1

Mapping of Saline Soils Using Remote Sensing and GIS in North

White Nile, Sudan

By

Ashraf Ibrahim Abdallah Abdallah

B Sc: (Honours) in Soil and Water Science

Faculty of Agricultural Studies

Sudan University of Science and Technology (2007)

A Dissertation

Submitted for partial Fulfillment of the Requirements for the Degree of

Master of Science

in

Soil Science (Remote Sensing)

Department of Soil and Water Science

Faculty of Agricultural Sciences

University of Gezira Wadmedani, Sudan

November, 2012

2

Mapping of Saline Soils Using Remote Sensing and GIS in North

White Nile, Sudan

By

Ashraf Ibrahim Abdallah Abdallah

Examination Committee:

Name Position Signature

Prof. Eltayeb Mohammed Abdelmalik Chairman ……………………..

Dr. Elfatih Elaagib Awad Elkarim External Examiner ……………………..

Dr. Muna Mohammed El hag Internal Examiner ……………………..

Date of Examination: 25/ 11 / 2012

3

To

DEDICATION

This work is dedicated with great love

My parents,

My brothers and sisters,

My relatives

and

those who supported me and giving all their efforts

and time waiting for nothing to be returned

4

ACKNOWLEDGEMENT

First, praise to Allah for giving me strength and patience to complete this

work successfully.

I would like to thank my major supervisor Prof. Eltayeb Mohammed

Abdelmalik for his close supervision, continuous follow up and encouragement

during the study. Also I would like to thank my co-supervisors Dr. AbdAlmagid

Ali Elmobarak and Dr.Hanan Osman Ali for their valuable suggestions and useful

comments during the course of this study.

This work would not have been possible without the help of my dearest

friends: Abdelrahim Altayb and seif mosalam for their kind help in work of the

(GIS) labs.

Special thanks are due to my family for their encouragement and endurance.

Last, I would like to thank my colleagues at the faculty of Agricultural

Sciences University of Gezira, in general and in the department of Soil and Water

Science in particular.

5

Mapping of Saline Soils Using Remote Sensing and GIS in North

White Nile, Sudan

Ashraf Ibrahim Abdallah Abdallah

M Sc: in Soil Science (November, 2012)

Department of Soil and Water Science

Faculty of Agricultural Sciences

University of Gezira

ABSTRACT

High levels of soil salinity in north White Nile resulted in low productivity of most crops

cultivated in the area. The traditional method is used for the detection of soil salinity is costly and

slow. The use of remote sensing (RS) and GIS as tools to detect salinity in large areas saves time

and money. The objectives of this study were to reduce time and cost of salinity detection by

using remote sensing techniques and GIS and to develop a model that integrates remote sensing

data with GIS techniques to assess, characterize and map the soil salinity of the study area. This

study was conducted by using two satellite images for the years,1979 and 2006 by using soil

survey report maps (1979) and (2006) and using soil salinity data for these years. Data were

analyzed using programs Arc GIS 9.1 and Earth Resource Data Analysis System program

(ERDAS imagine 8.5). The results of the study were different from the maps classification of soil

salinity, which was designed by the program Arc GIS observation. An increase in the area of non

saline as observation by visual interpretation of these maps and there is a shortage in soil salinity

for units rated salinity other (slightly saline, moderately saline and strongly saline) has been most

clear. The affected area was determined by percentage of salinity existed in it, and identifying

salinity for each year alone. Total affected area for the year 1979 was 277850.8 hectar and the

year 2006 was 277718.9 hectar.The percentage of salinity in the top soil (0-30) in the affected

areas in 2006 was less than the percentage in 1979 and its percentage in the sub soil (30-100) also

decreased in 2006 than in 1979. Can improve remote sensing (RS) when it is combined with other

data,compiled with the use of geographic information systems (GIS), which is an appropriate tool.

6

النيل في شمال ونظم المعلومات الجغرافية البعيدباستخدام الاستشعار ملوحة التربة تخريط

السودان,الأبيض

أشرف إبراهيم عبد الله عبد الله

2012ماجستير العلوم في التربة أكتوبر

قسم علوم التربة والمياه

كلية العلوم الزراعية

جامعة الجزيرة

الخلاصة

معظم المحاصيل انخفاض إنتاجية إلى النيل الأبيض في شمال ملوحة التربة ارتفاع مستوى أدى

الطرق مكلفة جدا وتستهلك وهذه , ملوحة التربة للكشف عن الطرق التقليدية وتستخدم المنطقة. المزروعة في للكشف عن كأدوات ونظم المعلومات الجغرافية البعيداستخدام الاستشعار الملوحة. للكشف عنزمن أطول

من هذه الهدف . أدت إلى توفير الوقت والمالالطرق الحديثة والتي تعتبر من في مناطق واسعة الملوحةتقنيات باستخدام الملوحة عند الكشف عنوذلك الوقت و تقليل التكلفة هو الحد من استهلاك الدراسة

مع بيانات الاستشعار عن بعد يجمع بين نموذج، ووضع ونظم المعلومات الجغرافية الاستشعار عن بعدهذه وقد أجريت. لمنطقة الدراسة ملوحة التربةخرائط وتوصيف لتقييم معلومات الجغرافيةنظم ال تقنيات

( 2006و )( 1979، )مختلفين لسنتين المستخدمة الأقمار الصناعية الدراسة باستخدام صورتين من صورانات استخدام بيو أيضا تم ( 1979)و (2006) لسنتين التربةخرائط لتقارير فحص استخدامتم وكذلك

وبرنامج, (GIS 9.1 )ملوحة التربة باستخدام برامج بياناتجرى تحليل و السنتين، لهاتين ملوحة التربة(ERDAS 8.5) التي تم ملوحة التربة و خرائط تصنيف عند النظر في اختلافات نتائج الدراسة أظهرتو

وعن طريق التفسير المالحة المساحات زيادة او النقص فيال لمراقبة GIS برنامج من قبلتصميمها مساحات الترب فيهناك نقصاً أوضحت الدراسة ان و هذه الخرائطحجم الملوحة فى تم تحديد البصري

شديدة و متوسطة الملوحةالملوحة، ملوحة التربة )قليلة تصنيف وحدات المالحة والتى تم تحديدها باستخدام حات المتضررة بالملوحه وذلك بتحديد نسبة الملوحة لكل تم تحديد المسا رسم البياني( من خلال الالملوحة

لسنة و هكتار 277850.8( 1979لسنة ) المنطقة المتضررة بالملوحة إجمالي وكان سنة على حده. في المناطق المتضررة( 30-0) التربة السطحية في الملوحة نسبة. هكتار 277718،9كانت و( 2006)

( 100-30التحتية ) في التربة نسبتها وكذلك 1979الملوحة فى عام نسبة أقل من 2006في عام ( عندما يتم RSويمكن تحسين الاستشعار عن بعد ) .1979عام بالمقارنة مع 2006عام فى أيضا انخفضت

دمجها مع غيرها من البيانات، ونظم المعلومات الجغرافية التي هي أداة مناسبة.

7

LIST OF CONTENTS

Dedication …….…………………………………….…………………………...……i

Acknowledgements……………………………………………………………….......ii

Abstract …………………………………………………………………….…...........iii

Arabic Abstract …………………………………………………….……………...…vi

List of contents……………………………………………………….………….…...vii

List of Tables…………………………………………………………..……..…….....ix

CHAPTER ONE:

INTRODUCTION.. ……………………………………………………………......…1

CHAPTER TWO:

2 REVIEW of LITERATURE ………….…………………………….……….…….5

2.1.Prelude...…………………………………………………….……...…........…..…5 2.2 Salt-

affected Soils ………………………………..……..…………......……….....6

2.2.1 Type of Salt-affected Soils……………………………………………………...7

2.2.1.1 Saline Soils……………………………………………….……………....……7

2.2.1.2. Sodic Soils………………………………………………………....………....7

2.2.1.3. Saline-sodic soils…….……………………………………………….……….7

2.3. Soil Salinization…………………………………………………………………8

2.4. Causes of Salinity in Soils………………………………………………...…........9

2.5. Composition of Salt in Saline Soils………………………………………..…....10

2.6. Soil Salinity: Definition, Distribution and Causes…………………………...…10

2.7. Sources of salts in soils………………………………………………………….13

2.8. Remote Sensing………………………………………………………….……...15

2.9. Soil Maps and Mapping……………………………………………….…...……19

2.10. Remote Sensing, GIS and Soil Mapping……………………………….....……21

2.11. Reflectance Characteristics of Earth’s Cover Types………………………..….24

2.11.1. Vegetation……………………………………………………………...….....24

2.11.2 Water…….……………………………………………………………………25

2.11.3 Soil…….………………………………………………………………...……25

2.12. Image Classification…………………………………………………………....26

2.12.1. Supervised Classification………………………………………….…………27

2.12.2. Unsupervised Classification……………………………………………..…...28

CHAPTER THREE:

3. MATERIALS AND METHODS………………………………………………..30

3.1. Materials………………………………………………………………………....30

3.1.1. Study Area Image……………………………………………………………...30

3.1.2. Hardware and Software used in the Study……………………………….…...30

3.1.3 The data………………………………………………………………………...34

3.2 Methodology………………………………………………………………...…...34

3.3 Soil samples and laboratory Analyses……………………………………….…...35

3.3.1 Salinity Data……………………………………………………………….…...35

3.4 Soil Salinity Mapping…………………………………………….…………...…38

8

CHAPTER FOUR:

4. RESULTS AND DISCUSSION ……………………………………..………...40

4.1.Using Arc GIS View Program……………………………………….……..…....40

4.2. Soil Units Affected by Salinity………………………………….……….…..…40

4.3 Soil Salinity Classification………………………………………………..……...45

4.4 Unsupervised Classification……………………………………….…….……….47

CHAPTER FIVE

5. SUMMARY AND CONCLUSIONS………………….…………………..…......51

5.1 SUMMARY……………………………………………………………………...51

5.2 CONCLUSIONS…………………………………………………………….…..51

REFERENCES…………………………………………………………………...….52

9

List of figures

Figure (2.1): Spectral Reflectance Curves for Three Different Types of Soils…..….26

Figure ( 2.2): A Supervised Classification Process …………………………………28

Figure ( 2.3): Unsupervised Cassification ……….……………………………...…. 29

Figure (3.1): Study Area Location Map………………………………………..……31

Figure (3.1) Landsat Thematic Mapper Satellite Image for 1979. ……………..…..32

Figure (3.2) Landsat Thematic Mapper Satellite Image for 2006. ……………..…..33

10

List of Tables

Table (1.1) El Dueim Meteorological Station Climatic Data……………………....…1

Table(2.1) General ranges for plant response to soil salinity………………………...11

Table(2.2) Salinity tolerance of some crops (Adapted from Doorenbos and

Kassam…………………………………………………………………………….…11

Table(3.1). Salinity and Sodicity Classes of the soil ………………………………...35

Table ( 3.2 ) Data of soil salinity rating .The text file.data laboratory analys……..36

Table(3.3) Attributes of boundry and soil salinity rating…………………….…..…..37

Table.(4.1) Salinity classes of the soil ………………………….…….…40

Table (4.2): Salt affected areas/ha in the top and sup soil of the study area for the years 1979 and

2006………………………………………………………………….45

.

