The Remote Sensing of Ancestral Puebloan Sites
by
David Eugene Witt
February 1, 2010
A project submitted to the
Faculty of the Graduate School of
the State University of New York at Buffalo
in partial fulfillment of the requirements for the
degree of
Master of Arts
Department of Geography
Copyright by
David Eugene Witt
2010
ii
Dedication
To Mom & Dad.
Acknowledgements
I would like to thank my advisor, Dr. Sean Bennett, and my second reader, Dr. Le Wang,
for their unfailing patience and flexibility. I would also like to thank Linda Wheelburger,
principal investigator of the Totah Archaeological Project, for her aid in obtaining
archaeological site locations within the Middle San Juan River region.
iii
Table of Contents
Dedication & Acknowledgements
iii
Table of Figures
vi
Abstract
vii
Introduction
1
Objectives
3
Geographical & Environmental Overview of the Colorado Plateau
5
The Modern Geography & Environment
5
The Historical Environment
7
Historical Overview of the Middle San Juan Region
11
Archaic (pre-1200 BC)
12
Basketmaker II (1200 BC – AD 500)
12
Basketmaker III (AD 500 – AD 750)
14
Pueblo I (AD 750 – AD 900)
14
Pueblo II (AD 900 – AD 1150)
15
Pueblo III (AD 1150 – AD 1350)
16
Pueblo IV (AD 1350 – AD 1600)
17
Historical Review of Remote Sensing Techniques
18
Aerial Photography
18
Electric Resistivity & Electromagnetic Surveying
21
Magnetometry
23
Ground Penetrating Radar & Sonar
24
iv
Analytical Chemistry Techniques
25
Satellite Photography
27
Application of Remote Sensing Techniques
to the Archaeology of the Southwest
Literature Review
31
33
―The Use of ASTER Satellite Imagery in Archaeological Contexts‖
33
―Classification of Multispectral ASTER Imagery in
Archaeological Settlement Survey in the Near East‖
35
―Archaeological Site Detection: The Importance of Contrast‖
36
Discussion
37
Methodology
39
Results & Conclusion
48
Works Cited
49
v
Table of Figures
Figure 1: Map of Project Area
2
Figure 2: Map of Colorado Plateau
6
Figure 3: ASTER Image of Project Area, Bands 1, 2, 3
41
Figure 4: Eigenvalue Graphs, Principal Component Analyses
on VNIR and SWIR
42
Figure 5: PCA Image, VNIR Bands 2, 3, SWIR Band 1
43
Figure 6: Unsupervised Isodata Reclassification
44
Figure 7: Location of Archaeological Sites
45
Figure 8: Maximum Likelihood Reclassification, on
Principal Component Analysis of NVIR Bands
vi
47
Abstract
This paper is the result of a master‗s project to incorporate the use of imagery
obtained by the ASTER space-borne instrument to aid in the detection of surface
archaeological sites along the San Juan River of northwestern New Mexico and the
surrounding area within the Four Corners region of the American Southwest. The
methodologies used various unsupervised (IsoData) and supervised (maximum likelihood,
minimum distance, and spectral information divergence) classification methods. These
methods were attempted, and subsequently compared, to determine if any method, or a
combination of the methods, would provide more accurate results within the project area.
If these methods determined any potential archaeological site locations within the
study area, these locations would have been communicated to Linda Wheelburger, principal
investigator of the Totah Archaeological Project, for field identification. Unfortunately, these
methods did not result any in usable data, and no potential site locations were identified.
vii
I.
Introduction
Archaeology is concerned with the detection, recovery, and conservation of artifacts and
features, material remains of past cultures which aid us in understanding the nature of those past
cultures. Artifacts are portable, such as fragments of pottery or glass, or stone tools. On the other
hand, features are non-portable, such as walls, building foundations, and alterations of the
ground and soil. Traditionally, the detection of these materials has been accomplished through
methods such as field walking, surveying, and random luck, but remote sensing techniques have
been increasingly used by archaeologists as a method to focus their efforts by identifying areas
with a higher likelihood of containing artifacts or by identifying features in the soil before
surveying or excavation has even begun. These techniques involve the incorporation of a variety
of near-earth, aerial-borne, and space-borne instruments.
This paper is the result of a master‘s project to incorporate the use of imagery obtained by
the ASTER space-borne instrument to aid in the detection of surface archaeological sites along
the San Juan River of northwestern New Mexico and the surrounding area within the Four
Corners region of the American Southwest. As such, it will first address the objectives of the
project. Afterwards, it will provide a brief geographical, environmental, and historical overview
of the study area, and then a review of the application of remote sensing techniques for
archaeological purposes. A more detailed literature review of selected case examples will follow,
proceeded by a discussion of the project methodology and results. Finally, a concluding
discussion and remarks will be addressed.
The study area of the project is located in the Middle San Juan Region and is centered on
the city of Farmington, New Mexico, located at 36° 45‘ 6‖ North Latitude and 108° 11‘ 23‖
West Longitude. This is located within the Colorado Plateau, in Universal Transverse Mercator
1
(UTM) Zone 12N, with a northing value of approximately 4,071,000 meters and an easting value
of approximately 750,900 meters from the southwest corner of the zone. The datum used during
the study was North American Datum 1927 (NAD83). This is because the archaeological sites
were recorded using the North America Datum 1927 (NAD27) as is required by New Mexican
state law. These differing datums were taken into account during the project undertaking.
The majority of the sites already known in the project area date to the Pueblo II to Pueblo
III periods (AD 900 – AD 1350), though sites dating to previous and later periods have been
discovered. Due to the dates of the known sites in the project area, any sites discovered by the
project will be assumed to date from the same periods and have the same cultural affinity. This
assumption, however, will need to be tested by future field work.
2
II.
Objectives
The primary objective of the project was to develop and test a regionalized methodology
for employing remotely sensed data in the form of ASTER imagery for the classification and
detection of archaeological sites throughout the project area. This was accomplished through the
use of two computer software suites, ITT Visual Information Solutions‘ ENVI 4.4 and
Environmental Systems Research Institute‘s (ESRI) ArcGIS 9.3. ENVI 4.4 was used to conduct
various analyses and post-processing techniques upon multispectral images, whereas ArcGIS 9.3
was used to manipulate and synthesize geographic data to highlight areas of interest which might
contain archaeological sites.
The methodologies incorporated include various unsupervised (IsoData) and supervised
(maximum likelihood, minimum distance, and spectral information divergence) classification
methods. These methods were attempted, and subsequently compared, to determine if any
method, or a combination of the methods, would provide more accurate results within the project
area. The methods were initially developed by several computer scientists and archaeologists,
and the development of these methods will be discussed in depth in Chapter VII. Unfortunately,
the methodologies adapted from these papers were not successful for this project.
Secondary objectives of this project were to produce a list of potential archaeological site
locations within the project area for comparison and incorporation with the records of the San
Juan Museum based at Salmon Ruins, New Mexico and the Totah Archaeological Project of San
Juan College. These sites were then to be investigated in the field to determine if the
methodology had correctly identified the sites. However, due to the unsuccessful nature of the
project, no sites were determined from analyzing the ASTER imagery and therefore it is
impossible to create this list. Also, the results of this project were to be delivered, as a conference
3
poster, at the 75th Annual Meeting of the Society for American Archaeology, to be held April
14th to April 18th, 2010. These results are still planned to be delivered, and it is hoped that future
work will refine a methodology that may help determine site location.
Finally, because no new sites were determined, it was deemed unnecessary to provide the
precise location of previously known archaeological sites. The presence and locations of these
sites are considered privileged and confidential information.
4
III.
Geographical & Environmental Overview of the Colorado Plateau
This chapter provides a brief overview of the geography and environment (modern and
historical) of the Colorado Plateau and the Middle San Juan Region. This is to familiarize the
reader with the geographical conditions in which the project was set, and because the geography
and environmental conditions influenced the development of the various cultures and the
settlement patterns investigated by the project. Sources of water and arable land were, and still
are, the most important resources available in the project area, and it is believed that any sites
discovered by this project will be located near the former locations of those two resources. The
historical environment as reconstructed will be discussed with evidence obtained from
archaeological research conducted throughout the region.
A. The Modern Geography & Environment
The Colorado Plateau is a physiographic region within the intermontane plateaus of the
southern Rockies, covering an area of approximately 337,000 square kilometers (130,000 square
miles) in western Colorado, Utah, northern Arizona, and northwestern New Mexico. It is
bounded on the northeast by Rocky Mountains in Colorado, and on the northwest by the Uinta
and Wasatch Mountains in Utah. The southeast edge is demarcated by the Rio Grande Rift, and
the southern by the Mogollon Rim. About 90% of the Plateau is drained by the Colorado River
and its tributaries, such as the Muddy, Virgin, San Juan, and Little Colorado Rivers (Larson &
Michaelson 1990: 229).
