Construção e avaliação de mapas

Abstract

The precision agriculture (PA) is defined as the use of site specific management techniques that allow cropping management according to its needs and soil, in order to reduce impacts on the environment. The models valuation that interpolates field data and generates thematic maps is a task that requires in-depth knowledge on this issue. The Cohen Kappa index (K) is the most widely statistics used to compare thematic maps. The accuracy with which the spatial distribution maps of soil attributes are produced influences the implementation and use of PA. However its use has the disadvantage of providing variation in accordance with the use of different numbers of classes, adopted during the map generation process. Thus, this work aimed at selecting the best method among four interpolation ones (inverse distance, inverse distance squared, ordinary kriging and cokriging) using the index of selection of interpolators (ISI), proposed here, based on the contents of clay, copper and manganese, area elevation data and the apparent soil electrical conductivity. The selection among mathematical models and geostatistical interpolation was simplified using the ISI. The study also evaluated the influence in agreement K and Tau (T) indices when varying the number of confusion matrix classes in results that come from a 15.5 ha area, with typical Red Dystrophic soil in Céu Azul countryside, Paraná, Brazil, where soil properties were interpolated with the inverse distance, inverse square of distance, ordinary kriging and cokriging. According to this trial, K and T indices have been confirmed and varied widely of agreement for different numbers of classes. Thus, in order to solve this situation, it was developed K and T equivalent indices to compare thematic maps of quantitative data, using the relative deviation coefficient ,the absolute averaged deviation of interpolated data, the average, and standard deviation of the attribute original data. The result proved to be a good alternative to K and T indices based on the error matrix since it is independent of the classes number and shows a simpler calculation. The methodology was more efficient for situations when more than four classes are used.