Digital analysis of salinity of soil using multisource data

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Digital analysis of salinity of soil using multisource data

The Procedure of Experiment

Investigating the Environment of Salinized Soil in the experimental Area

The region studied in this paper is located at the south west part of YANGGAObasin in SHANXI province nearly 113°50" E, 40°20" N this area in the temperate semi arid steppe belt where soil types are mostly meadow and salinized soil so the differentiation Is the distinct and the type of salinized soil are mostly mixed type there exists various degree of salinization.

It is saline soil and highly salinized soil that the salinity of topsoil is greater than 1.0 percent and 0.6 percent to 1.0 percent respectively which mainly distributes at the alluvial plan along BAIDENG river the soil salinity of which is from 0.4 percent to 0.6 percent namely moderately salinized soil is at the north of the highly salinized soil region the soil salinity of which is from 0.1 percent to 0.4 percent namely weakly salinized soil and or potentially salinized soil is located at a big sides of high weekly salinized soil and or distributes crisscross with moderately salinized soil.it is non salinized soil that the salinity of topsoil is less than 0.1 percent A long which drainages free is of lower mineralization rate to the north of alluvial inclined plan the soil that is weakly salinity and sufficient moisture is non salinized soil.

According to the field investigation in late June the variety of vegetations and it's growths are related with salinity the wheat with high coverage that is growing well is mainly planted in the area of non salinized soil. Other plants which are low coverage such as chestnut sweet potato watermelon soybean etc. are all sediments in the various salinized area the highly salinized soil areas appear a stretch of white flat land,.
The Preprocessing of the Tm image.

On the Tm image 1986 the highly salinized soil area in which the soil is high reflect rate for each band appears white the mon salinized spoil area in which the plants are growing well appears red only in the moderately and weakly and salinized soil areas there is little chromatic difference and the soil is difficult to be distinguished .

In order to get the understanding features of soil and to find the relation between brightness of pixels an the corresponding natural scences three images are compared and classified the false color image of first color image three components after K-L principal component transformation the false color image first three components after K-T Tasseled Cap transformation and the false color image of band 4,3,2, of Tm image the result of comparison is that the image after K-T has the most evident of soil characteristics and the best classification result with only three bands and become the main remote sensing sources of the multi information classification.

The Selecting of Non Remote Sensing Sources.

The salinized soil in the YANGGAO regions is principally affected by ground water The salinized soil of the ground water type ids formed with the water circulation within ground water soil and atmosphere the buried depth and the mineralization rate of ground water is definitely related to the salinity of soil in general the salinity of soil is weaker in the area with deep depth an low high mineralization rate of ground water the period of the peak of salification is in June and July Comparing the depth and mineralization rate with other factors such as topography surface water buried topography etc they are most important for the salinity research and as the non remote sensing sources in this paper .

From the soil experiment by expects the two probability tables are listed for the statistical algorithm.

Table 1. Probability for depth of ground water

Level	Depth of ground water (m)	Saline soil and highly salinized soil(%)	mid-salinized soil(%)	weakly salinized soil(%)	non-salinized soil(%)
1 2 3 4 5	>2.4 2.0-2.4 1.8-2.0 1.4-1.8 1.0-1.4	0 0 5 10 60-70	0 5-10 10-20 60-70 20-30	20-30 30-40 60-70 20-30 10	70-80 50-60 15 0 0

Table 2 probability for mineralization rate of ground water

Level	mineralization rate of ground water (mg./1.)	Saline soil and highly salinized soil (%)	mid-salinized soil (%)	weakly salinized soil (%)	non-salinized soil (%)
1 2 3 4 5 6	<600 600-800 800-1000 1000-3000 3000-6000 >6000	0 0 0 20-30 70-80 80-90	0 0 10-20 70-80 20-30 10-20	10-20 60-70 70-80 0 0 0	80-90 30-40 5-10 0 0 0

Generalized Bayes Analysis of Multisource Data

For the equation (1) n=3 the multi source data are multi spectral TM image as a remote sensing data the depth and the mineralization rate of ground water as the non remote as the non remote sensing data. The steps of processing are reading TM data into computer digitizing the contour maps of both depth and mineralization rate of ground water then matching them with TM image after transformation of vector format to raster format by the common like hood supervised classification the value P(w_j/x^TM) can be got from the training areas of the TM data after K-T class area to total area for each level in the training fields or the valuesP(W_j/x_s) from geography observation experiment this paper uses the latter one.

According maximum like hood classification rule in the condition of same prior probability the discriminating function of the probability P(w_j/x_TM)may be written.

P^TM= In |E_j|-(X-M_j)^T E_j^-1 (X-M_j)

Here M_j E_j are mean vector and covariance matrix of class J respectively then equation (1) could be simplified as

F_j(X) = P^TM + In [P^D(w_j/x)] + In (w_j/x)]---------------(2)

Here TM and D and M for TM remote sensing data depth and mineralization rate of ground water respectively if prior probabilities of the classes are not the same equation (1) should be used.

Observing of the Dynamic change

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