the region are developed based on best spectral response of sites.

Archaeology, remote sensing, satellite, Landsat, Radmat, survey, site prediction,

Chachapoyas, mountains.

This thesis is dedicated to my wife, whose unflagging belief in me and in the value of this work made the impossible corne me.

1 would like to acknowledge first and foremost my supervisor, Dr. Andrew Nelson, who in the most difficult of circumstances guided and supporteci me. His wisdom and comrnon sense were invaluable. 1 would also like to thank my advisor Dr. Christopher Ellis for help with statistics, and guidance when Dr. Nelson was not available.

1 thank Dr. Inge Schjellemp and Keith Muscutt for their help in locating archaeo- logical sites and providing access tn research documents and maps in their possession. Without this help my research effort would have been severely curtailed.

My work benefitted greatly from the gracious help provided by Dr Cheryl Pearce of the Department of Geography, both in technical advice and use of laboratory equipment. Advice from Dr. Philip Stooke and Dr. Jinfei Wang of the Geography Department about radar images and geo-referencing of satellite imagery is also greatly appreciated.

1 thank Callie Cesarini, the Anthropology Department graduate secretary for much needed administrative reminden and help through the schedules and paperwork.

I appreciate the professiohal editing and literary advice of my wife, Welwyn Wilton Katz, who read my work as an advocate of the reader. My tean during the editing process dried in view of the final product-

Finally, L wish to acknowledge the Canada Centre for Remote Sensing, and Dr. Robert Gauthier of that organization in particular for their generous grant of four Radarsat scenes, James Wers and Renée Lariviere of CCRS for their help in the ordering process, and Radarsat Corporation for the rapid response to my request for satellite alignment and acquisition of the daîa. This is an invaluable resource, which will continue to serve my research in the b e .

Table of Contents

Certificate of Examination Abstract Dedication Acknowledgements Table of Contents List of Figures List of Maps List of Tables List of Appendices

2. Technical Background Landsat &ka Radarsat &ta Image generation

3. Methods and Materials Data sources Primary Landsat images for exploration Site selection Image classification Statistical d y s i s

4. Discussion Control sites Known arc haeological site cornparisons Test sites

5. Conclusion !tesearc h results Resources from which this research could have benefitted Directions for future research

. . II ... 111

iv v vi vii vii

- *. Vlll

ix

Appendices

List of Fieares

Figure 2.1 Atmospheric trammittance of electromagnetic radiation

Figure 2-2. Reflectance of electromagnetic radiation by selected surface materiais.

Figure 2-3. Full Landsat TM image.

Figure 2-4. Geometry of synthetic aperture radar.

Figure 2-5. Principal components of two-dimwnsional data

Figure 3-1. Histogram of pixel counts in clusters based on ISOCLUST seed image.

Figure 3-2. Histogram of pixel counts in clusters after 3 iterations with 24 clusters.

Figure 3-3. Segment of clustered image.

List of Maris

Map 1 . 1 Chachapoyan archaeological site distribution

Map 3- 1. Natural colour image of Chachapoyan region

Map 3-2. False colour 4/3 band ratio image of Kuelap site

Map 3-3. False colour multiple band ratio image of Kuelap site

Map 4-1 (a). false colour bands 7,3 and 1 image of Chachapoyas region (b) ( c ) . magnifications of select regions of 4- 1. (a).

(d) 4 ) . magnifications of select regions of 4-1. (a).

Page

13

Page

5

33

36

vii

Table Page

Table 1 - 1. Cornparison of theoretical tirnelines of Chachapoyan development 4

Table 2- 1. Dark-object values for Landsat &ta of Chachapoyan region. 2 1

Table 2-2. Principal components calculated h m Landsat bands 1 to 5, and 7, of the Chachapoyan region. 25

Table 3- 1. Known archaeological sites used in calculations. 38

Table 3-2. Test sites with characteristics sirnilar to known archaeological sites. 44

Table 3-3. Control sites with no archaeological presence. 44

Table 3-4. Levels of significance of t-test compuïsons. 47

Table 4- 1 . Results of t-test cornparisons with anomalous known sites removed 5 1

Appendix

Appendix A Sample of the Process of Known Site Location

Appendix 8 Site Polygons and Surrounding Tomes

Appendix C Means and Variances of Site Polygons and Surrounding T o m s

Appendix D Software Used

Page

74

79

1. Introduction

Historical PerspectÏve ofrhe Chachapoyan Region

The established view of PenMan history from pre-ceramic times to the Spmish

conquest divides it into 'Intennediary Penods' of local cultural florescence punctuated by

'Horizons' of mi1ita.q andlor d s t i c influence which span the entire PenMan Andes and

coast (Rowe 1944). The Inca empire constitutes the Late Horizon, established by their

great conquests in the late 1400's.

Though the Incas spread into the northem Andes after 1460, there is indication

h m known archaeological sites and ethnohistoric accounts during the Spanish conquest

îhat the region to the east of the Marailon River was heavily populated through the

Middle Horizon and into the Early Intermediate Period, roughly 1500 yeus ago

(Schjellerup 1984, K a u h a n n Doig 1990, Moseley 1992). The Inca conquest of this

region was chronicled by Garcilaso de la Vega ( 1967 ( 1609)), who referred to the region

as Chachapoyas. The Inca and Spanish perpetuated the use of this name.

That the Inca, Tupac Yupanqui, used some forty thousand troops to conquer the

region is not only a testament to the logistical genius of the inca, but also an indication of

the power that the Chachapoyans were able to muster in their own defence. Yet,

acwrding to ethnohistoric accounts, they were not a single culture, but a loose

confederation of three or possibly four peoples (Reichlen and Reichlen 1950, Davis 1988,

Kauffmann Doig 1990). A cursory exploration of the region reveals both stylistic

sirnilarities and differences among the artifacts of these sub-cultures. Despite the fact

that many sites are known to local f m e r s and small civic museurns, no comprehensive

scholarly treatment of the Chachapoyan people as a whole exists.

Context of archoeo~ogicui research

Sporadic archaeological mention has been paid to the Chachapoyas region since

the 'discovery' of the fortress of Kuelap in 1843 by Don Juan Crisostomo Nieto, a local

judge (Rivero and von Tschudi 1854). The impressive structure of Kuelap remained the

focus of attention during the 1800's in M e r acfounts by the engineer Werthemann,

archaeologists Stubel and Reiss, and anthropologist Bandelier (Schjellerup 1997). This

was typical of the 1 9m century's fascination with the 'big find' . Bandelier published the

first comprehensive account of Chachapoyan settlement in 1907, breaking the mold of

previous work by combining ethnography, historical &ta and archaeological investigation

(Bandelier 1 WU( 1907)).

Unfomuiately, Bandelier's example wouid not be followed until late in the 20°

century. In 1933, Louis Langlois visited Kuelap and other sites, noting the association of

habitation sites with tenaces on momtain slopes (Schjellerup 1997). In 1948 Henri and

Paule Reichlen spent four months in the Chachapoyas region. Their report (1950) of their

investigations gives some modem ethnographie details, surveys previous studies, and

describes visits to 39 sites. As well, they performed excavations and collected surface

materials from four sites: Kuelap, Chippuric, Revash, and San Pedro de Washpa. From

their investigations they collected some 1400 kg. of pottery and other materials, which is

sîill crated and has yet to be catalogued and examined (Schjellemp 1997). It is not clear

from their article why they chose these four sites to do more extensive work than at the

othen, but one can surmise îhat their size made these sites seem important. The

Reichlens divided the Chachapoyans into three ethnic sub-groups: Kuelap, Chippuric and

Revash. Based on seriation of local pottery and pottery frorn other regions, they placed

the Kuelap group as the oldest, followed by the Chippuric and then Revash (Reichlens

1950) (see Table 1). Some problems with their study are addresseci in more recent work

discussed below, but X find it curious that their cultural divisions are so similar to the few

sites they actually excavated and that little mention is made of this correlation in eosuing

studies. In any case, this model of Chachapoyan culhna1 division becarne the standard,

and was followed into the 1990's. Horkheimer (1958) expands on the Reichlen model by

allowing for unknown cultures (see Table 1-1). Ruiz Estrada (1 972) focuses on Kuelap,

and develops a more detailed seriation of the pottery (see Table 1- 1 ).

Gene Savoy, a joumalist and explorer, did more than anyone to bnng the

Chachapoyan culture into the public eye by describing his attempts (Savoy 1970) to

follow the path of conquest of the Chachapoyans by Topac Inca Yupanqui as detailed by

Garcilaso de la Vega (de la Vega W67(I609)). Morgan Davis, another avocational

archaeologist, has spent several yean in the Chachapoyan region, visiting and describing

archaeological sites (Davis 1988) (see Map 1 - 1 ). Both of these exploren maintaineci the

Reichlen model. That model, however, has recently begun to be challenged by more

comprehensive, multidisciplinary studies which combine the materiakt archaeological

approach with li terary historicd study .

There has been traditionally an uneasy relationship between archaeology and

history (Ingold 1996, Thomas 1996). Nevertheless, stnctly archaeological inquiry has

been giving way in the 1st hventy years to multi-faceted stuciies which draw information

from historical records and 'traditional' behaviour of presentday peoples as well as from

investigations of material remaim. Several such studies have been applied in the

Chachapoyan region. I believe that multi-disciplinary studies have provided dramatic

irnprovements to our understanding of the Chachapoyans, and that such inclusive midies

must continue.

Table 1-1. Cornparison of Occupation models of Chachapoyas region. Adapted from Schjeilerup ( 1997).

Date AD.

Inka

Revash

ChippuRc

Kueiap

hka

Revash

chippuric

KueIap

lnka Kuelap

Kuelap

Li

E-

Middle rnaediap99

B w h ( 1977) presents a soci~ulturai study of the modem town of Uchucrnarca in

Peru to research agricultural methods and trading patterns which persist to this &y from

the pre-Inca Chachapoyan period. Lerche ( 1986) analysed the Jalca region of Peru to

study changes in resource use during colonial Spanish occupation of this Chachapoyan

region. Salomon ( 1986) used records of visitas, Spanish administrative field studies, near

Quito, Ecuador to study îrade links that pre-dated the S w s h occupation, including

material on a Chachapoyan community transplanted by the Incas. Inge Schjellerup

( 1 997) conducted a multi-dise i plinary midy of the southemmod part of the Chachapoyan

region. Her research incorporated ethnohistoric accounts of interactions between the

Chachapoyans and their conquerors, the Incas and Spaniards; ethnologicai &es of

modem life in the region; and archaeological investigations of twenty-four sites including

stratigraphie excavations, cerarnic analyses and physical anthropological studies. Her

objective was to shidy long-term processes of change among the Chachapoyans, to

determine to what degree the Inca presence was çtamped upon the people and theù

material arti facts, and to what degree ancient practices and beliefs persisted through

centuries of occupation and historical records.

These studies al! claim the importance of accounts such as the Spanish vlsitas and

compilations such as the description of the Inca conquest of the Chachapoyans (de la

Vega 196ï( 1609)) as sources of descriptive material most immediate to the time of the

Chachapoyans. One couid argue that these sources carry built into thern the conquerorj'

biases, and that the facts are altered to fit those biases, but Salomon takes pains to

validate his use of etho-historic material. He argues that Murra and other authors have

demonstrated the ethnologicaI usefulness of visiras. "No other class of sources rivals the

minute detail and methodological ngor with which they descn i village-level socio-

economic organization" (Salomon 1986: 13). In a fashion similar to Salomon's defence of

the use of ethnohistoric accounts, B w h (1977) addresses the issue of applicability of

studying modern behaviours in order to learn of prehistoric behaviours. Brush maintains

that while Uchucmarca is a Spanish constnict, the pre-Hispanic Andean patterns of

environmental adaptation are still evident there, as they are throughout the Andes. This

testifies to the resilience of andent Andean practices that have resisted four hmdred

years of Spanish cultural imposition Schjellemp argues that history and archaeology

have "long-standing disciplinary ties" in Scandinavia ( 1997: 10) and that she carries this

tradition into her work.