11

CHAPTER ONE

INTRODUCTION

The study area is about 555569.7 ha extends along Khartoum-Rabak highway

from (Km), 120 to Km, 180 and is subtended by latitudes 13o45 N and 14o20 and

longitudes 32o15 and 32o40 E.

According to the climatic zonation of the Sudan, the area falls within the arid

climatic zone (van der Kevie, 1976). The mean maximum temperature in the hottest

month (May) is 41.2o C, the mean minimum temperature of the coldest month

(January) is 16.2o C. And the annual rainfall is 232mm falling mostly between July

and September (Table 1.1).

Table (1.1) El Dueim Meteorological Station Climatic Data (Normal, 1971-

2000)

Elements

Month

Mean max

Temp oC

Mean min

Temp oC

R.H. % Sunshine

hr/day

Solar Radiation

M Jol/m2/day

Rainfall

mm

Jan. 31.5 16.1 31 10.1 20.9 0.1

Feb. 33.6 17.6 26 10.0 22.6 0.0

March 37.4 20.5 21 9.7 23.8 0.1

April 40.6 23.4 21 10.1 25.1 0.4

May 41.2 25.7 28 9.5 24.0 9.0

June 39.9 25.8 37 8.3 21.8 15

July 36.4 24.3 52 7.0 20.0 69

Aug. 35.1 23.8 60 7.1 20.3 103

Sept. 36.6 24.2 54 8.1 21.4 31

Oct. 38.5 25.4 41 9.3 21.8 4.0

Nov. 35.7 21.1 30 10.2 21.3 1.0

Dec. 32.4 7.70 33 10.2 20.4 0.0

Year 36.6 22.1 36 9.1 21.9 232

12

The study area is part of the central clay plain of the Sudan. William, (1968)

reported that the development of these soils is related to the White Nile alluvial

deposits (Holocene Deposits) and the Blue Nile alluvial deposits. Generally

speaking the slightly cracking grayish clays are related to the White Nile deposits

and the intensively cracking brown clays are most probably related to the Blue Nile

alluvial deposits.

The particle size class of the profile control section (25-100 cm) is fine (clay

ranges from 51 to 58%) permeability is very low to low (0.68 to 2.83 cm/hr) which

is most probably related to the high content of clay and Na ions in the exchange

complex that adversely affects the soil macroporosity on wetting and disturb soil

structure. The soils of the study area have high capacity of water retention

(saturation percentage >80%). The water holding capacity (WHC) of the studied

soils is rated as good, i.e. >4 and >12 cm in the 0-30 and the 30-120 cm soil depths,

respectively. The dry bulk densities of soils of the study area are high and increase

with depth as a result of overburden. The high bulk density usually signals a low

percentage of total soil porosity (Young, 1977).

The advantage of remote sensing lies in its high temporal sampling frequency

and rapid nondestructive characterization of a wide range of materials.

Multispectral remote sensing has been widely used by many researchers to map

salt-affected areas such as Rao et al., 1991 and Guan et al., 2001. Most of the

studies using conventional remote sensing data focused on mapping severely

affected saline areas or differentiating between saline and nonsaline soils, but it is

13

difficult to differentiate between low-saline and nonsaline soils using multispectral

remote sensing. Some studies (Peng, 1998; Guan et al., 2001) used geographic

information system (GIS) techniques to integrate multispectral data with field data,

such as groundwater mineralization data, groundwater depth data, and topography,

to overcome the weakness of multispectral images. These studies were successful in

mapping salinity classes. However, this technique needs a dense network of field

sample data to adequately characterize the spatial variability of salinity over an

area. Characteristic features for salt-affected soils are mostly observed in narrow

wavelength bands. In general, such detailed spectral signatures are lost when the

bandwidths are wide, as with multispectral remote sensing data. Compared to

multispectral remote sensing, hyperspectral remote sensing provides ample spectral

information to delineate material characteristics. This capability allows for the

identification of targets based on their established spectral absorption features

(Goetz et al., 1985). The spectral information enables quantitative assessment and

research of salt-affected soil (Ben-Dor et al., 2002). Howari et al. (2002) examined

spectral reflectance of soils treated with saline solutions containing NaCl, NaHCO3,

Na2SO4, and CaSO4·2H2O. Their study showed that spectroscopy can be used under

certain conditions to identify the presence of primary diagnostic spectral features of

salt crusts. Dehaan and Taylor (2002; 2003) used spectral feature fitting and

mixture-tuned matched filter mapping methods to map salinity classes, combining

airborne hyperspectral data (Hy map) with field measurement and image-derived

spectral end-members. Reflectance spectroscopy has been applied to predict the

14

physical, chemical, and biological properties of soils (Ben-Dor et al., 2002;

Shepherd and Walsh, 2002; Liu et al., 2004; Lu et al., 2005).

1.2. Justification:-

High level of soil salinity in North White Nile resulted in low productivity

of most crops cultivated in the area. A Traditional method is used for mapping

of soil salinity, this has high cost and takes long time to achieve detection of

salinity. Use of Remote Sensing and GIS as tools to detect salinity in large areas

save time and is cost effective.

1.3. Research Objectives:

To reduce time and cost of salinity detection by using remote sensing

techniques and GIS

To use a map that integrates remote sensing data with GIS techniques to

assess, characterize and map the saline soil of the study area.

15

CHAPTER TWO

REVIEW OF LITERATURE

2.1. Prelude:

The main problems associated with arid and semi-arid regions are salinization and

desertification. Soil salinization is a major form of land degradation in agricultural

areas, where information on the extent and magnitude of soil salinity is needed for

better planning and implementation of effective soil reclamation programs. To keep

track of changes in salinity and anticipate further degradation, mapping and

monitoring is essential for proper and timely decisions to be made to adjust the

management practices or to undertake proper reclamation and rehabilitation

measures. Mapping and monitoring of salinity means first identifying the areas

where salts concentrate and secondly, detecting the temporal and spatial changes in

their occurrence. Both largely depend on the peculiar way of salts distribution at the

soil surface and within the soil mantle, and on the capability of the remote sensing

tools to identify salts (Zinck, 2001). For monitoring and eventual control of salinity

problems, remote sensing techniques are very useful, especially for the study of

soils in arid and semi-arid environments due to sparse vegetation cover. Wiegand et

al. (1994) carried out a procedure to assess the extent and severity of soil salinity in

fields in terms of economic impact on crop production and effectiveness of

reclamation efforts. Their results illustrate practical ways to combine image analysis

capability, spectral observations, and ground truth to map and quantify the severity

of soil salinity and its effects on crops.

16

In recent years, methods for studying soil salinization have been

improved greatly. Techniques have evolved from using geographical analysis

alone, to using remote sensing analysis and visual interpretation of satellite

images combined with computer processing of satellite images. Moreover, it is

becoming increasingly popular to combine a remote sensing method with

Geographic Information Systems (GIS) to solve complex problems (Peng,

1998). Although remote sensing techniques have been used to diagnose general

salinity problems, only limited attempts have been made to evaluate their

effectiveness in identifying soils where the primary inhibitor of plant growth is

nutrient deficiency induced by either alkalinity or salinity (Weigand et al.,

1994). Ghabour and Daels [1993] concluded that detection of soil degradation

by means of a conventional soil survey requires a great deal of time, but remote

sensing data and techniques offer the possibility for mapping and monitoring

these processes more quickly and economically. However, to assess the

accuracy of satellite images to map and monitor salinity, it is necessary to

compare them with field measurements of salinity.

2.2 Salt-affected Soils

In general, salt-affected soils are soils with dissolved salt in a high

concentration to affect plant growth (Richards, 1954). Salt-affected soils could be

classified owing to the difference in electrical conductivity (ECe) of soil

saturation extract at 25Co, Exchangeable Sodium Percentage (ESP) and, or Sodium

Adsorption Ratio (SAR) (Brady and Weil, 1999) as follows:

17

2.2.1 Type of Salt-affected Soils

2.2.1.1 Saline soils

Saline soils are soils with easy dissolved salt in high concentration to

generate effects on plant growth, without ESP influencing.The soil is

described as saline if electrical conductivity of soil saturation extract is more than

4 dS.m-1 and the SAR is below 13 or ESP is less than 15, while soil reaction is

less than 8.5 (Brady and Weil, 1999). Normally, white alkali soils are found on of

surface of saline soils.

2.2.1.2. Sodic soils

Sodic soils are soils with ESP in high volume enough to reduce crop

productivity and affect soil texture in any kind. Sodic soils have ESP more than 15

and soil reaction being more than 8.5 (Brady and Weil, 1999).This because soil

reaction and ESP are in high levels.

Gradually, soil solvent wells up with groundwater and then silt on soil

surface. Black color sedimentation of sodic soils is always called black alkalisoils.

2.2.1.3. Saline-sodic soils

Saline-sodic soils are soils with high volume of easy-dissolved salt

and ESP enough to harm crop productivity in any type of soil texture and

crop.Electrical conductivity of saturation extract ECemeasured from

soil saturation extract is at least 4 dS/m in saline-sodic soils. Sodium

adsorption ratio SAR is more than 13 or ESP is more than 15. Soil reaction

may be more than 8.5 but less than 8.5 is normally found in saline-sodic

18

soils (Brady and Weil, 1999). Salt-washed out from saline-sodic soils will

increase soil reaction to be more than 8.5 and then the soils will transform

to sodic soils.

