809004130 geophysical soil sensing

8/2/2019 809004130 Geophysical Soil Sensing

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Student -809004130

AGSL -3010

Soil sensing modeling project

Hypothesis: The undisturbed land has a higher soil water concentration due to the vegetative

coverage than land used for agriculture.

Objectives

1) To examine disturbed and undisturbed field data from field station.2) To assess data quality for proper usage.3) To determine what the differences are between disturbed and undisturbed soil.4) To examine the difference between disturbed and undisturbed land.

Executive summary

Soil sensing or soil moisture sensing is an important capability in assessing the detrimental need for land

use. Land-use is particularly essential in determining as the name suggest what the land is particularly

useful for and how the vegetation is affecting the soil and vice versa.

The property of the soil itself affects the soil moisture content. In assessing the effectiveness of DUALEM

data the quality for usage was important in manually filtering and transforming to refit a real time image

of the land surface from SAR satellite.

The quality of assessment resulted in a high quality residual image of an electromagnetic induction of

the site.

It is concluded from the image that the hypothesis is proven true. It is proven true that the vegetation

coverage and less agriculture results in a higher soil moisture concentration within these areas.

The scope of the project was limited to resources and time but highly acceptable due to electro

magnetic induction.


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Introduction

The purpose of the study is to fulfill the objective in order to meet the criterias for fulfilling the

hypothesis, in answering true or false.

The objectives are clearly stated although limited with data. The areas assessed on the field station arebriefly described separately. The method for data collection was performed within separate groups. An

instrument known as the DUALEM instrument was used in obtaining raw data. The DUALEM sensor

received position coordinates along with time and speed. Other readings obtained were voltage,

temperature etc. known as aux_1, aux_2, aux_3 and etc. These values where then taken to be assessed

via the method in figure one.

Disturbed and Undisturbed fields- 1038'21.68" N 6125'45.58" W

Cocoa field - 1038'42.04" N 6124'09.62" W (Not described as is assumed to be exaggerated in

description by its name). The detail of the field station plot of land assessed is given below:

Disturbed

This area of land appears to have been used for cultivation, no trees can be seen in this area, flat

undulating land, entirely covered with grass vegetation, shrubs can be seen in small clusters about the

area and a recognizable square of densely clustered planted vegetation in located on the easterly side

of the plot. Rows of beds created by a tractor are visible in a horizontal, west to easterly direction and

crops are planted in neat rows on the top corner of the plot. Tracks with no vegetation are visible where

traffic and movement appear to be frequent.

Undisturbed

The undisturbed plot of land is heavily vegetated and appears to run in diagonal strips. Intermediating

between sparse and dense vegetation to small diagonal grassy plain. Trees line the outer region and run

in long diagonal clusters across the land. It does not appear entirely natural though because of the

positioning of the trees and they appear to be planted and then left to grow. A dense cluster of

vegetation is visible at the South eastern corner of the plot. With trees that appear larger than the rest

with larger canopies. Smaller trees or shrubs are scattered and less clustered but follow along a diagonal

path. They once again join denser vegetation along the middle. But overall clusters of shrubs and grass

are visible. The plain appears unkempt and not smooth, muddy patches are visible on the lower regions

of the field, with no trees seen but shrubs in small areas and the grass appears taller when compared to

the disturbed region.

The areas assessed were dampened that morning by early morning showers or rainfall. So the results

displayed below are unsure as to an average day -without bias.


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Study site and data

The image above depicts the study site: larger box area undisturbed and the smaller box area disturbed.


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Methods

The collection of data was accomplished through groups work where team players were needed in

separate groups called; disturbed soil sampling and the undisturbed soil sampling. The instrument used

to take the data was called a DUALEM sensor. The individual in the group took the back pack and the

cylinder along with the GPS coordinate receiver. Holding the cylinder approximately 10 cm above the

ground the person would walk in a specific manner desiring type of result. The diagram above (figure

one) depicts the way the data was transformed for use in the project.

In assessing the functionality a grid of 10m or 5m and then 2.5m was produced. The 2.5m grid was

found to be acceptable and was used in performing the kriging and NS back transform of data to format

to SAR image.

***An explanation of missing steps is the vesper usage where a grid is simply generated of the Global

Positioning Satellite coordinates. **An underlining of steps is missing, but the manner in approaching

hypothesis is needed. Therefore the figure above was produced.


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Results-

Cocoa aux_2 Column1 Disturbed aux_2 Column2 Undisturbed aux_2 Column3

Mean 29.9528061 Mean 15.4452282 Mean 19.8677122

Standard Error 0.19875215 Standard Error 0.15825188 Standard Error 0.50953512

Median 28.2 Median 15 Median 15.2

Mode 25.7 Mode 13.1 Mode 8.5

Standard Deviation 7.87018347 Standard Deviation 4.9134596 Standard Deviation 11.8624327

Sample Variance 61.9397879 Sample Variance 24.1420852 Sample Variance 140.71731

Kurtosis 2.17040038 Kurtosis 0.05103551 Kurtosis 2.41888912

Skewness 1.2879188 Skewness 0.40842749 Skewness 1.34848639

Range 49.2 Range 28.1 Range 73.3

Minimum 14.5 Minimum 4 Minimum 4.3

Maximum 63.7 Maximum 32.1 Maximum 77.6

Count 1568 Count 964 Count 542

Table 1: The data samples from different sites. These were used in producing a final image where a

complete data analysis was performed for all aux-2 with respect to the particular areas as show above.

Figure 2: The boxplot displaying the range of values and outliers using aux-2.