5
The modern environment of the Colorado Plateau is primarily one of strong zonation,
which is the division of the landscape into various microclimates based on elevation,
temperature, and precipitation. These different vegetation zones are the result of the presence of
microclimates, with the primary factor being elevation, but the degree of slope and exposure to
prevalent winds and sunshine are also factors. There are six zones, determined primarily by
vegetation: sage desert, short grass, pinyon-juniper woodlands, pine parkland, mixed coniferous
forests, and spruce-fir forests (Hevly 1988: 93).
Elevation can range from 150 meters to 3500 meters, and the effect of elevation is such
that with each 333 meter (1000 foot) increase in elevation, temperature is decreased by 2.5
degrees Celsius (Larson & Michaelson 1990: 229; Hevly 1988: 93). Along with the decrease in
temperature, precipitation increases, with the distribution of precipitation influenced by
6
exposure, winds, and the presence of mountains, which creates rain shadows (Hevly 1988: 93).
Due to the combination of these factors, it is hard to determine exactly which factors influence
growth patterns, and to what extent they influence the patterns.
The area is generally hot and arid, however considerable variability based on spatial,
annual, and seasonal diversity exists within the region (Dean 1988: 122). The region is known to
experience extreme cold, and temperatures below -18 degrees Celsius (0 degrees Fahrenheit) are
occasionally the result of cold air masses moving south from Canada during the winter
(Shepherd et al. 1999). Rainfall occurs during two seasons, July to September and December to
March, and most of the average annual rainfall of 322 millimeters (12.7 inches) occurs during
these two periods (Shepherd et al. 1999). The remaining months comprise two dry seasons, with
the dryer climate occurring during the April to June season (Hevly 1988: 95). The seasonal
nature of rainfall in the region is primarily due to the region‘s location between two sources of
atmospheric moisture, the Gulf of Mexico to the east and the Pacific Ocean to the west. The
thunderstorms that occur during the summer months are a result of air currents from the two
bodies interacting with dryer air over the continent (Dean 1988: 122). The winter rains, however,
originate from large frontal storms that originate from the northern Pacific Ocean and move
eastward over the continent. A third, rarer source of rainfall is hurricanes which affect the
western coast of Mexico, forcing warm moist air over the Colorado Plateau. The result of these
rain patterns is the unique bimodal precipitation pattern with two rainy seasons and two dry
seasons (Dean 1988: 123).
B. The Historical Environment
The regional geography has remained relatively stable over the past several millennia;
however, one major exception was a series of volcanic eruptions located north of Flagstaff,
7
Arizona. These eruptions, occurring between AD 1080 and AD 1150, built the 340 meter tall
volcanic cone now known as Sunset Crater. While the eruptions did have an effect on the local
Sinagua population, it is not known to have effected other populations in the Southwest (Berlin
et al. 1990).
The past environmental differed greatly, though. While the climate experienced the same
bimodal precipitation pattern previously discussed, the environmental history of the region
indicates shits from wetter to dryer climates over the past several thousand years. The late
Archaic/pre-Ancestral Puebloan period (approximately 1800 BC to 250 BC) was characterized
by a climate slightly dryer than the modern climate (Hall 1988: 587). As such, this corresponds
to an increase in relative levels of grassy, non-arboreal pollen levels throughout lower elevations
in the San Juan Basin and elsewhere in the Colorado Plateau. An absence of pinyon and juniper
in charcoal samples from the lower elevations during this time period likely illustrates an
absence of those species from the area, as they were both considered excellent fuel sources and
unlikely to be overlooked (Hall 1988: 588).
Beginning around 250 BC, pinyon-juniper pollen levels increased to one-third that of
modern levels, indicating a cooler, wetter environment (Hall 1988: 588). Pollen obtained from
rodent middens throughout Chaco Canyon illustrate that pinyon pine numbers increased from
AD 1 to AD 200 in that area to twice the modern levels, which would have been the result of a
change in climate to a wetter environment in that particular area (Hall 1988: 588). Nevertheless,
the increase of arboreal pollen in samples taken from throughout the Colorado Plateau indicates a
shift in the climate throughout the region.
Pollen records dating from AD 600 to AD 1000 indicate a shift back to dryer conditions
throughout the Colorado Plateau, with samples originating from Chaco Canyon, southwest
8
Colorado, and the Virgin River in Utah (Hall 1988: 588, Larson & Michaelson 1990: 237,
Kohler & Matthews 1988: 552). This dry spell continued until AD 800 when it intensified,
peaked around AD 900, and finally ended circa AD 1000 (Kohler & Matthews 1988: 552).
Throughout the plateau, pollen levels indicated a shift back to non-arboreal species, and the
environment shifted to desert shrub grassland similar to the modern environment (Hall 1988:
588). This shift to dryer conditions would have influenced the location and growth of
communities, which would have been located near larger rivers and other perennial sources of
water.
After AD 1000, moister conditions developed, as evidenced by growth rings, and this
settlement pattern reversed (Hall 1988: 590). The conditions resulted with an increase in
groundwater supplies, declining erosion (especially erosion of agricultural land), and increased
precipitation (Gumerman & Gell-Mann 1994: 21). These, in turn, influenced ―the development
of new traditions and the fluorescence of already established ones,‖ population increases
throughout the Four Corners region, the development of large regional systems such as the
Chacoan system and the establishment of permanent settlements in areas previously used only
seasonally (Gumerman & Gell-Mann 1994: 21). Again, a drought developed in AD 1130 and
lasted for 50 years, as illustrated by dendroclimatology. This drought is thought to have been a
major contributing factor to the decline and collapse of the Chacoan system circa AD 1150
(Fagan 2000: 328-329).
A return to moister conditions allowed for the resurgence of settlement construction
throughout the Middle San Juan Region, but again this was not a permanent situation. A ―Great
Drought‖ began circa AD 1275 and continued for 25 years. This placed enormous amounts of
environmental stress on the existing populations. Once again, population centers throughout the
9
region experienced decline; in an extreme example the Entire San Juan Basin, including Mesa
Verde on its northern periphery, was abandoned (Fagan 2000: 338). Circa AD 1350, which
corresponds to the end of the Pueblo III period and the traditional date of the Ancestral Puebloan
collapse, pinyon-juniper pollen levels increased in Chaco Canyon and along the San Juan River,
as well at higher elevations such as on Chacra Mesa. These probably indicate reestablishment of
pinyon-juniper forests following the Ancestral Puebloan collapse (Hall 1988: 589). These forests
were descendent from small isolated stands, perhaps located along cliff walls (Hall 1988: 591).
These stands served as sources of timber and fuel for the Ancestral Puebloans, and the
reestablishment of these species indicate a complete and prolong abandonment of the areas. (Hall
1988: 591).
10
IV.
Historical Overview of the Middle San Juan Region
The cultural history of the Southwest, and the Middle San Juan Region specifically, is a
long and complicated affair; while only a few cultures evolved in the area (this project focuses
on one), numerous phases of cultural growth and realignments occurred. As such, a full
recounting of the cultural history of the region is outside the scale of this project, and this chapter
serves only as a brief introduction to the history of the region to allow the reader to understand
terms used throughout the project report. It is arranged chronologically, and divided according to
the traditional Pecos Classification scheme generally used throughout the Ancestral Puebloan
area. Due to the imprecise nature of the Pecos Classification, the dates should be regarded as
approximations.
Currently known archaeological sites already located within the project area are limited
to the Basketmaker III to Pueblo III periods (AD 500 – AD 1350), but it is possible that sites
from the Archaic to the Pueblo IV periods may be discovered by this project. Because of this, the
chapter will discuss the history of the region from the Archaic to the Pueblo IV (circa 1200 BC
to AD 1600).