2.3. Soil Salinization

Normally, salinity in soils is related to 6 processes as

alkalization, dealkalization, salinization, desalinization, solonization and

desolodization. Alkalization or sometimes called solonization is the

process of sodium ion accumulation in ion exchanged areas of

calcium, sodium and magnesium. Dealkalization or solonization is the

process of sodium ion moving out from exchanged areas. This process

will make the sodium ion hydrated. Salinization is the process of

accumulation of dissolved salts such as sulfates and chlorides of

magnesium, sodium and potassium (Buol, 1989). In most salic horizons,

found in shallow water table, fine textured and poor drained soils exist.

This process will occur in humid climate (Brady and Weil, 1999).

Desalinization is the leaching of dissolved salts from soil layer, as it

happens after salinization. From a soil study in California, Whittig,

(1959) found that ancient evolution of soil is influenced by sodium ion that

A horizon is acid in reaction and B horizon is a base in reaction within

a fine texture due to the very high clay content. There is a low rate of

sodium exchange in the upper soil and will increase gradually with the

depth of soil layer that make, clay dispersion and positive ion exchange

19

different. Besides, salinization, solonization, solodization and time that

influence soil conditions and increasing of alkalinity will come from usage

of divalent ion fertilizers.

2.4. Causes of Salinity in Soil

Most saline soils occur due to geological effects but also caused by

geologic and formations containing salt sources like sodium at shallow surface

soil or high salt water table areas especially at the surface level. Due to the fact

that salt stone contains white salt rock, then this salt stone can be both salt

crystal and mineral chain in sand-powered stone, clay stone and silt stone

(Moormann and Breeman, 1978). When stone starts decaying, salt from stone will

dissolve to ground water, circulate and accumulate at other places as a cause of

salinity in soil by evaporation of water from soil surface. The cause of salinity

in shallow ground water may come from salt composite solvent scattering in

soil or stone layer (Sinanuwong and Takaya, 1974). The result of decaying stone

and other elements under the period of drought and semi drought include

water evaporation. Also the transferring of element from groundwater that

relates with the changing of groundwater level was found to influence salt when

groundwater level decreases and later generates salt layer (Al-Barrak and Al-

Badawi 1988). After that, salt tolerant plant species will occur in that area,

alkalinity comes from positive ion concentration of divalent in basin area

having more evaporation than rainfall volume. Nevertheless, soil reaction being

higher than 8.5 can happen when inner water circulation is alkaline and has

20

the related process such as decaying from oxidation reaction, ion exchange,

rainfall volume, increased salt and evaporation affecting the accumulated salt in

soil (Van Breeman, 1973).

2.5. Composition of Salt in Saline Soil

Salt in saline soils contains cations such as sodium, magnesium,

calcium and potassium with anions such as chloride, sulfate, bicarbonate, and

nitrate in forms of sodium chloride, sodium sulfate, sodium carbonate, sodium

bicarbonate, sodium nitrate, magnesium sulfate and magnesium chloride. Most

sodium and chloride ions can be found in saline soils. Bernstein, 1964 found that

sodium types which can harm plants can be arranged from more to less as

carbonates, chlorides, sulfates and nitrates. Salts of calcium and magnesium

which can harm plants can be arranged from more to less as chlorides, sulfates

and nitrates. (Joffe, 1953). Dissolved salt, such as gypsum, chalk, and magnesite

have no effects on plant but soluble salts such as chlorides, sulfates, nitrates,

carbonates, bicarbonates and borates mainly harm plants (Buringh, 1970).

However, several types of salts in soils are more harmful to plants in

comparison with single type of salt in soil at the same volume. Normally, plants

can be more tolerant to salinity in clayey than i n sandy soils because clay

have more capacity to absorb salinity and water. The water absorption can

reduce the concentration of salts in soils (Joffe, 1953).

2.6. Soil Salinity: Definition, Distribution and Causes .

Soil salinity, as a term, refers to the state of accumulation of soluble salts in

21

the soil. Soil salinity can be determined by measuring the electrical conductivity

(ECe) of a solution extracted from a water-saturated soil paste. The electric

conductivity as ECe (Electrical Conductivity of the extract) with units of

decisiemens per meter (dS m-1) or millimhos per centimetre (mmhos/cm) is an

expression for the anions and cations in the soil. From the agricultural point of

view, saline soils are those, which contain sufficient neutral soluble salts in the

root zone to adversely affect the growth of most crops (Table 2.1). For the purpose

of the definition, saline soils have an electrical conductivity of the saturation

extracts of more than 4dS m-1 at 25°C (Richards, 1954).

Table(2.1) General ranges for plant response to soil salinity .

Salinity (dS,m-1) Plant response

0 to 2

2 to 4

4 to 8

8 to 16

above 16

Mostly negligible

growth of sensitive plants may be restricted

growth of many plants is restricted

only tolerant plants grow satisfactorily

only a few, very tolerant plants grow

satisfactorily Source: Richards, 1954

22

Table(2.2) Salinity tolerance of some crops ( Adapted from Doorenbos and

Kassam (1979)

Crop

Threshold

value

10%

Yield loss

25%

Yield loss

50%

Yield loss

100%

Yield loss

ECe (dS m-1) ECe (dS m-1) ECe (dS m-1) ECe (dS m-1) ECe (dS m-1)

Beans(field)

Maize

Sorghum

Wheat

Sugar beets

Cotton

1.0

1.7

4.0

6.0

7.0

7.7

1.5

2.5

5.1

7.4

8.7

9.6

2.3

3.8

7.2

9.5

11.0

13.0

3.6

5.9

11.0

13.0

15.0

17.0

6.5

10.0

18.0

20.0

24.0

27.0

23

As salinity levels increase, plants extract water less easily from soil,

aggravating water stress conditions. High soil salinity can also cause nutrient

imbalances, which then result in the accumulation of elements toxic to plants,

and reduce water infiltration if the level of one salt element (like sodium) is

high. In many areas, soil salinity is the factor limiting plant growth. Table (2.2).

There are extensive areas of salt-affected soils on all continents, but their

extent and distribution have not been studied in details (FAO, 1988). Inspite of

the availability of many sources of information, accurate data concerning salt

affected lands of the world are rather scarce (Gupta and Abrol, 1990). Statistics

relating to the extent of salt-affected areas vary according to authors, but estimates

are in general close to one billion hectares , which represent about 7% of the

earth’s continental extent (Ghassemi, Jakeman, and Nix, 1995). In addition to these

naturally salt-affected areas, about 77 million ha have been salinized as a

consequence of human activities, with 58% of these concentrated in irrigated areas.

On average, salts affect 20% of the world’s irrigated lands, but this figure increases

to more than 30% in countries such as Egypt, Iran and Argentina (Ghassemi,

Jakeman, and Nix, 1995).

According to estimates by FAO and UNESCO, as much as half of the

world’s existing irrigation schemes is more or less under the influence of

secondary salinization and waterlogging. About 10 million hectares of irrigated

24

land are abandoned each year because of the adverse effects of irrigation, mainly

secondary salinization and alkalinization (Szabolcs, 1987).

2.7. Sources of salts in soils:

Salts in soils came come from the following sources. The salt can be present in the

parent material, e.g., in salt layers, accumulated in earlier times. This form may be

present in the Balikh basin in some gypsiferous deposits, and deeper formations,

originating from the era in which the extension of the Arabian Gulf became land

in the Mesopotamian depression.

The salt can be formed during weathering of the parent rock. Salts are generally

set free by rock weathering but they will be leached, as this process is very slow.

However, some kinds of rock have a chemical composition and porous texture so

that under warmer climates relatively high proportions of salts are formed.

The salt can be air borne. In this case it is transported through the air by dust or by

rainwater. In the Euphrates in valley transport of salt by dust is a common

phenomenon, but it has local importance.

The ground water can be saline. In this case where the water table is near to the

surface, salt will accumulate in the topsoil as a result of evaporation. When the

water table occurs deeper in the soil, the saline layer can be formed at some

depth, which may influence the soil after use, especially with uncontrolled

irrigation and inadequate drainage. In the Euphrates valley and at other places

25

in Balikh, the ground water table is near to the surface during part of the year so

that salt accumulation is present and even salt crusts are formed. In particular the

drainage water in gypsiferous areas contributes to the salinization.

Salts brought by irrigation water. Irrigation water always contains some salts

and incorrect methods may lead to accumulation of these salts. When

waterlogging is present at some depth the water evaporates again and the salt

transported with water from elsewhere is left behind (Zinck, 2001).

In principle, soil salinity is not difficult to manage. The first prerequisite for

managing soil salinity is adequate drainage, either natural or man-made. If the

salinity level is too high for the desired vegetation, removing salts is done by

leaching the soil with clean (low content of salts) water. Application of 15cm of

water will reduce salinity levels by approximately 50%, 30cm of water will

reduce salinity by approximately 80%, and 60cm by approximately 90%. The

manner in which water is applied is important. Water must drain through the soil

rather than run off the surface. Internal drainage is imperative and may require

deep tillage to break up any restrictive layer impeding water movement. Sprinkler

irrigation systems generally allow better control of water application rates;

however, flood irrigation can be used if sites and level of water application is

controlled.

However, the determination of when, where and how salinity may occur is vital to

26

determining the sustainability of any irrigated production system. Remedial

actions require reliable information to help set priorities and to choose the type of

action that is most appropriate in each case. Decision-makers and growers need

confidence that all technical estimates and data provided to them are reliable and

robust, as the economic and social effects of over- or underestimating the extent,

magnitude, and spatial distribution of salinity can be. To keep track of changes in

salinity and anticipate further degradation, monitoring is needed so that proper

and timely decisions can be made to modify the management practices or under-

take reclamation and rehabilitation. Monitoring salinity means first identifying

the places where salts concentrate and, second, detecting the temporal and spatial

changes in this occurrence. Both largely depend on the peculiar way salts

distribute at the soil surface and within the soil mantle, and on the capability of

the remote sensing tools to identify salts (Zinck, 2001).

