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1
Linking Remote Sensing and Social Science:
The Need and the Challenges
Ronald R. Rindfuss and Paul C. Stern
There is increased interest today in making scientific progress through the
use of remotely sensed datai in social science research. Space-based sensors are
scanning the earth's surface and sending back images with increasingly high
spatial, spectral, and temporal resolution, and data likely to become publicly
available within the next year promise to show considerably improved resolu-
tion.2 Government agencies that collect remotely sensed data, such as the Na-
tional Aeronautics and Space Administration (NASA) and National Oceanic and
Atmospheric Administration (NOAA), have a growing interest in making these
data useful to social scientists, and the increased availability of funding for re-
search on the human dimensions of global change provides incentives for social
scientists to study human activities with a strong spatial component, such as land-
use transformations. This confluence of events sets the stage for social scientists
to use remotely sensed data and for social scientists and remote sensing experts to
collaborate.3 This volume examines the potential for such use. It offers some
guidance for researchers and research sponsors in the form of reports of promis-
ing research, information on the state of the technology, and reflections on the
challenges of linking social science and remotely sensed data.
Remote sensing is not a new technology. Aerial photographs have been in
widespread use for a half-century (Carls, 1947) and satellite images for a quarter-
century (e.g., Estes et al., 1980; Morain, in this volume). These images have been
put to various socially useful purposes, including making crop forecasts, predicting
severe storms, and planning land development. Despite the apparent usefulness of
remotely sensed data for social purposes, however, remotely sensed images have not
been a popular data source for social science research, for several reasons.
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2 HNKING REMOTE SENSING AND SOCIA:L SCIENCE: THE NEED AND THE CHALLENGES
First, the variables of greatest interest to many social scientists are not readily
measured from the air. Many social scientists find visible human artifacts such as
buildings, crop fields, and roads less interesting than the abstract variables that
explain their appearance and transformations. Changing land use, road and build-
ing construction, and the like are regarded as manifestations of more important
variables, such as government policies, land-tenure rules, distributions of wealth
and power, market mechanisms, and social customs, none of which is directly
reflected in the bands of the electromagnetic spectrum. Thus social scientists are
likely to be skeptical that remote sensing can measure anything considered im-
portant in their fields of study (Turner, in press).
A related issue is that social science is generally more concerned with why
things happen than where they happen (Turner, in press). Even areas of social
science in which one might expect a spatial orientation are curiously aspatial. For
example, while it seems almost self-evident that spatial propinquity must be a
factor in the shaping of social networks, it is only recently that the spatial aspects
of social networks have been receiving attention (Faust et al., 1997~. Relatively
few social scientists outside the field of geography value the spatial explicitness
that remotely sensed data provide, nor do the typical social science data sets
contain the geographic coordinates that would facilitate linking social science
data and remotely sensed data.
Further, the scientists who participate in developing remote sensing tech-
niques have overlapped little with social scientists in their backgrounds, theories,
methods, jargon, or epistemological approaches, although this situation is chang-
ing. Integrating social science and remote sensing will require the fusion not only
of data, but also of quite different scientific traditions. Many social scientists do
not know what a pixel is, and few have ever considered how clouds may affect
data quality. Similarly, the average remote sensing expert is unlikely to be
conversant with a wide range of social science problems and solutions, such as
why fixed-effects statistical models were developed. It is easy for scientists on
one side to underestimate the difficulty of learning the approaches, theories,
methods, and jargon of the other. This difficulty is compounded by the fact that
those on each side are likely to have some familiarity with the other. Social
scientists are likely to have been watching news and weather reports for decades,
acquiring what they think is an ability to interpret satellite imagery. Some of the
images that appear on the television screen bear a close resemblance to familiar
objects, such as maps, making interpretation seem easy. But in fact, these pro-
cessed images can be several steps removed from the remotely sensed data on
which they are based. People who see only these products may have little appre-
ciation of the analysis necessary to produce them. Similarly, socioeconomic
patterns and trends are discussed frequently in the mass media. It is easy for
those not trained in social science to claim some understanding of it and to think
that incorporating it into their research would be straightforward. But as with
remote sensing, popularized presentations can mask the detailed analysis that lies
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RONALD R. RINDFUSS AND PAUL C. STERN
3
behind the summary data. We return to the issue of different scientific traditions
later, in discussing the problem of training future scholars.
Finally, bridging the social science and remote sensing fields undoubtedly
entails the risks frequently encountered by those who do interdisciplinary re-
search. For example, when we discuss the issues raised in this chapter with
remote sensing experts who were originally trained in the social sciences, they
frequently mention feeling marginalized from their original fields of study be-
cause the problems and concepts central to their remote sensing research are not
considered core to those disciplines. Estes et al. (1980) have discussed this issue
from the perspective of geography the discipline in which social science and
remote sensing have most closely converged and we are told that the situation
remains much the same in the late l990s.
Given this gap between the social sciences and remote sensing, why bother
trying to bridge the two fields? The question has different answers for different
kinds of scientists. For some remote sensing experts, a compelling answer is
social utility: remote sensing is expensive, and government spending on it is
more justifiable if it improves our understanding of the social system by being
incorporated into social science research. While remotely sensed data have been
employed for a variety of socially useful purposes, such as increasing yields
through precision farming or weather forecasting, there are, as noted, relatively
few examples of those data being used in social science research. The social
utility argument posits that remote sensing becomes even more valuable to the
extent that social scientists find it useful, and that efforts should be made to
identify and overcome the barriers to making this happen. In addition, the contri-
butions of social scientists might allow remote sensing experts to "see" landscape
features in the remotely sensed data not previously apparent. There are several
examples of this in the present volume.
From the perspective of social science, one important reason for using re-
motely sensed data is to gather information on the context that shapes social
phenomena. The role of context has been central to the theories and empirical
work of numerous sociologists, economists, and anthropologists. Remote sens-
ing offers an additional source of contextual data for multilevel analyses. An-
other consideration involves the growing interdisciplinary community of scien-
tists interested in sustainable development, pollution prevention, global
environmental change, and related issues of human-environment interaction who
need to compare data on social and environmental phenomena at the same spatial
and temporal scales. This community includes both social and physical scien-
tists. For them, fusing social and remotely sensed data should be an attractive
strategy.