Lerche (1995) challenges the Reichlen's research by pointing out that at least one of

the sites they describe, Plaza Pampa, is not where they say it is, and therefore could not

have been visited by them as they claim. In addition, 1 suggest that the categorization of

the Chachapoyans into Kuelap, Revash and Chippuric must be re-examined (this issue

has k e n discussed in personal communications among myself, Guillen, Muscut~ and

Schjellerup in 1998). Al1 known Revash sites are burial sites (Guillen 1998: persona1

w rnrnunication, Schj e llerup 1 99 8: personal communication). Given that Revash sites are

al1 in close proximity to Kuelap sites (Davis 1988, Schjellemp 1997), it is more

reasonable to assign Revash sites to the Kuelap cultural group. Schjellemp fin& a

homogeneity and temporal continuity among Chachapoym sites which le& her to

generate a di fferent cultural time-line (see Table 1 - 1 ).

Giwn these conflicting views of Chachapoyan development and organization, it is

clear that our understanding of the Chachapoyans is in flux, calling for a comprehensive

treatment that takes into account settiement patterns across the entire region.

Incentive for fhis research

Large, complex state-level societies such as the Moche and inca naturally attract the

majority of archaeological attention in Peru. Despite that, a fiontier region such as

Chachapoyas, isolated by the formidable b e r of the W o n River (over which the

first automobile bridge was built only in 196 1 ), should be studied for two important

reasom.

F i r s ~ two ment investigations indicate that Chachapoyas may hold important

information regarding cultural contacts between the Pacific coast and the tropical forest

to the east.

Thompson ( 1984) describes evidence of tram-Andean communication as ancient as

the Early Horizon of the hi& mountain Chavin culture, two thousand years before the

Chachapoyans. His evidence includes powerful iconography depicting birds and animals

of the tropical lowlands, and Chavin pottery that pervades the coastai region of the

period. Correspondingly, Thompson describes evidence of the typical U-shaped

ceremonial complex found at Chavin and on the Pacific coast (Moseley 1 992) and floral

rernains of tropical plants in pottery in the Chacbapoyan site of Inticancha (he does

concede the possibility of these plants growing in the lower elevations of the nearby

Mixailon River). Shamn (1994) notes the same U-shaped complex in Vira Vira, on the

verge of the tropical lowlands of the Arnazon. He believes this is the easternrnost

exarnple of this architectural feature discovered to date.

The traditional view of mobility in pre-Columbian P e n builds on the concepts of

horizontaiity and verticality (Murra 1956). Horizontality refers to trade and

communication dong similar elevation levels, for example up and down the coast and

dong major river valleys from the coast towards the mountains. Verticality refea to

travel and trade among different mountain settlements at different elevations across the

Andes to provide various resources. The Incas practised both hcrimntality and

verticaIity, but even in their extensive empire there is M e evidence to date of mobility

entirely across the Andes in either direction. However, if such a practice were to be

demonstrated convincingly for the Chachapoyans as a group, questions of origin and state

development could be re-addressed for the entire Andes.

Within the Andes, the Chachapoyan region is the best place to cross the mountains

for hundreds of kilometres. Primary routes leading east to the Amazon lowlands are the

Maraiion and Huallabamba valleys. Other valleys eventually lead west to the Pacific

coastal area bounded by modem-day Chiclayo and Trujillo. Therefore both geography

and archaeology provide Mme evidence that Chachapoyas is a prime area to study the

entire issue of trans-Andean contact.

Second, though the social organization of the Chachapoyans is not fully undentood,

it does not easily fit any of the bmad typological categories suggested in the mode1 of

cultural evolution described by Flannery (1972) and others. Contradktions arnong factors

such as site- and dwelling-size, cooperation in the face of invasion, and communal

construction and iconography suggest that the study of the Chachapoyans would offer an

opportunity to learn more about the processes that effect change in culturai complexity.

Without a complete rnapping of the pre-Columbian senlement of Chachapoyas,

both areas of research are hampered Away from the major river valleys the terrain is

extremely di fficuit, with preci pitous mountain dopes and dense cloud- forest cover.

Terrestrial exploration and mapping are v W l y impossible. The few aerial

photographic surveys done by the rnilitary do not cover the whole region becaw it is not

strategicaily important. However, comprehewive and relatively affordable maps of

Chachapoyas cm be created via the manipulation of satellite data in the fom of images

from visible light, infra-red radiation, and radar.

stmctwe

In this study 1 manipulateci such satellite data (primady Landsat Thematic Mapper

data) to locate eighteen hown Chachapoyan archaeological sites. Given the frontier

nature of the region and the deanh of previous archaeological attention, the accurate

location of these known sites presented a tirne-consuming problem. The solution

involved combining information from site reports and maps, handdrawn regional maps,

reports from travellers and exploren, clues from prelirninary scanning of satellite

imageiy, and anecdotal discussions with archamlogists farniliar with the area. 1

corroborated the location of some of these sites with Radanat Synthetic Aperhue Radar

data which showed site structures such as walls, terraces and larger buildings.

After locating the eighteen known archaeological sites, 1 analyseci them to

determine factors of visibility that set them apart from their smoundings. This enabled

me to generate new images which highlighted them visually. ûther locations in the new

images showed similar visual characteristics to the hown arc haeological sites. These

oew locations might be other archaeological sites whose existence or precise location

have until now not been hown.

Chapter 2 describes the basic teminology and fimction of satellite remote sensing

systems, and the rnethods used to derive infornation about the earth's sdace From

satellite irnagexy Issues of applicability of types of satellite &ta are addressed.

Chapter 3 describes the methods used in this study, first to isolate the eighteen

knowu sites h m their surroundings, second to compare these sites against their

irnrnediate surroundings in order to determine differences introduced by the presence of

arcbaeologicai remains, and third to suggest seven locations for fkue archaeological

exploration. Control sites with no archaeological presence provided a test of the

techniques useci, minimizing statistical hfacts that can be introduced by the

methodology .

Chapter 4 provides analysis of the results calculated in Chapter 3 and suggests

images that best use the information collecteci. As well, discussion of anomalous results

helps in understanding the imperfections of this method.

Chapter 5 concludes this study, surnrnarinng the results, suggesting materials and

rnethods that rnight have helped the present study, and proposing further research that

should be done to build on this work.

2. Satellite Data Collection and Image Ceneration

The niw data used to generate images and locate archaeological sites are derived

From two sources: Landsat Thernatic Mapper (TM) data and Radanat Synthetic Aperture

Radar (SAR) data. This chapter describes the technical aspects of the data collection and

image generation fiom the raw data The background information on satellite fùnction

and detail is readily available in many general sources (Gupta 199 1, Buiten and Cleven

1993, Dniry 1993, Richards 1993, Verbyla 1995, Vincent 1997).

LarrriSar T M d d a

Landsat is a satellite program which has been in seMce since 1972, with successive

satellites king launched as the previous ones neared the end of serviceability. TM

format data was first collected in 1984.

The TM multispectral scanner acts similarly to a regular camera, except that it uses

electronic recepton instead of photographic film. These receptors scan back and forth in

a line perpendicular to the path of the satellite, recording light as it is reflected frorn the

earth's surface. Thus the TM scanner functions only during daylight. Data are recorded

in seven difFerent bands of the electromagnetic spectnim, including visible light in the

blue, green and red regions and four bands in the inhred region of the spectrum.

n i e amount of radiation received by the satellite's camera depends on the

transparency of the earth's atmosphere to the radiation (see Figure 2-1 ), and on the

reflectiveness, or albedo, of the materials on the surface of the earth (see Figure 2-2).

These two factors strongly influenced satellite designers' choice of bands of radiation to

be recorded. Landsat was designed prirnarily for botanical research, and bands 1 to 6

were chosen for the trammissive properties of the atmosphere at those bands in the

electromagnetic spectnim and because these bands show information about vegetation

including species, stage of development in the life cycle and health, as well as soil

characteristics (Vincent 1997). Band 7 was added between bands 5 and 6 after the initial

design in response to lobbying by geologists, as band 7 is highly responsive to differences

in soil and rock (Vincent 1997). Band 6, at the longest wavelength (10.4 m to 12.5 rn) ,

is referred to as a thermal image, because the electromagnetic radiation at that

wavelength corresponds to heat. This band is the only one of the seven that records not

just reflected solar radiation, but also radiation generated at the eanh's surface. Also, the

resolution of the image is poorer. Thus, interpreting the information in this band is very

complicated and I make little use of this band in my research.

Figure 2-1. Atmospheric transmittance of electromagnetic radiation and relative locations of TM bands. Adapted From Drury (1 9936) and COSETI ( 1998).

TM Band: 1 2 3 4 5 7

Matter can react to radiation falling upon it by transmitting it, absorbing it,

reflecting it, or absorbing it and re-radiating it in a different frequency (Drury 1993).

niese factors combine to make a unique reflectance response for that particular matenal.

When a graph of reflectance venus wavelength is plotted for a particular material, it will

generate a reflectance response curve (see Figure 2-2). An example pertinent to this

research is that of a plant IeaE Plants with chlorophyll in their leaves absorb blue and red

light (the energy of which is used by the chlorophyll in photosynthesis), and reflect green

light. This reflection is why we perceive them as green. In the infrared region, invisible

to human eyes, plants strongly reflect band 4 radiation, and to a lesser extent bands 5 and

7. The reflectance response curve of vegetation, then, tends to have low points at bands I

and 3, and peaks at bands 1,4,5 and 7, with the highest at band 4 (sec Figure 2-2).

Figure 2-2. Reflectance of electrornagnetic radiation by selected surface materials, and relative locations of TM bands. Adapted From Dmry ( 1993: 1 2).

The data for each band are stored as a separate image. A full image is 185 km

along the flight path and 185 km wide. Once relayed to earth, the data are

mathematically transfonned to remove distortions introduced by the varying angle of the

camera with respect to the earth as it sweeps back and forth. Further distortions due to

the curvature of the earth are also removed. Finally, the image is oriented so that north is

towards the top ( s e Figure 2-3).

--

Figure 2-3. Landsat TM image, single band.

The 185 km by 185 km image for each band is stored in digital fom as a set of

numbers called pixels. Each pixel represents a section of the earth's surface roughly 30

m by 30 m. The actual ground size of a pixel depends on factors such as altitude of the

region king imaged, which affects the distance fiom the satellite, and longitude, which

affects both the shape of the earth and the orbital direction and altitude of the satellite. In

the Chachapoyas region these effecü make the pixels in the images 25.9 m square. For

each pixel, the TM data represent an average of the reflectance of the earth's surface for

that small region, and is recorded as an integer value which ranges fiom O for no received

radiation, to 255 for maximum brïghtness. The range of nurnbers fiom O to 255 is

commonly used because it can be stored in one byte of digital cornputer memory.

It is important to remember that the region of the earth7s surface represented by a

single pixel is still large enough to have many different materials present, each

contributing to the reflectance at that place. in each band image, a pamcular pixel will

represent the surn of reflectances in that band of the materials present.

With coverage of most of the earth's surface and multiple bands of data, Landsat

TM imagery is widely applicable to research in many fields. It suflers fiom the need to

record in daylight and susceptibility to atmospheric effects such as clouds, dust and haze

which can adversely affect the quality of the image, or even negate the usefulness of data

in parts of the image.

Radarsat data

Zadarsat began collecting data in 1995. It is an active system which broadcasts

electromagnetic radiation at a frequency of 5.3 Ghz, in the region commonly known as

microwaves, and collects the reflection from the earth7s surface to form an image.

Because radar has a much longer wavelength than visible light or infrared radiation, it can

penetrate clouds, dust, haze and other atmospheric effects to image the surface of the

earth. Because it generates its own radiation, it can function at any time of &y or night

(Dniry 1995). Within the Chachapoyan region, which often has signifiant cloud cover,

Radarsat holds an im-portant advantage.

