Abstract: Computer based geostatistical methods can offer effective data analysis possibilities for agricultural areas by using
vectorial data and their objective informations. These methods will help to detect the spatial changes on different locations of the large
agricultural lands, which will lead to effective fertilization for optimal yield with reduced environmental pollution. In this study, topsoil (0-20 cm) and subsoil (20-40 cm) samples were taken from a
sugar beet field by 20 x 20 m grids. Plant samples were also collected
from the same plots. Some physical and chemical analyses for these
samples were made by routine methods. According to derived variation coefficients, topsoil organic matter (OM) distribution was more than subsoil OM distribution. The highest C.V. value of
17.79% was found for topsoil OM. The data were analyzed
comparatively according to kriging methods which are also used
widely in geostatistic. Several interpolation methods (Ordinary,Simple and Universal) and semivariogram models (Spherical,
Exponential and Gaussian) were tested in order to choose the suitable
methods. Average standard deviations of values estimated by simple
kriging interpolation method were less than average standard
deviations (topsoil OM ± 0.48, N ± 0.37, subsoil OM ± 0.18) of measured values. The most suitable interpolation method was simple
kriging method and exponantial semivariogram model for topsoil,
whereas the best optimal interpolation method was simple kriging
method and spherical semivariogram model for subsoil. The results
also showed that these computer based geostatistical methods should
be tested and calibrated for different experimental conditions and semivariogram models.
Abstract: Various methods of geofield parameters restoration (by algebraic polynoms; filters; rational fractions; interpolation splines; geostatistical methods – kriging; search methods of nearest points – inverse distance, minimum curvature, local – polynomial interpolation; neural networks) have been analyzed and some possible mistakes arising during geofield surface modeling have been presented.
Abstract: Field mapping activity for an active volcano mainly in
the Torrid Zone is usually hampered by several problems such as steep
terrain and bad atmosphere conditions. In this paper we present a
simple solution for such problem by a combination Synthetic Aperture
Radar (SAR) and geostatistical methods. By this combination, we
could reduce the speckle effect from the SAR data and then estimate
roughness distribution of the pyroclastic flow deposits. The main
purpose of this study is to detect spatial distribution of new pyroclastic
flow deposits termed as P-zone accurately using the β°data from two
RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and
field observation were used for cross-validation of the SAR results.
Mt. Merapi in central Java, Indonesia, was chosen as a study site and
the eruptions in May-June 2006 were examined. The P-zones were
found in the western and southern flanks. The area size and the longest
flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The
grain size variation of the P-zone was mapped in detail from fine to
coarse deposits regarding the C-band wavelength of 5.6 cm.