Table 1: Pecos Classification
Archaic Period
Pre 1200 BC
Basketmaker II Period
1200 BC – AD 500
Basketmaker III Period
AD 500 – AD 750
Pueblo I Period
AD 750 – AD 900
Pueblo II Period
AD 900 – AD 1150
Pueblo III Period
AD 1150 – AD 1350
Pueblo IV Period
AD 1350 – AD 1600
11
A. Archaic (pre-1200 BC)
The American Southwest has been inhabited since the Archaic period (also known as the
Paleoindian period), and many well-known sites, such as Blackwater Draw at Clovis, New
Mexico (dating to 11,000 BC) and Folsom, New Mexico (dating to 9,000 BC) are located in the
region. However, despite the long history of human occupation in the area, the predominate
lifestyle of the inhabitants remained one of mobility and dispersion, with sustenance obtained by
hunting and gathering activities until circa 1500 BC (Gumerman & Gell-Mann 1994: 17). It was
approximately this time that maize horticulture was introduced in the region (most likely
imported from Mesoamerican contacts to the south, but possibly independently developed), and
while it would take several thousand years for agriculture to become the dominant source of food
in the region, the introduction of horticultural activities altered the cultural trajectory of the
people of the region (Gumerman & Gell-Mann 1994: 17). The development of horticulture
precipitated a slow move away from a mobile lifestyle to one of sedentism based upon the
control of geographical areas and their agricultural resources, with this sedentism resulted in the
growth of population centers in the region, primarily in the Rio Grande Valley and in the
southern Arizona desert (Gumerman & Gell-Mann 1994: 17). However, despite a relationship
between small population groups and local resources, archaeological evidence indicates that
artifact styles were similar throughout the greater Southwest region throughout this time.
B. Basketmaker II (1200 BC – AD 500)
It was not until circa AD 200 that the separate population concentrations began to diverge
into the three classic Southwestern traditions, the Hohokom (in southern and central Arizona),
the Mogollon (in the highlands of eastern Arizona and western New Mexico), and the Ancestral
12
Puebloan culture colloquially known as the Anasazi, located in the San Juan Basin and in the
immediate Four Corners area and west along the Arizona-Utah border into southern Nevada
(Gumerman & Gell-Mann 1994: 17). These traditions were derived from the diverging artifact
and architectural styles, and to a degree settlement pattern and location, rather than a specific set
of cultural behavior related to a specific cultural tradition (Gumerman & Gell-Mann 1994: 17).
However, the continued growth of the importance of agriculture as a food source independent of
hunting or gathering activities resulted in a loss of mobility and a degree of sedentism that was
unknown among adjacent areas such as the Great Basin and the Great Plains (Gumerman & GellMann 1994: 17). This dependence on agriculture even grew to include beans and squash, which,
when included with maize as part of a normal diet, contained all the amino acids and protein
necessary to maintain a healthy diet (Fagan 2000: 308).
Throughout the Ancestral Puebloan area, population continued to grow into the Sixth
Century AD. Seasonal communities consisting of pithouses (semi-subterranean dwellings) and
underground storage rooms were founded throughout the southern Colorado plateaus. The
development of these communities indicated a greater investment in sedentism and agriculture,
and the indigenous invention of several technologies, including the bow & arrow, the twohanded mano and the trough metate, ceramic pottery, and the domestication of the turkey are
evidence of the continued growth in the importance of local agricultural activities rather than
hunting and gathering techniques (Gumerman & Gell-Mann 1994: 18). This development in
community structure was termed the ―Basketmaker II‖ culture (―Basketmaker‖ because the
culture was one of the first to develop basketry, and ―II‖ because archaeologists at the time had
erroneously predicted a ―simpler‖ sedentary culture that made baskets but had not yet developed
pottery).
13
C. Basketmaker III (AD 500 – AD 750)
From about AD 500 to AD 750, the Basketmaker II culture continued to evolve, and the
artifacts and features associated with the cultural tradition took on stylistic attributes that would
later be identified as early Ancestral Puebloan (Gumerman & Gell-Mann 1994: 19). This was
termed Basketmaker III, and included new architectural features such as Great Kivas,
manifestations of inter- or intra-community ritual interaction (Gumerman & Gell-Mann 1994:
19). These Great Kivas developed architecturally from the pithouse, and similarly were semisubterranean structures (Fagan 2000: 320). Another architectural development was the
construction of adjacent, above-ground dwellings and storage rooms, resulting in the creation of
pueblos, the archetypal form for which the people were named (Gumerman & Gell-Mann 1994:
19). These architectural forms were standardized across the Ancestral Puebloan culture area,
indicating a degree of social connectivity throughout the area. However, this does not indicate
any degree of social control throughout the area (Gumerman & Gell-Mann 1994: 20).
D. Pueblo I (AD 750 – AD 900)
The developments of the Basketmaker III period continued into the Pueblo I period, and
there is some difficulty distinguishing between the two. Previously, archaeologists separated
Basketmaker III and Pueblo I on the basis of the presence of pueblos and the absence of
pithouses, but it is now apparent that both architectural forms were used simultaneously in the
same settlements, so the distinction between the two is false. The recognizable features of the
Pueblo I period are a continued growth of population and a growth of complexity in agricultural
and irrigation projects (Cordell 1997, 280; Fagan 2000: 318). Architecturally, communities
continued to use traditional forms such as pithouses, but above ground pueblos were gradually
14
replacing the pithouses (Fagan 2000: 317). Unlike before, Pueblo I communities were perennial
establishments, dependent upon agriculture to feed the population year round (Fagan 2000: 310).
E. Pueblo II (AD 900 – AD 1150)
Beginning circa AD 900, the Pueblo II period witnessed an explosion of population
growth through the Ancestral Puebloan area. By AD 1000, the Ancestral Puebloans had reached
their greatest territorial extent, occupying ―virtually every conceivable spot‖ (Cordell 1997: 280).
This coincided with a considerable increase in social complexity, with evidence of regional
hierarchy and region-wide cultural systems and trade patterns, namely that of the Chacoan
system, centered in Chaco Canyon, New Mexico (Gumerman & Gell-Mann 1994: 20-21). This
complexity even spun off smaller, daughter cultures such as the Kayenta, Virgin, and Winslow
branches of the Ancestral Puebloans (Cordell 1997: 280).
The Chacoan system evolved from a need to manage the scarce natural resources of the
San Juan Basin and is now evidenced by sophisticated small-scale irrigation works, especially in
Chaco Canyon, and the planning of large pueblos, both within Chaco Canyon, and 70 satellite
communities named ―outliers‖ scattered throughout northwest New Mexico and southwest
Colorado (Fagan 2000: 324). The nature and degree of the Chaco system, however, is a subject
of considerable debate within American archaeology, but the fact that the Chacoan system was a
substantially new phenomenon is not debated. As Gumerman & Gell-Mann state:
―The geographic extent of the large systems and of their exchange networks—and in the
case of Chaco the obvious central planning of large towns—indicates a level of
information sharing that required a high degree of coordination. The extensive regional
systems were not simply aggregations of smaller local traditions but were qualitatively
different in organization‖ (1994: 21).
The last century and a half of the Pueblo II period saw the contemporaneous expression
of many different cultural forms, but this in itself did not increase the amount of variation
15
expressed in the region. For example, what was once the Mogollon culture in the highlands of
the central Southwest lost its distinctiveness and began expressing Ancestral Puebloan traits
(Gumerman & Gell-Mann 1994: 22). At the end of this period, a severe drought developed in
AD 1130 throughout the Colorado Plateau and despite the expansion of population and culture
throughout the region, several cultures experienced decline or even collapse (Fagan 2000: 328).
F. Pueblo III (AD 1150 – AD 1350)
The start of the Pueblo III period circa AD 1150 saw striking population movement and
sociocultural realignment throughout the Southwest (Cordell 1997: 365-397). Within the
Ancestral Puebloan area, the Virgin Branch area in southern Nevada and Utah experienced a
population collapse and the Chacoan system as a whole experienced a political collapse
(Gumerman & Gell-Mann 1994: 22). Throughout the Ancestral Puebloan region, sites were more
clustered, indicating territorial reduction and in some places a decrease in organization
complexity (Gumerman & Gell-Mann 1994: 22). However, even after the collapse of the
Chacoan system, Chaco Canyon was not completely abandoned and some construction
continued, although in architectural forms apparently imported from the Mesa Verde to the
north. Chacoan outliers, especially those located in the Middle San Juan Region, continued to
function as community centers, but exhibited Mesa Verdean influence rather than Chacoan
(Fagan 2000: 329). The Mesa Verde and Sinagua (located near Flagstaff, Arizona) regions
experienced a growth in population, likely due to migration from other areas of the Southwest.
This population growth was accompanied by an increase in construction efforts, and there is
artifactual evidence for an increase in interaction with other cultural areas (Gumerman & GellMann 1994: 23).