2.8. Remote sensing

Remote sensing performs the detection, collection and interpretation of

data from distance by mean of sensors. The sensors measure the reflectance of

electromagnetic radiations from the features at the earth surface. The radiation

energy is transmitted through space in waveform and is defined by wavelength

and amplitude or oscillation. The electromagnetic spectrum ranges from gamma

rays, with wavelength of less than 0.03 nm, to radio energy with a wave-length of

27

more than 30 cm. In remote sensing applied to land resources surveys,

wavelengths between 0.4 and 1.5 mm are commonly used. A variety of remote

sensing data has been used for identifying and monitoring salt-affected areas,

including aerial photographs, video images, infrared thermography, visible and

infrared multispectral and microwave images (Metternicht and Zinck, 2003). The

use of the multispectral scanning (MSS) technology for natural resource surveys

concerns the images obtained by Landsat MSS/TM (Thematic Mapper) and Spot.

Type and variation of the images depend on the electronic scanners, which record

the reflected radiations in the separate bands. Landsat offers a much wider range

of bands (spectral diversity) than SPOT, which enhance the detection of surface

features.

The two of classification, unsupervised and supervised, were used for the

proper identification of salinity, mostly at regional levels multispectral scanning's

bands 3, 4 and 5 are recommended for salt detection in addition to TM bands 3, 4,

5 and 7 (Naseri, 1998). Interesting studies of using satellite images for

salinity detection were conducted by Chaturvedi et.al (1983) and Singh and

Srivastav (1990) using microwave brightness and thermal infrared temperature

synergistically. The interpretation of the microwave signal was done physically by

means of a two-layer model with fresh and saline groundwater. Menenti, et.al

(1986) found that TM data in bands 1 through 5 and 7 are good for identifying salt

28

minerals, at least when they are the dominant soil constituents. Moreover, salt

minerals affect the thermal behavior of the soil surface.

Mulders and Epema (1986) produced thematic maps indicating

gypsiferous, calcareous and clayey surfaces using TM bands 3,4 and 5.They found

that TM is a valuable aid for mapping soil in arid areas when used in conjunction

with aerial photographs. Sharma and Bhargava (1988) followed a collative

approach comprising the use of Land-sat2_MSS “FCC”(False Colour

Composite), survey of Topomaps and limited field checks for mapping saline

soils and wetlands. Their results showed that, the possibility of the separation of

saline and waterlogged soil that because of their distinct coloration and unique

pattern on false color composite imageries. Saha, et.al (1990) used digital

classification of TM data in mapping salt affected and surface waterlogged lands

in India, and found that these salt-affected and waterlogged areas could be

effectively delineated, mapped and digitally classified with an accuracy of about

96 per cent, using bands 3,4,5, and 7. Rao et.al. (1991) followed a systematic

visual interpretation approach using FCC of TM bands 2,3 and 4 in the sake of

mapping two categories, moderately and strongly sodic soils. Steven et.al (1992)

confirmed that near to middle infrared indices are proper indicators for chlorosis

in the stressed crops (normalized difference for TM bands 4 and 5). Mougenot and

Pouget (1993) have applied thermal infrared information to detect hygroscopic

29

characteristic of salts, and they found that reflectance from single leaves

depends on their chemical composition (salt) and morphology.

The investigations of Vidal et al (1996) in Morocco and Vincent etal.

(1996) in Pakistan are based on a classification-tree procedure. In this

procedure, the first treatment is to mask vegetation from non-vegetation using

(NDVI). Then the brightness index is calculated to detect the moisture and salinity

status on fallow land and abandoned fields. Dwivedi, (1969) applied the principal

component analysis of Landsat MSS bands 1, 2, 3and 4 in delineating salt affected

soils. Brena, et,al. (1995), in Mexico, made multiple regression analysis using the

electrical conductivity values and the spectral observations to estimate the

electrical conductivity for each pixel in the field based on sampling sites. They

generated a salinity image using regression equation and the salinity

classifications. Their experimental procedure was applied to an entire irrigation

district in northern Mexico. Ambast et,al (1997) used a new approach to classify

salt affected and water logged areas through biophysical parameters of salt

affected crops (albedo, NDVI). Thier approach is based on energy partitioning

system named Surface Energy Balance Algorithm For Land, SEBAL (Bas-

tiaanssen, 1995). It is clear that most of the published investigations based on

remote sensing distinguish only three to four classes of soil salinity. Moreover,

most of them focused on empirical methods by a simple combination of multi

30

spectral bands, and very few concentrated on the biophysical characteristics of

the plants. These parameters are those which remote sensing has proved to give

accurate measurements for. Another remark can be made that the newly launched

ASTER sensor with its variable spectral bands was not used for salinity detection

yet.

2.9. Soil maps and mapping:

One of the major occupations of soil scientists is the surveying and mapping

of soils, and the production of the soil maps (Fitzpatrick, 1986). Surveying soils is

a most exacting scientific task because soils do not normally have sharp boundaries

but gradually grade from one into another. At the outset of a soil survey, it is

essential to establish the purpose of the map; it may be a special or a general

purpose survey.

The initial part of the survey usually starts with an examination of the soils

map of the area and from which preliminary boundaries may be drawn and then to

be checked by field examinations. It is also important at this stage to determine

which properties are to be mapped. This leads to the choice of classes to be

mapped and the map legend and where necessary to establish any relationships

between mapping units and land use planning. The soil units that are mapped vary

with the purpose of the map and the nature of the soil pattern. Generally, the soil

surveyor delimits soil series which are areas of relative uniformity but because

31

soils are so variable, soil series are seldom absolutely uniform and may contain up

to 15 per cent of other soils (Fitzpatrick, 1986). There are various methods for

conducting soil surveys. Mostly free surveys and grid system. From the data

recorded on the map or photograph and in the notebook, the surveyor draws lines

on a map to enclose areas of relative uniformity to produce a soil map. Most soil

survey data are now computer stored. This allows for rapid retrieval of information

and production of special purpose maps to suit user requirements.

From the original soil map, it is possible to derive a number of other

maps and it is becoming customary to prepare a land suitability map which

is published together with the soil map and report. In the last few years,

there has been a very rapid development in remote sensing techniques.

Before the launch of Landsat-1 in 1972, aerial photographs were being

used as a remote sensing tool for soil mapping, and exhibited their

potential in analysing land use and erosion status (Manchanda et al.,

2002). Subsequently, from 1972 onwards satellite data in both digital and

analog have been utilized for preparing small scale soil resource maps

showing soil sub-groups and their associations. The high resolution

Landsat TM data, enabled soil scientists to map soils at 1:50,000 scale,

which is used for district level planning. At this scale, soils could be

delineated as associations of soil series or family levels. The SPOT data

32

offered stereo capability, which has improved the soil mapping quality

(Manchanda et al., 2002).

The soil maps are required at different scales varying from 1:1 million to

1:4,000 to meet the requirements of planning at various levels. The scale of a soil

map has direct correlation with the information content and field investigations that

are carried out. The soil maps at 1:250,000 scales provide information for planning

at regional or state level with generalized interpretation of soil information for

determining the suitability and limitations for several agricultural uses and require

less intensity of soil observations and time.

Landsat TM and SPOT satellites enable us to map soils at 1:50,000 scale at

the level of association of soil series due to the higher spatial and spectral

resolutions (Manchanda et al., 2002).

2.10. Remote sensing, GIS and soil mapping:

Soil maps have been used to provide chemical and physical data input within

ecological and hydrological process models (Burrough and McDonnell, 1998). The

soil survey program is simply not designed to furnish data for such applications.

The increasingly sophisticated use of soil data has led to a greater demand for data

about soil properties than the conventional soil map can accommodate (Cook et al.,

1996).

33

Soil surveyors consider the topographic variation as a base for depicting the

soil variability. Even with the aerial photographs, only physiographic variation in

terms of slope aspects and land cover are being practiced for delineating the soil

boundary. Multispectral satellite data are being used for mapping soil up to family

association level (1:50,000). The methodology in most of the cases involves visual

interpretation (Biswas 1987; Karale et al., 1981). However, computer aided digital

image processing technique has also been used for mapping soils (Epema 1986;

Korolyuk & Sheherbenko, 1994; Kudrat etal., 1990) and advocated to be a

potential tool (Kudrat etal., 1992; Lee et al., 1988).

The conventional soil surveys are providing information on soils but they are

subjective, time consuming and laborious. Remote sensing techniques have

significantly contributed to speeding up the conventional soil survey programmes

and have reduced field work to a considerable extent and soil boundaries are more

precisely delineated than in conventional methods.

The major problem facing conventional soil survey and soil cartography is

the accurate delineation of boundaries. The remote sensing data in conjunction

with ancillary data provide the best alternative, with a better delineation of soil

mapping units.

Several workers (Karale, 1992; Kudrat & Saha, 1993; Kudrat etal., 1990;

Sehgal, 1995) have concluded that the technology of remote sensing provides

34

better efficiency than the conventional soil survey methods at the reconnaissance

(1:50,000) and detailed (1:10,000) scale of mapping.

Recent advances in remote sensing and GIS technology have become very

cost effective, due to (a) satellite images are sufficiently accurate and reliable. (b)

changes over time can be identified (c) computers have the capacity to rapidly

process large quantities of data and (d) are object-oriented. Geographic information

system (GIS) provides enormous flexibility in storing and analyzing any type of

data, providing decision support modeling for effective management (Buchan,

1997).

Remote sensing and photogrammetric techniques provide spatially explicit,

digital data representations of the earth surface that can be combined with digitized

paper maps in geographic information systems (GIS) to allow for efficient

characterization and analysis of large amounts of data. The future of soil survey

lies in using GIS to model spatial soil variations from more easily mapped

environmental variables. The new generation of GIS that integrated satellite

images with maps data means that this technology can be successfully used for

remotely monitoring land use cover. According to Buchan (1997) when image

analysis and GIS are combined into one package, it can offer a very efficient and

cost-effective solution. Satellite images are used to identify what is growing, while

35

the GIS component is used to measure an area, categorize it, and locate its position

on the earth surface to provide a complete record of the site.

2.11. Reflectance Characteristics of Earth’s Cover types:

The spectral characteristics of the three main earth surface features,

vegetation, water and soil, are briefed below:

2.11.1. Vegetation:

The spectral characteristics of vegetation vary with wavelength.

