People and Pixels: Linking Remote Sensing and Social Science


From now, insyaallah I’ll share with you about my E-book in Remote Sensing. For the first is “People and Pixel: Linking Remote Sensing and Social Science”

OK, let’s begin…

Book Cover
Book Cover

People and Pixels: Linking Remote Sensing and Social Science

By: Ronald R. Rindfuss and Paul C. Stern

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 building 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 important in their fields of study (Turner, in press).

In the other hand, social science is generally more concerned with why things happen than where they happen.

Finally, bridging the social science and remote sensing fields undoubtedly entails the risks frequently encountered by those who do interdisciplinary research.


Measuring the Context of Social Phenomena

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 mapmaker’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 methods 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.

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 consequences of various social, economic, and demographic processes. For example, remote observations of land cover may show the footprints of

agricultural extensification, urbanization, and road development;5 observations of vegetation density 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.

Providing Additional Measures for Social Science

For example, agricultural intensification can be measured by using data from surveys of farmers’ behavior, sales figures on agricultural chemicals 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. Urbanization can be measured by counting building permits, sampling and observing city blocks, or remotely sensing the proportion of land covered by structures.

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 undercount. The use of satellite images with high spatial resolution might improve this process

Remotely sensed data have been used for measuring other socially significant variables, especially in urban and suburban contexts. the use of remote observation to classify land use and land cover into categories; to measure the area, height, and volume of buildings; to measure traffic patterns and road conditions; to estimate residential energy demand; and to build predictive models of residential expansion.

Clearly, remote sensing is well suited to providing comparable data for different geographic regions or at different times.

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, psychologists 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.

Providing Time-Series Data on Socially Relevant Phenomena

Time-series data can be helpful when social scientists attempt to trace relationships 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 processes of human-environment interaction. Examples; thinning of forests by human action, forest regrowth after clear-cutting, and development of algal blooms that harbor pathogens. In addition, remotely sensed time-series data can be essential for modeling human-environment interactions. Examples; the use of remotely sensed data to model the effects of access to forests on out-migration, and processes of land conversion to urban uses.


Validation and Interpretation of Remote Observations

Remote sensing specialists are well aware of the need for “ground truthing,” that is, for validating remote observations against data collected on the ground. An important 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

landscaping, 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 differences in tenure. Discovering such differences would likely require collaboration between remote sensing specialists who can distinguish spectral patterns and social scientists who can classify land-tenure types and land-management practices.


Interpreting, Modeling, and Predicting the Dynamics of Natural Resources

Example; To predict deforestation, habitat fragmentation, and secondary growth in a region from census data on population dynamics, economic activity, and other social indicators at the regional level. The dynamics of mutual causation between land cover and human migration, with implications for resource demands in both rural and urban areas, and the effects of human settlements and public policy on forest cover.

Finding the Appropriate Spatial and Temporal Resolution

Decisions about the appropriate scale, level of aggregation, and frequency of measurement of various data are driven by considerations of both theory and data availability. On the theoretical level, the appropriate units of analysis depend on the question being asked. For example, the debate over global warming rests mainly on questions about what is happening on a global scale and on a temporal scale of decades to centuries. By contrast, questions about population migration turn on the decisions of individuals and households. Some questions require analysis at multiple scales. For example, questions about land use and land cover typically require information at the level of individuals and households that may own the land and make many of the decisions on how it is used, local governments (because they often regulate land use and make decisions about the location of transportation infrastructure), and governments at higher levels.


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