Full text: Resource and environmental monitoring

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4). The 
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e. This 
iple size. 
and 80 
b) With the decrease of sample size, the differences between 
the relative variances of estimations, corresponding to separate 
sampling plans, do also decrease. The reason for this is that the 
total sample, which was used for the calculations of the error of 
separate sampling plans, does contain the data selected for the 
testing of the sampling plans, and in case of large sample sizes 
the overlap between the two data sets and samples is larger. 
c) There is not significant difference between the weighted and 
non weighted sampling plans. 
d) The estimation errors decrease from sampling stratum ] to 
stratum 4, but the errors of strata 3 and 4 are practically the 
same. The reason for the decrease of error is the decrease in 
GR. . 
  
Estimation error 
non-weighted sample 
  
  
  
  
  
  
  
  
2.5 
t o 
9 20 o g 2 
o LT 
g = 15 A4 strata 
E = 1.0 02 strata 
2 
2 ost — 
o | 
0.0 + } i } 4 
0 20 4 60 80 100 
Sample size 
  
  
Estimation error 
4-strata, non-weighted sample 
  
  
  
  
  
  
  
  
4 el ising 
5 AA Astratum 1 
£ 3 A A |Ostratum 2 
y 8 2 Eat © stratum 3 
E T. Ostratum 4 
fud 
o 
0 + + + + 
0 20 4 60 80 100 
Sample size 
  
  
Estimation error 
2-strata, non-weighted error 
  
  
  
  
  
  
  
  
  
  
  
  
  
gal 4 
£3 
EZ A 
25 AA A | Astrata 1-2 
& 3 2 O—OÓ Ó 
99 Ostrata 3-4 
= | 
8 
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0 20 40 60 80 100 
| Sample size 
  
Figure 5. Relative estimation variances and estimation errors 
of applying the field sampling plans 
CONCLUSIONS 
Our criteria for the field sampling plan are the following: 
a) The conclusions drawn from the sampling plan should be 
right. 
b) The sample size should be minimal. 
c) The sampling plan should be the simplest. 
Criterion a) excludes all sampling plans that result in 
'erroneous conclusion based on Table 3, and criterion 2 
excludes the sample size of 80. The third criterion is the 
weakest, since the difficulty of obtaining and evaluating an 
aerial photograph is much larger than that of its stratification, 
and weighting by intensity. Based on these, we suggest the use 
of 2-strata sampling plan with sample size of 20. There was not 
significant difference between the weighted and non weighted 
sampling plans. In that case when there are few samples falling 
into the sampling strata with large intensity, based on spatial 
statistical considerations, we suggest the use of weighted 
sampling plan. 
The suggested technique for the preparation of allocating plans 
of reclaiming materials is applicable not only for gypsum, but 
for other materials as well, if there is linear relationship 
between the intensity of aerial photograph and the required 
amount of reclaiming matenal. 
REFERENCES 
Csillag F., L. Pasztor, and L. L. Biehl. 1993. Spectral band 
selection for the characterization of salinity status of soils. 
Remote Sensing of Environment. 43 pp. 231-242. 
Kertész, M. and T. Toth. 1994. Soil survey based on sampling 
scheme adjusted to local heterogeneity. Agrokémia és Talajtan. 
43 pp. 113-132. 
Mc Bride, M. 1994. Environmental chemistry of soils. Oxford 
University Press. New York. 
Metternicht, G. I. and Zinck, J. A. 1996. Modelling salinity- 
alkalinity classes for mapping salt-affected topsoils in the 
semiarid valleys of Cochabamba (Bolivia). ITC Journal 1996 
(2) pp. 125-135. 
MSZ 9693/1-77 (National standard for the quality of soil 
reclaiming materials) (in Hungarian). 
Tôth, T and M. Kertész. 1996. Application of soil-vegetation 
correlation to optimal resolution mapping of solonetzic 
rangeland. Arid Soil Research and Rehabilitation. 10 pp. 1-12. 
Tóth, T. and L. Pásztor. 1996. Field reflectance measurements 
as a means of distinguishing different grades of salinity and 
alkalinity. pp. 23-34. in: Soil salinization and alkalization in 
Europe. Thessaloniki. 
Tóth, T., F. Csillag, L. L. Biehl and E. Michéh. 1991a. 
Characterization of semi-vegetated salt-affected soils by means 
of field remote sensing. Remote Sensing of Environment. 37 
pp. 167-180. 
Tóth, T., F. Csillag and Gy. Büttner. 1991b. Satellite remote 
sensing of salinity-alkalinity in the Great Hungarian Plain. 
Proceedings of International Symposium Impacts of 
Salinization and Acidification on Terrestrial Ecosystem and its 
Rehabilitation. September 26-28, Fuchu, Tokyo, Japan. pp. 
100-107. 
van Meirvenne, M., P. de Groote, M. Kertész, T. Tóth and G. 
Hofman. 1996. Multivariate geostatistical inventory of sodicity 
hazard in the Hungarian puszta. in: Monitoring soils in the 
environment with remote sensing and GIS. Proceedings of the 
ISSS International Symposium (working group RS and DM) 
Ouagadougou, from 6 to 10 Feb. 1995). pp. 293-305. 
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 181 
  
  
  
  
  
  
  
  
  
  
  
 
	        
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