Although linking of remote sensing and social science is difficult, it has been
and continues to be done, as evidenced by the case studies presented in this
volume. This chapter examines why it is important to join people and pixels,
addressing some of the challenges of doing so. Understanding the challenges is
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4 HNKING REMOTE SENSING AND SOCIA:L SCIENCE: THE NEED AND THE CHALLENGES
essential if progress is to be made. In considering the promise and opportunities
offered by collaboration between remote sensing and social science, we want to
strike a balance between overpromising and underselling. We do not believe
remote sensing will quickly revolutionize social science; rather, we suggest that
some progress can be made by joining social science and remote sensing perspec-
tives, techniques, and data. Hence, the majority of this volume consists of ex-
amples of the use of remotely sensed data mainly from space-based platforms-
in social science research.4 However, we do not want to be overly constrained by
the present, so we also speculate about additional, as yet untried, applications of
remote sensing to social science questions.
The volume is intended to stimulate dialogue between social science and
remote sensing experts, and in any dialogue, it helps to know something about the
participants. The Committee on the Human Dimensions of Global Change at the
National Research Council, which is responsible for this volume, consists of
social and natural scientists interested in the scientific understanding of human-
environment interactions. The majority of the committee members were trained
in the social sciences, and the authors of this chapter are typical in this regard.
One of us (Rindfuss) is trained in sociology, with research and teaching interests
in population studies or demography. He began graduate school wanting to
understand American fertility patterns and trends, and then branched out geo-
graphically and substantively, while continuing to publish on fertility in the United
States. He has used remotely sensed data in work on population migration and
social change in Nang Rong district, Thailand. He has worked mainly with
micro-level data sets in which individuals or households are the units of analysis.
He has been involved in various interdisciplinary activities through the Popula-
tion Association of America, the National Research Council, and other
multidisciplinary organizations and has directed an interdisciplinary research cen-
ter, but approaches the topic of people and pixels from the vantage point of a
sociologist/demographer.
The other author (Stern) is trained in social psychology, but has long been
interested in human-environment interactions, particularly in behaviors at the
individual and household levels that affect the use of natural resources and the
generation of waste and pollution. He has worked with data on individuals'
attitudes, beliefs, and behavior and has studied the effects of interventions aimed
at changing environmentally significant behavior at the local and regional levels.
He has published research together with colleagues from various disciplines in
the social and natural sciences, but despite his experience in interdisciplinary
collaboration and the potential of remote sensing to provide data on the environ-
mental effects of social interventions, he has not used remotely sensed data in his
research.
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RONALD R. RINDFUSS AND PAUL C. STERN
WHAT CAN REMOTE SENSING DO FOR SOCIAL SCIENCE?
s
One rationale for linking people and pixels is that doing so might result in
better social science research. This could happen in several ways, although the
realistic potential for making these improvements is in some dispute.
Measuring the Context of Social Phenomena
Many social science theories relate individual or household behavior to the
context within which the individual or household is located. "Context" can
denote a variety of entities, including a political or administrative unit, a social
network, a school, or a racial or ethnic group. When the individual is the unit of
analysis, the individual's household is also a context. People live their lives in
contexts, and the nature of those contexts structures the way they live. Contexts
can provide advantages (for example, growing up within a wealthy school dis-
trict) or produce constraints (young adults in rural areas with poor soil quality are
more likely to out-migrate). Hypotheses from theories of context may involve
additive effects (teenagers residing in high-crime neighborhoods are more likely
to become involved in crime than are teenagers in low-crime neighborhoods) or
interactive effects (the negative effect of education on fertility is stronger for
blacks than for whites) but in either event, the hypotheses concern the effects of
context on individuals or households.
Contexts can be measured in various ways. Censuses, because they obtain
information on almost all individuals and households in a country, can be aggre-
gated to various units (block, neighborhood, school district, city, county, or state)
to provide measures of the demographic or socioeconomic characteristics of
those units. The choice of scale and of characteristics depends on the theory and
the hypothesis being tested; the effects of the contextual variables are estimated
using statistical models. Sometimes individual respondents know the contextual
variables well enough to provide them directly. Race, ethnicity, and religion are
examples. Sometimes contextual variables must be measured with data that are
not gathered from individuals. Examples include public expenditures on educa-
tion and laws governing land tenure. In the case of social networks, researchers
are experimenting with several approaches to measuring the structure of net-
works and positioning individual respondents within that structure.
Remote sensing provides an additional means of gathering contextual data,
particularly in describing the biophysical context within which people live, work,
and play. First of all, remotely sensed data provide an alternative representation
of geographical context to that given by maps. Maps always include the map-
maker's selection of what is important to represent, and remotely sensed data,
though also imperfect representations of reality, have different biases. They can
therefore offer a check on what is in maps, additional information, and sometimes
a useful alternative perspective. A good general source of information on meth
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6 HNKING REMOTE SENSING AND SOCIA:L SCIENCE: THE NEED AND THE CHALLENGES
oafs of measuring and understanding geographical contexts is the recent volume
Rediscovering Geography (National Research Council, 1997~.
In addition, remote sensing has the potential to supplement georeferenced
social data by characterizing numerous aspects of the context, ranging from land
cover to soil moisture to weather. An example is the work of one of the authors
(Rindfuss) in Nang Rong district, Thailand, mentioned earlier. This work (the
subject of Chapter 6 in this volume) concerns the determinants of out-migration.
The Nang Rong district is a rural, agricultural area, with rice being the predomi-
nant crop. Over 90 percent of the adults are farmers, so access to land is essential
for young adults seeking employment in the district. Since forested land in Nang
Rong tended to have ambiguous legal titles, it was expected that the availability
of forested land would reduce the likelihood of out-migration of young adults,
and this was indeed found to be the case (Rindfuss et al., 1996~. Another example
comes from the work of Geoghegan and colleagues (see Chapter 3), who are
analyzing the effect of the mosaic of land uses surrounding a property on its
economic value and the probability of its future development.
Migration raises one of the thorniest issues related to the use of contextual
data to study social processes: individuals and households can change their
contexts through migration. People move for many reasons, including better
economic opportunities, better schools, and a preferred biophysical environment.
When they move and change the context in which they live, that context needs to
be modeled as an endogenous variable, rather than a simple external influence on
behavior. Even without migration, individuals can act to change their contexts-
a possibility that may be more easily uncovered when contexts are measured in
interviews than when they are measured by remote sensing. Thus, theoretical
care is needed when using remotely sensed data to supply contextual data for
models of individual or household behavior.
Measuring Social Phenomena and Their Effects
Remote sensing can provide measures for a number of dependent variables
associated with human activity particularly regarding the environmental conse-
quences of various social, economic, and demographic processes. For example,
remote observations of land cover may show the footprints of agricultural exten-
sification, urbanization, and road development;5 observations of vegetation den-
sity may be related to the effects of fertilization, irrigation, and other agricultural
practices; and observations of new building construction may be linked to the
effects of local policies on land use and property taxation. Remote sensing has
sometimes proven to be the best method for identifying archaeological sites and
relating them to key features of their geographical settings (see Chapter 7~.
