assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators

Assessment of hydrosaline land degradation

by using a simple approach of

remote sensing indicators

Nasir M. Khan a,*, Victor V. Rastoskuev b,Y. Sato a, S. Shiozawa a

a Graduate School of Agricultural and Life Sciences, The University of Tokyo,

Bunkyo-Ku, Tokyo 113-8657, Japanb Research Center for Ecological Safety, Korpusnaya Str. 18, 197110 St. Petersburg, Russia

Accepted 1 September 2004

Available online 10 May 2005

Abstract

This research deals with monitoring irrigated saline soils of Faisalabad, Pakistan. The analysis

is based on remote sensing data acquired from the Indian Remote Sensing satellite (IRS-1B) and

using a Geographical Information System (GIS). We have examined how different remote sensing

indicators work for salinity prone lands classification and assessment in part of the Indus basin of

Pakistan, which is facing extremely hydrosalinized land degradation problems. The study has

suggested some new but simple and practical approaches for assessing salinity. We have analyzed

the effectiveness of several indicators for the presence of salts in the area in terms of salinity indices

(SI), especially several combinations of the ratio of the signals received in the third spectral band to

others. As salt-affected soils are also characterized by stressed vegetation, vegetation indices were

also analyzed as concurrent indicators. The probability for obtaining a correct classification of the

satellite images has shown to be strongly dependent on the season for all indicators analyzed. The

best results can be achieved for the dry season (March–April), but not in humid or high temperature

periods. The most difficult part in the classification processes was to distinguish between salt-

affected areas and rural/village populated areas due to its muddy roofs producing similar reflection

as of patchy saline and dry barren distributed soils. We have come-up with two original schemes of

classification through the analysis of available satellite data for this specific test area. In the first

case, we tried to produce a new set of composite and stretched images out of four channels data

www.elsevier.com/locate/agwat

Agricultural Water Management 77 (2005) 96–109

* Corresponding author. Tel.: +92 51 4436432; fax: +92 51 4436431.

E-mail address: [email protected] (N.M. Khan).

0378-3774/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.agwat.2004.09.038

using special digital image processing (DIP) techniques and then analyzing their ratios. In the

second scheme, we analyzed isoclustering functions that perform classification based on speci-

fically created images (through principal component analysis (PCA) and salinity indices) instead of

the common practice of using just satellite sensor’s reflectance measurements. Both schemes have

shown the ability to perform good classification and assessment for hydrosaline degraded lands in

the study area using IRS-1B data.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Waterlogging and salinity; Remote sensing; GIS; Salinity indices; Indus basin

1. Introduction

Soil salinization is becoming an increasing problem, especially in arid and semi-arid

regions wherever irrigation is practiced. In some parts of the world, like Pakistan, the

population is growing very fast, and therefore, attempts are made to increase the

agricultural production, in many cases by land reclamation, but facing limited water

resources. It is reported recently that about 10% of presently arable lands of the world are

affected by salinity (Tabet et al., 1997). Salinity and sodicity affect an estimated 952 Mha

of land (Szabolcs, 1992). This is the case in Pakistan, where seepage and percolation from

large irrigation systems gave rise to watertable, and thus, gradually and substantially

contributed to the outburst of a twin menace, i.e., waterlogging and salinization. The

magnitude of the salinity/sodicity problem can be gauged from the fact that at one stage in

the country, the area of productive land was being damaged by salinity at a rate of about

40,000 ha/year (WAPDA, 1981). Salt-affected lands in the Indus basin of Pakistan cover an

area of 4.22 Mha, which is 26% of the total irrigated area (Ghassemi et al., 1995). Thus,

monitoring saline degraded lands has always been a primary issue for efficient irrigation

systems management and rehabilitation policies.

