View
0
Download
0
Category
Preview:
Citation preview
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
National Library Bibliothèque nationale du Cana&
Acquisitions and Acquisitions et Bibliographie Services s e W bibliographiques
395 W d ï i i Street 395. me Wellington OttawaON K1A ON4 Ottawa ON K1 A ûN4 Canada canada
The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or seil reproduire, prêter, distci%uer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfïche/nlm, de
reproduction sur papier ou sur format électronique.
The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fiom it Ni la thèse ni des extraits substantiels may be printed or othenivise de celle-ci ne doivent être imprimes reproduced without the author's ou autrement reproduits sans son permission. autorisation,
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.
Agache, Roger 1975 Aerial reconnaissance in northern France. IN D.R Wilson (d) Aerid reconnais-
sance for archeoiogy London: The Council for British Archaeology.
Ashton, Edward A- and Alan Scfiaum 1998 Algorithms for the Detection of Sub-pixel Targets in Multispectral Imagery.
Phoîogrammetric Engineering & Remote Seming. 64(7): 723-73 1.
Bandeber, Adolfo 1940 Los Indios y las ruinas aborigenas cerca de Chachapoyas en el norte del Peni.
C h d i . 1(2): 13-59.
Bernard, H- Russell 1994 Research Methuhodr in Anrhropdogy: QQuctuîive and Qwnîitutive Approaches. 2"d
ed. Thousand Oaks, Calif : Sage Publications, Inc.
Blatock, Hubert M. Jr. 1972 Socid Statisrics. 2* ed. New York: McGraw-Hill.
Brooks, Robert R and Dieter Johannes 1 990 Phytoarchaeoiogy. Portland: Dioscorides Press.
Bnrsh, Stephen K. 1977 Mountairt, Field. and h z l y : The Economy and Human E c o l o ~ of an Andeon
VafIey. Philadelphia: University of Pennsylvania Press.
Buiten, Henk J. and Jan G. P. W. Clevers (ed) 1993 Land Observation by Remde Sensing: Theory and Applications. Amsterdam:
Gordon and Breach Science Publishers.
Chavez, Pat. S. Jr. and J. A, Bowell 1988 Cornparison of the Spectral Information Content of Landsat Thematic MApper and
SPOT for Three Different Sites in the Phoenix, Arizona Region. Photogrammetric Engineering & Remote Sensing. 54: 1699- 1708.
C hurch, Warren 1 994 Ear 1 y Occupations at Gran Pajatén, Peru Andean Pm. 4: 28 1 -3 1 8.
COSETI 1998 Amiospheric Tr~~zsmission The Columbus Optical SETI Observatory, Ohio State
University. [http:////www.coseti.orgJ .
Davis, Morgan 1 988 Chuchapoyas: The Cloud People. Noelville, Ontario: sel f-publisheii
de la Vega, Garcilaso 1967(1609) Cornentarios reales de los Incus, Estudio prelimznur y notas de Jose Durand
2"6 ed Lima: Editores Cultura Popdar.
Dniry, S. A 1993 Irrtuge lnterpreiation in Geology. 2"d ed London: Chapman & Hall.
Ebert, James I a q Thomas R. Lyons, Bruce W. Bevan, Eileen L. Camilli, Sarah Denneît, Dwight L. Drager, Rosalie Fanale, Nicholas H a r t m a ~ , Hans Muessig, Invin Scollar
1983 Arc haeology, Amhropology, and Cultural Resource Management. IN John E. Estes (ed) Manual of Remote S e d g vol 2. Falls Church, Virginia: Amencan Society of Photogrammetry.
Flannery, Kent 1972 The Culhual Evolution of Civiluations. A n d Review of Ecology and
Systematiics. 3 :3 99-426.
Fowler, P. J. 1975 The Distant Vew. IN D. R Wilson (ed.) Aerial reconnaissancefor urchoeology.
London: The Council for British Archaeology.
Grandel 1, Jochen, Jouni Pull ianen, and Martti Hallikainen 1998 Subpixel Land Use Classification and Retneval of forest Stem Volume in the
Boreal Forest Zone by Ernploying SSMA Data Remote Sensing of Environment. 63: 140-154.
Gross, Hany N. and John R. Schotî 1998 Application of Spectral Mixture Analysis and Image fusion Techniques for Image
S harpening. Remore S e m g of Environ~ent. 63 : 85-94.
Gupta Ravi P. 1 99 1 Remote Sensing Geology. Berlin: S pringer - Verlag.
Horkheimer, Hans 1958 Algunas Consideraciones Acerca de la Arqueologia en el Valle del Utcubamba
Acres y Trubajos del II Congreso Nacioml de Historia del P e d 1 : 7 1 - 10 1 .
Huguenin, Robert L., Mark A. Karaska, Donald Van Blericom, and John R. Jensen 1997 Subpixel Classification of Bdd Cypress and Tupelo Gum Trees in Thematic
Mapper hagery. Pho1ogrc1rnrnettic Engineering & Remoie Sensing. 63(6): 7 17- 725.
M o l 4 Tim (ed.) 1996 Key Debates in Anthropology. London: Routledge.
IDRISI 1 997 IDRISI for Windows User k Guide. Worcester M A : Clark University. Institut0 Geologico Minero y Metalurgico 1995 I N G E M T J63I Primera Edicihn 1995. Lima: Ministerio de Energia y Minas.
Jasinski, Michael F. 1 996 Estimation of Subpixel Vegetation Density of Natural Regions Using Satellite
Multispectral Imagery. IEEE Trans~ctiom on Ceoscience and Rernote Sensing. 34(3): 804-8 13.
Johns Hopkins University 1 998 Ftp site for spectral signaîures. [[ttpY/roc ky.eps.j hu eddpub4.