Models that combine remote observation with ground-based social data have
the potential to improve understanding of the determinants of various land-use
changes. Geoghegan and colleagues in Chapter 3 and Cowen and Jensen in
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RONALD R. RINDFUSS AND PAUL C. STERN
7
Chapter 8 give examples of such modeling in which residential development is
the variable being predicted. It may also be possible to study the effects of
changes in agricultural commodity prices on cropping patterns and tillage prac-
tices by combining price data with remotely sensed data, and to improve under-
standing further by incorporating additional data on land-tenure systems or agri-
cultural policies.
Providing Additional Measures for Social Science
Social scientists frequently use aggregated units of analysis: cities or towns,
counties or districts, states or provinces, or countries. The substantive questions
vary, but investigators typically use multiple indicators for these aggregated units.
Remote sensing can provide a variety of additional indicators for these studies,
including land cover, moisture measures, locations of major roads and hydro-
graphic features, and indicators of crop fertility. Gathering such measures from
the ground might be possible, but often is prohibitively expensive because of the
need to collect large amounts of small-scale data for aggregation. Remote sens-
ing can sometimes provide highly aggregated data at less cost.
Indicators from remote sensing can complement indicators from ground-
based sources. For example, agricultural intensification can be measured by
using data from surveys of farmers' behavior, sales figures on agricultural chemi-
cals and farm equipment, or remotely sensed data on crop density and color.
Combinations of social and remote data can yield a deeper understanding of the
types of intensification possible, as shown in the analysis of Amazonian agro-
forestry in Chapter 5. Urbanization can be measured by counting building per-
mits, sampling and observing city blocks, or remotely sensing the proportion of
land covered by structures (see Chapter 81. Each data source has its imperfec-
tions, but combining sources with different limitations might provide a better
picture of the entire phenomenon. In this way, remote sensing even with its
imperfections can make a contribution to social scientific measurement by im-
proving on some measures and cross-checking others.
Because remotely sensed data are available with greater spatial and temporal
resolution than data from other sources, there has been discussion of the potential
for using the former data to conduct finer-grained studies than are possible with
typical social science data. These possibilities are expanding as higher-resolution
data become available from new satellites and satellite data collected by military
and intelligence organizations in the United States and the former Soviet Union
are declassified. For example, census data are remarkably accurate in most
countries, but they are collected infrequently, typically every 10 years. And there
are some countries for which census data are not available, and some in which the
data are reported inaccurately for a variety of cultural and geopolitical reasons.
Some have expressed the hope that remotely sensed data could be used during
intercensal periods to update the census reports. Cowen and Jensen (Chapter 8)
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8 HNKING REMOTE SENSING AND SOCIA:L SCIENCE: THE NEED AND THE CHALLENGES
report on correlations between remotely sensed indicators of dwelling units and
actual census counts in data from South Carolina. The validity of their indicators
in other geographical and social contexts or over time has not yet been demon-
strated, however. There have been some suggestions that remotely sensed data of
fine spatial resolution might be used in statistical models to generate estimates of
population counts. If that were possible, there would be numerous uses of such
estimates. It remains to be seen whether efforts along these lines will yield
accurate estimates. Before this becomes possible, however, a number of method-
ological studies are necessary because of certain inherent limitations in the use of
remote sensing for population estimates, such as the inability to sense the number
of people per housing unit or housing units per building, or to discriminate
between residential buildings and some others. Thus, ground-based studies are
necessary to determine how the number of people per dwelling unit varies with
the socioeconomic and physical characteristics of neighborhoods. If the variance
is sufficiently systematic, remote sensing might help improve intercensal popula-
tion estimates.
Remote sensing might also help with the census undercount problem. One
source of a census undercount is the failure to recognize a physical structure that
is a dwelling unit. For relatively remote rural areas, finding dwelling units is a
difficult undertaking, and missing a dwelling unit can contribute to the under-
count.6 The use of satellite images with high spatial resolution might improve
this process a possibility the U.S. Bureau of the Census has investigated using
aerial photographs (Carls, 1947~.
Remotely sensed data have been used for measuring other socially signifi-
cant variables, especially in urban and suburban contexts. Cowen and Jensen
(Chapter 8) describe the use of remote observation to classify land use and land
cover into categories established by the U.S. Geological Survey; to measure the
area, height, and volume of buildings; to measure traffic patterns and road condi-
tions; to estimate residential energy demand; and to build predictive models of
residential expansion. Some of these measurement methods are in the early
stages of development, so more experience is necessary to determine how well
they work across a variety of social and geographic conditions and over longer
periods of time. Nevertheless, these measures may provide important advantages
in cost or temporal resolution over conventional measures of the same variables,
and may make it possible to improve the quality of modeling used for planning
urban infrastructure needs and forecasting the need for utilities or other public
services.
It may be argued that remote sensing can support comparative social re-
search studies that attempt to draw conclusions by systematically comparing
the same phenomena in different countries or different regions of the same coun-
try by providing comparable measures of variables these studies need to inves-
tigate. Clearly, remote sensing is well suited to providing comparable data for
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RONALD R. RINDFUSS AND PAUL C. STERN
9
different geographic regions or at different times. The question is whether the
parallel social data are available in forms that are comparable.
Making Connections Across Levels of Analysis
Social science disciplines and subdisciplines have their preferred levels of
analysis and often do not communicate across those levels. For instance, psy-
chologists and sociocultural anthropologists tend to work with individuals and
small groups; political scientists and geographers tend to work at higher levels
defined by political units or geophysical features; while sociologists tend to
specialize in one level of analysis or another, from individuals to small groups to
communities to the world system. Remotely sensed data are essentially global in
coverage,7 composed of individual pixels that can be combined to allow work at
any scale or level of analysis more coarse than the pixel size. Thus remotely
sensed data offer some potential for encouraging social scientists to think across
levels of analysis and to develop theories that link these levels. An example is in
the work of Moran and Brondizio documented in Chapter 5 which, starting from
an anthropological and highly localized perspective, developed ways of examin-
ing land use in geographically disparate areas of Amazonia and thereby address-
ing regional-level questions. Similarly, Entwisle and colleagues (Chapter 6)
collected data on villages in a way that, with the help of comparable data across
villages from remote sensing and social surveys, has the potential to place the
behavior of individuals in the context of their villages and interpret the character-
istics of the village in the context of the region. The issue of linking levels of
analysis is explored in more detail in Chapter 3, which suggests some interesting
possibilities for combining remotely sensed and ground-based data to study the
effects of global economic forces on the behavior of individual land users. Chap-
ter 3 also considers a cousin of the linkage issue in the temporal dimension: the
property of path dependence in dynamic systems, which may be affected by their
histories as well as their current conditions.
