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• Spatially Continuous GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)

  56-61

  János Tamás

  István Buzás

  Ildikó Nagy

  Views:

  107

  The homogeneity of a study area of 20x20 m used for beetroot production in North-West Hungary was analysed with geo-statistical methods on the basis of measured plant and soil parameters. Based on variogram calculations (Equation 1 and 2), the yield surface showed homogeneity in North-South direction. Considering the results, decrease of sampling distance to 17 m can be suggested. The direction of the variability of yield (Figure 1) could be modelled with a direction variogram based on analysis of the variogram surface. In the study, developed methodological processes are presented for the analysis of spatial relationship between measured production and soil parameters. 5 spatial evaluation methods for yield surface were compared (Table 1). On the basis of the analysed methods, it can be stated that different methods (LP, RBF) should be used when the reasons for locally extreme yields are in focus than in case when the yield surface of the whole area is estimated (IDW, GP). Using adequate parameters the kriging method is applicable for both functions. Similarly to the results of an ordinary Pearson correlation analysis, spatial correlation analysis was shown using soil pH and Cu concentration data. The results of cross variogram analysis (Equation 2) and the North-South direction of the variogram surface showed negative correlation (Figure 3). Based on simulation calculations, decrease of 30% in sampling points resulted in increase of 12% in error for the total sample number considering Cu concentration. The method provides a tool to decrease the cost of sampling and sample analyses of spatially correlating features, and to increase the reliability of spatial estimation using a better sampling strategy with the same sample number.

• Usage of different remote sensing data in land use and vegetation monitoring

  7-12

  Péter Burai

  Views:

  114

  The use of remote sensing in forest management and agriculture is becoming more prominent. The rapid development of technology allowed the emergence of database suitable for precision application in addition to the previously used low-resolution and low data content images. The high resolution, hyperspectral images are not only suitable for separating the different land use categories and vegetation types but also for examining the soil characteristics and biophysical features of plants (Blackburn and Steel, 1999; Condit, 1970). We processed a multispectral satellite image (Landsat 7 ETM+) and a hypespectral areal image (DAIS 7915) about a farm on the plains and evaluated the different image classification methods. During our examinations, we examined the geometrical and radiometrical characteristics of images first, then assigning the training areas, we determined the spectral characteristics of land use categories. We performed a multispectral analysis for checking land use, where we compared controlled and uncontrolled classification systems to check their reliability. We used areal and spectral reductions to make the classifications more accurate and to reduce the length of calculations.