Fuzzy classification of JERS-1 SAR data: an evaluation of its performance for soil salinity mapping

Ecological Modelling 111 (1998) 61 – 74

Fuzzy classification of JERS-1 SAR data: an evaluation of its performance for soil salinity mapping G.I. Metternicht * Curtin Uni6ersity of Technology, School of Sur6eying and Land Information, GPO Box U 1987, Perth 6845, Australia Received 22 October 1997; accepted 23 April 1998

Abstract Remote sensing of surface features has been used intensively to identify and map salt-affected areas. Salt-tolerant vegetation is among the indicators used to separate saline-alkaline areas from non-affected ones. However, this type of vegetation causes spectral confusion and erroneous labelling between salinity and alkalinity classes when working with optical sensors such as Landstat TM or Spot. Accordingly, this paper evaluates the capabilities of the microwave range to map saline and alkaline areas. Fuzzy sets are used to model the information classes, and a fuzzy overlay model is implemented to classify the JERS-1 radar satellite image. The study shows that fuzzy classification of JERS-1 SAR data provides reliable detection (overall accuracy equal to 81%) of areas degraded by salinity-alkalinity processes. The main problems appear to be due to the interaction between soil roughness and radar backscattering, which determined erroneous allocation of alkaline and saline-alkaline areas to non-affected areas. © 1998 Elsevier Science B.V. All rights reserved. Keywords: Bolivia; Fuzzy modelling; Fuzzy sets; JERS-1 SAR; Land degradation; Salinity mapping

1. Introduction Land salinization is a major form of environmental degradation in agricultural areas, where information on the extent and severity of soil salinity is needed for better planning and implementation of effective soil reclamation measures. * Tel.: + 61 8 92667565; fax: + 61 8 92662703; e-mail: [email protected]

Remote sensing of surface features using aerial photography, videography, infrared thermometry and multispectral scanners have been used intensively to identify and map salt-affected areas (Robbins and Wiegand, 1990). Similarly, the advantages of using the microwave range of the spectrum for environmental mapping and resource management have been the subject of much investigation in recent decades. A synthetic aperture radar (SAR) system is an active mi-

0304-3800/98/$ - see front matter © 1998 Elsevier Science B.V. All rights reserved. PII S0304-3800(98)00095-7

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crowave sensor capable of transmitting and receiving polarised radar waves across a range of frequencies. The amount of energy returned to the radar antenna is known as radar backscatter. The nature of the backscatter returning to the sensor depends on: (a) geometric factors related to structural attributes of the surface and any overlying vegetation cover, relative to the sensor wavelength and incidence angle; and (b) electrical factors determined by the relative dielectric constants of soil and vegetation at a given wavelength (Ulaby et al., 1986; Dobson et al., 1995). Following the introduction of space-borne synthetic aperture radar in the 1900s (ERS1/2, JERS-1, ALMAZ, Radarsat), much research has been conducted on detecting and assessing features related to forest (Kattenborn and Nezry, 1997), agriculture (Brisco and Brown, 1995), wetlands (Ricard et al., 1997), rangelands and urban mapping. However, few studies have tested the possibilities of using microwave data to map areas degraded by salinity processes (King and Delpont, 1993). Successful discrimination of saline and alkaline areas using remotely sensed data also requires correct determination of the information classes. The approaches adopted by many soil scientists are based on crisp boundaries, where the ranking between saline, alkaline and non-affected areas is performed following the US Salinity Staff guidelines (Richards, 1954). In constructing these classification systems, it is difficult to restrict class boundaries to regions in the property space with small probability density, therefore it often happens that current classification systems representing discontinuous models fit poorly a reality with gradual changes. In a geographical context, the problem is reflected by the fact that broad zones of gradual transition are misrepresented by sharp boundaries that are arbitrarily located. The concept and advantages of using transitional instead of abrupt class boundaries has been widely discussed in the context of soil classification and land evaluation (Burrough, 1989; McBratney and de Gruijter, 1992), climatological applications (McBratney and Moore, 1985) and ecological research (Salski, 1992). As expressed by Burrough (1989), the use of strict Boolean algebra in combination with rigid, exact data models is often inappropri-