The problem of detection, monitoring and mapping salt-affected soils is known to be a

difficult matter because dynamic processes are involved. Recent advances in the

application of remote sensing technology in mapping and monitoring degraded lands,

especially in salt-affected soils, have shown great promise for enhanced speed, accuracy

and cost effectiveness. The approach to the problem of delineating saline soils using remote

sensing data and Geographical Information System (GIS) techniques has been proved

efficient in many recent studies (Sharma and Bhargawa, 1988; Rao et al., 1991; Dwivedi,

1992; Srivastava et al., 1997; Dwivedi and Sreenivas, 1998; Khan and Sato, 2001). The

combination of remote sensing with GIS is very promising, especially for the monitoring of

soil salinization (Goossens et al., 1993; Casas, 1995). This study is devoted for mapping

salt-affected soils in Pakistan. An approach using remote sensing indicators, different

salinity indices (SI), vegetation indices and principal component analysis (PCA), which are

based on spectral characteristics of different kind of surfaces, has been applied here as

digital image processing (DIP) and GIS techniques. The work has been accomplished with

the use of the capacity of GIS IDRISI for Windows (Eastman, 1995) through its special

modules and mathematical operator functions for remote sensing data processing and

analysis.

N.M. Khan et al. / Agricultural Water Management 77 (2005) 96–109 97

2. Area description and data used

The area under study is located in the center of Punjab province of Pakistan, latitudes

318020 to 318450N and longitudes 728500 to 738220E (Fig. 1), which is part of the world’s

largest contiguous Indus basin irrigation system. It is a part of subtropical continental low

land region and is designated as the semi-arid central Punjab. The climate conditions have

marked variations in temperature (mean monthly maximum temperature range from 19.4

to 41.2 8C) and rainfall over the year (<350 mm on average) occurs mostly (75%) during

the monsoon season (July–September). The mean annual potential evaporation is as high as

2100 mm (WAPDA/SMO, 1993–1994). Physiographically, the area is nearly flat to very

gentle slope (the average topo-gradient is 0.02%) from north-east to south-west direction

with mean elevation of 190 m. The soils are alluvial deposits classified as silt loam, loam

and silt clay loam and loamy sands. The cropping calendar has two seasons, called rabi

(winter) and kharif (summer). The main rabi crops are wheat, sugarcane, pulses, and

fodder, while corn, paddy, cotton, sugarcane and fodder occupy lands in kharif season. The

average yields of wheat, sugarcane, maize, cotton and rice in the whole study area are 1.5,

26.1, 0.85, 0.3 and 1.37 tonnes ha�1, respectively. Except for rice, yields of all other crops

are much lower than the average yields in Faisalabad administrative division.

Data were recorded by the sensor Linear Image Self-scanning Spectrometer (LISS-II) of

Indian Remote Sensing satellite (IRS-1B) (launched on August 29, 1991 and declared in

operation from September 16, 1991) having 22 day repeating orbit (857 � 919 km) at

905 km mean altitude and 99.258 sun-synchronous/inclination working in four spectral

bands: blue (B1: 0.042–0.52 mm), green (B2: 0.52–0.59 mm), red (B3: 0.62–0.68 mm) and

near infrared range (NIR) (B4: 0.77–0.86 mm). It exhibits a narrow field-of-view (FOV)

with 74 km � 2 swath-width. A spatial resolution of 36 � 36 m has been used in the

analysis. This data series extended the work potential for groundwater exploration, surface

water and land use problems, like salinity, land/water management, flood monitoring,

forest, etc. The parametric details of IRS LISS-II instrument are shown in Table 1.

N.M. Khan et al. / Agricultural Water Management 77 (2005) 96–10998

Fig. 1. Location of the study area.

Different time periods (pre-monsoon and post-monsoon) have been chosen for DIP

analysis because of considerable variation in soil surface salinity as well as in vegetation

cover (Table 2). Topographic maps of Pakistan at 1:50,000 scale were used as a main

supporting data to register all images. Many other data reports/maps showing extremely

salt-affected areas (manually surveyed) obtained from the International Waterlogging and

Salinity Research Institute (IWASRI) and Scarp Monitoring Organization (SMO) of Water

and Power Development Authority (WAPDA) were also used for identification of results.

3. Remote sensing indicators investigation

3.1. Spectral response patterns

The IRS LISS-II digital data were registered to the topo-sheets for the area using about

15 control points that were easily recognizable on the satellite images. It was presumed that

the topographic maps are the most reliable source of information. After this registration

procedure, satellite data were ready to be used in GIS. The Mercator projection (UTM) was

chosen for data presentation in GIS IDRISI for Windows, which was used throughout for

interpretation and classification procedures.