Plant pigment in leaves (chlorophyll) strongly absorbs radiation in the red

and blue wavelengths but reflects green wavelength. The internal structure

of healthy leaves act as diffuse reflectors of near infrared wavelengths.

Measuring and monitoring the near infrared reflectance is one way that

scientists determine how healthy a particular vegetation may be. Several

empirical indices have been used as quantitative indicators of vegetation

amount, which reduced the multidimensional spectral space of vegetation

scene to one dimension in order to sense variability in such properties as

biomass, leaf area indices, fractional cover and type (Jasinski, 1990).

Combining different ratios of red visible spectrum and near infrared, the

vegetation indices respond to the relatively high radiation absorption of the

red light by leaves due to the presence of chlorophyll and the high

36

reflectance of near infrared light due to scattering in the leaf internal

structure (Curran, 1980).

2.11.2. Water:

The majority of the radiation incident upon water is not reflected but

is either absorbed or transmitted. Longer visible wavelengths and near

infrared radiation are absorbed more by water than the visible

wavelengths. Thus water looks blue or blue green due to stronger

reflectance at these shorter wavelengths and darker if viewed at red or near

infrared wavelengths. The factors that affect the variability in reflectance

of a water body are depth of water, materials within water and surface

roughness of water.

2.11.3. Soil:

The majority of radiation incident on a soil surface is either

reflected or absorbed and little is transmitted. Soil reflectance properties

depend on numerous soil characteristics such as mineral composition,

texture (particle size), structure (surface roughness), percentage of organic

matter, and moisture content (Bunnik, 1981; Lillesand and Kiefer, 1987).

These factors are complex, variable, and interrelated. Mineral

composition, organic matter and moisture content are the main factors

governing spectral absorption of radiation. Azhar (1993) reported that

37

there are differences in reflectance of three types of soil namely peat,

paddy and forest soils (Fig 2.6).

.

Figure 2.1: Spectral reflectance curves for three different types of soils.

The curves for forest and paddy soils show higher reflectance values

compared to the peat soil. The high organic matter and soil moisture content of the

peat soil must have influenced the low reflectance of the peat soil. By measuring

the energy that is reflected by targets on earth surface over a variety of different

wavelengths, we can build up a spectral signature for that object.

2.12. Image Classification:

According to Canada Centre for Remote Sensing and Natural

Resources (2009), classification procedures can be broken down into two

Wavelength

38

broad subdivisions based on the method used: supervised classification

and unsupervised classification.

2.12.1. Supervised classification:

In a supervised classification, the analyst identifies in the imagery

homogeneous representative samples of the different surface cover types

(information classes) are of interest. These samples are referred to as

training areas. The selection of appropriate training areas is based on the

analyst familiarity with the geographical area and their knowledge of the

actual surface cover types present in the image (Fig 2.6). Thus, the analyst

is "supervising" the categorization of a set of specific classes. The

numerical information in all spectral bands for the pixels comprising these

areas are used to "train" the computer to recognize spectrally similar areas

for each class. The computer uses a special program or algorithm (of

which there are several variations) to determine the numerical "signatures"

for each training class.

Once the computer has determined the signatures for each class, each pixel

in the image is compared to these signatures and labeled as the class it is most

closely "resembles" digitally. Thus, in a supervised classification we are first

identifying information classes which are then used to determine the spectral

classes which represent them.

39

Sriharan (2004) in a study of analysis of land cover classes using

unsupervised and supervised classification of Stennis Space Centre (SSC) image,

found that the classification can be extended into agricultural research and is

especially useful for soil management and soil mapping.

Figure (2.2): A Supervised classification process (source: Canada Centre for

Remote Sensing and Natural Resources, 2009).

2.12.2. Unsupervised classification:

In essence it reverses the supervised classification process. Spectral classes

are grouped first, based solely on the numerical information in the data, and are

then matched by the analyst to the information classes (if possible). Programs,

called clustering algorithms, are used to determine the natural (statistical)

groupings or structures in the data (Fig. 2.8). Usually, the analyst specifies how

many groups or clusters are to be looked for in the data. In addition to specifying

the desired number of classes, the analyst may also specify parameters related to

40

the separation distance among the clusters and the variation within each cluster.

The final result of this iterative clustering process may result in some clusters that

the analyst will want to subsequently combine, or clusters that should be broken

down further - each of these requiring a further application of the clustering

algorithm. Thus, unsupervised classification is not completely without human

intervention. However, it does not start with a pre-determined set of classes as in a

supervised classification.

Figure (2.3): Unsupervised classification process (source: Canada Centre for

Remote Sensing, Natural Resources, 2009).

41

CHAPTER THREE

MATERAILS AND METHODS

3.1. Materials:

3.1.1. Study area image:

The study area is approximately a square area that is bounded by latitudes

13o45 N and 14o20 and longitudes 32o15 and 32o40 E, Fig 3.1. It is about 277850

ha, covered by two satellite images for two different years, 1979 and 2006 Fig 3.2

and Fig 3.3. The images are landsat Thematic mapper (TM) image which was

downloaded from website (GLCF-Landsat/WRS2 Path 173 Row 050).

Soil survey reports maps (No.170for 1979 and No.73for 2006) in North

White Nile.

3.1.2. Hardware and software used in the study:

(1) Scanner and color printers

(2) Arc GIS 9.1 program.

(3) Earth Resource Data Analysis System program (ERDAS imagine 8.5)

42

Figure (3.1): Study Area Location Map

43

Figure (3.2) Landsat thematic mapper satellite image for the study area 1979

44

Figure (3.3) Landsat thematic mapper satellite image for the study area 2006

45

3.1.3. The data

1) Maps covering the study area. Topographic map of the study area

2) Images for the years 1979 and 2006

3) Laboratory analyses to measure electrical conductivity (EC) for the soil

samples.

3.2. Methodology:

ERDAS imagine 8.5 software was used for band combination (2,3,4) for

landsat thematic mapper images of 1979 and 2006 . Conventional map was

inserted for water stressed regionn georefernce of the map was made after

converting from degree to UTM by (Geographic /UTM Coordinate Converter ) .

then Raster data was converted to vector (boundary, soil unit, augers) by ArcGIS

9.3 software .Salinity was then classified using salinity of Tom and Kevie Fig: 3.1

(Kevie, 1987) (None saline S1, slightly saline S2, moderately saline S3, strongly

saline S4) for every map depended on lab data (1979-2006 in the two depths (0-

30cm, 30-120cm) .A salinity map was produced depending on auger sites salinity

and then results of the two years were compared. The soils of the area are non-

saline to slightly saline in the 0-30 cm soil depth and slightly to moderately saline

in the subsoil. The soils are slightly sodic on the topsoil (0-30 cm) to moderately

sodic in the subsoil (table 1.2).

46

Table(3.1). Salinity and Sodicity Classes of the soil (Kevie,1987)

Classes

Salinity EC dS/m Sodicity ESP

0-30 cm

depth

30-120 cm

depth Class

0-30 cm

depth

30-90 cm

depth

None saline (S1) <4 <6 None sodic (S1) <10 <20

Slightly saline (S2) 4-8 6-12 Slightly sodic (S2) 10- 20 20- 35

Moderately saline

(S3) 8-16 12-24

Moderately sodic

(S3) 20-35 35- 50

Strongly saline (S4) >16 >24 Strongly sodic (S4) >35 > 50

3.3 Soil samples and laboratory analysis:

The soil samples collected 216 augers samples from the study area

were handed in to the laboratory of Land and Water Research Centre,

Agricultural Research Corporation (ARC) Wad Medani, Sudan, for

analysis of Electrical conductivity (EC) using a saturated soil paste (firstly

prepared and allowed to stand for one hour by adding soil to a known

quantity of water to paste consistency). Then the sample was extracted

using a vacuum pump. The extract was read for electrical conductivity

using an EC meter. The results were expressed as dSm-1 at 25°C; the

weighted average method was applied to calculate salinity for the auger

samples.

3.3.1 Salinity Data

The soil salinity and GPS data were saved as text file that will be later

incorporated into ArcMap. The file should be tab delimited with three columns:

longitude (X), latitude (Y), and estimated salinity data (Z). The headings of each

47

column should be written in the first row. The text file can be created easily in

Excel or any text editor.

Table ( 3.2 ) Data of soil salinity rating .The text file(data laboratory analyses

Land and Water Research Centre (LWRC/ARC)

depth Longitude Latitude salinity Calculation rating

0-30cm 432014 1564457 5.33 2

0-30cm 435324 1564181 7.07 2

0-30cm 437451 1562526 8.8 3

0-30cm 438673 1563826 1.4 1

0-30cm 439934 1565284 7.3 2

0-30cm 436742 1565481 1.2 1

0-30cm 433354 1565757 6.9 2

0-30cm 434575 1567176 10.7 3

0-30cm 437767 1566703 17.2 4

0-30cm 444209 1529908 11.2 3

0-30cm 453717 1530480 10.2 3

0-30cm 445987 1528450 2.2 1

0-30cm 445339 1531345 0.9 1

0-30cm 447312 1529709 1.24 1

30-90cm 432014 1564457 5.33 1

30-90cm 435324 1564181 7.07 2

30-90cm 437451 1562526 8.8 2

30-90cm 438673 1563826 1.4 1

30-90cm 439934 1565284 7.3 2

30-90cm 436742 1565481 1.2 1

30-90cm 433354 1565757 6.9 2

30-90cm 434575 1567176 10.7 2

30-90cm 437767 1566703 17.2 3

30-90cm 444209 1529908 11.2 2

30-90cm 453717 1530480 10.2 2

48

Table(3.3) Attributes of boundry and soil salinity rating (ArcMap)

SHAPE_Leng SHAPE Area rating land Suitability

714994.29350100000 408364515.26300000000 1 Non saline

12077.92931920000 3521000.49655000000 2 Slightly saline

2310.86092466000 324829.87657800000 2 Slightly saline

2940.17873495000 412162.48979100000 3 Moderately saline

6288.27352934000 1945989.90156000000 2 Slightly saline

24181.40355740000 7310974.05037000000 2 Slightly saline

2176.49177929000 345954.97478900000 3 Moderately saline

2002.57059294000 295334.94327800000 2 Slightly saline

1913.52572985000 279211.98232700000 3 Moderately saline

4057.49294852000 579501.59004500000 2 Slightly saline

6894.58186139000 1021260.97720000000 2 Slightly saline

8054.11776021000 1782273.01161000000 2 Slightly saline

28593.02157710000 10197273.38050000000 2 Slightly saline

1627.08374837000 185099.94028700000 1 Non saline

3242.30602079000 426053.50537300000 1 Non saline

8292.72951260000 1352640.93526000000 3 Moderately saline

1667.07805855000 199715.58501400000 2 Slightly saline

49756.73093610000 16058310.10790000000 2 Slightly saline

35782.32811090000 9024624.73486000000 2 Slightly saline

4212.93602828000 549436.42670800000 1 Non saline

49

The results were then plotted on the map and interapolated to

produce salinity map with different classes. ERDAS imagine and ArcGIS

were used as main GIS packages for building the model and running its

functions including input, output, analysis and processing. Raster and

vector GIS data sets were used to create different maps based on

overlaying, crossing and interpolation techniques. The verification

between mapping salinity using visual interpretation of remote sensing

data and mapping salinity through site observations were determined

through intersection. A correlation was developed between both maps to

arrive at a final method of integrating remote sensing data with spatial

modeling.