ate for soil survey and land evaluation because of the continuous nature of soil variation, the uncertainties associated with describing the phenomenon itself, and the existence of field soil attributes requiring the use of linguistic variables. Expanding the concepts highlighted by Burrough (1989), to the issue of soil salinity, it appears that transitional class boundaries derived from continuous salinity classes that intergrade gradually, better represent the real world situation. However, there is still a conceptual problem of how to define continuous classes in such a way that useful applications can be based on the definition derived. McBratney and de Gruijter (1992) mention that Zadeh (1965) first introduced a systematic solution to the ideas about continuous classes through the fuzzy sets theory. The concept in question was that of a fuzzy set, that is, a class with a continuum of grades of membership. Essentially, such a framework provides a natural way of dealing with problems in which the source of imprecision is the absence of sharply defined criteria for class membership. This paper explores an approach that uses the microwave region of the spectrum and a fuzzy supervised classification for mapping areas affected by salinity processes. Classes adopt transitional ‘fuzzy’ boundaries and the support of field and laboratory determinations allow for correlation of radar backscattering to soil surface roughness, the type and degree of salinity and the textural composition of the topsoil layer.

2. Data set composition and processing

2.1. Remotely sensed and field data set A SAR scene from the Japanese earth resource satellite (JERS-1) was obtained for the study area (path/row D419-330) on 15 May 1994. The JERS1 SAR sensor acquires data on the L-band of the microwave region (:23.5 cm wavelength) at a 35° incidence angle. The spatial resolution was 12.5 m. Section 2.2 describes the pre-processing steps carried out to reduce the speckle present on the image. The SAR scene was geo-referenced to a topographic map in a UTM co-ordinate system.

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Fig. 1. (a) The raw JERS-1 SAR data (Cochabamba, Sacaba and Punata-Cliza valleys). (b) JERS-1 SAR data after application of the gama MAP filter, three iterations and variable window sizes (3 ×3, 5 ×5 and 7× 7).

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As the study area (central part of Fig. 1) presented an almost flat topography, a first order polynomial transformation and nearest neighbour re-sampling were used. The root mean square error resulting from the polynomial transformation was less than one pixel. The field and laboratory data set consisted of 95 composite topsoil samples of 5 cm depth, collected from a surface area of about 9,000 ha concurrently with the acquisition of the JERS-1 SAR scene. A composite topsoil sampling was adopted to account for spatial variability. Samples were collected within a perimeter of 7 m from each central observation point, and soil structure, colour texture, stoniness, porosity and biological features were recorded according to the FAO guidelines for soil description (FAO, 1990). The field plots used as training sets and ground truth for accuracy evaluation were spatially referenced using a global positioning system (Magellan GPS NAV 1000Pro). Geographic co-ordinates were recorded in the three-dimensional (3D) mode of operation, using four satellites to obtain a position fix. Five to ten positionings of individual soil plots were made and averaged to reduce inaccuracies. Positional accuracy was checked using the position dilution of precision (PDOP). The latter is a measurement of the possible errors related to the geometry of the satellites used for triangulating a position. In 3D operational mode, good accuracy is obtained when the PDOP is B 6, implying field error locations between 30 and 100 m. Average PDOP values ranging between 2 and 4 were obtained, which were considered to be acceptable for the purpose of this study. Geographical co-ordinates were then transformed to UTM projection co-ordinates, plotted on topographic sheets at the scale 1:50000, and stored in a database. Soil saturation extracts were prepared to determine ion types and contents, electrical conductivity and soil reaction (pH). These and other ancillary data such as ground cover type and percentage, qualitative ranking of soil roughness, soil particle size distribution and organic matter content were incorporated into the database.