Kauffinann Doig, Fedenco 1990 Historia del Pem: Una Nueva Perspectiva Lima: Pacific Press SA.
Lake, M. W. , P. E. WOOdman and S. J. Mithen 1998 Taloring GIS Software for Archaeological Applications: An Example Concerning
Viewshed Analysis. Journal of Archaeological Science. 25: 27-38.
Lerche, Peter 1986 Hiàuptlingshun Jalca Bevolkerung und Ressourcen bei den vorspanischen
Chachupoyu, Peru Berlin: Dietrich Reimer Verlag.
Madry, Scott L. and Carole L. Cnunley 1990 An application of remote sensing and GIS in a regional archaeological setîlement
pattern anaiysis: the Arrow River valley, Burgundy, France. IN Kathleen M- S. AIlen, Stanton W. Green and Ezra B. W. Zubrow (eds.). hterpreting Space: GIS and Archoeology. London: Taylor & Francis.
Moseley, M. 1992 The Incas and Thezr Ancestors. London: Thames and Hudson.
Murra, John V. 1956 The economic organizaiion of the lnka stote. PhD. dissertation. Chicago :
University of Chicago.
Narvaez, A. 1988 Kuelap: Una ciudad fortificada en los Andes nor-onentales de Amazonas, Pem IN
Arquirectwa y Arqueo!ogia. Chiclayo: COYNTEC.
Radarsat 1 997 RADARSA T officia[ WWW Servez bttpY/radarsat space.gc.ca] 5 November, 1 997.
Reichien, Henri and Paule Reichlen 1950 Recherches Archéologiques dans les Andes du Haut Utcubamba. J o u d de fa
Société des Américanisfes. 2 19-246.
Richards, John A 1993 Remote Seming Digital Imge Analysxs: An Introduction. 2" ed Berlin: Springer -
Verlag.
Riley, D. N. 1987 Air Photography and Archneof~gy~ London: Duckworth.
Rivero, Mariano Edward and John James von Tschudi 1 854( 197 1 ) Pemian Antiquities. New York: AS. Barnes & Company. reprint: New
York: Kraus Reprint Co.
Rowe, John 1944 An Introduction to the archaeology of Cuzco. Papers of the Peabdy Museum of
American Archaeology and Ethnology. vol. XXW no. 2. Cambridge: Harvard University Press.
Ruiz Esîrada, Arturo 1 972 La AIfreriu de Cuelap. Tradition y Cambio. Tesis d e Bachil ler. Lima:
Universidad Nacional Mayor de San Marcos.
Saiomon, Frank 1986 Nutive Lords of Quito in the Age of the Incas: The political economy of north
Andean chiefdoms. Cambridge: Cambridge University Press.
Savoy, Gene 1970 Antisuyo: The seorch for the Lost Cities of the Andes. New York: Simon and
Schuster.
Schjellerup, Inge 1984 Cochabamba - An Lncaic adminimative centre in the rebellious province of
Chachapoyas. IN A. Kendall (ed.), Currenf ArchaeologicaI projects in the Central Andes. 4-P Internationcrl Congress of Americanists. Pmceedings. p. 1 6 1 - 1 87.
1997 Incas and Spaniurris in the Conquest of the Chachopoyas. GûTARC Series B. Goteborg Archaeological Theses No. 7. Goteborg: Goteborg University Press.
Sharon, Douglas 1994 VIRA VIRA in Andean Context. IN Keith Muscutt, Vincent R Lee and Douglas
Sharon, Vira Vira: A "New " Chachpoym Site. Wilson, woming: Sixpac Manco Productions.
Sherman, Stephen 1988 QuantzfLing Archaedogy. Edinburgh: Edinburgh University Press.
Showalter, Pamela Sands 1993 Thematic Mapper Analysis of the Prehistonc Hohokam Canal System, Phoeni~
Arizona. JO& of Field Archaeoi~gy~ 20: 77-90.
Taylor, C.C. 1975 Aerial photography and the field archaeologist. IN D R Wilson (ed) Aerial
reconnaissance for archaeohgy London: Duckworth.
Thomas, Nicholas 1 996 Out of Time: HLrroty and Evoluf ion in Anthropological Discocourse. Id ed AM
Arbor: University of Michigan Press.
Thompson, D. 1984 Ancient Highland Connections with Selva and Coast: Evidence fiom Uchucmarca,
Peru. IN: D. Browman, R. Burger, and M-Rivera (eds. ) Socid and Economic Organi=afion in the Prehisiponic Andes. Oxford: BAR International Series. p.73-78.
Toutin, Thierry 1998 SPOT and Landsat Stereo Fusion for Data Extraction over Mountainous Areas.
Photogrammetric Engineering & Remote Seming. 64(2): 109- 1 1 3.
Vachon, P. W., J. W. M. Campbell, C. A. Bjerkelund, F. W. Dobson, M. T. Rey 1997 Ship Detection by the RADARSAT SAR: Validation of Detection Mode1
Predictions. Canadian Journal of Remote Sensing. 23( 1 ): 48-59.
Vincent, Robert K, 1997 Fundantentds of Geological and Environmental Remote Sensing. New krjey:
Prentice Ml.
Verbyla, David L. 1 995 SateIIire Remote Seming of Nalural Resources. Boca Raton: CRC Press.
von Hagen, Adnana and Sonia Guillen 1998 Tombs with a View. Archneolqp. (March/Apnl): 48-54.
Wdd, Lucine, Thieny Ranchin and Marc Mangolini 1997 Fusion of Satellite Images of Different Spatial Resolutions: Assessing the Quality of
Resulting Images. Photogr~rnmerric Engineering & Remore Senszng. 63(6): 69 1 - 699.
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
Recommended