3.4 Soil Salinity Mapping:

The following applications are needed to produce soil maps: ArcMap. The

Spatial Analyst extension is required to create continuous surface maps from

discrete sample points. This Extension also provides numerous analysis and spatial

modeling tools, including converting features (points, lines, polygons) to rasters,

performing neighborhood and zone analyses, and modeling and analyzing raster

and vector data. Additional optional applications and extensions include ArcPad

and Geostatistical Analyst. ArcPad is an application used to delineate and

georeference the boundaries of the surveyed field via a hand held device integrated

50

with a GPS. The Geostatistical Analyst extension can be used to define a spatial

model that will be integrated in the kriging interapolation.

51

CHPTER FOUR

RESULTS AND DISCUSSION

4.1.Using Arc GIS view program:-

The maps in figure (4.1),( 4.2),(4.3) and (4.4) reflect the results of change cases

in the area of saline phenomenon which was determined by the data analyses of

saline soils by the isolation of unit image method, which represent the physiographic

unit by using Arc GIS program. By that, enabling to determine any physiographic

unit or saline phenomenon as a layer to obtain the area by special color for the saline

levels from four levels.

4.2. Soil units affected by salinity:-

The following classes were used to classify the soils of the study area and the

results by the levels indicated in the Table (4.1) were shown in

Table.(4.1) Salinity classes of the soil.

Classes depth

0 – 30 cm

depth

30 – 100 cm

Non saline < 4 < 6

Slightly saline 4 – 8 6 – 12

Moderately saline 8 – 16 12 – 24

Strongly saline >16 >24

Source : Kevie, 1987

52

Figure (4.1) Salinity Classification Map of the Top Soil of the study area,1979

53

Figure (4.2) salinity Classification Map of Top Soil of the study area, 2006

54

Figure (4.3) salinity Classification Map of Sub Soil of the study area, 1979

55

Figure (4.4) salinity Classification Map of Sub Soil of the study area, 2006.

56

4.3 Soil Salinity Classification

Using table (4.2) salinity classes, the soils were divided into non- slightly,

moderately and strongly saline abbreviated with the numbers 1,2,3 and 4

respectively. Fig.4.5 and 4.6 indicated that the area of the soil affected by salinity

in the year 2006 was less than that of 1979.

Table (4.2): Salt affected areas/ha in the top and sup soil of the study area

for the years 1979 and 2006 .

Salinity Rating

year

2006 1979

Top soil Sup soil Top soil Sup soil

None saline (1) 33.9 %

35.6 % 30.3 % 18.5 %

Slightly saline (2) 10.7 %

12.7 %

20.2 %

10.1 %

Moderately saline(3) 5.3 %

1.7 % 19.4 %

1.1 %

Strongly saline (4) 0.1 %

0.01 % 0.5 %

0.0 %

Total salt affected areas for the years 1979 and 2006 were 277850.8 and

277718.9 ha respectively.

57

Figure (4.5): The decrease in soil salinity soil from 1979 to 2006 in top soil in

(hectar).

Figure (4.6): The decrease in soil salinity from 1979 to 2006 in sub soil soil in

(hectar).

58

The origin of salinity in the study area is attributed to presence of the salts as a

basic component of the parent material of the soils and due to the desertification of

that area . The decrease of saline soils however is attributed to the presence of the

process of leaching due to high rainfall in the last few years and growth of salte

tolerant crops such as Acacia nubica (Loat) and Capparis deciduas (Tundub).

4.4 Unsupervised Classification:

Unsupervised classification process could not distinguish cultivated soil,

clay and bare soil units from other soil units. Sand units are generally well

identified by unsupervised classification. The data in unsupervised classified image

are not clear. It has generalized tones and reflectance values, which makes it

difficult to properly classify every pixel within the image. This causes

overestimation of one category and hence an underestimation of the other

categories in the process. However, this classification was also used along with the

supervised classification. The result of this technique were shown in Figures (4.7)

and (4.8) .

59

Figure 4.7 Unsupervised Classification of Study Area in 1979

60

Figure 4.8 Unsupervised Classification of Study Area in 2006

61

Once the classification is complete there are different classes in the image.

The pixels for each of the categories assigned to each land cover type were

grouped into the different land cover categories. They included: cultivated soil,

clay, bare soil and sand (Figure 4.7 and 4.8) . Performing this classification

generated some errors, especially in the vegetation. In this classification the most

common errors are observed in distinguishing between the cultivated soil and bare

soil classes.

The future of soil salinity maps lies in using GIS to model spatial soil

variations from more easily mapped environmental variables. The new generation

of GIS that integrated satellite images with maps data means that this technology

can be successfully used for remotely monitoring land use cover (Buchan, 1997).

62

CHAPTER FIVE

SUMMARY AND CONCLUSION

5.1 Summary

Variations in soil salinity led to detecting the change and consequently a soil salinity

map could be produced.

Changes in soil salinity result in change in surface soil color in spectral reflectance

and consequently influence sensors recorded signals.

Remote Sensing (RS) information can be improved when it is integrated with other

data, for which a GIS is an appropriate tool.

Results showed that preparing soil salinity maps and sampling using satellite images

is possible.

5.2 Conclusion

Soil salinity data can be used and integrated into a GIS environment for generating

surface maps and developing prescription maps.

The current study showed that satellite image and the laboratory data can

be used together to convert the information into soil salinity map.

63

REFERENCES

Abdel-Hamid, M. A. and D.P. Shrestha, 1992. Soil salinity mapping in the Nile

Delta, Egypt using remote sensing techniques. International Archives of

Photogrammetry and Remote Sensing, 29, (B7), 783–787. ISPRS Commission

VII/4, Washington D.C., USA.

Ali, H.O. 2009. Land degradation and sorghum productivity assessment using

spatial analysis at Gadambalyia Schemes, Gadarref State, Sudan. PH.D.Thesis

Dept of Soil and Water Sciences Faculty of Agricultural Sciences, University of

Gezira, Sudan.

Aldrich, R.C. 1975. Detecting disturbances in a forest environment,

Photogrammetric Engineering and Remote Sensing 41 : 39-48.

Al-Barrak, S. and M. Al-Badawi, 1988. Properties of some salt affected soils in

AL-Ahsa, Saudi Arabia. Arid Soil Research and Rehabilitation 2:85-95.

Ambast, S.K., 1997. Monitoring and evaluation of irrigation system performance

in saline irrigated command, using satellite remote sensing and GIS. Interne

Mededeling, Report No.471, DLO Winand Staring Centre, Wageningen, the

Netherlands, 106 p.

Azhar, H.S. 1993. Vegetation studies using remote sensing techniques. MSc.

Thesis, Univesiti Teknologi Malaysia.

Bastiaanssen, W.G.M., 1995. Regionalization of surface flux densities and

moisture indicators in composite terrain: A remote sensing approach under clear

skies in Mediterranean climates. Ph.D. Thesis, Landbouw Universiteie,

Wageningen, The Netherlands.

64

Bauer, M.E., Burk T.E., Ek, P.R, A.R. Coppin, S.D. Lime, T.A. Walsh, D.K.

Walters, Befort W., and D.F. Heinzen 1994. Satellite inventory of Minnesota

forest resources. Photogrammetric Engineering and Remote Sensing 60(3):287-

298.

Ben-Dor, R. Patkin A. Banin .A, and .A. Karnieli,. 2002. Mapping of several

soil properties using DAIS-7915 hyperspectral scanner data — a case study over

clayey soils in ( Israel). International Journal of Remote Sensing,. 23: 1043–

1062.

Bernstein L. Salt tolerance of plants. USDA Agric. Inf. Bull, USA; 1964.

Joffe, J.S. 1953. The ABC of soils. Oxford Book & Stationery Co. New Delhi.

Bear, F.E. 1967. Chemistry of the Soil. Reinhold publishing corporation, New

York.U.S.A.

Biswas, R.R. 1987. A soil map through landsat satellite imagery in part of the

Auranga catchment in Ranchi and Palamon district of Bihar, India. International

Journal of Remote Sensing 4: 541-543.

Brena, J .H. and .L. Pulido, 1995. Salinity assessment in Mexico. In Use of

remote sensing techniques in irrigation and drainage .A. Vidal and J. A.

Sagardoy,eds (179-184). Water reports 4: FAO.

Brady, N.C., and. R.R Weil, 1999.The Nature and Properties of Soils. Prentice

Hall. USA.

Brown, D.J. Shepherd K.D., Walsh M.G., Dewayne Mays .M. and .TG

Reinsch. 2006. Global soil characterization with VNIR diffuse reflectance

spectroscopy. Geoderma,. 132: 273–290.

Buydens, L.M.C. 2003. The potential of field spectroscopy for the

assessment of sediment properties in river floodplains. Analytica Chimica Acta,

484: 189–200.

65

Buringh, P. 1970. Introduction to the study of soils in tropical and subtropical

regions. 2nd Edition. Wageningen University,the Netherlands.

