6.IJAEST-Vol-No-7-Issue-No-1-SNOW-COVER-MONITORING-USING-DIFFERENT-ALGORITHM-ON-AWiFS-SENSOR-DATA-04 by ISERP ISERP

Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

SNOW COVER MONITORING USING DIFFERENT ALGORITHM ON AWiFS SENSOR DATA Devesh Khosla

Prof. J.K. Sharma

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

development activities of a region depends. Therefore knowledge of the dynamic snow cover changes is of major importance in Himalayas. Consequently, snow cover monitoring is becoming one of the most important current earth observation topics, as well as a significant component of digital earth studies. However, very few reliable largescale snow monitoring results were obtained before the application of satellite remote sensing, especially in sparsely inhabited areas. Since the 1970s, snow cover monitoring from space has become a routine operation using satellite optical imagery [1]. Snow is a mixture of ice crystals, liquid water, and air [2].Differentiation of land cover types is essential in the interpretation of snow information [3]. In particular, the effect of forested areas on SCA estimations [4] is a problem common to all approaches to snow remote sensing in seasonal snow covers [5] and complex landscapes [6-8].

Abstract: - Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface. Atmospheric and Manmade activities has great impact on snow, Snow melt dynamics generate a complex set of interactions, which dictates a need for long-term monitoring of snow cover in conjunction with other climatologically variables. Thus, snow cover area (SCA) monitoring is currently an important tool in studies of global climate change, particularly because satellite remote sensing data provide timely and efficient snow cover information for large areas. In this study snow cover map based on AWiFS data has a resolution of 56m. The processing steps include a topographic correction based on a digital elevation model (DEM) and snow cover ratio and analysis snow area by different methods. (1) NDSI (2) NDCI and (3) S3 is used for calculate snow pixel in vegetation area without reference data. NDSI and NDCI are using NIR band of reflectance for snow pixel area. Here we compared this algorithm with topographic correction on study area of lower and middle Himalaya, Himachal Pradesh, India. The experiment result shown that S3 with topographic correction gives more accurate and hidden information as compare to NDSI, NDCI with topographic correction.

Scientist (E), SASE, DRDO Chandigarh, India vd_mishra@rediffmail.com

Student, R.I.E.I.T, Railmajra S.B.S. Nagar, Punjab, India dekhosla@gmail.com

Dr. V.D. Mishra

Keywords: AWiFS, Topographic correction, NDSI, S3, NDCI I. INTRODUCTION

Snow play an crucial role in maintains the temperature of lower and middle Himalayan region and essential component for the hydrological system. In middle Himalayas the snow cover area (SCA) significantly increases with the onset of winter during November and subsequently decrease from April onwards. With this major seasonal variation, different parts of Himalayan region experience different snow-cover variation patterns. This localized snow cover variation extends from different ranges of Himalayas. This variation in the snow cover extent controls the surface heat exchange system, soil thermal regimes, melt runoff discharge and hydro-power generation capacity of an area. Not only this, the snow cover variation also influences tourist movement and extent of the cropping season on which socio-economic condition and other

ISSN: 2230-7818

Mapping and monitoring seasonal snow cover using field methods is normally very difficult in mountainous terrain like the Himalaya, so remote-sensing techniques have been extensively used for snow-cover monitoring. Snow-cover monitoring using satellite images began in April 1960 using the TIROS-1 satellite [9]. Since then, numerous satellites (e.g. GOES, Meteosat, NOAA, AWiFS and Resourcesat) have been used successfully for snow mapping [10]-[12]. In this investigation, the Advanced Wide Field Sensor (AWiFS) of the RESOURCESAT- 1 satellite was used to monitor seasonal snow cover in the lower and middle Himalayan. By using different algorithm (1) Normalized difference snow index (NDSI) [10], (2) NDVI [14] and (3) S3 [15]. In our AWiFS images we have large shadow area, so need topographic correction. We have many topographic correction methods available, divided in two main categories: (1) Empirical methods such as two stage normalization[16], (2) Lambertian methods such as Ccorrection, Cosine-T [17]etc.,(3) Non-Lambertian methods such as Minneart correction method [18], Slope match [19].It is reported [20]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 (India) and shown on AWiFS(Advance Wide Field Sensor)

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Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

image lies between latitude of 32.26 degree to 32.99 degree North and longitude of 77.00 degree to 77.49 degree East as shown in the Figure 1. The Lower Himalaya receives the highest snowfall (average 15-20 m) as compared to Middle Himalayan range (12-15m) during the winter period between October and May. 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 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). 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.