16
These changes were likely a result of severe environmental stresses introduced by the
Great Drought that occurred during the final quarter of the Twelfth Century AD. However, by
AD 1250 much of the population growth experienced by larger sites was reversed, and there was
again a period of large scale regional abandonment, resulting with the collapse of sites
throughout Mesa Verde, the Middle San Juan region, and Sinagua regions which were
thoroughly emptied (Fagan 2000: 338). Much of the Ancestral Puebloan population relocated to
areas now occupied by the modern Puebloan groups, and some even left the Southwest, moving
to sites such as Casas Grandes in Chihuahua and Trincheras in Sonora (Gumerman & Gell-Mann
1994: 23).
G. Pueblo IV (AD 1350 – AD 1600)
The Pueblo IV period was a time of massive realignment after the collapse of the
Chacoan system. Population movement depleted formerly densely populated areas of the
Southwest. As stated before, areas which enjoyed a greater supply of summer rains and perennial
rivers, such as the Phoenix Basin, the Rio Grande Valley, the Zuni region of west-central New
Mexico, and neighboring areas to the south absorbed the population (Fagan 2000: 343).
Simultaneously, populations aggregated from smaller villages to large communities, some of
which were larger than the largest pueblos of earlier times (Fagan 2000: 344). It was the
establishment of these larger settlements that led to the development of the modern Puebloan
cultures, and some of these settlements lasted for hundreds of years (the Hopi pueblo of Oraibi,
Arizona was founded sometime before AD 1100 and is the longest continuously occupied
settlement in the United States) despite impending contact with Europeans and the introduction
of foreign pathogens and technology (Fagan 2000: 345).
17
V.
Historical Review of Remote Sensing Techniques
The history of remote sensing for archaeological purposes extends several centuries, and
the principles of remote sensing have been correctly understood just as long. Because of the
time-sensitive nature of archaeology, remote sensing techniques, which promise the delivery of
large amounts of data in little time, have been adapted from other disciplines to great success.
This has continued with the homegrown development of other analytical techniques, such as
phosphates analysis, for use by archaeologists in their own research.
This section will trace the development and evolution of remote sensing techniques from
their beginnings to their modern forms and applications. However, it will focus primarily on
aerial and satellite photography, as it is those techniques that have been used most successfully
for archaeological surveying and because those techniques are employed in this study. The four
geophysical techniques (electric resistivity surveying, electromagnetic surveying, magnetometry
surveying, and ground penetrating radar surveying) and analytical chemistry techniques will
each be discussed briefly.
A. Aerial Photography
In the sixteenth century, the English antiquarian William Camden discussed the
occurrence of cropmarks in grain fields. Cropmarks are patterns of differential growth resulting
from underlying conditions of the soil, and Camden correctly reasoned that the intersecting
patterns of low crop growth (colloquially known as St. Augustine‘s Crosses) found in fields
throughout England were the result of the presence of ancient roads within Roman sites buried
underneath (Parrington 1983: 105). These former roads negatively influenced the growth of
plants, and the resulting patterns reflected the grid nature of Roman era streets. Though his
interpretation was common knowledge among antiquarians of that age, the knowledge of the
18
relationship between underlying archaeological features and overlying crop growth was not put
to practical use until the early 18th century. At that time, another antiquarian, William Stukely,
created the first archaeological plan of a cropmark, which he observed through a second story
window of an alehouse (Parrington 1983: 105). This cropmark was formed from the presence of
the buried foundations of a Roman era temple, and Stukely was able to accurately map the
temple foundations without actual excavation. At this point, it was understood that cropmarks
could convey much information concerning archaeological sites, but the utility of mapping
cropmarks was limited to those areas where a difference of elevation could allow access to an
overhead view of the cropmarks.
During World War I, it was quickly realized that the newly invented airplane could be
used as a platform to observe these cropmarks. Archaeological sites were noticed by pilots
during the war, but it was during the post-war period that aerial photography of archaeological
sites was first accomplished in large scale, as pilots who overflew the sites during the war
returned to satiate curiosity (Parcek 2009: 14). Pioneering this development was the English
archaeologist O. G. S. Crawford, who overflew much of southern England after the war (Parcek
2009: 15; Parrington 1983: 107). He and his partner, Alexander Keiller, eventually published the
illustrative and interpretive work Wessex from the Air in 1928. Likewise, in 1929 Charles
Lindbergh overflew and catalogued Pueblo sites in the American Southwest and Mayan sites
throughout Central America (Deuel 1973). As a result of these early efforts, aerial photography
was recognized as a valuable tool for archaeological prospecting and has been used throughout
western Europe and the United States (Parrington 1983: 108).
Aerial photography and its associated techniques have developed over the past ninety
years. Black and white photography revealed the same crop mark features that were visible from
19
high windows, while color photography could reveal slight differences in the color tone of
plants which could indicate the presence of subsurface features affecting the growth of plants,
but both of these were limited to recording the phenomena that were visible to humans (because
human beings had to first observe the phenomena before photographing it, and also because of
the technical limitations of the photographic film at the time). However, the development and
widespread availability of infrared photography in the 1930s allowed for the discovery of
invisible soil patterns and the differential release of residual heat due to alterations in the soil
and bedrock (Berlin et al. 1990).
Despite the utility of aerial photography, only four classes of archaeological features can
be recorded by such techniques: cropmarks, soilmarks, shadowmarks, and snowmarks
(Parrington 1983: 108, Scollar et al. 1990: 33-58). The first two are relatively permanent
features of the soil, whereas the last two are temporary phenomena that can be detecting and
subsequently used to locate buried archaeological features. As discussed previously, cropmarks
are areas of differential growth created by underlying archaeological features. The marks may
be either a decreased growth rate or an increased growth rate: better growth is the result of
plants growing in an area where the archaeological artifacts and features contribute nutrients or
trap moisture to aid plant growth. Conversely, decreased growth is a result of plants being
located over features or artifacts that stunt growth, such as stone foundations and walls that limit
root growth and therefore limit plant development (Scollar et al. 1990: 50-52). Soilmarks are
areas of differing soil color; these differences can be either in the visible spectrum or in portions
of the spectrum outside the human visible range. They can be observed in plowed fields before
plant growth obscures such differences, or in other areas of bare soil such as deserts, where
differences in soil color can be detected by a number of above-ground surveying techniques
20
(Altaweel 2005). Shadowmarks are temporary patterns detectable only in the early morning and
late evening, when the sun is low to the horizon. The position of the sun low on the horizon
results in exaggerated shadows. Low earthworks, which normally would not be visible to the
eye, might cast these shadows that highlight their location (Parrington 1983: 108). Finally,
snowmarks are the result of deferential melting of light snow, whether it is caused by
differences in heat retention by the underlying soil features or by a process similar to
shadowmarks where the snow is protected from the sun‘s rays by slight changes in elevation due
to underlying features (Scollar et al. 1990: 49). The presence of snow might highlight these
slight changes in elevation for short periods, and while positive results have been achieved from
the use of this method, it is by definition ephemeral and lack a consistent correlation to
underlying archaeological features (Parrington 1983: 108).
B. Electric Resistivity & Electromagnetic Surveying
Aerial photography remained the only form of remote sensing available for archaeology
until 1946 when resistivity surveying was developed by Atkinson (Scollar et al. 1990: 307, 371).
This relatively simple and inexpensive method measures the electrical conductivity of the soil,
hopefully illustrating the presence of non-conductive elements which could be archaeological
features. The technique involves the deployment of a pair of electrodes into the ground, and an
electrical charge is passed from one electrode to the other through the soil. The resistance of the
earth to the electrical charge is measured, and differences in resistance indicate differences in
the soil, which may or may not correspond to archaeological features. Parrington (1983) states
that the method works best in damp soil, and is most successful in locating buried stone walls
(as the electrical charge can simply bypass smaller features). Scollar et al. (1990: 359-371)
details, in depth, the effects of various features on the electrical resistivity of the soil. Due to the
21
nature of electricity, resistivity surveying cannot be used in dry soils—there simply is no way
for the electrical charge to be carried from one electrode to the other.
Resistivity surveying showed initial success, but the first application of the method was
conducted with ideal soil conditions on a site outside of Dorchester, England, a feat not easily
replicated in subsequent studies and the method was shown to be limited (Parrington 1983: 106).
There are other disadvantages: the time and effort involved to move and set the probes, as well
as recording the resistivity, made the use of electrical resistivity surveying for large areas
unrealistic (Scollar et al. 1990: 335). As a result, other related methods were developed, such as
electromagnetic surveying (Parrington 1983: 114). This method also measures the ground‘s
resistance to electricity, but unlike electrical resistivity surveying, the EM surveying instrument
does not need to be placed in the soil and so can be moved from location to location quickly. It
is usually mounted on a wheeled cart, allowing it to be pushed or pulled along predetermined
paths called ―instrument tracks.‖ It provides a continuous readout of the electrical resistivity
along the instrument track, allowing for quicker implementation of recovered data (Aitken 1974:
191-198, Parrington 1983: 114).