2.2. Pre-processing of raw satellite data The presence of speckle on the JERS-1 SAR

scene increases the variance and hampers visual and digital image analysis. Speckle is a form of multiplicative noise in the sense that the noise level, or standard deviation, increases with the magnitude of the radar backscattering (the mean). The speckle presents a random distribution, which is assumed as non-Gaussian and asymmetrical (Lee and Jurkevich, 1989). The statistical speckle model can be expressed as: Se=

SD m

(1)

Where: Se, strength of speckle noise; m, mean value of the image; SD, standard deviation of the image values. Several studies have evaluated the performance of different filtering techniques to reduce the speckle noise (Justusson, 1978; Frost et al., 1982; Durand et al., 1987; Mueller and Hoffer, 1989; Lee, 1990; Pan, 1990; Nezry et al., 1991; Metternicht, 1993). Quantitative and qualitative parameters are commonly used to assess the efficiency of a filter in reducing the speckle without loosing the features of interest. These include: visual inspection of edgeboundary retention; image smoothing; decrease of the image ‘granular aspect’; transformation of unimodal histograms representing different land cover types into multimodal ones, and computation of the speckle index defined as the ratio of SD to the mean of the selected image. A low speckle index indicates a good reduction of noise on the image by the filter. To reach a compromise between fine detail preservation and noise reduction, a lower limit must be established as a threshold value. Lee and Jurkevich (1989) suggested using the noise SD of the radar amplitude image as the lowest value for the SD/m ratio. Durand et al. (1987) defines the noise standard deviation of an amplitude image as 0.523/ N, where N is the number of looks used to process the image. Since the JERS-1 SAR data has three looks, the lower limit for speckle reduction was set at 0.30(0.523/ 3). The speckle index was selected as the quantitative parameter to evaluate filter performance, concurrently with a visual assessment of the edge-boundary preservation and the decrease of the ‘granular’ aspect on the radar image.

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Table 1 Results of the filtering procedure applied to the JERS-1 SAR subset Filter type

Sigma/damp

Gamma map

Number of iterations

Window size

Speckle index

1 2 1 2 1 3 4 3 3 2

3×3 3×3 5×5 5×5 7×7 3×3“ 5×5(2) 3×3(2)“ 5×5(2) 5×5(2)“ 7×7 3×3“ 5×5“7×7 3×3“ 5×5

0.406 0.375 0.357 0.329 0.329 0.324 0.318 0.303 0.309 0.344

Felee

1(damp)a

1 2

3×3 3×3“5×5

0.406 0.345

Lee

1.91(sigma) 2.45 3

1 1 1

3×3 3×3 5×5

0.408 0.38 0.34

Frost

1(damp)a

1

5×5

0.36

Raw data speckle index was equal to 0.53. The damping constant specifies the extent of the damping effect of the adaptive filter. Large values for DAMP preserves sharp edges better, but reduces the smoothing effect (PCI, 1994). a

To separate the speckle noise from the thematic information, the gamma maximum a-posteriori (MAP), sigma and median filters were tested on image subsets containing backscattered energy from surface features representing salt and sodium-affected areas. The gamma MAP filter has the ability to reduce the image speckle, while preserving linear and textural features of the surface cover. Texture preservation allows the separation of classes having similar spectral signature in the visible and infrared ranges, but different roughness. Once some heterogeneity is detected, the filter separates the most homogeneous areas containing the pixel to be filtered from the rest of the square moving window (Lopez et al., 1990). After the qualitative and quantitative evaluations were performed, the more effective template, found to be the gamma MAP filter, was applied over the whole area of interest. Table 1 presents the speckle index values obtained after applying the pre-selected filters on the JERS-1 SAR subset. Fig. 1(a) shows the appearance of the raw JERS-1 scene, while Fig. 1(b) depicts the same area after the application of the gamma MAP filter.

