Introduction

Remote sensing technology provides a wide array of opportunities and advantages to social and environmental research, however it remains vastly under-used by the majority of researchers in these fields. Remote sensing allows researchers to access surficial biophysical parameters of human and natural environments for virtually every area on earth since 1972. It offers a variety of micro-scale variables in digital format which are helpful towards measuring the context of social phenomena, the effects and extent of anthropogenic activity, change over time in human and natural environments, and the spatial relationships between social and environmental phenomena (Liverman et al., 1998). However, challenges remain in the process of integration of this data into larger social research projects and environmental studies. As with most cross-disciplinary work, obstacles stem from the differences between scientific traditions, methods, terminology, and analytical approaches (Liverman et al, 1998). This is especially true for the average social or environmental scientist who may know little about the spectral characteristics of vegetation or, for that matter, the difference between satellite imagery and fancy pictures from space. However, integration of remote sensing technology into mainstream social and environmental research is increasingly common and has been used in a wide array of studies on everything from climate change models, to agricultural development, to urban sprawl and population dynamics.

In working towards the integration of remote sensing technologies and social/environmental research, two primary questions should be discussed. What level of sophistication in data analysis is necessary and realistic for social scientists? And how can integration of remotely sensed data into mainstream research be improved? These questions, while important, cannot be answered simply in the time and space of this paper. Instead, I have used them as a guiding force in exploring the challenges and uses of remotely sensed data in a specific social and environmental research topic. Through this and further case studies we can ask more concrete questions and begin to propose more substantive answers to the questions above.

The case study presented here is an initial analysis of the environmental impacts of the Green Revolution in a region of western India. India, like many areas, is a region where reliable data on land use and land cover does not exist for most time periods. It also has extremely diverse and many times harsh environments with little transportation infrastructure for access into rural areas, making data collection in the field extremely difficult. Additionally, the issue of evaluating environmental changes due to social phenomena is precisely the type of problem that could benefit from remote sensing technology. This paper splits this issue into two main focus questions. How should vegetation change data be derived from satellite imagery for the purposes of evaluating environmental changes in a particular region of India during the 1970's? And how can vegetation change data be effectively utilized to evaluate the environmental impacts of the Green Revolution in this region? It is hoped that the answers to these questions will aid and encourage the incorporation of satellite imagery into environmental and social research in the future.

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