Table 1

Characteristics of IRS-IB satellite, LISS-II Sensor data used in the study

Serial no. Parameter LISS-II sensor

1 Spectral range (mm) 0.45–0.86

2 Number of bands 4

3 Spectral bands (mm)

B1 0.45–0.52

B2 0.52–0.59

B3 0.62–0.68

B4 0.77–0.86

4 Ground resolution (m) 36.25

5 Swath (km) 74 � 2

B2, B3, B4

6 Radiometric resolution (gray levels) 128

7 Data rate (Mbps) 10.4 � 2

Table 2

Archived dates of the satellite images used for the analysis of the study area

Image numbers Archived dates (dd/mm/yr)

1 31/03/1993

2 22/04/1993

3 05/06/1993

4 15/10/1993

5 06/11/1993

6 20/12/1993

Spectral response patterns of various terrain features are fundamental to a derivation of

information on natural resources and environmental degradation using space-borne

multispectral measurements. For different types of surfaces, the amount of reflected solar

radiation varies with the wavelength, which makes it possible to identify various kinds of

surfaces or classes in a satellite image and distinguish them from each other by the

differences in reflectance. To generate spectral response patterns of the various selected

major classes, LISS-II data were displayed and spectrally homogenous areas representing

various categories were then identified. In this study, five major classes were separated:

vegetation (crops), populated areas (towns and settlements), salt-affected areas (saline

soils), water bodies like lakes/ponds and irrigation-drainage channels (canal/drain) for

relative comparison (Fig. 2). Spectral responses in different spectral bands in the form of

digital number (DN) values were generated for different classes. A similar exercise was

carried out for all other LISS-II images to evaluate the results.

The ground truth data in terms of survey topo-maps were used to help in picking out

these training sites for the crop, town, lakes, airport and canal classes (Fig. 2). The training


Fig. 2. Red band image of LISS-II sensor showing the training sites for the different land classes: (1) cropped area,

vegetation, (2) town area, urban area, (3) airport, (4) salt prone lands, (5) water body, lake and (6) water channel.

sites for salt-affected areas, which have a relatively high reflectance coefficient in the first

three spectral bands of satellite data, were picked out from satellite images taking into

account all surface collected information. Examples of spectral response patterns for all

these classes at selected points are presented in Fig. 3. These results showed that the

spectral response pattern of salt-affected lands is higher than the other classes in all bands

and in all images whereas vegetation reflects maximum in NIR range, i.e., band 4 (0.77–

0.86 mm). The salt-affected soils have been found to reflect more incident light energy in

visible spectrum (0.45–0.68 mm) than those of other land cover features. This response for

saline soils is extremely useful as it helps the segregation of salt-affected soils from normal

soils and vegetation. Metternichit and Zinck (1997) also found that salt-affected lands had

high spectral reflectance in the visible window, particularly in the blue and red range of the

spectrum at low moisture content. There is also possibility that the spectral data would have

been affected due to certain external effects such as soil moisture and atmospheric factors.

In terms of DN values, the spectral pattern variation for the training class features of salt-

affected, waterlogged and cropped area were noted for all images and for all bands and then

normalized to band 4 (NIR) to have visual comparison of variation with time and each band

for that particular class. These results showed that there is a noticeable variation with time


Fig. 3. Spectral response pattern for various land cover features using LISS-II data.

in each band response for that class which could be attributed to a change in land cover,

canopy of the vegetation and atmospheric parameters like temperature, etc. Therefore, the

selection of the image for a certain time and for detection of a certain class has a main role

in any classification procedure.

3.2. Remote sensing indices

Indices are usually designed by combining two or more spectral bands and have been

widely acknowledged as powerful tools in identifying features of interest. Many studies

describe a wide range of band combination/indices developed for vegetation vigor, crop

assessment and land use change. However, there is paucity of information about the band

combinations meant for waterlogged and salt-affected lands. In the present study, the

proposed band combinations are hoped to discriminate salt-affected and water bodies/

waterlogged areas. Bands used were selected after examining the spectral reflectance

pattern of salt-affected soils. These indices were primarily related to the spectral

reflectance pattern of salts present in soil strata responsible for salinity/alkalinity. The term

‘‘alkalinity or sodicity’’ is used for sodic soils that are dominant in the test area and also

considered as the principal water quality concerns in irrigated areas receiving such waters

(Ayars and Tanji, 1999).