2.3. Surface roughness and radar backscattering Surface roughness is one of the major factors influencing the scattering properties of a target. If the surface of the object is smooth and radiation is reflected without interaction, then little information will be transmitted to the sensor. In the microwave range, the apparent roughness of a surface is a function of the wavelength, incidence angle and polarization of the radar. Objects that are small, relative to wavelength, produce broad scattering patterns, and short wavelengths commonly determine a more pronounced roughness effect (Dobson et al., 1995). A surface is considered ‘rough’ if its structure or shape has dimensions that are an appreciable fraction of the incident radar wavelength. One qualitative measure of roughness is the Rayleigh criterion, which considers a surface as ‘rough’ if: h]

l 8cosu

(2)

Where: h is the mean height of the surface; l is the radar wavelength; and u is the radar incidence angle (Ulaby et al., 1982).

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Table 2 Main characteristics of the classification categories, in terms of textural composition and soil roughness Category

Characteristics

Non-affected areas

Parcels tilled up to 30 cm depth; presenting clods of variable size (Fig. 6 (e)). Average height difference observed during field reconaissance: 15 – 25 cm. Average textural composition of the topsoils: 30% clay; 55% silt and 14% sand. Predominance of puffy soil surfaces, which are a mixture of soil aggregates and salts of different crystal forms, containing mainly sodium sulphate. These layers generally develop when pH values are above 9. Average height difference: 3 – 5 cm (Fig. 6 (a)). Areas with soil reaction values up to 9 are used for cultivation of salt-tolerant crops, mainly alfalfa (Medicago sati6a). These areas present a soil surface roughness similar to the ploughed non-affected areas (Fig. 6 (d)). Average textural composition of the topsoils: 38% clay; 56% silt; 10% sand. Variable soil surface roughness. Some areas are characterised by puffy soil surfaces, while other areas are dominated by loosely structured crust (Fig. 6 (b)). Under natural conditions, soluble salts concentrate in the topsoil, and no cultivation is performed. Patches of halophytic salt-tolerant vegetation are sparsely distributed on the soil surface. Average textural composition of the topsoil: 33% clay; 56% silt; 10% sand. Smooth, glassy salt crusts rich in sodium and magnesium characterise these soil surfaces (Fig. 6 (f), (c)). The crusts consist mostly of chlorides or equal proportions of chloride and sulphates. Average height composition of the topsoils: 55% clay; 41% silt; 4% sand.

Alkaline areas

Saline-alkaline

Saline areas

Applying this formula to the JERS-1 data (e.g. 23 cm wavelength and 35° incidence angle), surface components with a mean height E 3.6 cm will be considered ‘rough’ in the radar sense. Accordingly, it is hypothesised that surface roughness could help improve the distinctiveness among the information classes considered in this research. A qualitative ranking was developed using categorical variables, that is, ‘smooth’ when a surface presented an average mean height B 3.5 cm and ‘rough’ when the average height difference was \3.5 cm. Fig. 6 presents some examples of rough and smooth surfaces, according to the above definition and Table 2 describes the main characteristics of the T classification categories in terms of textural composition and surface roughness.

3. Method

3.1. Fuzzy modelling of the saline and alkaline classes

and zero when it does not (Fig. 2). For example, when applied to the detection of saline soil surfaces, an area may be saline or non-saline. This type of logic does not allow for the representation of vague concepts, unlikely fuzzy sets that introduce vagueness or uncertainty by eliminating the sharp boundary dividing members of a class from non-members. A fuzzy set can be defined mathematically by assigning to each possible individual (e.g. electrical conductivity) in the universe of discourse (e.g. saline areas) a value representing its grade of membership in the fuzzy set. This grade corresponds to the degree to which that individual is compatible with the concept represented by the fuzzy set. Thus, individuals may belong to the fuzzy set to a greater or lesser degree as indicated by a larger or smaller membership grade. The membership grades, also known as certainty factors or degrees of belief, are represented by values ranging in the interval between zero and one (Klir and Folger, 1988).