Providing Time-Series Data on Socially Relevant Phenomena
Time-series data can be helpful when social scientists attempt to trace rela-
tionships of cause and effect but cannot use experimental methods. Remote
platforms sometimes provide time-series data of good comparability (i.e., the
same variables measured in the same way across time) on variables of interest to
social scientists concerned with the effects of context on behavior or with pro-
cesses of human-environment interaction. Examples in this volume include data
on thinning of forests by human action (Chapter 3), forest regrowth after clear-
cutting (Chapter 5), and development of algal blooms that harbor pathogens
(Chapter 10~. In addition, remotely sensed time-series data can be essential for
modeling human-environment interactions. Examples in this volume include the
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10 HNKING REMOTE SENSING AD SOCKS SCIENCE: THE NEED AD THE CHALLENGES
use of remotely sensed data to model the effects of access to forests on out-
migration (Chapter 6) and processes of land conversion to urban uses (for ex-
ample, in Chapter 3~.
WHAT CAN SOCIAL SCIENCE DO FOR REMOTE SENSING?
As noted earlier, to the extent that remote observations provide uniquely
useful information for social research, these social science applications of remote
sensing can be used to provide additional justification for the money spent on
observational platforms and data management systems. In addition to this poten-
tial practical value of social science to remote sensing, there are several kinds of
scientific contributions to remote sensing that might come from its interaction
with social science.
Validation and Interpretation of Remote Observations
Remote sensing specialists are well aware of the need for "ground trothing,"
that is, for validating remote observations against data collected on the ground. A
standard example is the problem of measuring land cover by old-growth forests
(Lucas et al., 1993; Moran et al., 1994; Skole et al., 1994; Moran and Brondizio,
in this volume). It is necessary among other things to distinguish the spectral
signatures of old growth from those of forests regrown after deforestation. Doing
so requires comparsion of the remotely observed spectral properties of plots
known from ground observation to fall in these categories in order to develop an
algorithm that accurately discriminates between the two. Further ground obser-
vation is required, of course, to validate the algorithm on plots of land not used to
develop it.
Although classifying types of land cover requires observations on the ground,
it is not usually considered a social science activity. There are, however, some
kinds of ground truthing that involve classifying remote observations into more
obviously social categories, and thus depend on social science input. An impor-
tant example is classification of land uses, which are socially defined in ways that
do not correspond exactly to categories of land cover. Thus, some tree cover is
socially classified as forest land, some as park land, some as suburban landscap-
ing, some as orchard, and some as productive agroforestry land. It is frequently
necessary to rely on human informants to make these distinctions.
Similarly, different kinds of land tenure, such as family ownership, village
commons, and sharecropping arrangements, may all be used in the same kinds of
productive activity and may therefore fall within a single land-cover, or even
land-use, classification. It may be possible to associate different management
practices that can be distinguished spectrally by remote observation with differ-
ences in tenure. Discovering such differences would likely require collaboration
between remote sensing specialists who can distinguish spectral patterns and
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RONALD R. RINDFUSS AND PAUL C. STERN
11
social scientists who can classify land-tenure types and land-management prac-
tices.
Data Confidentiality and Public Use
As noted earlier, remotely sensed data are becoming available for public use
in ever finer spatial resolution, increasing the ability to discern the footprints of
socially important activities. Moreover, as high-resolution military observations
are declassified and made available, various organizations will gain access to
information about the landscape heretofore not considered by those responsible
for the landscape. As improved technical capabilities, collaboration with social
scientists, and especially the linking of remotely sensed data with social data
make remotely sensed data increasingly useful, new problems and conflicts may
arise over the use of the data.
Although there are legal precedents that limit privacy rights with respect to
high-resolution aerial photography, the courts have not yet directly addressed
questions of privacy and Fourth Amendment rights in the context of space-based
remote observation (Uhlir, 1990~. As such observation provides improved reso-
lution, new claims of infringements of privacy may surface. There may be new
calls for the restriction of access to remotely sensed data, stricter regulations or
legislation governing what can be collected remotely, or curtailment of public
resources invested in remote sensing technology. There are also unresolved
issues of international law (Hosenball, 1990~. By way of illustration, when one
of the authors (Rindfuss) first showed his Thai collaborators the satellite images
for our study site during a research seminar in Thailand, their first question was:
"Where did you get these?" Their tone and facial expressions suggested surprise
and perhaps a bit of concern that one could simply buy such images. Informal
discussions with others using remotely sensed data suggest that the reactions of
our Thai collaborators are not unique. Already, some landowners are concerned
that remote platforms will reveal secrets about their land-use practices (perhaps
revealing to government officials that those practices are violating land-use regu-
lations). We expect that notwithstanding legal precedents in the United States
dating from the early days of aerial photography, the increasingly finer resolution
and widespread availability of satellite images and their linking to ground-based
data sources will fuel public concerns about invasion of privacy and reopen some
previously closed issues.
Social scientists have experience in dealing with issues of confidentiality in
data collection and dissemination that may be of use to remote sensing special-
ists. Most social science data collection techniques, from face-to-face interviews
to participant observation to the analysis of administrative record forms, require
that those providing the information be motivated to remain open and honest,
which in turn requires that they trust the researchers to use the information
responsibly. There are many examples of censuses, surveys, and other studies in
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RONALD R. RINDFUSS AND PAUL C. STERN
17
being transformed primarily for the purpose of vacationing and tourism by indi-
viduals whose primary residences are elsewhere.
An additional problem is that of georeferencing the activities of individuals
who participate in the global economy and thereby affect the land in places where
they may never have traveled. People buy agricultural and other products from
widely dispersed places and manufacture products that produce effluents in the
widely dispersed locations where they are used, often without even knowing
where their activities are having an impact. Social science is a long way from
being able to georeference these human activities, but it is obvious that people in
modern economies are not easily linked to pixels for the purposes of understand-
ing the effects of their economic activities.
An analogous problem exists at other levels of analysis. Consider, for ex-
ample, linking firms to remotely sensed data in order to understand the influence
of different types of firms on land-cover or land-use patterns. Should one use
only the point locations of a firm's places of business, or should one also consider
the commuting patterns of the firm's employees and the locations of its suppliers
of raw materials?