III. SATELLITE DATA

Different pairs of cloud free satellite images of AWiFS (Advanced Wide Field Sensor) acquired on October, November, December 2009 and January, march 2010 are used in the present work to study influence of topography on different algorithm for snow monitoring. The salient specifications of AWiFS sensors are given in the Table 1.

Figure 1 AWiFS image of the study area.

Table 1 Salient Specifications of AWiFS Sensor Spectral wavelength 520-590 620-680 770-860 1550-1700

Spatial resolution 56 56 56 56

Quantization (bit) 10 10 10 10

Spectral bands B2 B3 B4 B5

IV. METHODOLOGY

A. Satellite data 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. This master image was used further to georeference AWiFS images. All satellite images were geocoded to the EVEREST datum by ERDAS/Imagine 9.1 (Leica Geosystems GIS and Mapping LLC) software with pixel accuracy. These geo-references it requires for good accuracy to extract meaningful snow cover information. B. Radio metrically corrected reflectance All geo-referenced satellite images are radiometrically (atmospheric + topographic) corrected. The atmospheric

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Maximum Radiance Solar Exoatmostpheric (mw/cm2/sr/μm) spectral Irradiance(mw/cm2/sr/μm) 52.34 185.3281 40.75 158.042 28.425 108.357 4.645 23.786 corrections are performed using the methods proposed in the literature [19]. The topographic corrections are then applied using slope matching technique [20] which is reported to be most suitable for Himalayan terrain. As shown in flow chart Figure 2. The topographically corrected spectral reflectance is estimated using the following equation [20]. =[

(

)

〉

(〈 〈

) 〉

]

(1)

Where is normalized topographically corrected reflectance, Rij is reflectance on the tilted surface and estimated using the model reported in the literature [17]. Rmax and Rmin are maximum and minimum reflectance of the image, <cosi>s is illumination on the south aspect, cosi is illumination (IL) image and estimated using [18] and C is an empirical coefficient.

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Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

DEM, aspect, slope

Satellite data (AWiFS) Geo-references

Illumination angle

Reflectance

because NIR band has higher value of vegetation area. Condition for snow area extraction is NDSI>0.4 and NIR band> 0.09. NDSI of AWiFS images of 20 November 2009 and 7 January 2010 are shown in Figure 4(a) and Figure 4(b) respectively

B. NDCI Index

Coefficient ( )

To identify snow we use NDCI which include difference of red and shortwave infrared band and divide by their addition [14].Show equation

Slope matching

NDCI =

Figure 2 Flow chart of topographic correction.

Snow area of each

Accuracy assessment

Slope matching

NDCI

(3)

where Red and SWIR are the reflectance's of the Red and shortwave infrared bands respectively. A threshold value for NDCI of 0.5(threshold) is defined for the pixels that are approximately 60% or greater covered by snow from the

V. ALGORITHM FOR SNOW AREA

NDSI

–

Figure 3 Flowchart for snow area. A. Normalized difference snow index (NDSI)

NDSI method is generally used for snow cover mapping using satellite data [10-11][21-22],NDSI uses the high and low reflectance of snow in visible (Green) and shortwave infrared (SWIR) region respectively and it can also delineate and map the snow in mountain shadows [11]. Additionally, the reflectance of clouds remains high in SWIR band, thus NDSI allows in discriminating some clouds and snow. NDSI is defined by following relation and it ranges from −1 to +1. –

(2)

where Green and SWIR are the reflectance of the Green and shortwave infrared bands respectively. A threshold value for NDSI of 0.4 is defined for the pixels that are approximately 50% or greater covered by snow from the imageries of different sensors. This threshold was set by visual interpretation. In this we use NIR band of reflectance