Buol, S.W., Hole F.D. and. R.J McCracken, 1989. Soil Genesis and

Classification.The Iowa State University Press, USA.

Burrough, P.A. and R.A. McDonnell. 1998: Principles of geographic information

systems. (Revised edition) Oxford: Clarendon Press.

Buchan, P. 1997. Satellite imagery for regulatory control. Modern Agriculture:

Journal for Site-Specific Crop Management, 1(2): 20-23.

Bunnik, N.J.J. 1981. Fundamentals of remote sensing relation between spectral

signatures and physical properties of crops. In A. Berg (ed). Application of Remote

Sensing to Agricultural Production Forecasting. A.A. Balkema, Rotterdam. 47-81.

Cablk, M.E., Kjerfve .B. Michener W.K. and J.R. Jensen 1994. Impact of

Hurricane Hugo on a coastal forest: Assessment using Landsat TM data. Geocarto

International 2: 15 – 24.

Chaturvedi, L., K.R. Carver, J.C. Harlen, G.D. Hancock, F.V. Small and K.J.

Dalstead, 1983. Multispectral remote sensing of saline seeps. IEEE Transactions

on Geoscience and Remote Sensing, vol. 21(3): 239-250.

Cook, S.E., Corner, R.J., Grealish, G., Gessler, P.E. and C.J. Chartres, 1996:

A rule-based system to map soil properties. Soil Science Society of America

Journal. 60: 1893–900.

Curran, P. 1980, Multispectral photographic remote sensing of vegetation amount

and productivity. in proceeding of the Fourteenth International Symposium on

Remote Sensing of the Environment, Ann Arbor, MI,U.S.A pp. 623-637.

De Dapper, M., Gossens .R. and E. van Ranst. 1997. Soil salinity and

waterlogging in the Ismailia Governorate, Egypt. Telsat III/13 project. Belgium

Egypt Scientific Collaboration.

66

Dehaan, R.L., and Taylor, G.R. 2002. Field-derived spectra of salinized soils

and vegetation as indicators of irrigation-induced soil salinization. Remote Sensing

of Environment, Vol. 80, pp. 406–417.

Dehaan, R.L., and G.R. Taylor,. 2003. Image-derived spectral endmembers as

indicators of salinisation. International Journal of Remote Sensing,. 24: 775–794.

Doorenbos, J., and A.H. Kassam, 1979. Yield response to water. FAO Irrigation

and Drainage Paper No. 33, Rome, Italy.

Dobson, E.L., Jensen J.R., Lacy R.B., and F.G. Smith 1995. A land cover

characterization methodology for large area inventories. ACSM/ASPRS

Proceeding,pp. 786-795.

Dwivedi, R.S., and B.R.M Rao,. 1992. The selection of the best possible

Landsat TM band combination for delineating salt-affected soils.

International Journal of Remote Sensing, 13: 2051–2058.

Dwivedi, R.S., and K. Sreenivas, 1998. Image transforms as a tool for the study

of soil salinity and alkalinity dynamics. International Journal of Remote Sensing,

19: 605–619.

Dwivedi, R.S., 1969. Monitoring of salt affected soils of the Indo-Gangetic

alluvial plains using principal component analysis. International Journal of

Remote Sensing, 17(10):1907-1914.

Elmobarak, A.A. 1991. Interpretation of land cover types based upon remote

sensing techniques (North Gezira-Sudan). MSc. Thesis. University of Gent,

Belgium.

Epema, G.F. 1986. Processing thematic mapper data for mapping in Tunisia. ITC

Journal 30-34.

FAO, 1988. Salt-affected soils and their management. Bulletin No. 39, FAO,

Rome. Italy

67

Farifteh, J., Van der Meer, F., Atzberger, C., and E.J.M. Carranza, 2007.

Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two

adaptive methods (PLSR and ANN). Remote Sensing of Environment, 110:

59–78.

Fitzpatrick, E. A. (1986). An introduction to soil science, 2nd edition, pp. 207-

214.

Frenkel, H., Goertzen, J.O. and J.D. Rhoades. 1978. Effect of clay type and

content, exchangeable sodium percentage, and electrolyte concentration on clay

dispersion and soil hydraulic conductivity. Soil Sci. Soc. Amer. J: 1978.

Gardener, L.R; Michener W; Kjerfve.K., and D.A. Karinshak 1991 The

geomorphic effects of Hurricane Hugo on an undeveloped coastal landscape at

North Inlet, South Carolina. Journal of Coastal Research 8: 181-186.

Ghabour, T.K. and Daels, L. 1993. Mapping and monitoring of soil salinity of

ISSN. Egyptian Journal of Soil Science 33: (4)355-370.

Ghassemi, F., Jakeman A. J. and H. A. Nix. 1995. Salinization of land and

water resources: human causes, extent, management and case studies.

Canberra, Australia: The Australian National University. Wallingford, Oxon,

UK: CAB International.

Goetz, A.F.H., Vane, G., Solomon, J.E., and B.N. Rock, 1985. Imaging

spectroscopy for Earth remote sensing. Science Washington, D.C, 228: 1147–

1153.

Guan, Y.X., Liu, G.H., Liu, Q.S., and Q.H. Ye, 2001. The study of salt affected

soils in the Yellow River delta based on remote sensing. Journal of Remote

Sensing . 5: 46–52.

Gupta, R.K. and I.P. Abrol, 1990. Salt-affected soils: Their reclamation and

management for crop production. Adv in Soil Sci .11: 223-288.

68

Howari, F.M., Goodell, P.C., and S. Miyamoto, 2002. Spectral properties of salt

crusts formed on saline soils. Journal of Environment Quality, 31: 1453 1461.

Jasinski, M.F. 1990. Sensitivity of the Normalized Difference Vegetation

Index(NDVI) to Sub-pixel canopy cover soil albedo, and pixel scale, Remote Sens.

Environ. 32:169 – 187.

Jensen, J.R. Cowen D. Althausen J.D. Narumalani S. and O. Weatherbee

(1993). An evaluation of the Coast Watch change detection protocol in South

Carolina. Photogrammetric Engineering and Remote Sensing 59(6): 1039-1046.

Karale, R.L. Bali Y.P. and K.V. Rao 1981. Soil mapping using remote sensing

techniques. Proceedings Indian Academy of Science and Engineering Sciences

3:197-208.

Kevie, W. and O.A.( El Tom . 1987). Manual for land suitability classification

for agriculture in Sudan . SSA/ Wad Medani, Sudan.

Khan, N.M., Rastoskuev, V.V., Shalina, E.V., and Y. Sato, 2001. Mapping salt-

affected soils using remote sensing indicators a simple approach with the use of

GIS IDRISI. in Proceedings of the 22nd Asian Conference on Remote Sensing

(ACSR), 5–9 November 2001, Singapore. Centre for Remote Imaging,

Sensing and Processing, National University of Singapore, Singapore.. 1: 25-

29.

Klute, A. 1965. Laboratory measurement of hydraulic conductivity of saturated

soils, In C.A. Black. Methods of Soil Analysis. Madison, Wisconsin, USA.

Kooistra, L., Wehrens, R., Leuven, R.S.E.W., and L.M.C. Buydens, 2001.

Possibilities of visible–near-infrared spectroscopy for the assessment of soil

contamination in river floodplains. Analytica Chimica Acta, 446: 97–105.

69

Kooistra, L., Wanders, J., Epemac, G.F., Leuven, R.S.E.W., Wehrens, R., and

Reeves, J.B., McCarty, G.W., and J.J. Meisinger, 2000. Near infrared

reflectance spectroscopy for the determination of biological activity in

agricultural soils. Journal of Near Infrared Spectroscopy. 8: 161– 170.

Korolyuk, T.V. and H.V. Shcherbenko. 1994. Compiling soil maps on the basis

of remotely sensed data digital processing: Soil interpretation. International Journal

of Remote Sensing 15: 1379-1400.

Kudrat, M., S.K. Saha and A.K.Tiwari. 1990. Potential use of IRS LISS II

digital data in soil landuse mapping and productivity assessment. Asian Pacific

Remote Sensing Journal 2: 73-78.

Kudrat, M., Tiwari A.K, Saha S.K. and S.K. Bhan. 1992. Soil resource mapping

sing IRS-1A LISS II digital data- A case study of Kandi area adjacent to

Chandigarh (India). International Journal of Remote Sensing 13: 3287-3302.

Kudrat, M. and S.K. Saha. 1993. Land productivity assessment and mapping

through integration of satellite and terrain slope data. Indian Journal of Remote

Sensing 21: 151-166.

Lee, K.S., Lee G.B. and J. Tyler. 1988. Determination of soil characteristics from

thematic mapper data of a cropped organic- inorganic soil landscape. Soil Sci. Soc.

of Amer. J 52: 1100-1104.

Lillesand, T.M., and R.W. Kiefer. 1987. Remote Sensing and Image

Interpretation. 2nd edition. John Wiley and Sons, New York. 08 :0-471-84517-5 -

Amazon.

Lillesand, T.M. and R. Kiefer, 1993. Remote Sensing and Image Interpretation.

3nd Edition, John Villey, New York.USA.

Lima, L. A. and Grismer. M. E. 1992. Soil crack morphology and soil salinity.

Soil Sci. Soc. OF Amer. J. 153(2):149-153.

70

Liu, W.D., Baret, F., Zhang, B., Tong, Q.X., and L.F. Zheng, 2004. Using

hyperspectral data to estimate soil surface moisture under experimental

conditions. Journal of Remote Sensing, 8: 8: 434–442.

Luney, P.R., and H.W. Dill, Jr.1970. Uses, potentialities, and needs in agriculture

and Forestry. in remote sensing with special reference to agriculture and forestry.

Nat. Acad. of Sci. p.1-34.

Lu, N., Zhang, Z., and Y. Gao, 2005. Recognition and mapping of soil

salinization in arid environment with hyperspectral data. In IGARSS’05,

Proceedings of the International Geoscience and Remote Sensing Symposium, 24–

(29 July 2005), Seoul, Korea. IEEE, Piscatawy, N.J. 6: 4520–4523.

Manchanda, M. L. 1981. Study of Landsat imagery and aerial photos for the

evaluation of salt-affected soils of north-west India. Unpublished MSc thesis, Soils

Division, ITC, Enschede.