The direction the satellite faces is different from Landsat because the Radarsat

system transmits a pulse of radar to the side of the orbital path, and a receiving antenna

collects the reflection of the pulse fiom the earth's surface (see Figure 2-4). The

retuming pulse represents the strip of the earth's surface struck by the projected radiation

and acts as a wave, with the portion stnking the nearer part of the stnp reîuming sooner

than the portion of the wave that travels to the farther part. The time for retum is

mathematically transformed to represent the distance along the surface of the earth and is

recorded as a set of pixels, just as Landsat data are. The image must be captured fiom the

side in order to prevent two different locations having the same time of r e m , as would

happen if the signal wavefiont were sent straight dom. (In that case, the pulse wvould

retum fiom both sides of the satellite at the sarne tirne, combining the two sides of the

terrain into one image.) Data collected this way are then mathematically transformed to

create an image as if viewed fiom directiy above. Since the satellite is in a continuous

orbit, it is possible to acquire different views of the same terrain, either in an ascending

orbit in which the satellite passes fiom south pole to north pole and faces east, or a

descending orbit in which the satellite is passing fiom north to south and facing west.

Figure 2-4. Geornetry of synthetic aperture radar. Adapted from Gupta ( 199 1 )

The intensity of reflected radiation fiom any point along the strip is a measure of

the texture of the surface at that point. A giassy smooth surface such as water would

reflect the pulse away from the receiver, and appear dark in the radar image. A rough

surface or a region with portions more perpendicular to the path of the radar pulse, such

as the remains of buildings, would reflect a larger portion of the signal back to the

satellite, and appear bright.

Successive strips of pixels are added together to make an image of the earth's

surface. Radarsat is a synthetic aperture systern because the motion of the satellite is

used to sirnulate a receiving antenna larger than the actual device on the satellite,

providing the ability to distinguish smaller features on the surface of the earth. A similar

technique uses radar telescopes separated by thousands of kilometres

resolve smaller astronomical objects than they could individually.

Resolution can be varied according to the needs of the research.

n conj unction to

The Radarsat

mode with highest resolution provides a pixel size of approximately 7 m by 7 m, and an

image size of approximately 50 km by 50 km, whereas the lowest resolution From this

satellite system provides pixels approximately 33 m by 33 m in an image approximately

150 km by 150 km (Vachon et of. 1997). The Radarsat data used in this research are at

the highest resolution, 7 m square.

The angle of the radar emitter and antenna can be adjusted within a range of 37" off

vertical to 48"off vertical (Radarsat 1997). There are 1 5 settings: five numbered settings,

with ' 1' being most vertical and '5 ' most horizontal, and within each of these three

adjustments, 'near', 'normal' and 'far'. Thus the most vertical setting would be ' 1 Near'

and the most horizontal would be '5 Far'.

Since the time of rehim is converted into distance along the sudace, anything which

interferes with that time will create a fdse reading for distance. For a perfectly flat

surface this aspect is not a probiem, but the presence of natural altitude variations, such

as mountains, causes the radar wavefront to strike high points early, and makes them

appear to be closer horizontally to the satellite than they really are. Since the radar pulse

will retum at the same time from a lower, closer point, the net result is a combination of

reflectances, creating a brighter spot showing on the image at the corresponding distance

fiom the satellite flight path. Over the whole image, the effect is as if the high regions,

such as mountains, are folded over in the direction of the satellite, increasing brightness

on surfaces facing the satellite and cornpressing the shape of the steep terrain in the

image towards the satellite. This effect is more pronounced the more closely to vertical

the pulse is directed towards the earth,

To minimize this effect, the radar can be directed to shoot at a shallower angle in

order to stnke the surface of the earth more fiom the side than above. This technique

reduces the likelihood that the curved wavefront will strike the high regions early, but

precipitous terrain such as that in the Andes will still present this effect to some degree-

Unfortunately, the shallower the angle of incidence, the more likely are regions to be

occluded by intervening terrain, causing shadow regions into whch the radar pulse

cannot go. This intervention would show in the image as regions from which no radiation

retumed, and are there fore totally black.

In extremely convoluted terrain such as in the Andes, in order to ensure that al1 of a

particular region is imagea the best solution, if one can afKord the multiple images, is to

acquire separate images from an ascending orbit and a descending orbit which overlap in

the required region. This way, the radar-shadowed regions of one image would be visible

in the other.

Image genemîion

The primary form the data takes for analysis is as cornputer-generated images of the

earth's surface. The Radarsat images and the individual Landsat band images are

greyscale, with the intensity of each pixel corresponding to a data number, O for pure

black to 255 for white.

The human visual system is much better equipped to discem differences in colour

than in intensity (Dniry 1993). As well, information about the earth's surface is

distributeci throughout six images in the Landsat data. Since it is difficult for a human

observer to draw information fiom several individual images, 1 useci image generation

techniques and mathematical transformations on the Landsat data to put as much

information as possible into single images. 'These modifications included band

stretching, dark-object removal, false colour, principal component analysis, band ratios

and altemate colour models such as the hue-saturation-intensity (HSI) model.

Band siretching

Some data sets do not Vary across the whole O to 255 value range. Band stretching

expands these values to fit the whole range. This procedure has a contrast-sharpening

effect, brightening a very dark image, darkening a very washed-out bright image, or

spreading data in a mal1 range in the middle values outwards in both directions. One can

vas, the effect by varying the stretch. This image manipulation is primady used to

increase discemability of image details to the human eye.

Dark-obiect removal

Dark-object removal is b a s 4 on the theory that in any large image, such as a

Landsat image there will be some pixels that should have a value of 0, for example lake

water or deep shadow (Vincent 1997). Atmospheric effects that create haze tend to add a

certain value to each pixel, so that no pixel is mily black, or value 0, anyrnore. This

effect is most pronounced in the shorter wavelengths, especially blue, and disappears by

the rnid-range infrared, roughly band 5. If one looks at a histogram of pixel values in a

band, and fin& the lowest value is not O, then by subtracting the dark-object value (which

is defined in Vincent ( 1997) as the lowest value minus 1 ) from each pixel in the whole

scene one can remove the effect of atrnospheric refraction, as well as any incidental small

electronic shift introduced by the satellite (Vincent 1997).

Although one eflect of this transformation is to sharpen contrast in the image, it is

not the same as stretching the image values, because dark object removal is a linear shift

downwards applied equally to the values of al1 the pixels in the image. Darksbject

values calculated for the Landsat scenes in this research are listed in Table 2-1. Data

submitted to principal component analysis and band-ratioing were first subjected to dark-

objec t removal.

- -- . . .- - - - - - -- - - -

Table 2-1. Dark object values for Landsat data of Chachapoyan region

Band Dark obiect vaiue 1 (blue) 39

2(g=n 1 9

3 W ) 5 4(near IR) 1

S(mid IR) O

7(mid iR) O

False Colour

Most modem computer programs that are used to manipulate images allow the user

io combine any three grey scale images of the sarne scene, one each for the primary blue,

green and red light components of colour, to make a fiill colour image. Together they

create an image which is a blend of these three primary coloun which can fabricate the

full range of visible colours. Given that each of the grey sale band images varies in

value fiom O to 255, this blending allows a colour image which has potentially 256 times

256 times 256 different colours. Any colour out of these roughly 16.5 million can be

completely defined by a combination of three numben in this system. For example,

values of 7% for re4 O for green and 255 for blue would give an intense purple. Values

of 127 for each of the three colours would create a middle level grey.

Bands 1,2 and 3 can be us&, king the blue, green and red reflectance bands

respectively, to give an image which corresponds to the natural colour view the human

eye would perceive. However, much information about vegetation and soi1 structure are

carried in near infia-red bands 4 and 5. Therefore, to maximize the information content

in an image, one or more infmred bands are often included in composite images, either as

a combination of bands 2,3 and 4 or bands 3,4 and 5. Landsat images are oAen

published commercially as one of these two composite colour images. These

combinations create colour images of the surface of the earth that do not correspond to

what the human eye c m see, and are therefore called false colour images. For example,

if band 4, which is strongly reflected by vegetation, is used as the red source, vegetation

appears strongly red in the final image.

Potentially, there are many combinations of sets of three bands, but in practice only

a few are used as the others do not give much different information and tend to create

colour combinations which are extremely unnaturai to the observer's eye. D q ( 1993)

suggests that the psychological resistance to such unnatural combinations, and the

pursuant reduced absorption of information from them, can negate the wfulness of any

increased information content.

Each of the six Landsat bands of data used in this research potentially carries

important information about the terrain. Each can be visualized as a variable which is

one dimension within a sixdimensional space describing the surface reflectance. It is

difficult to conceptualize this multi-dimensional space, and impossible to represent it in a

visual form. Furthemore, among the six bands in Landsat data there tends to be

significant levels of correlation, creating redundancy. A statistical device known as

principal component analysis (PCA) can be used to reduce the number of dimensions of

data needed to describe the total variability, or most of it, within a data set. Because

Landsat image data are stored as rectangular matrices of numben, they readily conform

to such analysis.

PCA creates a new set of dimensions which are orthogonal, or uncorrelated with

each other (see Figure 2-5). From the variances and covariances of the original data set,

the fim principal component is calculated so as to define as much of the variance in the

data as possible. This procedure is analogous to a linear regression which detines a line

of best fit through a field of data

The second principal component is calculated such that it is perpendicular to the

first in the sixdimensional space and best represents the variance for which the first

Figure 25. Principal components of two-dimensional data.

Band A

cannot account. Successive components are generated similarly. For data sets of many

dimensions, typically 95% or more of the variability is contained in the first three

components, with random elements and noise consigned to later components (Shennan

1988). The result is a new set of dimensions uncorrelated to each oîher and equal in

number to the first set of dimensions. In this research, then, six new dimensions which

are themselves images are calculated fiom the six images of surface reflectance (see

Table 2-2).

Two sets of numbers produced by a PCA are illustrated in Table 3-2, loadings and

eigenvectors. Though related to each other, they express dif'Ferent aspects of the

relationship between the new set of dimensions and the original, and have their own

unique applications in image manipulation.

Loadings represent the contribution of each original image to the new image, and

are therefore a measure of the correlation of the new image to each of the original

images. Together, the loadings of an individual component describe the spectral makeup

Table 2-2. Principal components cdculated from Landsat bands 1 to 5, and 7,

of the Chachapoyan region.

of the component, and carry information about the physical characteristics of the surface

of the earth depicted in that component. The 1' component almost always shows strong

positive correlation to al1 of the original images, which makes sense since this component

was generated to contain as much of the variability overall as possible. Later components

can be seen to have more specific information about the terrain k ing depicted (Dniry

1993, Richards 1993, Vincent 1997). The 2" component, for example, has positive

correlation with bands 5 and 7 and negative correlation with bands 1 to 4. This evidence

indicates that it shows details of soils and soi1 moisture content. The 3" component, as

another example, shows a positive correlation to band 4 and a negative correlation to

band 3, and so carries detail related to vegetation. Interpretation of the information

depicted in later components can be difficult, since they tend to be washed-out and noisy,

but moa authon suggest that they can still have use, depending on the application.

Eigenvecton are used to regenerate the old set of images h m the new set. This

can be usefiil once the new dimensions have ken submitted to effects such as band

stretching to enhance detail. Furthermore, a fom of filtering or noise reduction can be

applied to the old images by dropping one or more of the later principal components and

refabricating the original images from the remaining components.

ûther applications arise from this data tmnsfomation. The fint principal

component can be used as a greyscale image by itself to represent overalt albedo and

topographical detail across the range of bands. Together, the first three components can

be used to generate a full colour image which cames most of the information of the six

original bands. In this example, the first three components account for 98.6% of the

variability in the original data set.

Band ratios

Often, opposing effects for the sarne surface feature appear in different TM bands.

For example, the reflectance of vegetation is hi& in band 4 and low in band 3 (Drury

1993, Vincent 1997). Slight diReremes from the surroundings, when cornpounded by an

arithmetic operation Iike division, can be made much bigger. Band ratioing involves

dividing pixel values in one image by the correspondhg pixel values in another to create

a new image. When a new image is created by dividing the band 4 pixel values by the

band 3 pixel values, soils, which have similar values in both, receive a value of roughIy 1.

