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Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

Topographic Influence on Improved Change Vector Analysis using MODIS Satellite Data of Western Himalaya Sartajvir Singh1

2

Prof. J.K. Sharma2

3

Scientist (E), R.S. Research Group SASE, DRDO Chandigarh, India vd_mishra@rediffmail.com

Director, Engineering Deptt. R.I.E.I.T, Railmajra S.B.S. Nagar, Punjab, India sharma_jks@hotmail.com

M-Tech Student, E.C.E Deptt. R.I.E.I.T, Railmajra, S.B.S. Nagar, Punjab, India sartajvir.dhillon@gmail.com

The enhancement change detection techniques have the advantage of generally being more accurate in identifying areas of spectral change reported by[2]that includes: (1) image differencing [3],(2) principal component analysis [4], (3) change vector analysis [5] and other is post classification technique that involves the independent production and subsequent comparison of spectral classifications for the same area at two different time periods [6]. It is reported [7] that improved change vector analysis is valuable technique for change detection analysis which includes a semiautomatic method, named double-window flexible pace search (DFPS), which aims at determining efficiently the threshold of change magnitude, and a new method of determining change direction (change category) which combines a single image classification and a minimum-distance categorization based upon the direction cosines of the change vector.

A

ES

Abstract: - Change detection is a technique used to determine the change between two or more time periods of a particular area of study. Change detection is an important process of monitoring and managing environment resources. There are many methods available for change detection. In this paper, we are dealing with improved change-vector analysis on western Himalayas (usually snow covered area) using MODIS Satellite data. This method performed in two stages: in first stage double- window flexible pace search (DFPS), which aims at determining the threshold of change magnitude, and in second stage, the direction cosines of change vectors for determining change direction that combines single-date image classification with a minimum-distance categorizing technique. On other side, the topographic variability causes a problem of differential illumination due to steep and varying slopes in rugged Himalayan terrain. Therefore, topographic corrections are essential for qualitative and quantitative analysis of snow cover applications. Here we compared the improved change vector analysis (ICVA) with and without topographic correction on study area of lower and middle Himalaya, Himachal Pradesh, India. The experiment result shown that ICVA with topographic correction gives more accurate and hidden information as compare to ICVA without topographic correction.

Dr. V.D. Mishra3

T

1

Keywords: - Improved change vector analysis, direction cosines, topographic correction, MODIS.

INTRODUCTION

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I.

In the past few years, there has been a growing interest in the development of change detection techniques for the analysis of multi-temporal remote sensing imagery. This interest stems from the wide range of applications in which change detection methods can be used, like environmental monitoring, agricultural surveys, urban studies and snow cover monitoring. Change detection, by definition, requires images from two dates. Ideally, change detection procedures should involve data acquired by the same sensor, having the same spatial resolution, viewing geometry, spectral bands, same spatial location, radiometric resolution, and acquired at the same time of day [1]. There are many techniques available for monitoring land cover changes, fall into two main categories:

ISSN: 2230-7818

The topographic variability causes a problem of differential illumination due to steep and varying slopes in rugged Himalayan terrain. Sun-facing illuminated slopes (south aspect) show more than expected spectral radiance or reflectance, whereas the effect is opposite in shaded relief area (north aspect) [8]. Therefore, topographic corrections are essential for qualitative and quantitative analysis of snow cover applications. There are many topographic correction methods available, fall into three main categories: (1) Empirical methods such as two stage normalization[9], (2) Lambertian methods such as C- correction, Cosine-T [10]etc.,(3) Non-Lambertian methods such as Minneart correction method [11], Slope match [12].It is reported [13]that slope match is most suitable technique for topographic correction on Himalayan terrain. II.