3.2. Defining the membership functions Crisp set theory is driven by a logic that permits a proposition to possess one out of two values: one, when the element belongs to the set,

The membership function of a fuzzy set, usually expressed as fA (x), defines how the grade of mem-

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Fig. 2. Crisp membership function and fuzzy set membership function.

bership of x in A is determined. There are two possible ways of deriving these membership functions. The first approach, named by Robinson (1988) as the similarity relation model, is similar to that taken by cluster analysis and numerical taxonomy in that the value of the membership function is a function of the classifier used. A common version of this model is the fuzzy kmeans or c-means method, used for soil grouping, remote sensing image classification for cloud cover, vegetation analysis, land use, etc. by McBratney and de Gruijter (1992) and Wang (1990a). The second approach, known as the semantic import model, uses an a priori membership function with which individuals can be assigned a membership grade. This approach has been adopted in this research because it is useful in situations where users have a good, qualitative idea of how to group data, but for different reasons are constrained from using the exactness associated with the standard Boolean model, as stated by Burrough (1989). The first step in the fully supervised fuzzy classification is class determination. This research adopted the variables and ranges established by the United States salinity laboratory staff (Richards, 1954) for saline, saline-alkaline and alkaline areas. This knowledge can be represented by a set of linguistic rules of the form IF Žpremise, THEN Žconclusion. The following rules define the four information categories considered: 1. if electrical conductivity is 5 4 dS/m and the pHB8.5 then the soil plot is ‘non-affected’; 2. if electrical conductivity is 5 4 dS/m and pH]8.5 then the soil sample belongs to ‘alkaline areas’;

3. if electrical conductivity is \ 4 dS/m and pHB 8.5 then the soil plot belongs to ‘saline areas’; 4. if electrical conductivity is \ 4 dS/m and pH] 8.5 then the soil sample belongs to ‘saline-alkaline areas’. Accordingly, four fuzzy sets, namely ‘non-alkaline’, ‘alkaline’, ‘saline’ and ‘non-saline’ are necessary to characterise these categories. The selection of class boundaries and class intervals can be an objective or subjective process, depending on the way scientists agree to define classes (Evans, 1977). That does not mean that selecting class intervals is an arbitrary process, as often careful consideration is devoted to the selection of sensible boundaries between classes. The same holds for assigning the membership function of a fuzzy set. According to Burrough (1989), the membership function should ensure that the grade of membership is equal to one at the centre of the set and that it falls off in an appropriate way through the fuzzy boundaries to the region outside the set where it takes the value zero. The membership function must be defined to hold these conditions. There are several suitable functions that can be used for defining flexible membership grades, which can be easily adapted to specific requirements. A bell-shaped model as described by Dombi (1990) is adopted in this research, where the membership function is written as: mA(x)=[(1− n)l − 1 (x−a)l] /[(1−n)l − 1 (x−a)l + n l − 1(b−x)l] x [a, b]

(3)

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Fig. 3. An S-shaped membership function, where the shape of the curve is controlled by variations of the sharpness (s) and inflection (i ).

mA(x)=[(1− n)l − 1 (c −x)l] /[(1−n)l − 1 (c −x)l +n l − 1(x − b)l] x [b, c]

(4)

