Digital analysis of salinity of soil using multisource data
Peng Wanglu
Dept. of Geography Beijing Normal University,
Beijing, China
Li Tianjie
Institute of Environmental Sciences Beijing Normal
University, Beijing, China
Abstract
This paper the research work of Stalinization of soil at the YANGGAO region YANBEI China. For the highly precise quantitative analysis of the Stalinization not only remote sensing data Tm or MSS but also two non remote sensing data are needed depth of ground water and mineralization rate of ground water according to the theory of genesis of soil for the analysis of compounded multisource generalized Bays classification is used on the that various information sources are independent global membership function with probability are used to combine various information in order to make direct operation to the pixels and classifications of the salinity of Soil The experiment u order to make direct operation to the pixels and because increased speed of processing it's simplicity and improved precision of classification of the salinity of soil. The experiment indicated that this analysis method is sound of salinity At last MSS data of 1977 and TM data of 1986 after processing are compared for getting change of Stalinization during 10 years This worm indicates that the computer quantitative analysis of compounded multisource is one of effective research mean of salinized soil.
Introuction
The research of soil salinization ad the harnes of land -generation is one of emphasis of pedology geography and environment science. The interpretation of land sat remote sensing images and the computer processing are the important means to make qualitative quantitative and dynamic analyses. But the landsat remote sensing images are synthetic reflection of spectral features of various factors such as type soil combination soil covering structure and soil forming factor Consequently it is very incomplete for analyzing solonetz-solonchak\with only spectral feature because the influence of other factors can not be ignored in the view of genesis of soil it is indispensable to combine remote sensing data with ground in the view of genesis relationship among regional topography hydrology hydrogeology and soil data to study to realize the quantitative analysis of salinized soil it is imperative to improve the accuracy of discrimination and to make macroscopic analysis of the interrelation to improve between soil water motion and other factors of geography environmental conditions.
Recent years ynthetic quantitative mostly has used the methods of step by step with remote sensing data and non-remote sensing data (1) after the discrimination with remote sensing data the fuzzy parts of the different type of targets and same spectrum are found then further analysis will be made according to non remote sensing data 2 after classifying the level with non remote sensing data such as slope ,altitude etc the data in the region of each level are reclassified with remote sensing data so as avoid some indistinct surface features of different levels Geographic information system can be used a tool for these kind of operation.
In order to make quantitative analysis of salinized soil by means of the theory of genesis of soil the experiment use both remote sensing data and non remote sensing data experts experiences and increases processing speed and accuracy After registering land sat data of different times the dynamic change can be compared this work can be considered as a scientific basis for the work of transforming local soil.
The principle of classification with compound multi-information.
According P.H.Swain J.A Richards and T.Lee
(1) remote sensing data or non remote data may be regarded as independent sources data their locations must be matched accurately A pixel can be regarded as measurement
X =[x
1,x
2,......x
s....x
n]
T, s=1,2,3,......n, n is the number of independent sources the information class of a pixel denotes w
j,
J =1,2 .......M . M is the number of information classes The D
si (i=1,2,…. M
s ) indicates the ith c;lass of the sth source the function f (D
si/x
s) indicates the strength of association between x
s of sth source. and ith class D
si the function indicates the strength of association between the ith data class d
si of the data source S (relating to x
s) and the information class w
j last global membership function
F'j{f[Wj/dsi(xs)] rs | i = 1,2,...m, s = 1,2,..........m}
is used. The r
s is the weight (the "quality factor" for the source S. In consequence the discrimination rule the pixel X of all source is
If
F°= Max.Fj(j = 1,2,..M)
Then
X is in class W*
From byes classification theory
(Fj(X) = P(Wj/X)= P(Wj/x1,x2,x3.........xs.......xn)
according to the hypothesis "statistical independence of sources" (ignore the weight of sources)
the discrimination can be simplified as
The synthesis processing method extends the Bayes classification for remote sensing data brings the thematic elements of non-remote sensing in to the probability statistics theory for the analysis of classification which can be called generalized Bayes classification.