Vegetation, on the other hand, wouid have a large value divided by a smatl value, and

would receive a value many tirnes larger than the soi1 (see Figure 2-2). In the resulting

band ratio image (with the fractional values converteci back to the O to 255 scale in order

to generate a standard greyscale image), vegetated areas would appear much brighter than

open, bare soil. As noted earlier, the region depicted by a single pixel may have many

materials within it, but the magnimnp effect still applies. So, an area which is 50°,6

forest cover and 50% bare soil would still be brighter than bare soil. Archaeological

artifacts, such as building remains, would themselves most likely be smaller than a single

pixel, but their contribution would still be affected by the appropriate band ratio.

Furthermore, nuances within a material, such as different vegetation species, or different

levels of maturity or water content in the same species, al1 of which can be indicators of

the presence of archaeological remains (Brooks and Johannes 1990), also can be

highlighted by this technique.

Much work has been done in recent years to determine properties highlighted by

band ratios (Vincent 1997). Ratio value tables are avaiiable, such as in Vincent (1997:

350), and reflectance curves for hundreds of materials also exist (Johns Hopkins

University 1998) that can be used to determine appropriate image ratios to use to

highlight many different mineral or vegetation fmtures.

A major problem in using Landsat imagery in regions of high relief, such as

rnountains, is that large portions of the terrain are in shadow (Vincent 1997). This makes

the task of categorizing pixels more dificult because the same feature would present

di fferently in shadow and in light. Furthemore, shadowed areas are illuminated by

ambient atmospheric illumination, rather than the direct light of the sun, and this factor

too cm affect the spectral characte~stics of the reflectance in those regions.

When two bands with similar responses to shadow conditions and ambient light are

used to create a ratio by dividing pixel values of one image by pixel values of the other

image, that mtio efiectively eliminates the shadow effects of terrain and creates an image

that shows the pertinent characteristics of a pixel regardles of which slope it occupies

(Dniry 1993). Band 3 and band 4 respond similarly to shadow conditions, as well as

ofken king used to display details of vegetation. With the application of dark-object

removal, this usefulness is further enhanced, and c m be transferred to other ratios which

use bands less similar to each other in their response to ambient conditions.

Two problems, however, are inherent in the creation and application of band ratios.

First, the process of creating a ratio, through division of one pixel value by another,

tends to rnagniQ the effect of random values such as noise as much as it does srna11

nuances and changes frorn one band to another, since noise in a pixel in one band does

not usually appear in the same pixel in a different band. Thrrefore, ratio images are often

smoothed with a filter such as a median filter which does not change tnie values to any

great degree but eliminates anomalies such as individual noisy pixels. Inherent in any

smoothing, however, is loss of some detail which may be valuabie information, not noise.

Second, the corollary of rernoving effects of shadow and other terrain features is

that ratio images tend to make identification of terrain features difficult. Locating

regions or sites in such an image can be difficult. One common way to address this

problem is to introduce terrain detail contained in another image. This detail cm be

provided by dealing with the image in the HSI mode1 of colour.

hue-saturation-intensitv (HSI) mode1

Blue, green and red channels combine to make an image which can contain the Ml

range of coloun visible to the human eye. Colour can also be separated into factors of

hue, saturation and intensity (Dniry 1993). Hue is the value which represents colour in a

range from red to violet, as in a rainbow. In computer representation this would be O for

reâ, 255 for violet. Saturation is the arnount of colour present, with value O being grey

and value 255 being the pure colour. Intensity 1s the brightness of the pixel, From O,

which is black, to 255, which is white. Just as the three values of re4 green and blue

cornpletely define any particular colour, so do the three values of hue, saturation and

intensity.

By replacing the intensity channel with an enhanced detail image, such as a fint

principal component image or an appropriately georeferenced and size matched radar

image (IDRISI 1997, Toutin 1998), terrain features would be added and the colour data,

which carries the information about presence or absence of surface materials, would

remain in the hue and saturation channels. Of course, introducing non-ratio data is

counter-effective to the removal of shadows, but judicious tone control in the intensity

channel can lighten the shadows so that colour information is not masked.

Experimentation with al1 of the above techniques was necessary to develop the

methodology used in this study to develop images that were then used visually to locate

known archaeological sites and to predict the previously unknown location of othen.

3. Methods and Materials

Duta Sources

Landsat 5 acquired TM data employai in this saidy on 23 August, 1986 starting at

1414335, and stored the data in archives at the US. Geological Survey EOS Data Centre

in Colorado. At my request, in order to centre on the Chachapoyan region, the Data

Centre composed a combination image, to include the southem half of Landsat scene

LT50090640086235 10 tand the northern half of Landsat scene LT50090650086235 1 O.

The data are georegistered (see Chapter 2) and aligned to have north at the top of the

image. They provided the fwus of analysis in this research. With them 1 formed the

images for initial exploration and submitted them to various image manipulations and

statistical analysis to detect archaeological sites.

The Radarsat data are contained in four Fine Mode images collecteci specifically for

this project and provided for by a gant from the Canada Centre for Remote Sensing.

With the counterbalancing problems of layover and shadow in mind, 1 arrangeci for hvo of

the four images granteci me to be ascending views and two to be descending views, with

overlaps in regions containing known archaeological sites. Given the time constraints of

this research, there were tradeoffs made among issues of date of data acquisition, image

location and angle of incidence. The ascending images are consecutive, acquired on 6

F e b m q , 1998. They consist of image MO1 541 16 starting at 23:42:46.5 12 and image

MO 154 1 17 starting at 23:42:54.034. These are in the shallowest possible setting of the

satellite's view, ' 5 Far' (see Chapter 2). The descending images are from different dates:

image MO153063 on 3 1 January, 1998 starting at 10:46: 14.41 1 in '3 Near' mode and

image MO 154 154 on 7 Febniary, 1998 starting at 1 O:42: 16.337 in ' 5 Near' mode.

Different levels of mathematical correction exist for dealing with the distortions of

the earth's surface in the radar imagery, and for representing the detail on the surface.

These radar images were fine-tuned fiorn 7 m pixels to a higher resolution, with data

pixels of 3.125 rn to minimize data loss when the images are mathematically transfomied

However, 1 was not granted the more expensive images registered to the ground.

Registering the radar imagery to the Landsat TM imagery is possible, but requires a great

deal of time and painstaking effort, even for small sections of the image. Having

obtained the Radarsat data so late in the research, 1 was not able to perform such

registration for use in combined irnagery with the Landsat data.

Nevertheless, in the radar images one can follow river valleys, lake shores, and

ridges. Therefore, it is possible to identify locations in the images fiom the two satellite

systems that correspond to each other. With the higher resolution of this technology,

some archaeological sites can be detected by their shape, or even the shape of buildings

and walls within the site. My primary use for the Radarsat images, therefore, was to

corroborate the locations of known archaeological sites.

Primay Landral images for Exphration

Three basic ideas govemed my choice of images and the manipulations I performed

on them. First, the Chachapoyan archaeological sites 1 have seen were visibly different

fiom their surroundings, but tended to be covered and even masked by vegetation.

Second, much work indicates that archaeological sites cm have strong effects on the

vegetation in and around them (Taylor 1975, Riley 1987, Brooks and Johannes 1990).

Thus 1 decided to concentrate on the effects archaeologicd rernains have on their

surrounding vegetation. My research was influenced by work done in Europe and North

America with aerial reconnaissance for archaeology (Agache 1975, Fowler 1975, Ebert et

al. 1983, Riley 1987, Brooks and Johannes 1990) and with satellite imagery (Chavez and

Bowell 1988, Madry and Cnunley 1990, Showalter 1993). Third, my survey of the

literature suggested that the building materials used in Chachapoyan archaeological sites

were for the most part standardized (limestone rock is prirnary), and so if they were at al1

visible to satellite imagery, there might be characteristics of these particular materials

that could help in detecting sites.

I decided to generate a natural colour image, using bands 1.2 and 3 for the blue,

green and red channels respectively (see Appendix D for a list of software used). Based

on Dniry's (1993) concepts mentioned in Chapter 2, I thought that the most natural

colouration promised to be easiest to understand and to relate to the terrain if used as a

field map. Having compensated for the different responses of satellite receptors in the

three bands and differing atmospheric effects with dark object removal and band

stretching, 1 was able to generate a first step image (see Map 3-1) in which the terrain had

similar colouration to photographs 1 took in the region in 1993. Not only is this image

visually appealing, but important landmarks such as roads, rivers and towns clearly stand

out due to their strong reflectance in these bands.

The effects of the presence of archaeological sites on vegetation include alterations

in plant species, changes in maturation rate and size, and changes in rnoisture content.

This evidence is particularly relevant to the data 1 use4 as the pixel size in Landsat TM

imagery is larger than individual Chachapoyan buildings but smaller than most sites.

Since several techniques exist to detect the changes in vegetation using TM

imagery (Dniry 1993, Verbyla 1995, IDRISI 1997, Vincent 1997), it made sense to me to

3-1. Subsection of nahiral colour composite image generated from Landsat TM bands 1,2, and 3.

- 5 km (approx.)

5

apply these techniques as a second step in exploration for archaeological sites. These

techniques are broadly labelled vegetation indices, and involve the mathematical

manipulation of two or more of the T'M bands to generate an image. The simplest is a

ratio of band 4 divided by band 3. This procedure makes use of the dramatic shift in

reflectance that vegetation exhibits in the transition from band 3 to band 4. Because

simple band ratioing generates a data set which is not distributed nonnally, it precludes

the use of certain statisticd procedures. Also because it hazards the possibility of division

by zero, this ratio is usually mathematically norrnalized For example, the NDVI

(Normalized Difference Vegetation Index) applies the transfomation '4-3 / 4-3' to bands

3 and 4. This method still magnifies the information carrieci in the difference between

bands 3 and 4, but reduces the chance of division by zero and generates a normaily

distributed data set ranging from - 1 -0 to 1 .O. Negative values are regarded as non-

vegetation and positive values as vegetation. This modification forms the basis for a

Iarge number of transformations which rely on this normalization (IDRISI 1997). I

explored this technique but did not use it because the simple 4/3 band ratio located

archaeologicai sites more reliably.

Another set of vegetation indices involves the use of several bands in a linear

equation in which each band value is multiplied by a factor and added to the rest. For

example, in the Tasselled Cap Green Vegetation index, the index is calculated in this way

using bands 1,2,3,4,5 and 7 (IDRISI 1997). I found this transformation to show terrain

features which other indices did not. Given that it uses al1 six of the purely reflective TM

bands, it is not surprising that the Tasseled Cap bore some similarity to the first principal

component image, which also showed these features clearly. Clearly, then, this method

was redundant.

Vincent (1997) argues that of al1 of these, the 4/3 ratio is the most robust, in that it

changes the least between images generated from data collected at different times, and

requires only one gromd tmth sarnple (onsite venfication or collection of materials

present) to calibrate Iaboratory reflectance measures. Both of these factors have practical

value in archaeological exploration. Therefore i believe that the simple 4/3 band ratio is

the best for highlighting indirect effects of archaeological sites on the surrounding terrain,

and this research used that ratio image to hetp tocate known archaeological sites.

1 composed a false colour image (see Chapter 2) in which hue, saturation and

intensity were defined by three separate dark-object-removed grey-scale images as

follows. The 413 band ratio was applied positively in the hue channel to give red at one

end illustrating non-vegetation and purple at the other end for lush vegetation. Although

this full range gives a less natuml look than restncting the colour range to, say, red to

green, 1 believe the gain in detail and separation of nuances of colour pays off. For

example, in the red to purple range of colour, pixels in the region of some stretches of

wall in the site of Vira Vira clearly stand out, but are hard to discem in the red to green

range. 1 also used the wtme 4/3 ratio invened to create the saturation chamel, thereby

making full vegetation du11 and grayish and soil or rock a more intense colour. In this

way, I attempted to maximize the Msibility of small inclusions of soil or rock in a larger

field of vegetation, as might occur with archaeological remains of buildings. Finally, in

order to retain the details of terrain, I used the first principal cornponent as the intensity

channel (see Map 3-2). The terrain details are necessary to ease coordination with maps

and photographs in which these details are present.