STUDY AREA

The study area is a part of Lower and Middle Himalaya and shown on MODIS image(Moderate Resolution Imaging Spectroradiometer) lies between latitude of 32.254 degree to 32.999 degree North and longitude of 77.00 degree to 77.497 degree East as shown in the Fig. 1. The lower part of the area is surrounded by forest and tree line exists up to 3100 m. The upper part (Middle Himalaya) is devoid of forest. The average

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Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

IV.

P RE-PROCESSING

A master scene of 56m spatial resolution of AWiFS (Advance Wide field sensor) of study area is prepared after rectification with high spatial resolution 23m of LISS-III (Linear Imaging self-Scanning) with 1:50,000 toposheet. All satellite images of MODIS were than geo-coded with AWiFS to the EVEREST datum by ERDAS/Imagine 9.1 (Leica Geosystems GIS and Mapping LLC) software with sub pixel accuracy. The preprocessed uncorrected image of 02nd November 2009 and 21st November 2009 are shown in Fig. 2 (a) and (b) respectively.

ES

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minimum temperature in winter is generally observed to be 12oC to -15oC in lower Himalaya (Pir-Panjal range) and -30oC to -35oC in Middle Himalaya (Greater Himalaya range). PirPanjal receives the highest snowfall (average 15-20 m) as compared to Greater Himalayan range (12-15m) during the winter period between October and May. The altitude in the entire study area varies from 1900 m to 6500 m with a mean value of 4700 m. The slope in the study area varies from 1-86 degree with mean value of 28 degree and aspect ranges from 0-360 degree with mean values of 180 degree. Most of the slopes in the study regions are oriented to south aspect.

(b)

(a)

Figure 2 Uncorrected MODIS images (a) Pre image (02nd November 2009) (b) Post image (21st November 2009)

Figure 1 MODIS image of study area

III.

V.

S ATELLITE DATASETS

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Two almost cloud free satellite images of MODIS (Terra platform) of 02nd November 2009(Pre image) and 21st November 2009(Post image) are used in the present work to study the influence of topography on improved spectral change vector analysis. The salient specifications of MODIS sensor are given in the Table 1.

ESTIMATION OF REFLECTANCE

Without topographic consideration the atmospherically corrected surface spectral reflectance under lambertian assumption for MODIS is computed using (1) as defined in [14, 15]: (

) )

(

T ABLE 1 SALIENT SPECIFICATIONS OF MODIS SENSOR

Spectral bands

B1 B2 B3 B4 B5 B6 B7

Spectral wavelength (nm)

620-670 841-876 459-479 545-565 1230-1250 1628-1652 2105-2155

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Spatial Resolution (m)

250 250 250 250 250 250 250

Quantization (bit)

Radiance Scale (mw/cm2/sr/μm)

Radiance offset

12 12 12 12 12 12 12

0.0026144 0.0009926 0.0027612 0.0021087 0.0005568 0.0002572 0.0000787

0 0 0 0 0 0 0

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Solar Exoatmostpheric spectral Irradiance (mw/cm2/sr/μm)

160.327 98.70 209.071 186.4 47.6 23.8 8.7

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(1)


Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

Where and are the transmittances of the atmosphere in the view and illumination directions respectively calculated for MODIS bands using the equation proposed by authors [15], [16]. is the exo-atmospheric spectral irradiance (refer Table1), is the solar zenith angle and calculated for each pixel [17], d is the earth – sun distance in astronomical units and calculated using the approach of [18], is the downwelling diffused radiation and assumed zero according to [19]. is the path radiance and computed using [19]. VI.

T OPOGRAPHIC C ORRECTIONS

Topographically corrected spectral reflectance using MODIS imagery are estimated using slope matching method given below [13]: )(

)

(2)

DEM

Geo-referencing

Slope, Aspect

A

MODIS Images

Illumination Angle

Estimation of Reflectance

Estimation of coefficients

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Atmospheric Corrections

Topographically Corrected Reflectance

Figure 3 Flow chart of pre-processing steps and Slope matching topographic correction technique.

VII.