Manchanda, M.L., Kudrat, M and. A.K Tiwari, 2002. Soil survey and mapping

using remote sensing. Regional Remote Sensing Service Centre, India.

McKensie, N.J., Gessler, P.E., Ryan, P.J. and D. O’Connell, 2000: The role of

terrain analysis in soil mapping. in Wilson, J. and Gallant, J., (eds). Terrain

analysis: (principles and applications). New York City, NY: John Wiley and

Sons,Bubl. 245–265.

Metternicht, G., and J.A. Zinck, 1997. Spatial discrimination of salt- and

sodium-affected soil surfaces. International Journal of Remote Sensing, 18: 2571–

2586.

Metternicht, G.I. and J.A. Zinck, 2003. Remote sensing of soil salinity:

potentials and constraints. Remote Sensing of Environment, 512 : 1-20

Menenti, M., Lorkeers A. and M. vissers, 1986. An application of Thematic

Mapper data in Tunisia. ITC Journal 1 : 35-42.

71

Moormann, F.R. 1978. Breeman. Salinity and Alkalinity in Rice Lands.

International Rice Research Institute, Philippines. IRRI, Los Bonas, the Philippines

pp. 185.

Mouazen, A.M., Karoui, R., De Beardemaeker, J., and H. Ramon, 2006.

Characterization of soil water content using measured visible and near infrared

spectra. Soil Sci. Soc. of Amer. J. 70: 1295–1302.

Mougenot, B., Epema, G.F., and M. Pouget, 1993. Remote sensing of affected

soils. Remote Sensing Review. 7: 241–259.

Mougenot, B. and M. Pouget, 1993. Remote sensing of salt-affected soil. In

Remote Sensing Reviews .7:241-259.

Mulders, M.A. and G.F. Epema, 1986. The Thematic Mapper: A new tool for

soil mapping in arid areas. ITC Journal 1: 24-29.

Muchoney, D.M. and B.N. Haack 1994 Change detection for monitoring forest

defoliation. Photogrammetric Engineering & Remote Sensing 60(10): 1243-1251.

Nanni, M.R., and J.A.M. Dematte, 2006. Spectral reflectance methodology in

comparison to traditional soil analysis. Soil Sci. Soc. of Amer. J., 70: 393–407.

Naseri, M.Y., 1998. Characterization of salt-affected soils for modeling

sustainable land management in a semi-arid environment; a case study in the

Gorgan region, North east Iran. Ph.D. Thesis, Ghent University, Belgium, 321 p.

Nielsen, D.R., Biggar, J.W. and J.N. Luthin, 1966. Desalinization of soils under

controlled unsaturated conditions. Int. Commission on Irrigation and Drainage, 6th

Congress, New Delhi, India.

Nualchawee, K. 1984. Remote sensing in agriculture and the role of groundtruth

as supporting data. Proc. the symposium. Third Asian Agricultural Remote Sensing

Symposium, February 6-11, 1984. Chiangmai, Thailand, pp.269-285.

Olsson, H. 1995. Reflectance calibration of TM data for forest change detection.

International Journal of Remote Sensing 16(1): 81-96

72

Peng, W. 1998. Synthetic analysis for extracting information on soil salinity using

remote sensing and GIS: a case study of Yanggao basin in China. Environmental

Management, 22: 153–159.

Pupisky, H., and Shainberg. I. 1979. Salt effect on the hydraulic conductivity of

a sandy soil. Soil Sci. Soc. of Amer. J 43 (3):429-433.

Rao, B.R.M., Dwivedi, R.S., Venkataratnam, L., Ravishankar, T.,

Thammappa, S.S., Bhargava, G.P., and A.N. Singh, 1991. Mapping the

magnitude of sodicity in part of the Indo-Gangetic Plains of Uttar Pradesh,

northern India using Landsat-TM data. International Journal of Remote Sensing,.

12: 419–425.

Reichert, P. 1984. Remote sensing for agricultural resources survey. Proced. Of

the symposium. Third Asian Agricultural Remote Sensing Symposium, February

6-11, 1984. Chiang Mai, Thailand, pp.33-37.

Rhoades. J.D., 1990. Soil salinity – causes and controls. in Techniques for

desert reclamation, School of Geography University of Oxford.

Richards, L.A., 1954. Diagnosis and improvements of saline and alkali soils.

U.S. Salinity Laboratory DA, US Dept. Agr. Hbk 60, 160 p.

Rowell, D.L., Payne, D. and N. Ahmad, 1969.The effect of the concentration and

movement of solutions on the swelling, dispersion, and movement of clay in saline

and alkali soils. Soil Sci. Soc. of Amer. J 20 (1): 176-188.

Rowell, D. L. 1963. Effect of electrolyte concentration on the swelling of oriented

aggregates of montmorillonite. Soil Sci. Soc. of Amer. J 96: 368-374.

Saha, S.K., Kudrat, M. and S.K. Bhan, 1990. Digital processing of Landsat

TM data for wasteland mapping in parts of Aligarh District, Uttar Pradesh, India.

International Journal of Remote Sensing, 11: 485-492.

Sehgal, J.L. 1995. Land resource appraisal for landuse planning to meet the

challenges of 21st Century. Journal of Indian Society of Soil Science 43: 504- 528.

73

Sharma, R.C. and G.P. Bhargava, 1988. Landsat imagery for mapping saline

soils and wet lands in north-west India. International Journal of Remote Sensing.

9: 39-44.

Shepherd, K.D., and M.G. Walsh, 2002. Development of reflectance spectral

libraries for characterization of soil properties. Soil Sci. Soc. of Amer. J, 66:

988–998.

Silapathong, C., and F. Blasco. 1992. The application of geographic information

system to mangrove forest management: Khlung, Thailand. Asian Pacific Remote

Sensing Journal, 5(1): 97-104.

Sinanuwong, S and Y. Takaya. 1974. Saline Soil in Northeast Thailand.

Southeast Asian Study 12(1): 105-120..

Singh, R.P., and S.K. Srivastav, 1990. Mapping waterlogged and salt affected

soils using micro-wave radiometers. International Journal of Remote Sensing

11:1879-2592.

Sirois, J. and F.J. Ahern 1989. An investigation of SPOT HRV data for

detecting recent mountain pine beetle mortality. Canadian Journal of Remote

Sensing 14(2): 104-108.

Sriharan, S. 2004 . analysis of land cover classes using unsupervised and

supervised classification of stennis space center (SSC) Image. NASA Faculty

Fellowship Program 2004, Stennis Space Center. USA.

Steven, M.D. Malthus, J. Jaggard F.M. and B. Andrieu, 1992. Monitoring

responses of vegetation to stress in remote sensing from research to operation :

proceeding of the 18th annual conference of the remote sensing society, university

of dundee, 15-17 September 1992, (A. P. Cracknell and R. A. Vaughan(eds),

369-377. Remote Sensing Society Nottingham, U.K.

Szabolcs, I., 1987. The global problems of salt-affected soils. Acta Agronomica

Hungarica, 36:159-172.

74

Tricatsoula, E. 1988. Digital image processing and geographic information

system for soil and land use mapping with emphasis on soil moisture and

salinity/alkalinity determination.Unpublished MSc thesis, Soils Division, ITC,

Enschede.

Van Breeman, N. 1973. Dissolved aluminum in acid sulfate soils and in acid

mine waters. Soil Sci. Soc. Amer. J 37:694–697.

Verma, K.S., Saxena, R.K., Barthwal, A.K., and S.N. Deshmukh, 1994.

Remote sensing technique for mapping salt affected soils. International Journal

of Remote Sensing, 15: 1901–1914.

Vidal, A., Maure, P. Durand H. and P. Strosser, 1996. Remote sensing applied

to irrigation system management: Example of Pakistan. In EURISY Colloquium:

Satellite Observation for sustainable development in the Mediterranean Area, 132-

142. Paris: Promotion of Education and information Activities for the

Advancement of Space Technology and its Application in Europe.

Vincent, B. Vidal, A. Tabbet, D. Baqri A. and M. Kuper, 1996. Use of

satellite remote sensing for the assessment of water logging or salinity as an

indication of the performance of drained systems. In Evaluation of performance of

subsurface drainage systems: 16th Congress on Irrigation and Drainage, Cairo,

Egypt, 15-22 September 1996.

Wang, Z.Q., You, W.R., Zhu, S.Q. 1993. Saline–alkali soil of China. Science

Publishing, Beijing.

Wang, H.W. 1999. Partial least-squares regression – method and applications.

National Defence Industry Publishing, Beijing.

Walsh, S.J. and P.A. Townsend 1995 Comparison of change detecting

approaches for assessing a riverine flood hydroperiod. ACSM/ASPRS

Proceedings, pp. 134-143.

75

Weng, Y.L., Gong, P., and Z.L. Zhu, 2008. Reflectance spectroscopy for the

assessment of salt content in soils of the Yellow River Delta of China.

International Journal of Remote Sensing.

Westman, W. E., L. L. Strong, and B. A. Wilcox, 1989. Tropical deforestation

and species endangerment: the role of the remote sensing. Landscape Ecology,

3(2): 97-109.

Whittig, L.D. 1959. Characteristics and genesis of a Solodized-Solonetz of

California. Soil Sci. Soc. Amer. J 23:469-473.

Wiegand, C. L., Rhoades, J.D., Escobar, D. E., and J.H. Everitt, 1994.

Photographic and videographic observations for determining and mapping the

response of cotton to soil salinity. Remote Sensing of Environment, 49: 212-223.

Wold, S., Sjostrom, M., and L. Eriksson, 2001. PLS-regression: a basic tool of

chemometrics. Chemometrics and Intelligent Laboratory Systems, 58: 109–130.

Wu, Y., Chen, J., Wu, X., Tian, Q., Ji, J., and Z. Qin, 2005. Possibilities of

reflectance spectroscopy for the assessment of contaminant elements in suburban

soils. Applied Geochemistry, 20: 1051–1059.

Zinck, J. A. 2001. Monitoring salinity from remote sensing data. In Proceedings

of the 1st Work-shop of the EARSeL Special Interest Group on Remote Sensing

for Developing Countries, R. Goossens, & B. M. De Vliegher (eds) 359– 368.

Belgium: Ghent University Belgium .