False colour image using band ratio 4 3 in hue and saturation channels and fint principal component in intensity channel.

False colour image using band ratios 3/1 in red channel, 413 in green and 1/7 in blue channel. Intensity c h m e l replaced with first principal component.

Multiple band ratios, when combined into one colour composite image, provide a

possibility for M e r information. Based on suggestions and reflectance data in Vincent

(1997). as a third aep 1 nied to combine as much information as possible related to

Chachapoyan archaeological sites into one band ratio image. 1 generated a colour

composite image in two steps. Fint 1 used the RGB mode1 of colour and generated the

blue channel as the band ratio 1/7, the green as 4/3, and the red as M . Then 1 used the

HSI mode1 on this image, and replaced the intensity channel with the first principal

component image (see Map 3-3). The 117 ratio is bright for the mineral calcite (Vincent

19971, which is a strong component in limestone, the building material most available to

the Chachapoyans. The 311 ratio highlights iron compounds in rock (Vincent 1997),

which appea. red because of their strong reflectance of band 3. Red rock often appears in

Chachapoyan construction (Schjeliemp 1997). The first principal component image, as

before, supplied terrain details masked by band ratio images.

Unfominately, although there are interesting nuances displayed both in vegetation

cover and rock outcrops, I was unable ro extract understandable new information from

this image. 1 suspect M e r geological midy and on-site exploration and calibration are

necessary to fully utilize such a multiple ratio image.

Site Selection

In determining what characteristics of archaeological sites cm be detected from

satellite data, 1 needed to locate known archaeological sites in the images derived from

that data. Locating them accurately enough to use hem as factors in site prediction

proved di fficdt. The dearth of accurate maps of the region was the fint hurdle. The

Andean climate itself was another lirniting factor in locating sites. On the Landsat TM

images 1 haci, several sites were hidden by clouds, and therefore excluded. The site of

Kuelap, some 700 rn long, is cuvered in the middle by cloud, and effectively split into

two. However, using site maps and site descriptions from archaeologists and exploren,

discussions with archaeologists familiar with the region, and the Radarsat data, 1 located

eighteen archaeological sites with enough confidence to use them as a set to define site

characteristics (see Appendix A for an example of the process). As well as habitation

sites, they include stone terracing by itself and a threequarter kilometre long stairway

built during Lnca occupation. Table 3-1 lists these sites. I provide grid references for the

approximate location of the unmarked archaeological sites on the 1 : 100,000 geological

maps by the Peruvian Imtituto Geologico Minero y Metnlurgico (INGE-T).

Table 3-1. Known archaeological sites used in calculations. References marked ' pc ' are persona1 communications.

Site - Boveda Cabildo Pata Escalera incaica

Huepon Inticancha

JO ya Jubit Kuelap Papamarca Pena Calata P i Pirka Pornio Revash

( subs id iq ) Tambu

Tajopampa terraces in forest Torre Pukm Vira Vira Yalape

References Schjeilenip ( 1998) pc Schjeflenip ( 1997) Davis (1 988)

Schjeliemp (1 997) Thompson ( 1984), Schjellerup ( 1997) Schjellerup ( 1997), Davis ( 1988) Davis (1988) Narvaez ( 1988 ), Muscutt ( 1998) pc Schjellerup ( 1 997) Schjelienip (1 997). ( 1998) pc Schjderup ( 1 997)

SchjeUerup ( 1997)

Davis ( 1988)

Schjeiienip ( 1 997) Schjeilemp (1 998) pc Schjeilenip (1 997) Muscutt, Lee and Sharon (1993) Davis ( 1 988)

MGEMMET nrid reference Leimebamba 14-h 870-464 Leimebamba 14-h 926-360 Lonya Grande 13-g 590-045

(approximate) Leimebamba 14-h 852-326 ~olivar 1 5-h 924- 184 Leimebamba 1 4-h 9 1 0-452 Chac hapoyas 1 3 -h 003-903 Chachapoyas 1 3 -h 76 1 -902 Leimbamba 14-h 856-380 Leimebamba 14-h 936-360 B O ~ ~ V U 1 5-h 952- 198 Leirnebarnba 14-h 880-278

Leimebamba 14-h 846-758

Leimebamba 144 890-430 Leimebamba 14-h 1 20-302 Leimebamba 14-h 930-376 Leimebamba 14-h 966-264 Chachapoyas 1 3-h 792-048

Having located known archaeological sites, the next step was to determine the

features cornmon to the sites on the Landsat imagery.Two processes came to minci

I . Image Classrfication

This technique is commonly used in geographical information systems (GIS) to

describe the kinds of terrain that are depicted in an image of the surface of the earth

(Richards 1993, Verbyla 1995, Vincent 1997, IDRISI 1997). It is a clustering scheme

whereby pixels of like characteristics are grouped together. The general pwpose of such

classification is to provide a scheme for defining what each pixel in an image represents.

There are two broad categories of classification, unsupervised and supervised.

UnsupeMsed classi fication requires no previous knowledge by the researc her of the

categories into which the terrain may cluster. It submits the image to clustering based on

the variables provided, in this case the reflectance values in each of the bands, with no

previous categorization or information provided. The terrain under snidy is cornplex, and

1 had no set of ground tnith data on the pdcular kind of vegetation or surface rnineds

in the image. Therefore, it was appropriate to start with an unsuperviseci classification to

explore the data.

1 used the ISOCLUST routine in IDRISI (see Appendix D for software description),

which is an iterative self-organizing cluster routine. In this fom of clustering, the user

does not know how many clusters exist in the data A nurnber of clusters is arbitrarily

requested, and then the data are clustered in several successive passes. After each pas,

the cluster mean is calcdated, and data points are reclustered based on distance fiom this

mean. Iterations are continued until no significant change occurs in cluster structure. h

IDRISI, the routine does not start completely blind, but begins with a colour composite

image to speed the calcuiation (which can be very lengthy). IDRISI'S suggestion is to use

a band 3,4,5 image which can-ies a great deal of information (IDRISI 1997: 1 1-23). The

actual calculations used the full raw data sets, however. The program clusters the &ta

based on this begiming image and presents a histogram of the clusten (see Figure 3-1 ).

Based on heights of cluster bars and inflection points, the user decides how many clusten

are important, and where to cut off the ensuing iterations. 1 used the default of three

iterations as suggested in the manual (IDRISI 1997: 1 1-24). From the histogram ( see

Figure 3- 1 ), 1 selected for the three iterations the first nine of the twenty-four clusten

presenteci, because of the dramatic change in the cuve of the histogram at that point.

This selection also Iirnited the final set of ctusters to nine.

- - -

Figure I I . Histogram of pixel counts in clusters based on ISOCLUST seed image.

160000 1

This process classified most of the known archaeological sites as part of the last

two clusten of nine. Unfominately, these clusters also containeci much other terrain

clearly different from the archaeological sites. This result suggested to me that the cutoff

1 chose was too high, and that 1 had forced the program to fit pixels as well as it could

into those nine clusters. The IDRISI manual (1997) suggests the same in its notes on the

ISOCLUST routine. 1 therefore resubmitted the data asking for twenty-four clusten. The

histogram of the result is in Figure 3-2.

This histogram shows several clusters which correspond strongly to features in the

terrain. The turbid water of the Rio Maranon appears almost exclusively in cluster 24.

The dark water of lakes, and the deep cloud shadows appear in cluster 3. The full clouds

(not the wispy edges) appear in cluster 13. No such uniformity can be found for the

archaeological sites 1 selected. Within many of the sites, sorne areas appeared in either

clustea 16 or 9, but the shapes defined by pixels grouped in these clustea are not wholly

congruent with the shapes of the sites. Figure 3-3 shows a subscene of the resulting

image.

Although the appearance of cornmon clusters was encouraging. I saw no way to

bend this classification scheme in a way that would allow me to be confident in a

category strictly for archaeological sites. 1 believed the terrain to be too complex for such

classification at this level of resolution. and felt that 1 needed much better knowledge of

the kinds of vegetation. soil and other terrain features present. fherefore 1 abandoned the

attempt to fmd archaeological sites through either kind of image classification. However.

the supervised method of classification suggested a method for using the iDRIS1 software

to aid in a promising statistical technique.

Figure 3-3. Segment of clustered image demonstrating partial congruence of clusters with archaeological sites. Circled area highlights wall of Kuelap (compare to Maps 2-2. 2-3). Cluster includes part of wall, and clear soil to the east of the wall.

2. Sfatisticai Analysts

SupeMsed classificaîion requires the researcher to detine a beginning set of

caregories in which to place the pixels. This preliminary categorization usually involves

the creation of training sites, small regions of the image which are good examples of the

terrain types into which the researcher wants to divide the image. nie pixels in the whole

image are categorized according to how similar their characteristics are to these training

sites.

In the same way that IDRISI allows one to create training sites by drawing polygons

around the required region, I drew polygons around each archaeological site. To define

the polygons, 1 used information from the unsupervised classification above, ffom images

of bands 1 to 5 and 7, from the first principal component image, from site maps for shape

and orientation, fiom the 4/3 band ratio image, and fiom the natural colour image

composed of bands 1,2, and 3. I used the Radarsat images to fine-tune shape and

location. I also àid this for the region around each site polygon to create a new polygon

for the terrain surrounding the archaeological site. Given limits such as cloud cover or

precipitous terrain, for the areas surrounding archaeological sites I drew polygons that

were t o m s . Initially, each t o m was arbitrarily sized so that the distance between the

imer and outer circumferences was equal to the diameter of the archaeologicai site

@Y gon-

1 also selected four test sites (see Table 3-2) which appeared on the preliminary

Landsat images to be visually similar to the selected archaeological sites, though no

literahire existed to indicate that bey were actuai sites. Again visually, 1 selected three

more (see Table 3-2) that were in the approximate area of known but unmapped

archaeological sites. 1 wanted to see how the method behaved in questionable regions

and to provide areas for M e r on-site research if the results were positive.

Finally, I selected four control sites which I knew had no archaeological remains

present, as 1 had passed through al1 of them myself in 1993. These contml sites are

Table 3-2.

Site - Test 1

Test2

Testj

Test4 Tests

Test6

Test7

Test sites with characteristics similar to knom archaeotogicd sites.

Terrain

forest dope above lake, 'circIe' near cIiff tombs river valley floor - ?Yerva Buena? forest slope above river, beside lake forest dope above river scrub gras mountaintop - ? San Pedro De Washpa? mixed forest, grass - ?Chonta Cruz? forea dope above river

INGEMMET Location

Leirnebamba (1357) 14-h 995-308

Bolivar ( 1356) 15-h 244-950 Leirnebamba (1 357) 14-h 070-717

Lonya Grande (1258) 13-g not available Jumbilla (1 359) 12-h 1 90-3 80

(apT?rox. [no< available, but off 13-h]

homogeneous terrain, two of high grassland and two of foresteci slope. The terrain types

are similar to those in which most Chachapoyan archaeological sites are found

(Schjellerup 1997), and their homogeneity is useful in the statistical analysis which

fotlows ( see Table 3-3). Their purpose was to detemine to what degree significant

results on known site cornprisons could be trusted.

IDRISI presents summary stati stics about the pixels withïn the boundaries of the

delimiting polygons, including the mean and variance (see Appendix C). Given this

information, it is possible to compare an archaeological site with its irnmediate

sunoundings using statistics. 1 submitted the known sites, the seven test sites and the four

control sites, each in compouison with its respective surro~~1ding region, to a two-tailed t-

test in each of the six TM reflectance bands and the three band ratios developed earlier:

bands 3/ 1,4/3 and 1 /7,

Table 3-3. Control sites with no archaeological presence.