70

60

50

40

30

20

10

Cocoa

Boxplot of Cocoa

UndisturbedDisturbed

80

70

60

50

40

30

20

10

0

Data

Boxplot of Disturbed, Undisturbed


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Unfiltered Scatter Plot with the R Squares value:

Figure 3: The linear regression of unfiltered aux-2 with fairly little relation amongst values.

Figure 4: The linear regression of unfiltered aux-2 with fairly little relation amongst values.

Figure 5: The linear regression of unfiltered aux-2 with little or no relation amongst values.

y = -0.0064x + 34.984

R = 0.1385

0

20

40

60

80

0 1000 2000

Aux-2

Range

Aux-2 Cocoa

Aux-2 Cocoa

Linear (Aux-2

Cocoa)

y = 0.0054x + 12.837

R = 0.0934

0

10

20

30

40

0 500 1000 1500

Aux-2

Range

Aux-2 Disturbed

Aux-2

Disturbed

Linear (Aux-2

Disturbed)

y = -0.0211x + 25.561

R = 0.0775

0

20

40

60

80

100

0 200 400 600

Aux-2

Range

Aux-2 Undisturbed

Aux-2

Undisturbed

Linear (Aux-2

Undisturbed)


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Filtered data analysis-

Histograms of unfiltered disturbed sample and the other NST transform data.


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Histograms of unfiltered undisturbed sample and the other NST transform data.


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Histograms of unfiltered cocoa sample and the other NST transform data.


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Image of disturbed sample with 2.5m grid and Aux-2 NST transform.

Image of undisturbed sample with 2.5m grid and Aux-2 NST transform.


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Image of cocoa sample with 2.5m grid and Aux-2 NST transform.

Variogram Aux-2 NST of disturbed sample.


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Variogram Aux-2 NST of undisturbed sample.

Variogram Aux-2 NST of cocoa sample.


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EMv.3- The cocoa sample electro magnetic induction image after-

Analysis

Figure 6: The electro magnetic induction of image and site: undisturbed and disturbed respectively.


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Figure 7: The variance of the electro magnetic induction of site: undisturbed and disturbed.

Figure 8: The electro magnetic induction of cocoa field (not mentioned in the introduction).


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Figure 9: The variance of the electro magnetic induction of the cocoa field (not mentioned above)

Discussion

The spatial structure of soil moisture is linear or non linear to particular dynamics of the size of area

being assessed (Narendra, et al. 2008). The size is important in understanding the moisture fluctuation

of wet or dry. Soil moisture of wet to dry fluctuates even if the soil is of one class. Example, the above

figures 69 show images or rather parts of the areas known as the St. Augustine soil series. The data

from the cocoa field is from 2006 and the disturbed and undisturbed is 2011.

There is a time line difference in the soils development but the generality remains the same for the soils

series. Spatial and temporal variations in vegetation indices have been have been found to be linked to

prevailing climate, ecosystem, terrain and physical soil properties (Muldera, et al. 2011). Mulder gives

an advance article of how remote sensing is capable in scanning soil. The complexity of the subject

matter is that the vegetative indices or vegetation data is translated from satellite and explained in how

trees or vegetation affects the soil. From these google earth images on figure 6 we can easily see the

translation of the DUALEM scan of the two separate areas merged unto the SAR image.

(***It should be noted rain fell that morning.)

Although biased with the down pour of rain and this project was based on soil moisture content and the

difference on the weight of vegetation difference -affecting the soil moisture. There still can be seen


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purpose as the resulted image (figure 6) did display large amounts of blue area to display the difference

of high and low in water content in the soil. The reason for the state and evolution of soil moisture are

primarily forced by precipitation (Narendra, et al. 2008). Another answer as to the variation of soil

moisture is explained that, variations in soil texture, topography, crop cover and irrigation practices

result in large spatial and temporal variability in soil moisture (Lunt, et al. 2005). Soil texture and crop

cover are two key essential to be explained here.

Before moving further ahead table 1 and figure 2 should be analyzed along with the Variograms above.

Table one is the data analysis performed for aux-2 only and the boxplot further depicts the data in a

suitable manner. The boxplot displays the measure of dispersion amongst the sample areas, seen as

widely spread with a high census of outliers. This meant a great disproportion in the wet to dry in the

sample areas. The Variogram graphs confirms the great change needed in kriging of the data and the

suitability fit made in assess the electro magnetic induction data in trying to make a model to fit the area

scanned as only points. This is seen in the image with 2.5m grid and Aux-2 NST transform seen above.

NST stands for normal score transformation where the raw data results in the final Normal score backtransformation of product. EMv.1-3

Now the crop coverage is seen along with the electro magnetic induction over mapped of the SAR

image. The soil texture conforms to the crop coverage as the relation soil moisture shares to soil is

shared through CAC and other functionalities as mineral composition and electrical conductivity. These

are in fact the result of crop coverage. The roots of the vegetation have a greater effect upon the soil

than soil with less natural undisturbed larger trees but smaller crops. This is confirmed in the figure 6.

The cocoa field image confirms this although with a time lag difference. The purpose of assessing the

hypothesis is proven true as the higher vegetative coverage results in higher soil water content. Serrano,

et al. 2010 performed a similar research project but more advanced where he did an assessment of the

variability of soil and vegetation in a permanent pasture using the same non-contact electro magnetic

induction probe. The conclusion is seen in the figure 6 9. A greater in-depth explanation of how roots

affect the zone of moisture is given by (Crow, et al. 2008).

Conclusion

The undisturbed land has a higher soil water concentration due to the vegetative coverage.

Number of words: 1994 excluding reference.


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