ABSTRACT
This paper examines the use of remote sensing satellite data to predict food shortages among different categories of households in famine-prone areas. Normalized Difference Vegetation Index (NDVI) and rainfall estimate data, which can be derived from multi-spectral satellite radiometer images, has long been used to predict crop yields and hence famine. This gives an overall prediction of food insecurity in an area, though in a heterogeneous population it does not directly predict which sectors of society or households are most at risk.
In this work we use information on 3094 households across Uganda collected between 2004--2005. We describe a method for clustering households in such a way that the cluster decision boundaries are both relevant for improved-specificity famine prediction and are easily communicated. We then give classification results for predicting food security status at a household level given different combinations of satellite data, demographic data, and household category indices found by our clustering method. The food security classification performance of this model demonstrates the potential of this approach for making predictions of famine for specific areas and demographic groups.
- M. E. Brown, J. E. Pinzon, and S. D. Prince. Using Satellite Remote Sensing Data in a Spatially Explicit Price Model: Vegetation Dynamics and Millet Prices. Land Economics, 84(2):340--357, 2008.Google Scholar
- Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119--139, 1997. Google ScholarDigital Library
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 2009. Google ScholarDigital Library
- J. U. Hielkema and F. Snijders. Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO. Acta Astronautica, 32:603--616, 1994.Google ScholarCross Ref
- C. F. Hutchinson. Use of satellite data for famine early warning in sub-Saharan Africa. International Journal of Remote Sensing, 12(6):1405--1421, 1991.Google ScholarCross Ref
- M. M. Khan, N. Mock, and W. B. Bertand. Composite Indicators for Famine Early Warning Systems. Disasters, 16(3):195--206, 2007.Google Scholar
- E. Mwebaze, W. Okori, and J. Quinn. Causal structure learning for famine prediction. In In Proceedings of the Association for the Advancement Artificial Intelligence (AAAI) Spring Symposium on AI-D, 2010.Google Scholar
- M. Shrestha, G. Artan, S. Bajracharya, and S. R. R. Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin. Journal of Flood Risk Management, 1:89--99, 2008.Google ScholarCross Ref
- H. Silvia, R. Fensholt, K. Rasmussen, S. R. Proud, and A. Anyamba. Improving early warning systems for food security in Africa with geostationary earth observation data. Earth and Environmental Science, 6(47):472007, 2009.Google Scholar
- UBOS. Uganda National Household Survey 2005/2006, Report on the Agricultural Module. Technical report, Uganda Bureau of Statistics, 2007.Google Scholar
- USAID. Uganda Food Security Outlook, January to June 2010: Famine Early Warning Systems Network. Technical report, USAID, 2010.Google Scholar
- P. Xie and P. Arkin. Analyses of global monthly precipitation using gauge observations, satellite estimates and numerical model predictions. Journal of Climate, 9:840--858, 1996.Google ScholarCross Ref
Index Terms
- Increased-specificity famine prediction using satellite observation data
Recommendations
Forest fire monitoring and burnt area mapping using satellite data: a study over the forest region of Kerala State, India
The present study describes the night-time active forest fire detection capabilities of Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) satellite data over the forest region of Kerala State, India in 2004. Kerala State ...
Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months
The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary ...
Estimation of forest biophysical variables from Indian Earth Observation Satellite Cartosat-1 stereo data
Indian Earth Observation Satellite Cartosat-1 data were evaluated for the estimation of biophysical variables, including as tree height, crown diameter, canopy density and canopy gap, that are crucial for the estimation of stand volume, biomass and ...
Comments