The overall trend pattern of the spectral reflectance had been observed similar in all

scenes as shown in Fig. 3, indicating quite clearly the relative higher reflectance for salt-

affected soils compared to that of all other classes. In order to evidence the saline zones and

suppressing those with vigorous vegetation, the investigation of various indices (for

salinity and vegetation) were carried out (Khan and Sato, 2001) with the aim to compare

their effectiveness for the study area and the satellite sensor used. In view of spectral

reflectance of individual bands approximated for salt-affected soils, various combinations

were tested for LISS-II sensor (Khan, 2002) but the followings main indices, including the

water index (explained in the next section of classification), performed relatively better

were as:

salinity index ðSIÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiB1 � B3

p(1)

normalized differential salinity index ðNDSIÞ ¼ B3 � B4

B3 þ B4(2)

brightness index ðBIÞ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiB32 þ B42

p(3)

where in all above expressions (1)–(3) B1, B3 and B4 are first, third and fourth spectral

bands, respectively.

The data recorded in the third spectral band were also used in the analysis as an index

based on its especial reflectance characteristics for salt class, to check an idea to use just the

spectral satellite data in this band for salt-affected areas delineation. To play a role of an

index this data should be normalized; for example, that can be normalized to the average

value of an image for salt class. Salt-affected soils are usually characterized by poorly

developed vegetation areas, thus, such state of stressed vegetation could be an indirect sign


of the presence of salts in the soils. Two well-known vegetation indices were, therefore,

also included in the analysis as:

normalized differential vegetation index ðNDVIÞ ¼ B4 � B3

B3 þ B4(4)

and the ratio of two spectral bands as:

ratio ¼ B3

B4(5)

In the process of choosing the index that produces the best results in determining salt-

affected areas, it was supposed that the values of that index in two other adjacent points

belonged to two different classes (salt versus any non-salt class) should be different enough

in the scale of values of that index. To simplify the comparison of different indices, we

operated with the ratio of the indices for two adjacent classes, one of them being a non-salt

class. It should be pointed out that such ratio takes different values from one satellite scene

to another, which is caused by non-concurrent changes in spectral characteristics of

different surfaces. We concluded finally to mention here two most suitable solutions that

gave the best results for our data analysis approach. Using the third channel data of the

instrument as an index (SI3) for the presence of salt’s assessment in the area could be one

good solution, and other is normalized differential salinity index (NDSI) or normalized

differential vegetation index (NDVI), either could be used as the absolute values (NDVI or

NDSI) are equal, as explained below.

Fig. 4 shows the ratio of the signals measured in the third channel (SI3) for different

classes: ‘Salt’, ‘Town’ and ‘Crop’ to the third channel signals of the ‘Salt’ class for every

processed image. It can be concluded that in most cases the signal in the third channel of the

instrument received from salt-affected areas is substantially higher than the signal received

from the other surfaces (the water classes are not shown in the figure because they have


Fig. 4. Variation of the ratio of signatures in the third spectral band (SI3) for different classes to the B3 signature of

the ‘Salt’ class.

very low signal in this spectral band and can not stand in the way of delineating salt

surfaces). The only one class that could mimic the ‘Salt’ class was the ‘Town’ class

observed in this case study. Some training sites belonged to the class ‘Town’ had the same

spectral signature as the class ‘Salt’ in June, as shown in Fig. 4, in case of image no. 3. This

problem might be due to the fact that villages have muddy roofs similar to dry barren soils

along with patchy saline areas within the village, therefore, showing almost similar

signatures to saline soils.

