Digital Mapping of Farmland Classes in Three Landscape in Mexico

Abstract

The cartography of farmland classes allows generating land maps, using a methodology based on local knowledge, rapidly and at low cost, and with a greater number of cartographic units than conventional soil surveys maps. However, the results found when producing these maps with automated cartography techniques are contrasting. Precision and accuracy were evaluated in 324 computer generated farmland class (FLC) maps by applying the Inverse Distance Weighted (IDW) interpolation model. These maps were obtained by varying the sample size for the training, its spatial design, and the Power value of the interpolator. Moreover, the effort needed to obtain maps with acceptable reliability was quantified. The procedure was applied to FLC maps obtained from surveys with producers from three contrasting environmental zones in Mexico. The results show that the best sampling scheme in the three areas is the systematic sampling, and Power 8, giving the maps with the highest reliability. Through the criterion of map reliability and effort needed for sampling, the recommended sample size is 10% to 25% of the total plots.

Potential Distribution of Humid Mountain Forest in Mexico

“…Hernandez et al (2006) considered 50 records represent a reasonable size to carry out modeling exercises; however, the study area size needs to be considered. Cruz-Cárdenas et al (2010) suggest that sample size correspond to 15-20% of pixels covering the study area if satisfactory modeling results are expected. Therefore, considering that the scale of Rzedowski's potential vegetation map (Rzedowski, 1996) is 1:4 000 000, the pixel's size is 4 × 4 km (Bishop et al, 2001); if we divide the surface of HMF estimated in such a map (11,719 km 2 ), we fi nd that 733 pixeles occupy the surface of this biome.…”