One possible approach involves aggregating social data to larger geographi-
cal units. This approach assigns individuals to larger areas in which their envi-
ronmental effects are more likely to be confined. This approach can answer some
scientifically important questions and produce interesting results. Examples can
be seen in the work of Wood and Skole in Chapter 4. However, there are
limitations to this approach. First, there are numerous processes that are not
visible at high levels of aggregation. To exploit fully the potential of integrating
social science and remote sensing, one should also examine finer-grained rela-
tionships. Second, surveys have become much more important than censuses in
most social science disciplines, and the majority of surveys are designed to repre-
sent a broad geographic area (a country, region, or state) and not to be represen-
tative of smaller geographic divisions within that area. This is true even of very
large surveys, such as the Current Population Survey in the United States. It is
unusual to have comparable surveys conducted in multiple geographic units, and
when they are, the units are usually countries, which are too large for many
processes of interest.
INSTITUTIONAL ISSUES: MAKING IT HAPPEN
As this chapter and the case examples presented in other chapters of this
volume make clear, social science and remote sensing are being linked in efforts
to address important scientific and public policy questions. The potential of such
collaborations is considerable, although there are also significant challenges.
The question for the future is not whether this sort of activity should go on it
will but how much of it should go on and how it can be facilitated. This section
addresses some of the key institutional questions about the future, for example,
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18 HNKING REMOTE SENSING AND SOCKS SCIENCE: THE NEED AND THE CHALLENGES
how to create a productive community of scholars who combine social science
and remote sensing, how to train future scholars for participation in this commu-
nity, and how to support the community with needed data.
Building a Community of Scholars
For the next 5 years, most individuals who work on projects that involve the
linking of social science and remotely sensed data will be experts in one of these
fields, but probably not both, and will work in collaboration with researchers
whose training is complementary. They will acquire some knowledge in the area
in which they are not expert, but only the exceptional individual will be expert in
both. The volume of research literature in both areas all but precludes an estab-
lished researcher's becoming an expert in both. Thus in the near term, research
combining social science and remote sensing is likely to be multidisciplinary and
interdisciplinary, and will involve all the problems associated with such research.
One of these problems is peer review of scientific proposals that incorporate
both social science and remote sensing, and that are likely to make contributions
to both areas. Ideally, one would want to fund proposals that are of the highest
quality and that will make cutting-edge contributions to both the social science
and remote sensing components. Social science, however, is a very broad field,
and we do not know of anyone who has the audacity to claim expertise in all its
subfields. The same is probably true of remote sensing. The peer review process
would be best served if it included representatives of all the major subfields of
both social science and remote sensing that are represented in the proposals
submitted. Given the diversity on both sides, the selection of appropriate review-
ers will be a challenging task. Moreover, it may be desirable to fund some
projects that break new ground by applying knowledge or techniques that are
familiar in either social science or remote sensing in a new and important way,
and that are therefore not equally innovative in both fields. Such proposals might
be viewed as exciting and innovative by experts in one field, but uninteresting by
experts in the other. It will be necessary to find ways of preventing vetoes of such
projects by experts in a subfield who do not see the overall value of proposals that
go beyond their expertise.
Communication of scientific results is another problem for any new interdis-
ciplinary scientific field. Communication normally occurs through the auspices
of scientific associations that are built around disciplines (e.g., economics, geog-
raphy) or problem areas (e.g., population, natural resource management). The
communication media range from small workshops to scientific meetings to jour-
nals. To date, there is no scientific association or journal for scholars who are
integrating social science and remotely sensed data, and this lack of an institu-
tional base is likely to impede the development of research at the intersection of
the two fields. There are certainly examples of sessions at professional meetings
that include papers incorporating both social science and remotely sensed data
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RONALD R. RINDFUSS AND PAUL C. STERN
19
(e.g., the 1996 and 1997 meetings of the Population Association of America, the
Pecora 12 conference, and the 1997 Open Meeting of the Human Dimensions of
Global Change Research Community). But each of these meetings attracts only
a small fraction of those bridging the fields of social science and remote sensing,
and further, only one member of a research team usually goes to these meetings
(e.g., the Pecora conferences tend to attract only remote sensing experts).
Scientific journals are perhaps the most important communication mecha-
nism. For teams working on projects that use both social and remotely sensed
data, the most obvious publication outlets are ones that specialize in only one of
the two fields. The peer review process in such journals involves the same
problems already noted for the review of proposals: the social science reviewers
are generally not competent to review the remote sensing components of the
paper, and the remote sensing reviewers are generally not competent to review
the social science.
The ability to build a community of scholars depends greatly on publications
because they are central to the reward system in science. Universities are the
primary employers of social scientists and remote sensing researchers, and they
tend to be organized along disciplinary lines. Graduate degrees, faculty appoint-
ments, and tenure tend to be determined by disciplinary bodies within a vertical
structure in which department chairs report to deans, deans to provosts, and so
forth. Uniting social science and remote sensing involves horizontal links across
departments or even schools. While these horizontal links are crucial for the
research, the fact that they are orthogonal to the decision and reward structure of
the university means that tensions will inevitably arise. Even a simple question
such as where to publish findings will make collaborators consider their self-
interests. Further, many department chairs are wary of interdisciplinary research
efforts and centers they want their faculty spending time and effort in their
departments. Although interdisciplinary research is clearly possible, it takes place
against resistance in most universities.
The difficulties of peer review, communication, and publication are typical
of new interdisciplinary fields, and they are sometimes successfully overcome.
Among the ways this has been accomplished are the provision of resources tar-
geted to the field during its developmental period and the orientation of that
development around a few applied problems for which there are preexisting
communities of scholars. The developing scientific interest in the human dimen-
sions of global change, and within that field the growing attention to and research
support for work on land-use and land-cover change, provides a context that can
bring together many of the researchers who are combining social science and
remote sensing.
Training Future Scholars
As noted earlier, most current researchers who work on projects that com
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20 HNKING REMOTE SENSING AND SOCKS SCIENCE: THE NEED AND THE CHANGES
bine social science and remote sensing do not have solid skills in both; rather,
they are expert in one and collaborate with experts in the other. One model of
training would replicate this pattern and add special training of students to be
effective interdisciplinary collaborators. A drawback to this approach is that
collaboration would increase the expense of research. Another is that collabora-
tions would often be across institutions, creating an issue of distance that would
have to be addressed. A third drawback is that graduate students and junior
faculty members who were trained and employed in a disciplinary field would
have to worry about whether their interdisciplinary research would hurt their
chances of getting a job or tenure.