(a)

(b)

Figure 4 Topographic corrected MODIS images (a) Pre image (02nd November 2009) (b) Post image (21st November2009)

Sohl [22] concluded that change vector analysis produced the best result of all the techniques tested, due to its graphically rich content and its ability to detect urban and agricultural change with fairly good location information. A change vector can be described by an angle of change (vector direction) and a magnitude of change from date 1 to date 2 [23].It is reported [7] that improved change vector analysis is a valuable technique for change detection which includes a semiautomatic method, named Double-Window Flexible Pace Search (DFPS), which aims at determining efficiently the threshold of change magnitude of multi temporal image and a new method of determining change direction (change category) which combines a single image classification and a minimum-distance categorization based upon the direction cosines of the change vector provided a way to find change type discrimination. The improved change vector analysis is implemented in this paper as change detection analysis. The sequence of steps required to perform improved change vector analysis [7] is given in Fig. 5.

ES

Where is topographically corrected spectral reflectance, is spectral reflectance on the tilted surface, and is maximum and minimum spectral reflectance and estimated from topographically uncorrected image, is mean value of reflectance illumination on the south aspect and is illumination and calculated using equation proposed by [9]. is normalization coefficient for different satellite bands and estimated using equation given in the literature [12]. The flow chart of detailed methodology for topographic correction is given in the Fig. 3. Topographically corrected pre and post images are shown in Fig. 4 (a) and (b) respectively.

T

(

A. Change Magnitude CVA is a multivariate technique, which accepts the desired number of bands as input or spectral features from each scene pair [24].Change Vector is defined in (3). ∆G = H - G = (

)

(3)

IMPROVED CHANGE VECTOR ANALYSIS

Malila [20] gave a general idea of change-vector analysis (CVA). CVA is used widely to detect multispectral change detection, and is one of the most effective pre-classification change detection techniques [21].

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Where ∆G includes all the change information between the two dates , for a given pixel by G=( ) and H =( ) respectively and n is the number of bands.

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Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

Multi-date without/with topographic Correction

Image Classification

Change Magnitude Image Selection of training Sample

Find Search range and pace Extraction of seed points

Put values of threshold in search process

| ∆G | = √(

)

(

)

(4)

Using (4), change magnitude was computed. A decision on change is made based on whether the change magnitude exceeds a specific threshold which is calculated as explained in following sections. B. Threshold search using Double-Window Flexible Pace Search (DFPS)

DFPC mainly aims at determining the threshold of change magnitude. This method based upon selecting a threshold from a training sample as shown in Fig. 6 (a), (b), and (c), that contain all possible types of changes in our study area such as snow, soil, vegetation, shadow. As reported in the literature [7], training samples were selected based upon: (1) it covers all types/ as much as possible changes, (2) it must include only change type information and (3) training samples should be encircled by no-change pixels. A training sample has an inner boundary and outer boundary. Inner boundary is an area of interest to find the change, outer boundary is used to prevent the threshold from being too low.

ES

Calculation of testing Parameter

The Change magnitude is given by

T

The magnitude of change vector is given according to Euclidian distance to produce magnitude of change.

Condition to exit

(b)

(a)

A

Determination of optimal Threshold

Change / No-change Image

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Calculation of Direction of change vector for every change pixels Change Type Discrimination

Accuracy Assessment Change Detection

(c) Figure 6 Training sample area (Highlighted with white boundary) (a) Pre-image (b) Post image (c) Change magnitude

The threshold search range can be set as a difference between the minimum value x, and the maximum value y, of change in 1st search process, using formula:

Comparison ICVA with and without Topographic Figure 5 Flow chart of Methodology for Improved Change Vector Analysis

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=

(

)

(5)

Where is pace search and z is positive threshold value, which can be set manually. Succession rate is used to find out

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Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

best suitable threshold value for change. Succession rate ( ) can be calculated using following equation: =

(

)

%

(6)