The development of electromagnetic meters and their associated surveying techniques
has advanced this particular field of remote sensing (Parrington 1983: 114). It can be used by a
single person, another improvement on electric resistivity surveying, which required groups of
people to set and move the electrodes. Due to the nature of electromagnetic surveying, EM
meters can deliver verifiable results in dry soils, something that was not possible with electric
resistivity surveying. Finally, EM meters can detect ditches or the remains of earthen mounds;
these features are differences in soil rather than differences of material such as the stone walls
detected by electric resistivity surveying (Parrington 1983: 114).
22
C. Magnetometry
Related to electromagnetic surveying is magnetometry. Like EM surveying instruments,
the magnetometer is deployed on a wheeled cart and pushed along tracks, and measures slight
disturbances in the magnetic field of the earth, mapping out the location of such disturbances.
These disturbances are the result of culturally derived disturbances in the soil, specifically the
presence of iron artifacts or burnt features in which the temperature of the feature has been
raised above the Curie point, the point at which magnetic grains are ―reset‖ and the direction of
the grains become random in relation to the magnetic field of the earth (a common occurrence in
hearths and furnaces). The resulting randomness of the magnetic grain is easily detected by the
magnetometer. Pits and linear features, such as ditches, are quickly determined, but the
instrument can also detect walls and stone structures that have not reached their Curie points, as
many stone materials contain varying amounts of ferrous compounds that, unless perfectly
aligned with the Earth‘s magnetic field, can also be detected by the magnetometer (Parrington
1983: 115; Scollar et al. 1990: 435-438).
After a four year period of theoretical and methodological development, magnetometry
was first deployed as a prospecting technique in 1958 (Scollar et al. 1990: 514), and has
exhibited much success in archaeology: Scollar et al. (1990: 450) states ―it is probably not an
exaggeration to state that the … magnetometer has been more widely used in archaeological
prospecting than all other instruments taken together.‖ The technique‘s utility is notable,
especially when it is used to map out the extent of individual sites prior to excavation, such as at
the site of Titrus Hoyuk, Turkey (Matney & Algaze 1995; Parrington 1983: 106).
Magnetometers work best in large, open areas with no magnetic disturbance from ferric objects
such as vehicles and utility objects like power lines and sewer pipes (Parrington 1983: 115).
23
However, the technique is often limited to the uppermost occupation level, yet if one wants to
map out the most recent site features, it is a useful and inexpensive alternative to traditional
excavations (Matney & Algaze 1995: 299).
D. Ground Penetrating Radar & Sonar
Following the development of magnetometry is the use of ground radar, which has
proven to be one of the most useful of all remote sensing techniques (Parrington 1983: 115).
Developed in the early 1970s and first deployed at Chaco Canyon National Historical Park, New
Mexico in 1974, it uses the same principles as the aerial radar used by aircraft and weather radar
deployed throughout the world (Scollar et al 1990: 575-580). Like EM surveying and
magnetometry, a radar antenna is deployed on a wheeled cart where it can transmit signals into
the ground. If the radar waves hit a disturbance, the waves are then bounced back to the radar
antenna, which records and displays the data on a monitor.
Ground radar can detect small and weak variations of soil density, except when used in
clayey soils (Scollar et al 1990: 582). Under the right circumstances, such as dry volcanic soils,
it has detected stratigraphic layers of archaeological sites, and features and artifacts of various
sizes almost as well as traditional excavation techniques (Parrington 1983: 117, 119, Scollar et
al 1990: 584). Ground radar does not suffer the same interference that affects magnetometry;
however, limitations of the technique include the inability to determine the identity and
composition of the targeted features, their depth, and their volume (Scollar et al 1990: 582-583).
In other words, it can indicate that something is there, but nothing else. Also, the relative high
price of the equipment (even to this day) and the inability to use it in thickly wooded areas or on
rough terrain, as trees block the movement of the radar over the ground (Parrington 1983: 115).
24
Similarly, sonar, also known as acoustic sensing, was developed in Japan and has been
used in much the same manner (Parrington 1983: 120). A sound generator sends a lowfrequency acoustic pulse into the earth, which is then reflected by artifacts and features. This
pulse is then received by the antenna and the signals are interpreted and displayed on a monitor,
much like ground radar, and the utility of the tools are similar.
E. Analytical Chemistry Techniques
Though they are not technically remote sensing, analytical chemistry techniques have
been used to great success by many archaeologists throughout both Europe and North America,
with successful applications also in Mesoamerica and the Middle East. The most widely used
technique is phosphate analysis, which involves the removal of soil samples from the field to the
lab for chemical processing to determine relative or absolute concentrations of phosphates,
chemical compounds derived from human activity (see Holliday & Gartner 2007 for an
exhaustive list of phosphate analyses in archaeology).
Phosphate analysis in archaeology was first developed in the early twentieth century, and
like many archaeological techniques and principles it first developed elsewhere, in agricultural
studies (Bethell & Máté 1989). Russell (1957) claims that Hughes in Egypt in 1911 first noticed
that human occupation increases the concentration of phosphorus in soil over time, and that this
concentration will stand out over a background phosphorus content. However, it was not until
the early 1930s that this property was used in an archaeological context. Olaf Arrhenius, a
Swedish agronomist, also noticed that raised phosphate levels occurred in conjunction with
human settlement while he worked with the Swedish government to map soil conditions (Bethell
& Máté 1989). However, Arrhenius developed this principle further and realized that one could
25
locate settlements by measuring for enhanced phosphate levels (Bethell & Máté 1989). He then
used this principle in the first large scale systematic archaeological analysis of Sweden.
In the 1960s, the archaeological discipline was focusing on finding more scientific
methods to conduct archaeology, and phosphate analysis was more widely used as a surveying
technique. In 1965 Cook and Heizer authored a paper entitled ―Studies on the Chemical Analysis
of Archaeological Sites.‖ In this paper, the authors argued that an understanding of the entire soil
system is necessary in order to interpret any recovered data concerning soil concentrations, and
that phosphates in particular cannot be considered without also considering the entire
environment (Cook & Heizer 1965). Cook and Heizer‘s advice was followed, and during the
1970s phosphate analysis was taken in conjunction with other remote surveying methods, such as
resistivity and magnetometry surveying and aerial photography (Bethell & Máté 1989). Also,
phosphate analysis was being used on a much smaller scale, this time in an inconclusive attempt
to determine whether or not a body was at one time present in the Sutton Hoo ship (Barker et al.
1975).
The use of phosphate analysis continued to evolve during the late 1970s and 1980s with
the advent of processualism, a movement toward scientifically quantitative archaeology. In 1973,
Provan excavated a Norwegian farmstead, and in his report included the results of phosphate
analysis as well as soil descriptions, density, and elemental measurements (Provan 1973). This
follows with his statement that phosphate analysis was able to locate a site, determine its limits,
illustrate the diet of its inhabitants, and determine the use of structures (Provan 1971). At Cat‘s
Water, in Fengate, Great Britain, phosphate analysis confirmed the existence of a deeply
stratified site prior to excavation in 1984. Also, the phosphate analysis was used to map the
three-dimensional extant of the site prior to the excavation. A second round of testing was
26
including during the excavation to aid in the interpretation. Finally, the use of phosphate analysis
prior to excavation illustrates a step towards adopting phosphate surveying as a standard
technique (Bethell & Máté 1989).
In 2000, E. Christian Wells coauthored a paper describing the chemical analysis of
anthrosols at Piedras Negras. He first states that ―a number of phosphate compounds are
associated with human activities, such as food preparation and consumption, and disposal of
food products and fecal materials‖ (Wells et al. 2000). On top of this, he illustrates that
phosphate analysis can differentiate between elite compounds and structures associated with
non-elites (Wells et al. 2000). Other chemical analyses can determine if structures were painted
or the contents of middens (Wells et al. 2000). These analyses indicate the methodology bridges
the role of remote sensing methods to a more traditional, archaeological analysis of past human
activities.