An alternative training model would train young researchers in both a social
science and remote sensing. This strategy would address some of the drawbacks
of the first model, but has drawbacks of its own. First, it would increase the
length of graduate training, which some argue is already too long. Second, such
training might have to occur across departments, and most universities are not
structured to do such training. Third, young scholars with interdisciplinary train-
ing might have difficulty finding employment, especially in universities orga-
nized along disciplinary lines.
A third model involves gradual expansion of the community through inter-
disciplinary research activities that may provide both students and established
scholars with what is essentially on-thejob training in remote sensing or social
scientific fields that are new to them. This model is an extension of a process that
is already occurring in some research institutions. Its chief advantage is that
training in the context of ongoing research is likely to be highly effective. How-
ever, the process is likely to be slow at first, and it may train people idiosyncrati-
cally in narrow segments of a field that are related to a specific research topic.
At this juncture we would not want to recommend one model over another.
We anticipate that the models will vary across universities, and depend on the
strengths and existing institutional arrangements within each. We would suggest,
however, that the time has come for funders, both federal and private, to train the
upcoming generation of scholars who could bridge the social science and remote
sensing fields.
Providing Necessary Data
Linking remote sensing with social science presents special challenges for
data systems. A straightforward but significant problem is to provide georefer-
encing for social data so as to link them to remotely sensed data, which are
normally geocoded. Preexisting social statistics such as those collected by gov-
ernment agencies are typically coded at highly aggregated levels, such as politi-
cal units. They can be geocoded to some geographic point within the unit, but
cannot be disaggregated below the lowest level at which they were made avail-
able. 9 If they are geocoded for general use, it is important to select an appropri
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RONALD R. RINDFUSS AND PAUL C. STERN
21
ate geographic point so that researchers can move the point as their scientific
purposes require, and to store the geocoded data in an easily accessible place,
such as the NASA-supported Socioeconomic Data and Applications Center
(SEDAC). New social data could in principle be coded to the location of the
individual, firm, farm, or other unit from which they are collected, but as already
noted, concerns about privacy and confidentiality often prevent this, and even
when it can be done, questions remain about how best to geocode actors in highly
interdependent world markets. Again, the geocoded data should be stored, with
appropriate documentation, in an easily accessible location. It would be useful to
have an organized discussion within the research community about whether it is
advisable to develop standard methods of geocoding social data for storage.
Researchers also face the problem of finding appropriate social data to match
with remotely sensed data, or vice versa. NASA's support for the SEDAC is
intended to address this problem, and an indicator of the SEDAC's success will
be the extent to which researchers find it useful for locating the matching data
sets they need.
Another challenge is to match the level of resolution of remotely sensed data
with that needed for social data. Different social science questions require differ-
ent levels of resolution in space and time, and perhaps also spectrally. The
different needs are illustrated by Cowen and Jensen in Chapter 8 in the context of
research on urban dynamics. They are also illustrated implicitly in other chap-
ters. For example, although Chapters 4 and 5 both report studies of deforestation
in Amazonia, they rely on data at very different levels of spatial resolution. Each
research area probably has its own diverse needs for resolution, depending the
scientific questions being asked. This variety of needs in terms of resolution
suggests that any standardized system of data storage and geocoding should be
highly flexible.
Data maintenance is another important challenge. Many research applica-
tions require intact time series of remotely sensed data and benefit increasingly as
the time series are extended. For example, improvement of the famine early
warning systems described in Chapter 9 depends on continued enhancements to
models that account for past data on climate variations, crop production, human
response, and famine. The same is true for achieving the promise of public health
early warning systems, as described in Chapter 10; for modeling population and
land-use dynamics, as described in Chapters 4 and 6; and undoubtedly for many
other scientific purposes as well. Thus, maintenance of old data sets is a matter of
continuing importance.
The cost of remotely sensed data is a matter of considerable concern among
researchers. A major issue for the research community is the increasing cost of
data maintenance. The sheer volume of remotely sensed data is increasing rap-
idly as more platforms are launched and as they provide increasingly finer reso-
lution. The costs of transforming the raw data into useful forms, of cataloguing
them, of storing and maintaining them, and of making them available increase
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22 HNKING REMOTE SENSING AND SOCKS SCIENCE: THE NEED AND THE CHANGES
more rapidly than the volume of data because data archives must continually
maintain the old data as well as the new. The volume of data increases as new
data come in, and new technology often brings the additional problem of translat-
ing between different forms of data storage. The cost issue is multiplied further
as data from military satellites are declassified and become available. In many
cases, military and intelligence organizations have no use for the old data and
therefore no incentive to make them useful for social science, regardless of their
potential value. Yet the older material from military sources may be especially
important for social science because it extends time series further backward with
levels of spatial resolution that are not available from any other source for his-
toric data. Thus, the cost of data systems is growing in importance just as the data
are becoming increasingly useful for social science.
Another cost issue concerns the cost of data to potential users. Here, the
situation is highly fluid. U.S. government policy has so far kept remotely sensed
images in government data systems relatively inexpensive to scientists. How-
ever, cost depends on the platform and on whether any government agency has
ordered a particular scene. In addition, budgetary pressures and tendencies to-
ward commercialization of data systems may alter the situation. We believe it is
important for science that the government maintain its policy of keeping remotely
sensed data inexpensive or make sufficient research funding available so that
scientists have access to the necessary data.
PLAN OF THE VOLUME
This volume is intended to be useful to researchers and research sponsors
who are considering what they can do to foster or participate in collaborations
between remote sensing and social science. It is divided into two main sections.
The first, consisting of Chapters 1 through 3, provides conceptual and historical
background. The second, consisting of Chapters 4 through 10, offers case ex-
amples that illustrate the uses and potential applications of remote sensing for
social scientific purposes. Each of these case examples describes a research area
in which the effort to link remote sensing and social science shows promise for
advancing knowledge. The cases also indicate what is involved, both intellectu-
ally and in practical terms, in achieving that promise. The most intensive cover-
age is given to research on land-use change in rural areas of developing countries
(Chapters 4 through 7) because this is currently the area of the most intensive
research activity involving collaboration between social scientists and remote
sensing specialists. Chapters 8 through 10 present applications to urban land-use
issues, famine early warning, and public health that illustrate some promising
frontiers for social scientific use of remotely sensed data. The volume does not
include other actual or potential social science applications of remote sensing.