Eqs. (3) and (4) represent the monotonically increasing and decreasing parts of the function. In this equation, two parameters, l (inflection) and n (sharpness), govern the shape of the function and the standard point (b) characterises the value of the variable ‘x’ (e.g. saline soils) having a grade of membership equal to one. The typical points (a, c) represent the limits of the class. By varying the inflection and sharpness values, the form of the membership function can be controlled easily (Fig. 3). In selecting the sharpness and inflection parameters, it is commonly accepted that a set of memberships that is not too discrete or too diffuse is best. This criterion was applied in this research, concurrently with practical experience gained from literature review and discussions with soil salinity experts. No rigorous analysis is currently available for choosing the sharpness and inflection of the membership function, determining its shape, although McBratney and Moore (1985) suggested an ad hoc procedure, and Jiang (1996) recommends an inflection of about 3 and sharp-

ness of 0.8. After several tests using sharpness values of 0.6, 0.7 and 0.8, and inflection of 3 and 4, the membership function judged to better model the variables of interest was obtained by setting the inflection value equal to 3 and sharpness to 0.7. Fig. 4 illustrates the fuzzy membership function obtained for the soil reaction (pH), where a pH value of 7 has a membership degree of 1 in the fuzzy set ‘non-alkaline’, while a pH equal to 10 has a certainty factor of zero in that same set but of 1 in the fuzzy set ‘alkaline’. The membership grades of the soil samples to the four different fuzzy sets are then computed. Thus, a soil sample can be alkaline to a degree of 0.7, saline to a degree of 0.2, while also having a possibility of being saline-alkaline (0.2) or non-affected (0.9).

3.3. Membership grades Subsequently, the backscattering values of the soil samples pertaining to the four sets (e.g. saline, alkaline, etc.) were extracted from the geo-referenced JERS-1 SAR scene using a neighbourhoodbased operator. Knowing the geographic co-ordinates of the soil samples collected in the field as described in Section 2.1, the user places

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Fig. 4. The application of an S-shaped membership function with inflection of three and sharpness of 0.7 to the pH variable determines the fuzzy sets ‘alkaline pH’ and ‘non-alkaline pH’.

the cursor on the image to reproduce the same spatial location. Spectral (e.g. 910 digital numbers from the value of the central pixel) or spatial (e.g. a radius of 10 m) constraints are specified by the user in order to extract a mean backscattering value (in digital numbers) representing a specific soil sample. These backscattering values were allocated to one of the four categories, according to their degree of membership to the sets (Fig. 5). For instance, the backscattering value of a soil plot with a membership degree of 0.7 to the alkaline set, and of 0.2 to the saline set is representative of the alkaline class. The median value of each class was adopted as the standard point, with a membership degree of one to the set under consideration. Other fuzzy classification models take the mean value of a cluster as the standard point (Key et al., 1989; Wang, 1990b; Foody, 1996). However, the median is preferable to the mean, as the latter is very sensitive to extreme scores or measurements. Histogram tails were taken as the typical points, thus having a membership grade of zero.

3.4. Per-layer fuzzy classification of the JERS-1 scene This approach also requires the user to define

the type of membership function (e.g. linear, sigmoidal) to be applied for fuzzy modelling of the layers representing the ‘non-alkaline’, ‘alkaline’, non-saline’ and ‘saline’ classes. Because an Sshaped (sigmoidal) function was used in the fuzzy modelling of the informational classes, the same function type of the form: m = cos 2a

(5)

was chosen. Where, in the case of a monotonically increasing function, a= (x − a)/(b − a) · pi/2

for xB b

(6)

being x the backscattering value of the pixel analysed, a the typical point and b the standard point of the function (IDRISI, 1995).

3.5. The fuzzy o6erlay model After generating four different class outputs a fuzzy overlay model as described by Jiang (1996) was implemented in a geographic information system (Fig. 5). Individual layers are modelled for each of the categories under consideration, and processed using the fuzzy minimum and maximum operators described by Zadeh (1965). The layers are combined by a set of user defined rules of the IF–THEN type statements, as follows:

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Fig. 5. The fuzzy model.