F. Satellite Photography
During the 1960s, various governments began launching satellites with the capability to
observe features of the earth from their vantage point in orbit. Naturally, the first satellites
served the interests of the government bodies funding the development and deployment of the
technology, and were primarily used for reconnaissance of enemies or potential enemies, such as
the Soviet Union and its dependencies. While the instruments of these satellites were usually
focused on sites of military importance, many times they observed archaeological and historical
sites. An example of this was the observation of the historic city of al-Raqqa, Syria, by the
Corona program. Because these images were classified until the 1995, they remain
underutilized, but they represent the first large body of remotely sensed images (and as Challis
27
et al. 2002 illustrates, they offer a cost-friendly alternative to other, though more recent,
photographs).
The military and civilian development of remote sensing satellites was a continuation of
the development and utilization of aerial photography, and early satellites were limited to the
same portions of the electromagnetic spectrum that photography was: the visible and near
infrared portions of the spectrum expressed through black and white or color photographs. The
first satellite system to take pictures of the Earth and its atmosphere was the Television and
Infrared Observation System (TIROS), a meteorological observation system launched by the
United States in 1960 (Parcek 2009: 19). The United States military quickly saw the utility in a
space-based observation system, and launched a series of military reconnaissance satellites, with
specific satellite systems codenamed Corona, Argon, and Lanyard. These satellites, eventually
numbering six in total, were designated the ―Keyhole‖ program and took images of the surface
of the earth from 1960 to 1972 (Parcek 2009: 19).
During the 1960s and 1970s, methodological development of archaeological uses of
aerial photography demonstrated that the observable portions of the EM spectrum needed to be
expanded from visible and near infrared to incorporate other, previously unused portions such as
the microwave and far infrared portions (Parcek 2009: 20). This methodological development,
along with coinciding theoretical and problem-orientated developments in the new subfield of
archaeological remote sensing, was applied to satellite systems as well as airborne sensors.
During the late 1960s and early 1970s, The U.S. Department of the Interior developed
the Earth Resources Technology Satellite (ERTS), with the goal of achieving a non-militarized
(and non-supervised) satellite observation system with multispectral capabilities for peaceful
purposes. The first satellite of the program was launched in 1972, and the United States
28
government invited scientists from throughout the world to study and analyze the data obtained
from ERTS-1 (Parcek 2009: 22). In 1975, the program and its seven associated satellites were
renamed ―Landsat.‖ The Landsat program provided the first space-borne data applicable to
archaeological investigations on the earth, with most of the data related to the geomorphology of
large geographic regions (Parcek 2009: 22). Likewise, in 1978, the government of France began
development of another multispectral satellite system named the Systeme Pour l‘Observation de
Terre (SPOT). Launched in 1986, SPOT was the first satellite system capable of stereoscopic
imaging (Parcek 2009: 22).
Early efforts to incorporate satellite imagery for archaeological purposes proved quite
successful, and several projects investigating prehistoric and anthropogenic landscapes
throughout northern Africa and the Yucatan were able to effectively employ the Landsat system
despite an 80 meter spatial resolution (Parcek 2009: 26). These successes led to the organization
of the first conference on Remote Sensing in Archaeology in 1984, and the papers and
discussions from this conference resulted in the continued application of Landsat and other
satellite imagery to regions throughout the world: archaeological investigations in England
(Donoghue & Shennan 1987, 1988), Sweden (Lunden 1985), Delaware (Custer et al. 1986),
Syria and Palestine (Cleave 1985), Libya (Dorsett et al. 1984), and Egypt (Wendorf et al. 1987;
McHugh et al. 1988, 1989) resulted in seminal works on space-borne remote sensing
applications for archaeology.
Applications of satellite imagery for archaeological projects increased throughout the
1990s, and the topic of the integration of remote sensing imagery with Geographic Information
Systems (GIS) technology was first addressed (Parcek 2009: 28). GIS was able to easily
integrate knowledge of previously determined areas of environmental and cultural importance
29
with areas under investigation, but due to a lack of training GIS remained relatively unused by
archaeologists and this fact was critiqued by archaeologists (Parcek 2009: 29). Also, while there
was an emphasis on the development of predictive models in other fields, there was a noticeable
lack of articles on the topic and theoretical ignorance of the issues of predictive modeling was
not addressed within archaeology (Davis 1993). Finally, the use of satellite imagery in
archaeology was discovered by global media, and successful projects from New Mexico and
Costa Rica (Sheets and Sever 1991), Greece (Joyce et al. 1992), Thailand (Parry 1992), and
especially the discovery of the lost city of Ubar in Arabia (also known as Iram, or the City of a
Thousand Pillars, and mentioned in the Qur‘an) were brought to the public‘s eye (Fiennes 1991;
Blom 1992).
Satellite technology continued to develop during this same time. Microwave and radar
sensors were deployed on space-borne systems, and the spatial resolution of the satellites
increased. SPOT 4 was launched in 1998 with a spatial resolution of 5 meters, and IKONOS
was launched by Lockheed Martin in 1999 with panchromatic and multispectral band spatial
resolution of 1 meter and 4 meters, respectively. Quickbird, with a panchromatic band spatial
resolution of 0.6 meters and a multispectral band spatial resolution of 2.4 to 2.8 meters was
developed and launched by the private corporation DigitalGlobe in 2001. Finally, hyperspectral
sensors, instruments that can observe the EM band in hundreds of individualized portions, were
developed and deployed on a variety of satellite vehicles. These improvements in sensor
sensitivity continue to aid archaeological investigations, as the increased resolution allowed the
discrete discernment of increasingly smaller portions of project areas.
30
G. Application of Remote Sensing Techniques to the Archaeology of the Southwest
Despite the growing number of archaeological projects employing remote sensing, there
are very few publications on the topic of the utilization of space-borne remote sensors to
determine landscape patterns within the American Southwest. Past projects have included nearground surveying techniques such as magnetometry and ground radar (Scollar et al. 1990: 575580), and there are examples of airborne surveying projects (Deuel 1973 discusses Charles
Limburg‘s flights over Chaco Canyon, New Mexico) or projects where airborne sensors were
used to track the condition of archaeological sites (Palumbo & Powlesland 1996). Perhaps the
best known project to incorporate the use of aerial imagery may be George Gumerman and
Thomas Lyons‘ 1971 study ―Archaeological Methodology and Remote Sensing‖ which was the
first overview of remote sensing in an archaeological publication. In it, Gumerman & Lyons
discussed the utility of aerial multispectral remote sensing and demonstrated the ability to
differentiate between soils, rock types, vegetation, geomorphology, and sites. They were also
able to perform soil moisture analysis, vegetation pattern analysis, plant vigor analysis, and land
use analysis (Gumerman & Lyons 1971). Despite the clear utility demonstrated by the article, it
appears to have been unheeded by Southwestern archaeologists.
As mentioned previously, the first conference on Remote Sensing in Archaeology was
held in 1984, and discussions concerning the remote sensing of Chaco Canyon were included.
One of these, presented by conference organizer Tom Sever, discussed the inability of Landsat to
aid most archaeological investigations due to the instruments‘ 30 meter spatial resolution (Parcek
2009: 26); Sever switched to an airborne Thermal Infrared Multispectral Scanner to continue his
project (Sever & Wagner 1991). Though the poor spatial resolution of past instruments is
admittedly a factor, Sever‘s articles may have negatively impacted the field, as very little
31
research has been done on the application of space-borne remote sensing despite the increase
spatial resolution of modern sensor technology. It is the goal of this project to apply such
technology to an area in which it has not yet been applied.
32
VI.
Literature Review
This chapter will review three different approaches to the application of remote sensing
techniques to archaeological problems. They involve various methods to classify multispectral
imagery obtained from two space-borne instruments. These instruments are the declassified
Corona system, the IKONOS system, and the Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) instrument onboard the Terra satellite launched by NASA in
1999. Unlike Corona, which delivered black and white images of the visible spectrum, ASTER is
a multispectral instrument capable of delivering true and false color images from the visible, near
infrared, short-wave infrared, and long-wave infrared/thermal portions of the electromagnetic
spectrum. ASTER data has traditionally been used for geologic, environmental, and population
studies (Altaweel 2005: 151), but because of the capabilities of the ASTER instrument, ASTER
data was chosen for this project. Because the approaches reviewed by this chapter have
successfully implemented multispectral data within the semi-arid environment of the Middle
East (specifically portions of the nations of Syria, Turkey, and Iraq), they have been selected to
determine if any of the approaches may be successfully implemented in the Middle San Juan
Region of New Mexico.