Site - Forest t Forest2 Grasstand 1 Grassland2

INGEMMET Location

Chachapoyas(l358)13-h 688110 Chacbapoyas ( 1358) 13-h 689- 105 Chachapoyas (1358) 13-h 740-170 Chachapoyas ( 1358) 13-h 836-1 12

The purpose of this statinical test, in general, is to determine whether the means

of two samples are different enough to indicate that the samples themselves are different

populations. Specific to this study, then, the t-test determines whether any difference in

mean reflectance value between the archaeological site polygon and its surrounding

polygon is significant, indicating that they are two different kinds of terrain. My primary

assumption in doing this was that the surrounding terrain would be sirnilar to the terrain

on which the site was built, and that any differences detected would have been introduced

by the presence of archaeological remains.

I used two-tailed values as 1 had no prior bowledge of how the values of the two

means would be related to each other. 1 accepteci the standard -05 level of significance as

the cutoff value.

hmediately afler calculation, a problem appeated Not only did moa of the

archaeologicai sites appear significantly different from their surroundings, so did the non-

archaeological control sites which were chosen to be the same as their surrounding

regions. 1 amibuted this to the arbitrary radius I chose for the outer tom, which covered

an area containing usuaily six to ten times as many pixels as the site it sunounded. This

large difference in the number of data values causeù the sunounding area to appea.

different fiom the site it surrounded, even in relatively homogeneous terrain. Afier some

experimentation with the hornogeneous terrain, a better shape and size for the area

surrounding the site of interest presented itself: a thin toms containing roughly the same

number of pixels as the site it surrounded

1 regenerated strrrounding pol ygons for the eighteen known arc haeological sites, the

seven test sites and the four control sites and recalculated t-tests for each band and band-

ratio. As seen in Table 3-4, the r-tests for the control sites showed linle significant

di fference between site and surroundings. Therefore, 1 believe that using this narrow

t o m for areas surrounding other sites eliminates numeric artifacts introduced by

improper size and shape of the surrounding region (see Appendix B).

The resulting significance levels are iltustrated in Table 3-4. They are based on the

table of distribution of [-test significance in Bernard (1994). Greyed cells indicate a

significance level weaker than the .O5 level, that is, no significant difference.

The t-values shown in Table 3-4 were calculated using the formula in Bernard

(1 994). Blalock (1972) suggests that, aithough the results in practise often differ little,

especially in samples of size fifty or more, a modifieci method involving much more

computation is necessary for samples in which the standard deviations are not known to

be similar. Since 1 codd not predict the relationship of standard deviations for reflectance

data, 1 subrnitted al1 the data listed at the borderline -05 level of significance to the

modified

in the set

test. Two of these tests attained a slightiy weakened level of significance, one

of known sites, and one in the control set. In the interem of consenratisrn,

those cells were therefore also rejected as not significant Since these two changed their

t-value only slightly, and the remainder of the borderline cases, even those of small

sample size, did not change or in fact improved slightly, 1 retained the values calculated

with the simpler method for the table as a whole. The amount of change dernonstrated in

the -05 level cells would not be enough to move any of the other cells into or out of the

broad categories delimited by the -05 level boun*.

Table 3-4. Levels of significance of t-test cornparisons between known archaeological sites and their surroundings. The sites are ordered ascendingly by number of pixels in the inner polygon ('Inner N'). Test locations appearing similar to the known sites in preliminary images and locations known to be free of archaeological remains are also included. Recall the band ratios Y I , 4/3, 1/7 are for disceming reddish rock, vegetation and calcite respectively.

. . . . ... . --. ... . .. .

O 01 O 0 5 005 0001 O O1 O os O Ot O 001 O 01 oc6 O 01

u 001 O 001 0001 O M I

4. Discussion

At the beginning of this research my belief was that archaeological sites could be

disthguished fiom the surrounding terrain using satellite imagery of the region, and that

this distinction couid be used as a tool in conjunction with other factors to help locate

archaeological sites. Given the results of the above statim'cal analysis, 1 conclude that

Landsat imagery can be used to discem variations in the surface reflectance of

archaeological sites, and so, both visually and mathernatically, separate them from their

surroundings. Furthemore, the relative effectiveness of different bands and bands ratios

has k e n dernonstrateci by the results of the analysis, and the most effective bands can be

used to generate colour images which highlight known and potential sites.

Control Sires

The four pain of control regions, two forest and two grasslanà, generally

demonstrate their homogeneity for the purposes of this test. Twenty-eight out of the

thirty-six comparisons show no significant difference between a control site and its

surroundings. Of the eight comparisons that do indicate significance, two are in the 4/3

band ratio traditionally used to rnaxirnize the visibility of minute difierences in type,

health and season of vegetation, and one is in band 4 which is the most sensitive of al1 the

individual bands to those differences. The remaining five show significance at a

relatively weak level. The general lack of differentiation between homogeneous regions

and their surroundings indicates that any mathematical artifacts introduced by using the

method in Chapter 3 to define the terrain surrounding a site were minimized.

Known Archaeological Site Cornparisons

Since the known sites varied so widely in size, I wanted to detexmine if site size had

an effect on the significance values measured for the sites. The results of a simple

corrdation test in the six reflectance bands between the size in pixels of known

archaeological sites and the number of wignificant comparisons show very litde

correlation, 4.07. The band ratios, however, show a much stronger correlation, 4.67.

As discussed in Chapter 2, band ratios tend to highlight noisy pixels since noise tends to

appear in one band but not another. This highlighting wouid increase the variabitity

within a band ratio data set and tend to reduce the resulting calcuiated levels of

significance. My choice to avoid washing out the evidence of small sites as opposeci to

the common practise of subjecting band ratios to a smoothing filter seems not to be

justified

Well over half the comparisons on known arc haeological sites s howed significance

at the .O5 level or better. Although one cannot extrapolate from a single t-test between

two samples to the population as a whole, a significant result rules out the likelihood of

randomness or error, in that test at lest (Blalock 1972). Many of the [-values were much

stronger than needed to provide even the .O0 1 level of significance. So many significant

differences, over many different bands, support my contention that there is a phenomenon

present which is not random and which does separate the various target regions fiorn their

surroundings. Given the criteria 1 used to select the target regions, that phenomenon must

be the presence of archaeological sites.

Four of the known archaeological sites were anomalous. Torre Pukru, Pena Calata,

Boveda and Cabildo Pata showed littie or no difference from their surroundings. I

believe the variation in size among these four sites reaffhs that size of site has little to

do with the dearth of significant comparisons.

Pena Caiata and Cabildo Pata are on opposite sides of a narrow valley, bracketing

the hamlet of Atuen. From photographs in Schjellerup ( 1997) it c m be seen that nearby

f m i n g is spreading up the hillsides into these two sites, possibly compting their

etectrornagnetic reflectances. The same is tme of Huepon, but it is a larger site, and

seems to have been af3ected to a lesser degree.

Torre Pukm is about one and a half kilometres north of Pena Caiata and Cabildo

Pata A photograph of it in Schjellemp (1997) shows it to be isolated on a hilltop, and so

the issue of spectral contamination by famikg does not apply. Boveda is aiso on an

isolated mountainside. For these two sites, it is possible 1 just missed the site locations,

and am in fact cornparhg bare hillside to bare hillside. Altematively, if a site polygon is

made smaller than the site, even king in the right spot would place the outer t o m over

some of the remainder of the site, thereby causing a cornparison of a site with itself.

There is some support for these suggestions below.

Removing these four anomalous sites fiom the table for clarity produces a new

significance table presented in Table 4-1.

A clearer pattern appears hem. Whereas the band ratio comparisons still fail to

show significance in the smaller known archaeological sites, a reversal of this trend

appears in the single band comparisons, with insignificant values appearing mostly in the

seven larger sites. I am at a loss to explain this dichotomy, but wonder whether the

susceptibiiity of band ratios to magnifjmg noise might explain part of this phenornenon

Having set aside the four anomalous sites, some bands clearly show more

significant ciifferences than othen. Among the band ratios, the 4/3 ratio shows the fewest

insignificant values, and the most comparisons with the highest level of significance.

This lends support to the choice of this band ratio as a tool in exploration, especidly

considering that most Chachapoyan archaeological sites are situated in highly vegetated

terrain.

Table 4-1.

Known S b Pirka Pirka Revash 2

Jow Kueiap

Tambu Tajopamp Pomio JuM

y-ge Huepon

lnticancha Vira Vita

Papsrnarca Escaiera lncaica

terraces

Test S b Test2 TesQ Testfi Test6 Tesn Test7 Test4

Results of t-test comparisons with anomalous known sites removed Band1 Band2 Band3 Band4 Band5 Band7 311 ratio 4l3 ratio mraîio

Among the individual bands, band 7 and band 3 each have ody one non-significant

value, and many values are at the highest level of significance. Out of the two next best

individual bands, band 1 seems slightly better than band 2 for the known sites. Therefore,

it makes sense to generate a colour image out of bands 7-3 and 1 to see if the numeric

significance is matched in the visual image. 1 generated such an image with red, green

and blue channels king bands 7,3 and 1 respectively. Samples are provided in Map 4- 1.

MapC l(a). Chachapoyan region displayed in bands 7 .3 , 1 as red, green. and blue respectively. Compare to Map 3- 1 . Forest is dark green-blue, higher elevation scrub is green-brown, fârmland and some bare soi1 and rock is red, river water is bright blue-green, lake water is black.

5 km (approx.)

Map Cl(b). Enlargement of Kuelap region.

Map 4-l(c). Enlargement of Joya region.

Map 4- l(d). Enlargement of Vira Vira region.

Map 4- l(e). Enlargement of Jubit region.

The result is an image in which forest is a very dark blue green, fmland shows

bright red, higher altitude scrub gras is brownish-green, and many of the known

archaeological sites selected for this study show as brighter areas of blue-green againa

the darker background. For example, a town wall and row of buildings show in Vira

Vira, as does the main wall of Kuelap. Of interest is the fact that modem artifacts of

human presence such as roads and t o m are a bright white and watercourses of even

small size are art extremely clear blue-green in the new image. As a map for on-site

exploration, therefore, it rivals the natural colour image for orientation clues, and it

carries much more archaeological information. The somewhat unnamal colour scheme,

however, does require accommodation.

Through study of this image I cm now postulate with more certainty why Torre

P u h and Boveda failed to show significantly in the statistics. Roughly ninety metres

southwest of the peak 1 had selected as Torre Pukm is another smaller peak which has a

highlight of the colours (built from the three bands chosen fiom the table) sirnilar to those

of other sites such as Vira Vira, Yalape, and others. Although 1 had created a region of

the nght size to be Torre Pukru., it appears 1 was three pixels away from the actual site.

At Boveda, a similarly highlighted region, smaller than the region I chose, was included

in my Boveda polygon. Thus the polygon I chose to represent the archaeological site

appears to have included an area outside of that site, and so included part of the

surrounding region.

For both Boveda and Torre Pukni, the dues 1 used from various sources were

enough for me to place them on the Landsat images with some degree of confidence.

From these examples, however, it is clear that this method is not perfect, and some sites

may slip unseen or misplaced even with the mon sophisticated satellite imagery.

Two patterns appear in the full set of reflectances which rnay help in site location.

A generaily brighter reflectance shows in montain-side sites when they are compared to

their surroundings in al1 bands except band 4, which tended to show a darkening for these

sites. The two valley floor sites, Tambu Tajopamps and Papamarca responded in

opposite fashion, including in band 4. In the band ratio comparïsons7 a more regular

pattern appeared. Valley floor sites were brighter than their surroundings in the 311 and

413 band ratios and darker in the 7/1 band ratio. Mountain-top and -slope sites were

brighter in the 31 1 and 7/ 1 ratios and darker in the 4i3 band ratio. If these patterns appear

in further studies, they wuld become as good or better indicaton of the presence of

arc haeologicaf materials.