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Table 3 Error matrix of the fuzzy supervised classification Classified data

Non-affected Alkaline Saline Saline-alkaline Total identified Error of omission (%)

Reference data Non-affected

Alkaline

180

40 10 10 10 70 86

10 190 5

Saline

220 220 0

Saline-alkaline

Total classified

Error of commission (%)

50 10 20 220 300 27

270 20 250 240 780

33 50 12 8

Overall accuracy is computed by dividing the number of correctly classified samples positioned at the diagonal of the matrix by the total number of pixels checked, that is (630/780) · 100= 80.7%. For the alkaline class the error of commission is calculated as [(10)/20] · 100 thus equal to 50%. The error of omission is computed as [(40+10+10)/70] · 100 =86%.

1. IF ‘non-alkaline’ and ‘non-saline’, THEN ‘non-affected areas’; 2. IF ‘alkaline’ and ‘non-saline’, THEN ‘alkaline areas’; 3. IF ‘non-alkaline’ and ‘saline’, THEN ‘saline areas’; 4. IF ‘alkaline’ and ‘saline’, THEN ‘saline-alkaline areas’. The membership grade of a pixel (x) to the new categories (e.g. non-affected areas) is computed using a minimum operator: mnon-affected (x)= MIN [m‘non-alkaline’ (x), m‘non-saline’ (x)]

(7)

The membership grade of a pixel (x) in the fuzzy set ‘non-affected areas’ is the smallest of its membership grades in the sets ‘non-alkaline’ and ‘non saline’. Membership grades for the other sets are computed in the same way, using the minimum operator (Zadeh, 1965; Negoita, 1985; Klir and Folger, 1988). Accordingly, a group of membership grades is attached to each pixel to indicate the extent to which the pixel belongs to the four different classes. Pixels with mixed classes or in transitional conditions can now be described. For example, if a ground cell contains two classes such as ‘saline areas’ and ‘saline-alkaline areas’, it has two membership grades indicating the extent to which the pixel is associated with the two classes. The closer the value of f‘non-affected’ (x), which represents the membership grade of (x) to the ‘non-affected’ set,

is to one, the greater the association of the pixel with that class. The union of the classes provides the final map. A pixel will take the label of the group where it has the highest certainty factor. This is implemented by using a fuzzy maximum operator: m’final

map’

(x)=

MAX [m‘non-affected’, m‘alkaline’, m‘saline’, m‘saline-alkaline’] (8) The membership grade of a pixel (x) in the output map is either its membership grade in ‘alkaline’ or ‘saline’ or ‘saline-alkaline’ or ‘non-affected’, whichever has the largest value. The number of layers may be a disadvantage associated with the modelling process. For instance, the combination of four salinity-alkalinity classes and one satellite band generates a 4-layer output. However, if the number of bands increases to six, 24-output layers will be created.

4. Data interpretation An error matrix was computed to assess the classification accuracy (Table 3). The error matrix is a square array of numbers set out in rows and columns which expresses the number of sample units assigned to a particular category relative to the actual category as verified on the ground. The columns represent the reference data while the rows indicate the classification generated from the

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Fig. 6. Examples of rough and smooth surfaces present in the study area. (a) Puffy soil crusts characterizing alkaline areas; (b) soil roughness variations in saline-alkaline areas; (c) glassy smooth saline surfaces; (d) alkaline areas with pH values up to 9 are used for cultivation of salt-tolerant crops; (e) rough surface of a ploughed land parcel; (f) smooth saline soil surface.

remotely sensed data. Accuracies of individual categories are plainly described along with both the errors of inclusion (e.g. errors of commission) and errors of exclusion (errors of omission) present in the classification (Congalton, 1991). Table 3 presents the overall accuracy, errors of omission and commission resulting from the classification of the radar scene. Although the percentage of pixels correctly identified equals 81%, large numbers of alkaline areas were wrongly mapped. The error matrix shows that 40 of the reference points mapped as non-affected areas were actually alkaline, while 20 sample points corresponding to alkaline areas were included in the saline or saline-alkaline categories. This misinterpretation between alkaline and non-affected categories is mainly attributed to the roughness of the soil surface which, as mentioned in the previous sections, significantly affects the radar backscattering. During the field campaign, it was observed that farmers cultivate land parcels with soil reaction values up to nine, corresponding to