A. “The Use of ASTER Satellite Imagery in Archaeological Contexts”
This article, written by Mark Altaweel and published in the journal Archaeological
Prospection in 2005, investigates the applicability of ASTER data for determining large-scale
archaeological features in northern Mesopotamia, in northern Iraq. These features include roads
(called ―hollow ways‖ by Altaweel), canals, and sites that could not be easily distinguished
within Corona images due to the inability of the Corona system to detect separate spectral
signatures for portions of the electromagnetic band outside the visible range, despite the two to
33
three meter resolution of the Corona cameras (Altaweel 2005: 151). By using ASTER data, the
author was able to verify the presence of features located in the Corona imagery, as well as
discover features not previously discernable within Corona images.
The first feature class investigated by Altaweel, road beds (or hollow ways), were
investigated using a simple qualitative technique. After performing a series of conversions and
analyses on bands 1 through 9 of the ASTER image in question (corresponding to the visible,
near infrared, and short-wave infrared portions of the EM spectrum, which can indicate the
presence of water, plant health, and different soils and minerals), Altaweel visually compared
known roadbeds with similar features. The known roadbeds and the unknown features were
qualitatively similar, and exhibited similar patterns such as direction and terminating at known
archaeological sites (Altaweel 2005: 157). As such, he was able to successfully identify
previously unknown roadbeds.
Canal features were similarly investigated. However, these features were verified using
the spectral analysis capabilities of the ASTER instrument, which permitted the comparison of
known feature spectral signatures with the spectral signatures of potential features (Altaweel
2005: 158). Rather than being a purely qualitative comparison between known and unknown but
similar features, this method allowed for a quantitative comparison of known and suspected
canalways. However, Altaweel admits that similar spectral signatures of two features only
indicates a degree of likelihood that the two features are of the same type, and that field work
needs to be undertaken to positively identify the suspected feature as a canal (Altaweel 2005:
160).
Finally, Altaweel investigated potential archaeological sites throughout the ASTER
imagery by employing a supervised classification method. Supervised classification methods
34
involve the identification of known sites in an image. These known sites then are analyzed, and a
range of reflectance values for a subset of the spectral bands are identified. Suspected sites are
then compared to this range of values, known as a classifier, to determine if they exhibit similar
reflectance values. This method is also a quantitative method, and Altaweel was able to identify
a previously unknown archaeological site in the image (2005: 161). Again, the author stated that
a field visit was required to determine positively if the suspected site was truly an archaeological
site.
B. “Classification of Multispectral ASTER Imagery in Archaeological Settlement
Survey in the Near East”
This paper, authored by Bjoern H. Menze and Jason A. Ur, was presented at the Tenth
International Symposium on Physical Measurements and Signatures in Remote Sensing, a
symposium organized by the International Society of Photogrammetry and Remote Sensing in
2007. It is available in the Proceedings of that same symposium.
Similar in methodology to Altaweel (2005), it discusses a project to identify
archaeological sites in the Middle East by developing a spectrum signature. However, the
authors go further than Altaweel by illustrating how differences of climate, time of day, angle of
the sun, and season can mask or alter the spectrum signatures of archaeological sites (Menze &
Ur 2007: 1). To counteract these effects, the authors combine all available ASTER images of
their study area (16 total) and average the spectrum signatures to obtain a composite signature
(Menze & Ur 2007: 3). By using the composite signature in a supervised classification method,
the authors were able to obtain a correct classification rate of approximately 80%, significantly
higher than the average of 60% correctly classified when they used spectrum signatures obtained
from individual ASTER images (Menze & Ur 2007: 3). By combining multiple ASTER images,
35
the authors were able to develop a methodology that consistently outperformed methodologies
reliant upon single observations. It also relieves the investigator of the choice of selecting the
―best‖ available image, because all images of sufficient quality are used to develop the classifier.
However, it is often difficult to obtain multiple ASTER images of sufficient quality to duplicate
this technique.
Finally, the authors discuss the importance of understand false-positive and falsenegative results, and discuss the potential of using a classifier explicitly trained to identify
problematic results, such as archaeological sites located near modern settlements (Menze & Ur
2007: 6). They claim that ―false positives in the area of modern settlements might result from
covariates in the (current) training data, or might resemble the spectral characteristic of ancient
debris indeed, eventually indicating the presence of (unrecorded) former sites at the same place‖
(Menze & Ur 2007: 6). These issues need to be kept in mind when developing a supervised
classification scheme, otherwise sites may go undetected.
C. “Archaeological Site Detection: The Importance of Contrast”
This paper, written by A. Beck and delivered to the Annual Convention of the Remote
Sensing and Photogrammetry Society in 2007, also focuses on archaeological sites in the Middle
East. However, rather than relying solely on a supervised classification method that depends
upon the spectral signatures of archaeological sites differing from the signatures of non-site
areas, the author developed a method that relied upon the relative amounts of reflectance
detected by the IKONOS instrument. Beck states:
―Unfortunately archaeological sites do no exhibit spectral signatures that can be used for
generic detection purposes (however, see Altaweel (2005) for use of spectral signatures
in a constrained environment). Rather, it is hypothesized that archaeological residues
produce localised [sic] contrasts in the landscape matrix which can be detected using an
appropriate sensor under appropriate conditions‖ (Beck 2007: 1).
36
While we know, and Beck even admits, that his claim is not entirely true, Beck does
develop a unique methodology to detect archaeological sites. He compares his methodology to
the detection of soilmarks in aerial photography and Corona imagery, noting that archaeological
residues, when dry, were significantly lighter in color than the surrounding soil matrix (Beck
2007: 4). Beck claims that the contrast is not only visual, but would also extend outside of the
visual wavelengths.
Beck‘s methodology attempts to detect sites not by difference in the spectral reflectance,
but rather by the strength of reflectance. He states that many of the enhancements undertaken to
enhance the visual contrast of archaeological sites to their surrounding matrix mask the subtle
reflectance differences that may be more effective in determine the presence of archaeological
remains (Beck 2007: 5). Rather, the presence of any differences in the strength of signal
reflectance can be highlighted by the application of a moving average kernel based upon the
average background soil pixel. After the application of the filter, any areas of unmodified soil
should have an average approaching zero, but areas significantly deviating from the background
values (such as archaeological sites and modern construction) should exhibit positive or negative
values (Beck 2007: 5).
D. Discussion
These papers illustrate increasing levels of sophistication in the interpretation of
multispectral imagery, and the methods presented in the papers were analyzed for this project.
All three papers discuss the utility of supervised classification methods, though the authors might
disagree on its use. While Altaweel‘s methods are relatively simple compared to the other
authors‘, his use of supervised classification is common, and is repeated in this project. Likewise,
Menze & Ur employed supervised classification methods, but mentioned the importance of
37
minimizing false-positive and false-negative errors. As such, this project takes into account the
methods discussed by Menze & Ur to avoid, as much as possible, those errors. Unfortunately,
due to the limited resources of this project, it will not attempt Menze & Ur‘s method to combine
and average the spectral signatures of multiple ASTER scenes. Finally, Beck, who disagreed
with the use of classification methods, created a method to determine the presence of
archaeological sites by highlighting reflective differences that is simple, but unique. Beck
illustrated that many of the traditional analyses actually mask subtle differences that may
indicate the presence of archaeological sites; this must be accounted for when designing a
project.
38
VII.
Methodology
This chapter will discuss the ASTER instrument and the images taken by that instrument,
and also the methodology undertaken to analyze the ASTER imagery for the project. The
instrument was launched in December 1999 onboard the satellite Terra, the first craft launched in
the multi-craft series ―Earth Observing System‖. It was given the designation EO-1 as a result.
Terra and the Earth Observing System is a multinational cooperative mission, with teams of
scientists located in the United States, Canada, and Japan. The satellite‘s primary mission is to
―research into the ways Earth‘s land, oceans, air, ice and life function as a total environmental
system‖ (JPL 2009). It orbits the Earth in a sun-synchronous orbit at 705 kilometers, and is
inclined 98.3 degrees from the equator. Its orbit period is 98.99 minutes, crossing the equator at
10:30 AM. It takes 16 days for the ground track to repeat itself; this is known as its temporal
resolution. The vehicle carries five instruments: ASTER (Advanced Spaceborne Thermal
Emission and Reflection Radiometer), CERES (Clouds and the Earth‘s Radiant Energy System),
MISR (Multi-angle Imaging SpectroRadiometer), MODIS (Moderate Resolution Imaging
Spectroradiometer), and MOPITT (Measurements of Pollution in the Troposphere) (JPL 2009).
ASTER‘s primary missions are related to the environment: it was launched to monitor
land surface climatology, vegetation and ecosystem dynamics, volcano and other hazard
monitoring, hydrology, geomorphology, and land cover change (JPL 2009). However, as
mentioned before, it has found some utility in archaeological projects, particularly the surveying
of regions for settlement sites. It conducts these missions with three instrument subsystems,
which focus on different portions of the electromagnetic spectrum. These are listed in Table 2,
adapted from Altaweel (2005).
