Exploring the spatial associations between census based socioeconomic conditions and remotely sensed environmental metrics in Assam northeast India

Abstract

This thesis explores and quantifies the associations between socioeconomic variables and environmental metrics. Remotely sensed satellite data is often used to monitor environmental conditions. However, it is less frequently used for socioeconomic purposes. Several studies have attempted to use remotely sensed data to monitor socioeconomic conditions in urban areas. Non-causal associations between poverty and development and environmental conditions are frequently found in the scientific literature for rural areas of developing countries. This research uses environmental metrics derived from remotely sensed imagery from an Earth observation satellite to explore if associations, similar to those in the literature, can be found for extensive spatial areas. If non-causal associations can be found between census-based socioeconomic variables and remotely sensed environmental metrics it may be possible to use remotely sensed imagery as a limited, but valuable source of information regarding socioeconomic conditions of rural communities. Socioeconomic data is collected in national census datasets at the household level. However, this fine spatial resolution means that it is an expensive process and is typically only conducted once every 10 years. This coarse temporal resolution limits the relevance of census data for planning resource allocation by governments and targeting development assistance, especially in rapidly changing economies. Therefore, the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may provide a way of increasing the understanding of information available to policy makers for monitoring socioeconomic conditions.

An extensive area of Assam in northeast India was used as a case study to explore the associations between socioeconomic variables derived from the Indian national census and remotely sensed environmental metrics derived from Landsat Enhanced Thematic Mapper Plus (ETM+) data. Field work first identified; (i) two socioeconomic variables that appeared to be associated with poverty which were female literacy and participation in economic alternatives to agricultural work, and; (ii) a series of land cover types that appeared to be associated with broad level socioeconomic conditions. Cloud and transparent cloud cover were removed from satellite data prior to an object-based land cover classification which defined nine land cover types identified as having potential associations with poverty in the literature and a field work study.

Socioeconomic and environmental data were integrated at the village level prior to statistical analysis. No village boundary information was available and therefore, research aimed to identify the most appropriate method of approximating the village boundary using Thiessen polygons and several radial buffer zones. Statistical analyses were conducted to explore; (i) the associations between female literacy and economic alternatives to agricultural work and several environmental metrics, and; (ii) which village boundary approximation provided the lowest AIC model fit statistic. Logistic regression and generalised autoregressive error models explored the associations between socioeconomic conditions and environmental metrics on a global level. Geographically weighted logistic regression was also used to explore the spatial variation in the associations.

Findings indicated that significant associations exist between female literacy and economic alternatives to agricultural work and remotely sensed environmental metrics. Many of the associations identified could be interpreted meaningfully in relation to both the understanding gained from field observations and in relation to generally accepted associations in the literature. Thus, the quantitative findings of the research were in keeping with expectations and research hypotheses, lending credibility to the associations observed by other researchers. The methods used here could be developed further and the increased temporal resolution that remotely sensed imagery offers over the traditional ground survey methods may, in the future, increase the relevance and understanding of information available to policy makers for monitoring socioeconomic conditions.

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Identifiers

ORCID for P.M. Atkinson: orcid.org/0000-0002-5489-6880

ORCID for Craig W. Hutton: orcid.org/0000-0002-5896-756X

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