the alkaline category, as defined in this research (Section 3.1). Accordingly, the alkaline areas would present a ‘rough’ surface texture similar to the non-affected areas, as shown in Fig. 6 (d) and (e). The statistical analysis performed between radar backscattering, soil particle size distribution and the soil variables used to derive the classification categories, showed significant correlations at a P-level of 0.5 (Table 4). Variables characterising soil alkalinity (pH, carbonates and bicarbonates) were negatively correlated to the mean backscattering values observed in the JERS-1 scene, meaning that increases in soil alkalinity would lower the amount of energy returned to the radar antenna. As mentioned in Section 2.3 backscattering decreases because a surface becomes smoother for the radar wavelength. In the study area, the latter occurs when the land becomes unsuitable for cultivation (Fig. 6 (c), (f)). The degree of soil roughness also explains the accurate discrimination of saline areas (e.g. 100%

G.I. Metternicht / Ecological Modelling 111 (1998) 61–74

accuracy is reported in the identification of these areas). Saline topsoils are generally smoother than non-saline ones, thus presenting lower backscattering values as less energy returns to the radar antenna. Table 4 shows a significant negative correlation between electrical conductivity (− 0.36), sulphates (− 0.33) and chlorides (− 0.28), which characterise saline areas. It is also interesting to observe the correlation between soil particle size distribution and radar backscattering. Clayey soil particles exhibit a negative correlation (− 0.16), while silt and sand present positive correlations with the amount of energy backscattered to the antenna. Accordingly, increases in the clay contents of the topsoil are related to a decrease in the radar backscatter. Table 2 presents the average clay, silt and sand contents of the four categories (e.g. saline, alkaline, etc.) considered in this research. While nonaffected, saline and saline-alkaline topsoils present similar textural composition (e.g. above 50% of silt content, between 30 to 38% of clay and 10 to 12% sand), saline areas exhibit a distinct textural composition characterised by high clay contents (55%). Consequently, the high accuracy in the identification of saline areas is attributed to the synergistic effect of soil surface roughness and the clayey textural composition of saline areas, both causing a ‘smooth’ surface roughness to the radar wavelength.

Table 4 Correlation between radar backscattering and selected soil variables

73

5. Conclusion Based on the results of this research, it can be concluded that: The weakest part of the semantic model used to determine the salinity and alkalinity classes in the frame of fuzzy set theory is the way in which membership functions, standard and typical points are chosen. Research on a standard way to determine these parameters from the data themselves may yield more objective results, although it has been shown here how these factors can be linked to practical experience.The S-shaped membership function defined by Dombi (1990) proved to be suitable for defining flexible membership grades, covering the specific requirements of the information categories used in this research. Fuzzy classification of JERS-1 SAR data, using a fuzzy overlay model provided reliable (81% overall accuracy) detection of areas degraded by salinity-alkalinity processes. Radar backscattering was most affected by the roughness of the surface, which is produced by land cultivation. This was identified as the main factor causing an erroneous allocation of alkaline and saline-alkaline soils to non-affected areas. The findings of this study add to the growing evidence of the valuable capabilities of imaging radars in the detection and monitoring of land degradation processes, due to the sensitivity of radar to a variety of surface feature indicators. Additionally, further research is being conducted to explore the capabilities of optical and microwave data fusion for detection of land salinity processes.

Variable

Radar backscattering

References

Electrical conductivity Soil reaction (pH) Carbonates Bicarbonates Chlorides Sulphates Clay (%) Silt (%) Sand (%)

−0.36a −0.11 −0.25a −0.23a −0.28a −0.33a −0.16 0.08 0.21a

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a

Marked correlations are significant at P-level of 0.05.

74

G.I. Metternicht / Ecological Modelling 111 (1998) 61–74

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