39
Table 2: Subsystems of the ASTER Instrument, from Altaweel (2005)
Subsystem Type
ASTER Band
Band width (µm)
Spatial Resolution (m)
VNIR
Band 1
0.520 – 0.600
15
VNIR
Band 2
0.630 – 0.690
15
VNIR
Band 3n
0.760 – 0.860
15
VNIR
Band 3b
0.760 – 0.860
15
SWIR
Band 4
1.600 – 1.700
30
SWIR
Band 5
2.145 – 2.185
30
SWIR
Band 6
2.185 – 2.225
30
SWIR
Band 7
2.235 – 2.285
30
SWIR
Band 8
2.295 – 2.365
30
SWIR
Band 9
2.360 – 2.430
30
TIR
Band 10
8.125 – 8.475
90
TIR
Band 11
8.475 – 8.825
90
TIR
Band 12
8.925 – 9.275
90
TIR
Band 13
10.250 – 10.950
90
TIR
Band 14
10.950 – 11.650
90
Bands 1 and 2 focus on the red and green/yellow portions of the visible spectrum,
respectively. Band 3 focuses on the near infrared, while Bands 4 through 9 focus on the
shortwave portion of the EM spectrum, the portion sensitive to moisture, soil, and vegetation
differences (Altaweel 2005: 153). As such, these bands were used by archaeologists to determine
site location, and they were used in this project to attempt the same.
40
As stated before, this project was an attempt to determine archaeological site locations in
the Middle San Juan Region of northwestern New Mexico. An ASTER image of the area was
obtained on April 4, 2008, with the aid of Dr. Le Wang of the State University of New York at
Buffalo. The image‘s upper left coordinate was centered above 108º 21‘ 10.85‖ West, 37º 5‘
39.20‖ North. This translates to a Universal Transverse Mercator position of grid 12N,
735250.432 meters East, 4108603.669 meters North. The image has a rotation of -8.700343
degrees. This image was subset using ENVI 4.4 to include an area of 22.55 miles east-west by
19.66 miles north-south, with the upper left corner measured at 740859 meters East and 4090090
meters North.
41
Because the analyses used both VNIR and SWIR bands, the SWIR bands were resampled
to match the 15 meter spatial resolution of the VNIR bands. This resampling was conducted in
ENVI 4.4, and involved a first degree Rotation, Scaling, and Translation (RST) and nearest
neighbor algorithms. A forward Principal Component Rotation using a covariance matrix was
then conducted on both VNIR bands 1 through 3 and SWIR bands 4 through 9.
Principal Component Analyses is used to produce uncorrelated bands; that is, the first of
these bands contains the highest amount of variance, the second band contains the second
highest, et cetera. This variance is the amount of data that is not directly related to its
neighboring data. Three bands for each set were created; this correlates to an Eigenvalue of
14.222 for the VNIR band 3, and 0.0175 for SWIR band 3. Because VNIR only contains three
bands, one hundred percent of information contained within the VNIR band was used to create
the PCA VNIR bands, but because the SWIR information is initially held within six bands, only
99.87% of the information was used to create the three PCA SWIR bands.
The resulting images then underwent a Hue Saturation Value (HSV) Sharpening color
transformation. This transforms the image by resampling the hue and saturation values according
Figure 4: Eigenvalue Graphs, Principal Component Analyses on VNIR and SWIR
42
to a nearest neighbor algorithm, but as it only masked the presence of previously located
archaeological sites, the transformation was removed and succeeding analysis was conducted on
the PCA bands.
PCA VNIR bands 2 and 3, and PCA SWIR band 1 were then displayed in a Red-GreenBlue (RGB) colorspace. While multiple other combinations were tried, this combination
presented the most data as determined by the principal component analyses while also
maintaining a degree of visibility for the visual identification of previously recorded
archaeological sites and modern features. It was upon this image that the classification
techniques were attempted. While classification was attempted on other band combinations, this
combination presented the most accurate classification.
43
The first set of classifications attempted was with the unsupervised IsoData classification
technique included within ENVI 4.4. This technique calculates class means evenly distributed
throughout the data space, and then iteratively clusters the remaining pixels using minimum
distance (Tou & Gonzalez 1974). The software was instructed to create five to ten classes within
twenty-five iterations on a variety of band combinations, including PCA VNIR bands 2 and 3,
and PCA SWIR band 1. This was done to provide a visual reference for comparing future
supervised classifications; however, while the classifications seem relatively accurate on a large
scale, previously located archaeological sites were not consistently identified. This attempt to
qualitatively match the classes of known archaeological sites provided a quick method to
determine the accuracy of the various classification attempts.
44
After attempting a series of unsupervised classifications, supervised classifications of
various kinds of undertaken. To conduct these classifications, a series of Regions-of-Interest
(ROIs) had to be identified and selected. These regions corresponded to the various classes being
identified: water, crops/plants, highland/bare rock, sand, road, urban areas, and archaeological
sites. The ROIs for the classes followed the traditional ―ten times the number of bands‖ formula
and were randomly selected from throughout the image, except for the case of the archaeological
sites. The locations of the sites were determined previously using hand-held Garmin Global
Positioning System (GPS) handsets. These locations were then brought into ENVI 4.4, and the
ROIs corresponding to archaeological sites were developed. The shape of the individual site
ROIs was determined from field visits and site records, and were attempted to match the physical
site boundaries as much as possible.
45
It was decided that a mask would not be placed on the image to hide any one class (such
as urban areas). The proximity of the various archaeological sites to the other classes, especially
the urban class, was a factor in this decision. The fact that previous inhabitants of the region built
in the same location as modern people seems unavoidable. As was discussed earlier, errors ―in
the area of modern settlements might result from covariates in the (current) training data, or
might resemble the spectral characteristic of ancient debris indeed, eventually indicating the
presence of (unrecorded) former sites at the same place‖ (Menze & Ur 2007: 6). The use of a
classifier explicitly trained to identify problematic results, such as archaeological sites located
near modern settlements, might mitigate these issues (Menze & Ur 2007: 6).
Three supervised classification methods were employed for this project. These were
maximum likelihood, minimum distance, and spectral information divergence. Maximum
likelihood assumes that the statistics for each class in each band are normally distributed and
calculates the probability that a pixel belongs to a specific class; each pixel is assigned to the
class to which it has the greatest possibility of belonging (Richards 1999: 240). On the other
hand, minimum distance calculates the mean vectors of the classes and measures the Euclidean
distance from each pixel to the mean vector of each class. It then classifies each pixel to the
nearest class (Richards 1999: 240). Finally, spectral information divergence matches the
spectrum of each pixel to a reference spectra obtained from the ROIs. The pixel is then matched
to other pixels that exhibit similar spectra (Du et al. 2004: 1777 – 1786).
These three methods were also performed on a variety of band combinations. However,
one appeared to give the best result: the maximum likelihood method on the PCA bands of the
VNIR portion. However, even this failed the qualitative assessment of whether or not the various
archaeological sites were classified similarly. As can be seen in the image below, the
46
archaeological site class, colored red, has even been confused for crop growth, river sediment,
and bare soil. Rivers (colored blue) and roads (colored magenta) are also confused, as are silt
(cyan) and roads (magenta). Due to the obvious failure of the qualitative assessment, a
quantitative measure such as a confusion matrix was deemed unnecessary.
47
VIII. Results & Conclusion
The results of the analyses prove disappointing. The project was unable to develop and
verify a methodology to detect archaeological sites in the project area. This may be due to the
proximity of larger archaeological sites to modern urban areas; the spectral signature of those
sites is masked by the larger urban areas. However, as Tom Sever mentioned in 1984, the spatial
and spectral resolutions of the systems may not yet be at the point where they are able to
distinguish between archaeological sites and their surrounding matrix (Parcek 2009: 26). This is
true for ASTER imagery, whose 15 meter spatial resolution and 14 bands were unable to detect
the smaller sites throughout the region, many of which are less than 15 meters square. Perhaps
the implementation of other, more sensitive, sensors can advance the field of remote sensing for
archaeological surveying in this area.
The results of this project will be delivered to Linda Wheelburger, principal investigator
for the Totah Archaeological Project based at San Juan College, Farmington, New Mexico, as
well as delivered as a poster at the 2010 conference of Society for American Archaeology. It is
desired that future work and collaboration of the project will bring about successes where the
work for this project has only indicated the inadequacy of the methodology and sensors
employed.
48
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