For example, remote sensing is used in mapping the impacts of and recovery
from natural disasters and can be used in research on disaster response. It may
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RONALD R. RINDFUSS AND PAUL C. STERN
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also be possible to link remotely sensed data on atmospheric trace gas concentra-
tions to ground-based data on industrial activity in order to improve models that
link human activities to their environmental consequences.
In Chapter 2, Morain provides a historical perspective by examining past
relationships between remote sensing and social science, focusing especially on
the long history of the Landsat program. Although there has recently been a
proliferation of remote sensing platforms that have great potential usefulness for
social science, Landsat and AVHRR have until now been the main space-based
platforms used in social science research. The chapter, though perhaps fragmen-
tary from a remote sensing perspective, provides a good account of the history of
the sources of space-based data most commonly combined with social science
data. A good source for more detail on the full range of remote sensing technol-
ogy is Ryerson (1996~.
In Chapter 3, Geoghegan and colleagues discuss the major issues that emerge
from the most extensive current effort to link remote sensing and social science-
the Land-Use/Cover Change (LUCC) research program, sponsored by the Inter-
national Geosphere-Biosphere Programme and the International Human Dimen-
sions Programme on Global Environmental Change. Among these issues are
those of spatially explicit modeling in the social sciences, analyses that make
links across spatial scales and levels of analysis, and the problem of developing
effective concepts and analytical methods for simultaneously analyzing changes
in time and in space.
Chapters 4 and 5 describe research projects in Amazonia that use remote
sensing of land cover to examine such issues as the effects of human population
dynamics on deforestation and the effects of deforestation on land-use change.
The two studies, though focused on the same geographical region, differ greatly
in the levels of analysis at which they examine data and in the variables they
select for study. Chapter 6 examines population dynamics and land-use and land-
cover change at the village level in one district in Thailand. Of particular interest
is the project' s effort to use both social and remotely sensed data in time series to
illuminate mutual causation between population dynamics and land-use change.
Chapter 7 examines land-use change in the Peten region of Guatemala. As in
other areas where deforestation is progressing, remote sensing is used to track the
process and its relationships to social driving forces such as road construction and
land development or preservation policies. The chapter, which focuses on an
important region for archaeological research, shows how remote observation has
been used to identify sites not previously discovered from the ground.
Chapters 8, 9, and 10 present some promising frontier areas for linking
remotely sensed data and social science. These areas are promising because
remote sensing has already demonstrated its relevance to socially important phe-
nomena; greater integration of social science concepts is likely to yield further
practical and scientific advances. In Chapter 8, Cowen and Jensen identify a
variety of remotely sensed attributes that could be used for analysis of urban
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24 HNKING REMOTE SENSING AND SOCKS SCIENCE: THE NEED AND THE CHANGES
dynamics in the United States. They specify the degree of spatial and temporal
resolution required to measure change in these attributes and, in an important
contribution, compare these data requirements with the capabilities of existing
remote sensing platforms. The comparison suggests some areas in which exist-
ing remotely sensed data could be used more extensively in social science re-
search and others in which such uses are likely to become possible as soon as
higher-resolution remote data become available. For example, remote platforms
might be able to measure an underinvestigated and potentially important set of
social variables represented by the physical characteristics of neighborhoods.
Arguably, the behavior of significant outsiders, such as social service providers
and mortgage lenders, is shaped more by stereotyping based on a neighborhood's
observable physical characteristics than by the actual attributes of its inhabitants.
Chapter 8 also provides an illustration of the use of remotely sensed data to model
and predict the course of urban development.
Hutchinson (Chapter 9) discusses some applications of remote sensing and
social science to famine early warning systems in Africa. Remote data are
increasingly useful for monitoring and forecasting crop production the supply
side of famine and also valuable for measuring roads and other infrastructure
important to food distribution. However, famine also depends on the economic
demand for food and the economic and political institutions that allocate food or
restrict its availability factors much more easily measured by standard social
scientific methods. Thus, famine early warning can best be accomplished by
collaborative efforts. The same can probably be said for basic understanding of
food systems and their implications for the nutritional status of populations. It
has often been argued, for example, that famine is more often a result of inad-
equate food distribution due to war, economic inequality, or the practices of
repressive governments than of supply shortfalls. Nevertheless, famine and nu-
tritional deficits are almost certainly the result of an interaction of factors: often,
both supply shortages and interferences with distribution are necessary condi-
tions. The study of how food production interacts with various political and
economic forces is likely to be advanced by combining remotely sensed and
ground-based data within the same analytical schemes.
In Chapter 10, Epstein reports on uses of remote sensing to monitor and
anticipate the emergence of infectious disease outbreaks. Until now, this work
has involved contributions from remote sensing experts, ecologists, epidemiolo-
gists, and public health specialists without explicit social science involvement.
However, there are opportunities for the involvement of researchers in such fields
as disease prevention, health promotion, and risk communication, as well as for
interdisciplinary research linking the ecolo~v of disease organisms and vectors
with human ecology.
C7 C7 ~C7
Two appendices are intended as resources for social scientists who are rela-
tively unfamiliar with remotely sensed data. Appendix A provides a guide to
numerous major sources of remotely sensed data. Because the data sets change
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RONALD R. RINDFUSS AND PAUL C. STERN
25
so rapidly, this appendix will be updated periodically on the World Wide Web.
Appendix B offers a glossary of technical terms used both in remote sensing and
in social science with which experts in either field might need to be familiar when
venturing into the other.
ACKNOWLEDGMENT
We thank John Estes and B.L. Turner II for their very helpful comments on
earlier drafts.
NOTES
1 Definitions of this and other technical terms can be found in Appendix B.
2 Availability of satellite data changes rapidly. Appendix A identifies a number of current
information sources and a Web site that will provide updated information. At the time of this
writing, we are told that several improvements in resolution are just over the horizon. The French
Systeme Pour ['Observation de la Terre (SPOT) satellite, scheduled for launch in 1998, is to include
a 20-meter resolution mid-infrared band in the 1.55-1.75 microns range, 10-meter resolution in the
red and green visible bands and the near infrared, and 2.5-meter resolution in the panchromatic band.
Updated information on this satellite may be obtained from WWW.spot.com. A private company,
EarthWatch Inc., launched the EarlyBird satellite in December 1997 with 3-meter resolution and
plans to launch the QuickBird satellite in 1998 with 1-meter resolution, both panchromatic. Updated
information on these satellites may be obtained at WWW.digitalglobe.com. India plans to launch a
series of satellites with 1-meter resolution, IKONOS 1 and 2, in late 1997 and 1998. In addition,
declassified high-resolution data from military and intelligence satellites from the 1960s and early
1970s are beginning to be made available.