The next index that gave satisfactory results in retrieving ‘Salt’ class areas was NDSI

(Eq. (2)). One may also use NDVI (Eq. (4)), since these two indices are equal in absolute

values. Fig. 5(a) shows for six different images that the NDVI values for the four classes:

‘Crop’, ‘Canal’ ‘Town’ and ‘Salt’ range from the maximum to the lowest, respectively. It

may be easy to decide which classes are lying closer to ‘Salt’ class in terms of NDVI-index

values if one observes results in Fig. 5(b) where is shown the ratio of the NDVI indices

relative to those classes to the NDVI index of the ‘Salt’ class. It can be concluded again that

similarly in using measurements of the third channel as mentioned in above paragraph, the


Fig. 5. (a) Variation of NDVI for different land surface classes, (b) ratio of NDVI calculated for different classes to

the NDVI derived for the ‘Salt’ class at various times: (1) 31/03, (2) 22/04, (3) 05/06, (4) 15/10, (5) 06/11, (6) 20/12.

main difficulty in retrieval of salt-affected areas from satellite data lies in distinguishing

‘Salt’ and ‘Town’ classes. To overcome this difficulty one can use up-to-date topographic

maps to get information about the settlement/urban area boundaries. Such areas can be

vectorized and excluded from the calculation process while monitoring salt-affected soils.

If topo-sheets are not available, then a possibility is to avoid using that specific period when

satellite data do not provide for appropriate distinction between these two classes.

According to this study, the optimal time period for taking satellite data to assess salt-

affected soils using the NDVI (or NDSI) index is March (Fig. 5(b); Table 2). Then it is

possible to avoid any mix-pixels confusion with urban areas, which are expected during

peak summer. Each index image was generated using GIS IDRISI through mathematical

operator functions. The respective histograms were then created using stretch module

functions for better understanding the distribution of the pixels. A reclassification was then

performed after visualizing the histogram for water, salt and vegetation classes to create a

new image for that particular class.

4. Classification approach

In this last part of the paper, new possibilities of classification on the base of using indices

are discussed. This part of the work has been accomplished using special functions of GIS,

called image enhancement procedures. In GIS IDRISI, they are FILTER (different kinds of

filtering), STRETCH (rescaling image values to fall within a given range) and COMPOSIT

(producing new images with new characteristics on the base of received satellite data). These

GIS functions increase the ratio of signal to noise in satellite data and suggest the most

efficient use of important information contained in different spectral channels.

The problem of delineation of salt-affected areas is often accompanied by the problem

of determination of regions under risk of waterlogging. The latter task is usually solved

using the fourth band (NIR) data processing (Dwivedi and Sreenivas, 1998; Sujatha et al.,

2000). The main obstacle for obtaining a good classification is the low contrast between

areas to be identified relative to other surface classes. To overcome this difficulty, the

following water index was suggested for the study area (Khan, 2002):

WATER ¼ COMP124

B4 STR(6)

where, COMP124 is a composite image produced on the basis of B1, B2 and B4 spectral

data, i.e.:

COMP124 ¼ B1 þ 6 � B2 þ 36 � B4 (7)

It is obtained by using the COMPOSIT module of IDRISI for Windows with the type of

contrast stretch specified to be linear with a saturation level of 2.5%. The parameter

B4_STR concerns stretched satellite data received in the fourth spectral band (NIR) using

the stretch module in the regime of histogram equalization.

The analysis of the image and its histogram shows that the values of water index for

irrigation channel pixels are close to 0, being more than 3.0 for other water objects (lakes/


ponds and waterlogged areas). The main achievement of the described procedure is the act

of getting an image where water objects (including waterlogged areas) are definitely

separated from other surface classes. This has become possible due to stretching the B4

satellite image and using it in the way that is described in expression (6). This index image

was used in further classification procedure as described below.

To get a classified image of the surface under study one can use various classification

procedures of GIS. Here, we have tried to describe how to get satisfactory results using

unsupervised classification criteria. In IDRISI for Windows, unsupervised classification is

usually applied to a composite image. The routine procedure to get that composite is

through the use of satellite data sets of different spectral channels. It could be B1, B2 and

B4 data of the LISS-II sensor. Obviously, a composite image can be produced on the other

basis and composition according to different spectral ranges available in various

commercial satellites. In our case, we have calculated a specialized composite image using

three indices: NDSI (Eq. (2)), water index (Eq. (6)) and NDVI (Eq. (4)). Then we have

applied to that image an ISOCLUST module of IDRISI for Windows under unsupervised

classification procedure. The result is shown in Fig. 6 where salt-affected areas are

identified in white tones in the resulted image. The comparison with topographic maps


Fig. 6. Unsupervised classification (using the ISOCLUST Module) relative to salinity (white), vegetation (gray)

and water bodies (dark).

proves reliable results of the classification procedure adopted. One of the main advantages

of the described procedure is that water objects can be classified correctly in this case with a

high probability of success.

