The quality of poverty alleviation programmes relies heavily on appropriate targeting and priority setting. Major problems in assessing poverty include identification of the indicators of poverty and the methods used for its assessment. Nutritional status, expressed by anthropometric indices, has been proposed as a poverty indicator because of its validity, objectivity, reliability and feasibility. This study was conducted to explore the application of remote sensing to poverty mapping based on nutritional status at the community level. Relationships between the nutritional status within a community and the ecological characteristics of the community were investigated. Multiple linear regression tests were executed, and the resultant equations were tested for their validity in predicting communities with poor nutritional status. Among geographical and ecological indicators used, distance to the nearest market, main soil type, rice field area, and perennial cultivation area were found to be most useful predictors for the ranking of the communities by nutritional status. Among non-ecological determinants, food consumption, health service status and living conditions were also found as predictors. The highest correlation was found if total population was also taken into account in the regression model (R2 = 0.69; p < 0.0001). In the assessment of the sensitivity and specificity of the eight models studied, ‘undernutrition’ was defined as a condition where a community belongs in the first quartile for nutritional status (highest prevalence of undernutrition), and the baseline nutritional survey was considered as a standard method for final diagnosis. Most models which included only ecological factors in the equations had lower sensitivity and specificity than models which included all determinant factors in the equations. All models which took into account the total population had higher sensitivity and specificity than those that did not take total population into account. The best model of those that took into account only the geographical and ecological characteristics of the community’s living environment had similar sensitivity and specificity (80% and 94.1%, respectively) as the models that considered non-geographical and non-ecological variables in addition to geographical and ecological variables. In the case of West Sumatra, only four ecological and geographic characteristics were sufficient to predict poverty in villages. Since these characteristics could be surveyed by remote sensing, it may well be possible to use remote sensing for a rapid method for poverty mapping.
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