Where, is number of change pixels detected inside an inner training window, is number of change pixels detected inside outer training window incorrectly and I is total number of pixels in inner training window. It should be noted that outer window include one or two pixel only from every

side. When highest succession rate is achieved, the iteration is stopped, then a threshold value that have maximum succession rate is applied to an entire change magnitude image as shown in Table 2 (with topographic correction) and Table 3 (without topographic correction). The topographic uncorrected change/no-change image(white shows change part, black shows no-change part) shown in Fig. 7(a) and the topographic corrected change/no-change image(white shows change part, black shows no-change part) is shown in Fig. 7(b).

T ABLE 2 R ESULTS OF DFPS WITH TOPOGRAPHIC

Range 90-50 Pace 10 Range 65-55 Pace 5 Range 64-56 Pace 2 Range 61-59 Pace 1 Threshold Success % Threshold Success % Threshold Success % Threshold Success %

2.50 19.40 41.55 61.03 51.90

65 60 55

55.80 58.44 57.15

64 62 60 58 56

58.44 58.44 61.03 59.74 57.16

61 60 59

58.44 61.03 58.44

60.5 60.0 59.5

T

90 80 70 60 50

Range 60.5-59.5 Pace .5 Threshold Success %

58.40 61.03 59.70

T ABLE 3 R ESULTS OF DFPS WITHOUT TOPOGRAPHIC

100 90 80 70 60 50

32.5 55.0 42.5 42.5 42.5 25.0

95 90 85

Range 94-86 Pace 2 Range 91-89 Pace 1 Range 90.5-89.5 Pace .5 Threshold Success % Threshold Success % Threshold Success %

ES

Range 100-50 Pace 10 Range 95-85 Pace 5 Threshold Success % Threshold Success %

40 55 45

94 92 90 88 86

40 47.5 55 50 47.5

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A

C.

(a)

No-change

(b)

Change

Figure 7 Change magnitude image (a) Without topographic correction (b) With topographic correction

ISSN: 2230-7818

91 90 89

52.5 55 52.5

90.5 90 89.5

52.5 55.0 52.5

Change type Discrimination

It is reported in [7] that change type discrimination can be obtained using a method which combines single image classification with minimum-distance categorizing based on direction cosines of change vectors. The direction of a vector can be described by a series of cosine functions in a multidimensional space. This series is called direction cosines [25].Moreover, using the direction cosines instead of angle measurements can avoid the difficulty of “baseline� establishment for angle measurement. First, Pre-image is classified using supervised classification based on parametric rule Maximum Likelihoods shown in Fig. 8. Pre image classification included four classes: (1) Snow, (2) Soil, (3) Vegetation and (4) Shadow. These classes are generated using signature file. Then the seed points were extracted from different class types. Seed points play an important role for change type discrimination for all change pixels. Spectral change vector between any two kinds of change type can be calculated based on classification and then Euclidean distance of corresponding change is obtained by transplanted these values in direction cosine. The direction of cosine is defined as [25].

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Sartajvir Singh* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 077 - 084

Cos

=

, Cos

Where X ( ,

=

… Cos

=

(7)

) is vector, n is number of bands

T

(a)

Snow (a)

Unclassified

Vegetation

Soil

Shadow

D. Accuracy assessment

) is calculated using following

A

Soil

Figure 9 Minimum Distance Classified image (a) Without topographic correction (b) With topographic correction

Shadow

Figure 8 Pre image classifications (a) Without topographic correction (b) With topographic correction

Change magnitude ( equation

Unclassified

Vegetation

ES

Snow

(b)

(b)

(8)

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Change type is obtained by applying the minimum distance rule because its effectiveness and its simple requirement of only the estimation of the mean vector of each spectral class [26] based upon this unknown pixel is assigned to certain class. By superimposed the minimum distance classified image on pre classified image, the classified post image has been obtained as shown in Fig. 9 (a) (without topographic correction) and (b) (with topographic correction).

