Remote sensing and Geographical Information Systems (GIS) are important tools used for assisting agricultural surveys. Such tools can be used to stratify the population samples in a study area, optimizing and reducing the costs of field work. Nevertheless, defining the number of samples to be visited in the field is a challenging task. In the presented research, the sampling strategy for agricultural survey was addressed by integrating GIS, remote sensing techniques and a Monte Carlo simulation. A study case was carried out in the Taita Hills, Kenya to test the operational viability of the method. The applied approach allowed the estimation of crop areas with reduced uncertainties and management of the errors.
Key words: Agricultural survey, Taita Hills, Monte Carlo simulation, remote sensing.
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