3 Partly for economy of exposition and partly to make some of our points more emphatically,
we refer to social science and remote sensing as fields. One reviewer questioned whether remote
sensing is a "field," a point that raises the question of whether social science is a "field." The latter
question is the easier of the two: social science is actually a collection of fields. The status of remote
sensing is more ambiguous. As the reviewer correctly noted, remote sensing is in one sense only a
technology or a tool. Courses in remote sensing are taught in a variety of departments, including
geography, geology, landscape architecture, oceanography, and forestry. However, remote sensing
also has many of the characteristics of a field. There is a specialized language that those in the
remote sensing area know and use, and outsiders do not understand. There are scientific journals
devoted to remote sensing topics, and there are annual meetings for those who speak the remote
sensing language. Thus, despite the room for disagreement, we refer to both remote sensing and
social science as "fields."
4 With few exceptions (e.g., Chapter 8), the researchers represented in this volume have used
satellite data rather than aerial photographs. Among the studies we know that use remotely sensed
data and social science data together, the vast majority use satellite data rather than aerial photo-
graphs. There are probably a number of reasons for this. While the situation varies from country to
country and region to region, in general it is more difficult to find the needed images in the form of
aerial photographs than as satellite images. Satellites, because they orbit the earth, collect data for
much of the earth's surface, while aerial photographs cover much smaller sections of the earth.
Further, the various organizations that have emerged to sell and distribute satellite data tend to have
regional or global coverage. On the other hand, those that distribute or sell aerial photographs tend
to operate at the national level, or even finer scales. In addition, for many areas of the world, the
search costs to determine the availability of aerial photographs are formidable. Satellite data cover
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26 LINKING REMOTE SENSING AND SOCIAL SCIENCE: THE NEED AND THE CHALLENGES
more of the spectral range, sometimes including the near and thermal infrared, whereas aerial photo-
graphs are typically panchromatic (black and white). Finally, satellite data typically come in digital
format allowing for easier incorporation into a geographic information system (GIS), but aerial
photographs are typically in analog format, thus requiring additional work before they can be incor-
porated into a GIS.
5 Roads illustrate some of the limits of remote sensing for measuring visible phenomena. Most
sensors "see" the highest level of cover between the satellite and the earth, although there are excep-
tions, such as radar data. Thus on a cloudy day, most sensors will see the clouds. If a road is tree
lined and well shaded by the trees, on a clear day the typical satellite platform will provide an image
of the trees and not the road.
6 The specifics, of course, depend on the constellation of techniques being used to locate all
households.
7 Most satellite platforms do not capture the poles. However, given the limited human popula-
tion at the poles, we can consider the data from most satellite platforms to be global from a social
science perspective. Even though the data from many satellite platforms are essentially global, the
uses of the data need not be global. Indeed, the examples in this volume are much more localized.
8 Aerial photographs are not subject to this pixel constraint. Instead, properties of the photog-
raphy determine the minimum mapping unit, which can function like a pixel in that it determines
what one can "see" in an aerial photograph.
9 Market research firms typically compile social data at much lower levels of aggregation than
governments do in the United States, at the level of the postal zip code or even the zip-plus-four,
which typically corresponds to a geographic area that encompasses the residences of a few dozen
households. Some elements of these privately held data sets, such as data on consumer expenditures,
are parallel to data collected by governments but available to clients at finer resolution. These data
have not to our knowledge been used much in social science research.
REFERENCES
Bureau of the Census
1993 1990 Census of Population. Social and Economic Characteristics: United States. Bu
reau of the Census, U.S. Department of Commerce. Washington, D.C.: U.S. Govern
ment Printing Office.
Carts, N.
1947 How to Read Aerial Photographs for Census Work. Washington, D.C.: U.S. Govern
ment Printing Office.
Colwell, R.R.
1996 Global climate and infectious disease: The cholera paradigm. Science 274:2025-2031.
Estes, J.E., J.R. Jensen, and D.S. Simonett
1980 Impacts of Remote Sensing on U.S. Geography. Remote Sensing of Environment 10:43-
80.
Faust, K., B. Entwisle, R.R. Rindfuss, S.J. Walsh, and Y. Sawangdee
1997 Spatial Arrangement of Social and Economic Networks among Villages in Nang Rong,
Thailand. Paper presented at the annual meeting of the Sunbelt Social Network Confer-
ence, San Diego, Calif.
Hosenball, S.N.
1990 International and U.S. domestic law governing remote sensing. Pp. 125-140 in Earth
Observation Systems: Legal Considerations for the '90s. Bethesda, Md. and Chicago,
Ill.: American Society for Photogrammetry and Remote Sensing and American Bar Asso-
ciation.
OCR for page 27
RONALD R. RINDFUSS AND PAUL C. STERN
27
Lucas, R.M., M. Honzak, G.M. Foody, P.J. Curran, and C. Corves
1993 Characterizing tropical secondary forests using multi-temporal Landsat sensor imagery.
International Journal of Remote Sensing 14:3061-3067.
Moran, E., E. Brondizio, P. Mausel, and Y. Wu
1994 Integrating Amazonian vegetation, land use, and satellite data. BioScience 44(5):329-
338.
National Research Council
1997 Rediscovering Geography: New Relevance for Science and Society. Rediscovering Ge-
ography Committee. Washington, D.C.: National Academy Press.
Rindfuss, R.R., S.J. Walsh, and B. Entwisle
1996 Land Use, Competition, and Migration. Paper presented at the annual meeting of the
Population Association of America, New Orleans, La.
Ryerson, R.A., ed.
1996 Manual of Remote Sensing. 3rd edition (CD-ROM). Bethesda, Md.: American Society
for Photogrammetry and Remote Sensing.
Skole, D.L., W.H. Chomentowski, W.A. Salas, and A.D. Nobre
1994 Physical and human dimensions of deforestation in Amazonia. BioScience 44(5):314-
322.
Turner II, B.L.
in Frontiers of Exploration: Remote Sensing and Social Science Research. In Proceedings
press of Pecora 13. Bethesda, Md.: American Society for Photogrammetry and Remote Sens-
ing.
Uhlir, P.F
1990 Applications of remote sensing information in law: An overview. Pp. 8-22 in Earth
Observation Systems: Legal Considerations for the '90s. Bethesda, Md. and Chicago,
Ill.: American Society for Photogrammetry and Remote Sensing and American Bar Asso-
ciation.
Representative terms from entire chapter:
sensed data