With the experiment results, Influence of topographic can be evaluated using accuracy assessment of “Change/ No-change” error matrix. A kappa coefficient of 0.8403 and overall accuracy of 92% were achieved with topographic correction as shown in Table 4. A kappa coefficient of 0.7136 and overall accuracy of 86% were achieved without topographic corrections as shown in the Table 5. Further evaluation has been made for accuracy assessment of “From-To” change. A kappa coefficient of 0.7779 and overall accuracy of 86% were achieved with topographic correction as shown in Table 6 and a kappa coefficient of 0.7572 and overall accuracy of 82% were achieved without topographic corrections shown in Table 7.

T ABLE 4 E RROR M ATRIX USING 50 SAMPLES FOR “C HANGE /NO -CHANGE ” DETECTION WITH TOPOGRAPHIC

Change Pixels

Classified change

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Referenced Change No-change Pixels Sum

Change Pixels 23 3 No-change Pixels 1 23 Sum 24 26 Commission Error 4.16% 11.5% Overall Accuracy= 92 %, Kappa Coefficient = 0.8403

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26 24 50

Commission Error

11.50% 4.16%

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T ABLE 5 E RROR M ATRIX USING 50 SAMPLES FOR “C HANGE /NO -CHANGE ” DETECTION WITHOUT TOPOGRAPHIC

Referenced Change No-change Pixels Sum

Change Pixels

Change Pixels 26 6 No-change Pixels 1 17 Sum 27 23 Commission Error 3.70% 26.08% Overall Accuracy= 86 %, Kappa Coefficient = 0.7136

Classified change

32 18 50

Commission Error

18.75% 5.55%

T ABLE 6 ACCURACY ASSESSMENT USING 50 SAMPLES OF “FROM -TO ” C HANGE DETECTION ICVA WITH T OPOGRAPHIC

22 /11/09 02/11/09

Unclassified

Sum

28 19 2 1 50

T

Shadow Snow Soil Vegetation Shadow Snow 2 25 1 Soil 18 1 Vegetation 2 Unclassified Sum 4 43 2 Overall Accuracy =86%, Kappa Coefficient =0.7779

1 1

22 /11/09 02/11/09

Shadow Shadow 1 Snow 5 Soil 3 Vegetation Unclassified Sum 9 Overall Accuracy =82%

CONCLUSION

Snow

Soil

2 20 15

1

37

1

Unclassified

3 3

Sum

3 26 18 3 50

REFERENCES

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In this paper, we have concluded that improved change vector analysis is viable change detection technique to find the threshold of change magnitude and change type for snow cover regions of Himalaya terrain. DFPS method is implemented to estimate a threshold value for change/no change area on satellite images with good succession rate. The change magnitude combines a single image classification and a minimum-distance categorization based upon the direction cosines of the change vector provided a way to find change type discrimination. Topography plays a significant role in change detection analysis. The inclusion of topography significantly improved the accuracy of change detection. Overall accuracy of 86% (Kappa coefficient 0.7779) has been achieved with topographic model inclusion as compared to 82% (Kappa coefficient 0.7572) for uncorrected images.

ACKNOWLEDGEMENT The authors would like to thank Director, Snow Avalanche Study establishment, Department of Defence Research and Development Organization. We are also thankful to Arun Chaudhary, Scientist, Snow Avalanche Study Establishment for technical discussions.

ISSN: 2230-7818

Vegetation

, Kappa Coefficient = 0.7572

A

VIII.

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T ABLE 7 ACCURACY ASSESSMENT USING 50 SAMPLES OF “FROM -TO ” C HANGE DETECTION ICVA WITHOUT TOPOGRAPHIC

[1]

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11.IJAEST-Vol-No-7-Issue-No-1-Topographic-Influence-on-Improved-Change-Vector-Analysis-using-MODIS-S