One might ask how an archaeological site can be differentiated From a limestone

outcrop on a forested hillside. By itself, this technique would not be able to tell the

difference. Both would show similarly in the statistics and in the image generated corn

bands 1-3, and 7. Further clues would be needed, such as shape, nearness of water,

elevation, soi1 type, vegetation type, etc. Ln short, the above statistical analysis and the

colour image h m bands 1,3, and 7 are tools to be used along with other traditional tools

of archaeological research for site location. The statistical procedure cm also be used to

test ami corroborate results garnered by other means, such as the band 2 - 4 - 7 image

suggested for w in the arïd region near Phoenix, Arizona by Chavez and Boweli (1988)

and Showalter (1993).

Test Sites

Originally, 1 selected as test prediction sites regions in rny preliminary images that

exhibited visual characteristics similar to the known archaeological sites, as well as

having location characteristics similar to known archaeological sites.

Test2. Test5. and Tesr6 are locations within broader regons in which poorly

mapped archaeological sites are known to exin Test2 is a location near Vira Vira, a

valley bottom site in a different ecological zone that would fit the zona1 complernentary

mode1 (Murra 1956, Salomon 1986) mentioned in Chapter 1. As well, it is in the

approximate region of an archaeological site called Yerva Buena (Schjellerup 1998,

personal communication). Test5 is a mountain ridge location, roughly in the region of the

Chachapoyan t o m San Pedro de Washpa. Test6 is visually typical of many locations east

of the Rio Utcubamba, a region claimed by Davis (1988) to have roughly one hundred

and fi@ Chachapoyan sites. According to his hand-drawn map, this site is roughly in the

location of the archaeological site Chonta C m

Tesr3, Test4 and Tesr? are distinctive locations in three widely distributed areas of

the forested eastem slopes, above riven as are so many known Chachapoyan

archaeological sites.

Testl is a circular feature high in the mountains above a recent archaeological find:

cliff tombs at the Laguna de los Condores (Lake of the Condors) (von Hagen and Guillen

1 998).

The consistent and significant differences displayed by the r-test comprison of

these sites strongly suggest that these locations should be investigated as archaeological

sites. Only on-site verification wiil ascertain their true nature. Unfomuiately, such on-

site verification is beyond the means of this study, but the locations of Test 1, Test2 and

Test3, al1 near the Laguna de los Condores, have been cornmuoicated to the

archaeological team investigating there in the sumrner of 1998.

5. Conclusion

The purpose of ths thesis was to develop a technique for finding archaeological

sites with satellite imagery. As a focus for this development I chose archaeologicd sites

of the Chachapoyan people of the northem Andes of Peu.

Discoveries in ment years indicate that before the arriva1 of Inca and Spanish

conquerors in the late 1 Sm and early 16'h centuries, the Chachapoyans rnaintained a

thriving culture with unique architecture, pottery and art Leaming more of their way of

life could provide insight into theoretical issues such as cultural evolution and migration

into the Andean region of South Amerka. Yet, despite these oppommities and the

presence of scores of archaeological sites known to tnwellers and residents in the area,

the archaeological community has paid little attention to this region In part, this can be

biamed on the great cultural wealth Pem possesses, with numerous cornplex coastal

kingdoms and the overarching presence of one of the greatest empires of the ancient

world, the Inca. As well, travel in the Chachapoyan region is extremely difficult, and

much of the axa is remote and sparsely settied Therefore, exploration of the region by

the usual methods is impractical.

A tool capable of circurnventing distances, forests, steep slopes and high altitudes

to detect archaeological sites wodd be extremely helpful in the Chachapoyan a r a and

other sirnilar locations. In this concludïng section of my study, 1 shall summarize what I

have lemed about how to use satellite imagery as such a tool, discuss how aspects of my

study could have k e n improved, and project how this research could be expanded

Research Resulrs

Archaeological sites ciiffer £Yom their surroundings in the way they reflect visual

and infrared electromagnetic radiation. These differences can be detected, measured and

displayed in a way which can be used to aid exploration and the development of

prediction models.

A method tu determine whether a smpected site differs from its surroundings in a

statistically significant way is demonstrated in this study. This method requires the use of

cornputer software to display a Landsat image and superimpose upon it an image layer in

which one cm create and display polygons to delineate regions of interest in the image. 1

used IDRISI to perfonn this function, but many geographicai information systems (GIS)

packages also do so.

The first step in the method consists of creating a set of pixels delimited by a

polygon drawn around the suspected region, using knowledge gained frorn site reports,

maps and visual inspection of the satellite imagery for guidance in locating the site and

defining its shape. ïhen, another polygon is defined sunounding this region, in the form

of a toms of area roughly equal to the site polygon and following the border of the site as

closely as possible. The shape and size of the outer polygon have been demonstrated to

be important to the hinction of the m e t h d Finally, these two regions are compared to

each other statisticaily. In each Landsat Thematic Mapper (TM) band (except band 6 in

which surface reflectance is compounded with direct radiation from the surface), and in

band ratio images built h m Thd bands, the reflectance data in the two regions are

submitted to a [-test. I f the t-test demonstrates a difference between the two sets of pixels

at a significant level, the assumption is that the difference between the site and its

surroundings is not a result of chance variation. In the case of a region chosen using

information about a known archaeological site, such a significant difference supports the

use of the set of pixels as characteristic of the 'archaeological nature' of that site. in the

case of a location not known to contain an archaeological site, but differing £tom its

surroundings and exhibiting characteristics similar to known archaeological sites which

have k e n submitted to this method, one c m support the argument that this location has

potential for k ing an archaeological site and that it should be explored M e r .

The sites tested in this study show differences that are statistically significant in al1

six of the pure reflectance bands of Landsat data, and in the band ratio images 3/1,4/3,

and 1/7. These band ratios were chosen to highlight vegetation differences and mineral

components in consmiction materials common in Chachapoyan buildings.

The three reflectance bands most consistently showing significant differences

between site and surroundings were bands 1-3, and 7. A false colour image (see Map 4-

1) made fiom tfiese bands highlights known archaeolopical sites used to develop the

methodoiogy and also brings to the eye regions that display characteristics sirnilar to the

known sites but which are not listed in the literature as archaeological sites. These

regions therefore are suggested as likely areas for exploration. This image also highlights

tenain features useful in orientation, such as rivers, roads and towns.

The lack of accurate mapping of the region can be overcome to some degree by

making available to researchers and explorers this composite image made from bands 1,

3, and 7. Also, a tnie colour image made from bands 1,2 and 3 is helpfùl. Especially

useful in the early phase of my exploration was the 4/3 band ratio image (see Map 3-2)

which hiwighted differences in vegetation and overcame some of the conking efkcts

of shadow in precipitous temin. These three composite images wouid provide a uniform

platform on which to mark the location of known and newly discovered sites.

In the band comparisons, a generally brighter reflecuince was measured in

mountain-side sites than in their sunoundings, except with band 4 which was darker in

just over half the significant sites. The two valley floor sites, Tarnbu Tajopampa and

Papamarca had responses which were largely the opposite of the mountaintop- and slope-

sites, with reduced reflectance often appearing in bands other than band 4, and increased

reflectance in band 4. Much more regularly, the measurements showed a different

pattern in the band ratios. The valley Boor sites both showed increased values in the 3il

and 4/3 band ratios, and decreased values in the 1/7 band ratio. The mountaintop and

slope-sites a11 showed increased values in the 3/ I and 2/7 ratios and decreased values in

the 4/3 ratio.

This study demonstrates that Landsat TM irnagery has value as an exploration tool,

even though it is older technology and is often data collected a decade or more aga This

value is due in part to its low cost, large number of bands of data and wide coverage of

the earth's surface, and in part to the symptomatic way in which archaeological sites

differ fiom their surroundings in the particular regions of the electromagnetic spectrum

perceived by this technology.

Resources from whzch this research couhi have benefiled

Another Landsat TM data set taken on a different day with different cloud cover

would have k e n the most usefid addition to this study, as several sites for which 1 had

locating-information were completely covered by cloud in the image I used. These sites

include large ones such as Patron Samana, Gentil, Churro, and Runashayana, and smaller

ones such as Michi Mal and Sinchipata Also, a second set of Landsat TM dam would

have allowed testing of the robustness of this study's methodology. On the same site, in

data collected on different &YS, how sirnilar would the results be? Vincent ( 1997)

suggests that band ratio images, at least, should be very robust this way. 1 suspect that

single bands would show differences from one image to the next. This result would have

to be accounted for by developing a &ta set of sites that exist in both images, and

describing and fonnuiating the differences.

A geo-registered radar image, or a higher resolution visual image such as a SPOT

panchromatic 10 m X 10 m resolution scene, could have k e n used in &ta fusion with the

Landsat TM data as the intensity charnel in a colour image (Wald, Ranchin and

Mangolini 1 997, Toutin 1 998). This information would have provided finer details of

terrain shape, or even individual building shapes, both to guide the location of h o w n

sites and corroborate the location of suggested sites. The first principal component from

a principal compooents analysis of the six reflectance bands served this role during this

study, providing as much terrain detail as possible within the TM pixel resolution. Of

course, the negative factors in such combination of data of different resolutions are image

size and cost.

The finer the resolution of an image, the more cornputer memory is required to hold

data pertaining to a particdar region, or conversely the smalier the region that is covered

for the same amount of data As an example, a single band of Landsat TM data covering

the study-region occupies roughly 15Mb of disk space. The same area covered by one

band of data nom the new EOSAT Im X Irn hi&-resolutioo imagery which will soon be

available to the public would occupy roughly 1 I Gb of disk space, almost 700 times the

space. The cost of seven bands of archival Landsat TM &ta ( 10 years old or more),

covering 185km X 185km is approxirnately $400 US. The cost of archival EOSAT for a

region outside North America is $54 US per square km. Between these extremes are

several available options, such as the SPOT data mentioned above, but the point is that

there is a trade-off between coverage and cost, with the Landsat TM data king very

inexpensive compared to moa of the other options.

Ln retrospect the grant 1 obtained for four Radarsat images, two pain partially

overlapping to overcome the radar shadows, would have been better spent by acquiring

fewer images that were geeregistered The grant arrived late in the research period, and

there was no oppommity to do ths tedious and time consuming mathematicai

transformation myself Nevertheless, the Radanat images I possess are available for

fùture work. They show detail mavailable in the Landsat imagery , suc h as the traces of a

heretofore undocurnented second earthwork branchhg north-est of the one mentioned in

Schjellerup ( 1997) and walls and tenaces in Torre Pulau, Vira Vira, Churro, and Huepon.

Also, they are important in corroborating known site location, shape and size. It is in

level of detail and through the ability to penetrate adverse atmospheric conditions that

radar imagery outperforms TM irnagery. Geo-registering the image would likely allow

other sites to appear out of the slopes that were cornpresseci or shadowed by the

mechanism of the Raciarsat imagery. For example, I think Patron Samana would show if

the ridge on which it is located in Schjellemp (1 997) could be fitted to the tenain.

Similarly, in conjunction with a different TM data set, sites obscured by cloud in my TM

data codd have been located.

A digital elevation model (DEM), combined with global positioning system (GPS)

readings for known sites, would have helped locate known sites as accurately as possible.

The only DEM I could find for the region was fiom the US Geological survey with data

points one km aparf which is too coane a remlution for accurate location of sites. The

creation of a DEM would greatly increase the possibility of generating a prediction mode1

of archaeological sites, as discussed below.

Finally, on-site verification of vegetation and soi1 types, especially at and around

known archaeological sites, would allow calibration of spectral response cwes for those

locations. Given this kind of knowledge, it would have been easier to venQ site locations

and shape the site polygons to the best fit.

Direcliûns for Future Researclî

Many directions for friture research exist. These can be broadly grouped into

technical aspects and culturaI aspects, which together can combine to develop a model

both to describe Chachapoyan settlement, and to predict new sites.