The next example of classification of satellite data described below uses a different

approach based on the principal component analysis. This sort of analysis of a set of images

produces a new set of images, i.e., components that are uncorrelated with each other and

explain progressively less of the variance found in the original set of spectral bands. In our

case, we have built principal components (PCACMPi) with the use of all satellite

measurements: B1, B2, B3 and B4 spectral bands. Results show that the first component

explains 92.52% of the variance in the original set of band and the second one explains

6.81% of variance, giving together more than 99%. The percentage of the first component

(PCACMP1) was not enough to use it alone for classification. We have, therefore, taken the

two first components and produced a composite image using as the following:

PCACMP2 þ 6 � PCACMP2 þ 36 � PCACMP1 (8)

where, PCACMPi is the ith component in the principal component analysis.

The described composite image has been used as a base for unsupervised classification

(ISOCLUST module). The analysis of the results (Fig. 7) proves also a good quality of

classification. The classes ‘Salt’, ‘Crop’, ‘Canal’ and ‘Lake’ are delineated correctly in

the displayed part of the satellite image (Fig. 7), which was observed as the most difficult


Fig. 7. Classified results after principal components analysis of LISS-II data.

land-use/cover area in the classification procedure. We have applied this procedure to

different scenes taken at different times in the year (Table 2) to check the potential of PCA

approach. It always produced good results for classification of salinity prone areas when

compared with the available topographic sheets and field reports/maps of WAPDA.

However, some other classes like ‘Town’, ‘Bare land’ and some ‘Crop’ types have not

come-up with clear difference in PCA analysis.

5. Conclusions

The two most suitable solutions producing the best results for assessing saline lands

using LISS-II sensor data were the selection of third channel due to its substantial higher

spectral reflectance compared to other wavelength ranges, and the NDVI or NDSI indices,

that are same in their absolute values.

The main difficulty in retrieval of salt-affected areas from satellite data is to distinguish

between ‘Salt’ and ‘Town’ classes. This can be overcome using up-to-date topographic

maps to get information about the settlement/urban area boundaries and excluding it after

vectorizing from the calculation process while monitoring salt-affected soils. The other

possibility when that information is not available is to avoid using satellite scenes where

such distinction cannot be made but other images taken at different times. The optimal time

period for taking satellite data (IRS LISS-II) in order to assess salt-affected soils in the test

area with the NDVI (NDSI) index is March to avoid spectral confusion of mixing with

urban areas that is occurring in peek summers times.

To identify various types of water objects (channels, rivers and waterlogged areas) the

newly developed water index may be used, which is capable to correctly differentiate water

objects; it can be used with confidence for the classification of waterlogged areas. This

became possible because of using a B4 stretched image (NIR) of the LISS-II sensor in the

model used for classification procedure.

It is concluded that a simple but practical approach based on remote sensing

indicators as well as the specialized classification procedures through PCA has shown

promising potential in delineating the hydrosalinized degraded lands for this specific

area using LISS-II data. This approach is the first study adopted for the selected area of

Indus basin (Pakistan) using the LISS-II sensor of IRS-1B satellite. The approach stated

above has also been carried out in further studies to monitor and assess the status and

cause-effect relationships of waterlogging, groundwater quality indicators and salini-

zation through an integrated methodology of remotely sensed and field data as input for

GIS analysis.

Acknowledgements

The authors are grateful to the Japan Society for the Promotion of Sciences (JSPS) for

the fellowship provided for this research study. Thanks are due to Dr. S. Uchida of Japan

International Research Center for Agricultural Sciences (JIRCAS) for providing the IRS-

1B satellite data, and the International Waterlogging and Salinity Research Institute


(IWASRI), Lahore, Pakistan, for providing the necessary field data, maps and reports used

in the study analysis. We also acknowledge the contributions of the reviewers of this

manuscript.

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