Technical asmcts

A great deal of data on individual mineral and vegetable responses to

electrornagnetic radiation is available (Vincent 1997, Johns Hopkins University 1998). A

thorough understanding of the eiectmmagnetic response properties of the materials used

in construction of Chachapoyan sites would allow development of a set of target response

curves for which to search in such exploration. While Landsat data contains seven broad

ban& of response, hypenpectral data collected with other satellites can contain hundreds

of narrower bands that can describe an individual material's response curve in more

detail, and deal more accurately with factors such as moimire content (a variation 1

believe will be useful in differentiating construction materials before and d e r use).

Pursuant to such a finer definition, Chachapoyan construction materials, such as the

lirnestone blocks, should be submitted to laboratory reflectance analysis to create a

controlled baseline for a response curve.

Given that for practical reasons research wili probably continue with pixel

resolution at the level of Landsat data, a fhutful avenue to develop would be sub-pixel

andysis of terrain. In this technique, response curves of known materials are used to

determine relative proportions of presence in a mixed pixel (Jasinski 1996, Huguenin et

ai. 1997, Ashton and Schaum 1998, Grandell, PuHianen and Hallikainen 1998, Gross and

Schon 1998). in conjunction with a better understanding of the materials used in

Chachapoyan sites, this technique could be a way to overcome the limitations introduced

by using this pixel size.

Working in such precipitous terrain, more research into band ratios is needed, as

they are a key way of dealing with shadows and enhancing the visibility of rninerals and

vegetation. Eliminating shaâows in this study àid not aiways work well, even though 1

took the precaui-ion of removing the dark-object value from the images. The location of

sites on one side of the cordillera, east or West. may be made more difficult by this

discrepancy. in conjunction with this problem, it is important to answer to what degree

the perceiveci differences between one side of a ridge and the other are due to failure of

the technique to account for shadows, or due to real differences in surface rninerals and

vegetation caused by fol& and fiachues in the earth exposing different mils or rninerals

on opposiog slopes.

A digital elevation mode1 can be developed from multiple satellite imagery that

uses stereuscopic differences in images to calculate altitude. The DEM of the region

would allow more accurate rnapping and analyses of site elevation, distance From water

sources, view from and to sites (viewshed anaiysis) (Lake, Woodman and Mithen 1 998),

and the effort required to reach one place fkom another (cost surface analysis) (IDRISI

2997).

Fid ly , armed with as much of the above as possible, it should be possible to

devetop a cornputer program that autornatically scans a large scene, making the

cornparisons developed here, searching for response patterns determined to be of

archaeological significance. The writing of such a program, of coune, would be no small

task, as it would be hampered by factors such as varying site shape and surface

vegetation, but the basic principles of programming are well established.

Cultural aspects

Fundamental questions regarding the Chachapoyans remain to be mwered. Where

did they corne fiom? Were they a forest people expanàing into the Andes or an Andean

people spreading down the eastern slopes? Every new site discovered, especially in the

transition zone between the mountains and the tropical forest plain, c m help answer these

questions. Of interest is recent work done by Church (1995) in the Abiseo region,

including Gran Pajatén. He fin& two distinct occupation periods separated by hundreds

of years, the latter bearing strong similarity in pottery, architecture and iconography to the

Chachapoyan remains to the north. 1s the later occupancy conquest, occupation of an

abandoned site, or re-occupation of a site abandoned by forebears? Similar, detailed

excavation work must be doue in other Chachapoyan sites in the forested eastem slopes

to determine if a pattern of occupation and time depth appears. Questions of ecological

adaptation (How does the model of verticality apply to Chachapoyan settlement

patterns?), cultural evolution (What does Chachapoyan social organization Say about the

stages of cultural complexity?) and migration (1s there a pattern of expansion or

movement in Chachapoyan occupation sites?) al1 are addressed by research into sites

within this border regioa None of these questions can be answered without accurate

knowledge of where Chachapoyan occupation sites are.

Mul ti-discipliw approac h to research

Remote sensing technology, geographical techniques and anthropological theory

can combine to develop a model to describe Chachapoyan settlement patterns and predict

the location of sites, despite the rernoteness and dificulty of the terrain in which the

Chachapoyans lived Surface reflectance data, elevation data from a DEM developed

from stereo image pain, distance of occupation sites from rivers and Iakes, viewshed

analysis, dope information, cost surface analysis, site excavation, ecological adaptation

models such as verticality, and cultural complexity theory al1 have parts to play in

developing an understanding of the Chachapoyans. The tool I have developed leads

naturally into broader research combining these methods.

Myriad forces of conquest and colonialism have contrived over the centuries to

eradicate the artifacts and cultural presence of a popdous and thnving culture. Through

their archaeological remaios they call to us. The research describeci here is a small part

of the response to that call.

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Amendix A. Sample of the process of known site location.

The process of locating a known archaeological site in the Landsat TM irnagery involves several steps. An example of the process for the site of Kuelap is demonstrated here. 1 chose Kuelap for this example because locating it involved many of the resources 1 used for the various sites. including site-reports and -maps. travellen' descriptions. persona1 communication with archaeologists who had been there and Radarsat imapry.

The first step was to l e m as much as possible about the shape and location of the site. From descriptions in Savoy ( 1970). Davis ( 1988) and Narvaez ( 1988). Kuelap is an impressive site. I t is built along a ridge m i n $ north to south. with the West face of the ridge a long. steep drop and the east dope dropping more gently towards the Utcubamba River. The outer dimensions are roughly 700 m by 150 m. enclosed by a wall which is as much as 20 rn hi& on the eastem. down-dope side.

Davis ( 1988) describes one way to approach the site. The town of Tingo. on the Utcubarnba River at the confluence of the Tingo River. is the starting point. One walks south from Tingo along the West bank of the Utcubarnba River for nearly two kilometres. then tums West. uphill. A switchback trail leads uphill to the north ridge of the Sicash Canyon. From here one travels W e s t along the ridge and then uphill when it joins the mountain ndge on which the fortress is built. At this point the village of Kuelap is visible. One travels through the village and up the long eastem slope of this final ridge to the fortress. Based on this information. 1 chose a likely ridge for the location of the fortress in the natural colour made fiom TM bands t, Z and 3 (see below). Part of this ridge is covered in cloud in my TM image. and it was not clear at this point where the fortress was.

Narvaez ( 1988) gives a contour map of the ridge on which the fortress sits and a plan o f the outer wall. Given that the east wall is so high, 1 sumiised that this wall shodd show on the Radanat image which was acquired with the satellite lookmg west over this region. Although the Radarsat image is not geo-referenced to the Landsat TM image, the terrain features such as riven and ridges can be correlated (see the radar image below).

The intensity of the Radarsat image in this region does not extend the full range availabte. Its histogtam below shows no pixels with intensity values above 160.

To maximize the visibility of the bright reflectance of the vertical wall, 1 stretched the intensity values to the full 256 value range (see bëlowj.

The result is illustrated in the magnifieci image below. The line of bright pixels corresponds closely to the descriptions and maps of the east wall of the fortress of Kuelap.

Given this corroboration of the shape and location of Kuelap. I took orienting dues :om the Radarsat image below to help place the site on the Landsat TM image.

With these locating dues, I was able to find the wall of Kuelap in the Landsat TM lage (see below).

The feature 1 identified as the east wall of Kuelap dso shows in other colour composite images (see Map 3-2). The final step was to create site- and surrounding- polygons for this archaeological site. Since the site is split in two by the large cloud, 1 made each data set frorn two polygons, the north and south visible parts of the wall (see below and Appendix B for examples).

Appendix B: Site Polygons and Surrounding: Tomses

Known Sites

Torre P h

Revash ( secondary )

Pirka Pirka

Pena Calata

Boveda Joya

Tambu Tajopampa Kuelap

Pomio

Jubit

Cabildo Pata

Inticancha

Huepon

Vira Vira

Escalera incaica terraces

Test Sites

Test 1

Contrd Sites

Forest 2 Forest 1

Grassland 2 Grassland 1

Amendix C: Means and Variances of Pixel Refîectance Values for Site Polveons and their Sn mondine Toruses

Location

Pena Calata m- vaqance

surroundrngs mean vmance

Boved. m." valplce

moundrngs rnean vanance

Band l

34.64 4.45

34.42 3.36

31.21 5 - 7 2

28.3 1 8.06

24.65 12.74

22.28 1.74

20.39 15.66

21.95 24.94

36.30 10.3 1

35.17 32.67

26.66 23.59

22.71 20.36

42.84 213.81

29.60 16.59

Tambn Tajopampa mF 29.72 vaqance 26.83

surroundrngs mean 32.50 vanance 6-71

Pomio me" 25.42

17.84 s u c ~ o u n ~

m- 23.34 vanance 20.66

Location

Cabüdo Pata m- vanance

surroundnigs mean Vanaace

Jubit m- vanance

surroundings m- variance

Ydape m- vanance

surroundings m- vanance

Euepon m- vF!nance

surroundlngs - vanance

tnticancha m- vanance

surroundings rn- vanance

Vira Vira m- vqance

surroundmgs mean vanance

Band l

32.49 8.43

32.70 18.84

26-12 8.50

23.15 4.87

31.02 11.85

27.58 14-22

28.32 41.85

26.97 16.88

39.53 33.20

3 1.53 21.30

27.67 10.34

25 +97 16.9 1

32.36 3 1.36

33.84 31.14

21.80 7.02

20.65 6.07

19.62 5 .O9

19-20 4.23

Known Sites

311 ratio 4/3 ratio 1/7 ratio

Pena Cala- m- varplce

surroundngs "- vanance

Kueiap *- vqance

nirrowidings *- vanance

Tamba Tajoprmpa - *ance

surroundlngs rn- vanance

Pomio m- va?ance

surroundings m- VananCe

Location 3/1 ratio 413 ratio 117 ratio

Ju bit m- vqance

surroundmgs *- vanance

Yala pe ='- v q m c e

surroundmgs m- variance

Papamama m- varplce'

surroundings m." vanauce

temces mean var!axlix

moundlngs m- vanaDce

Test Sites Locatmn Tm 2 -

vanance surroundiags

m- vanance

Test 3 *- vaflance

surroundings mean Y8nance

Test 5 m- vallance

surroundmgs meazt

variance Test 1

ControI Sites Location F o m 2

m- w==

surrmdlngs - vanance

Fom4t 1 mean

variance

Bandl

24.27 4.02

26.10 4.69

21.09 4.72

20.39 6.16

34.44 19.95

29-17 8.73

30.58 10.52

27.20 8.98

24.40 5.55

22.28 6.33

21.14 11-97

17.64 3.69

22.97 1 1 .O3

19.16 5.72

Band 1

25-11 1.11

23.56 2.28

24.W 3.50

23.81 1.63

48.00 14.09

48-09 17.99

37.47 49.47

36.80 44.57

Test Sites Location 3/1 ratio 4/3 ratio 1/7 ratio Tm 2

'=- vanaace

~~~~o l lnd i f lgs m-

Tat 3 m- vanance

surroundings m- mance

Test 5 m-

mundurgs - T i 6

m- vanance

surrowictuigs meaLl

Test 1 m- vanance

surroundings mean

Tat 7 vm- m- vanance

surroundings - vartance Tut 4

meao vanance

surrouadinps m- vanance

Control Sites Location 3/1 ratio 4/3 ratio 1/7 ratio Forest 2

m- vanance

surroundings m- vanance

Forest 1 m- vaRance

sunoundings *- vanance

Grassland 2 m- v q c e

surroundings m- vanance

Grassland 1 m- vaqance

=n-mdings *- vartance

Software

IDRISI for Windows, version 2, Clark Laboratones

Amendix D: Software used

Picture Publisher for Windows, version 7, Micrografk Inc.

-band ratio dculation -principal component analysis aark-object removd -image classification -polygon creation and calculation of

means and variances of pixel values

-stretching of tonal values -colour compositing of images -work in Hue-Saturation-Intemity

and Red-Green-Blue colour models

l MAGE EVALUATION TEST TARGET (QA-3)

APPLIED IMAGE. lnc - t 653 East Main Street - -* - Rochester. NY 14609 USA -- -- - - M e : 71 6/482-0300 -- -- - - F a 71 W288-5989

O 1993. Appüed Image. Ine, All Rig#m Resanmd