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(a)

(b)

Figure 4 NDSI images of AWiFS (a) 21 November 2009 (b) 7 January 2009 Snow area

Snow area

Non Snow

imageries of different sensors. Condition used for snow area to calculate is as NDSI>0.3, NDCI> 0.5, Green band >0.09 and NIR band >0.09. NDCI of AWiFS images of 20 November 2009 and 7 January 2010 are shown in Figure 5(a) and Figure 5(b) respectively. Where Black color represent non snow area(shadow, soil, vegetation) and other is snow area. C. S3 index To identify snow under vegetation, normalized snow index S3 was also proposed (Saito and Yamazaki 1999)

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Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

VI. SNOW COVER RATIO (SCR) AND SNOW COVER AREA(SCA) In the calculation of SCR both snow cover area and snow free area is normalized. The number of pixels of each index value was divided by the total number of pixel. For example, the number of pixels of snow cover area at certain value of index are divided by total number of pixel of snow covered area. SCR=

(b)

( (

) )(

(4)

)

where NIR, Red and SWIR are the reflectance's of the nearinfrared, red (visible) and shortwave infrared bands respectively. S3 uses the reflectance characteristics of snow and vegetation to reduce the errors caused by snow covered areas mixed with vegetation. Shimamura[15], found the threshold value of S3 index for snow cover area greater than 0.18 and for the snow under vegetation it is distributed from 0.05 to 0.18. As shown in Figure 6, S3 of 20 November 2009 and 7 January 2010 respectively. Snow area is of white and grey color and non snow area is of black color.

(a)

(b)

Figure 6 S3 image of AWiFS (a) 21 November 2009 (b) 7 January 2009

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⁄∑

(5) [15]

Here i is index value, Sci is number of pixel in snow cover area, and Sfi is the number of pixel in snow free area. Shown in table2 The snow cover area from summer to winter is shown in the figure 7. These represent that by using different algorithm (NDSI, NDCI and S3) gives different area as shown in graph. All area depends upon threshold which set visually (manually) NDSI>0.4, NDCI>0.5, S3>0.19.

Figure 5 NDCI image of AWiFS (a) 21 November 2009 (b)7 January 2009

⁄∑

(a)

⁄∑

VII. RESULT AND DISSCUSION A. Determination of Threshold The threshold process is carried out by visual interpretation of NDSI, NDCI and S3 with original image. By the method we get threshold of NDSI = 0.4, NDCI = 0.5, S3 = 0.19. B. Snow Cover Area of Different Months Snow cover area calculation is carried out on bases of threshold. As shown in Figure 7.We have seen that as the snow cover area (SCA) significantly increases with the onset of winter during November and subsequently decreases from April onwards. Lesser area of snow in 13 October 2009 and after 20 November 2009 there was abruptly increase in the snow area. But in winter seasonal there is less difference in the snow area in different date’s image. Different graphs line of S3, NDCI and NDSI respectively in Figure 7. C. Accuracy Assessment The accuracy assessment has been carried using 200 random samples. To get optimized threshold we considered best value over which all snow is covered. The threshold is different in all cases NDSI(0.4), NDCI(0.5) and S3(0.19) and there accuracy is above 90 percent in all case. Snow cover area is calculated on base of these thresholds as show in graph Figure 7. Accuracy assessment is given as correctness/(correctness + error)*100. The overall accuracy and kappa coefficient of S3 is 95% and 0.883 but NDSI and NDCI has 91.5%,0.725 and 93.5%,0.778 respectively .This results

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Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

show that accuracy of the S3 method is best for snow area under dense vegetation.

Figure 7 Snow cover area of different dates

Table 2 Accuracy Assessment With Topographic Of AWiFS

NDCI S3

DATE 20nov 7jan 20nov 7jan 20nov 7jan

VIII. CONCLUSION

THRESHOLD 0.40 0.40 0.50 0.50 0.19 0.19

COORECTNESS 185 183 188 187 187 190

INDEX NDSI

We investigated that advantage of using NDSI is that the NDSI value for flat terrain same as for a steep slope facing the sun and a slope turning away from the sun if the snow conditions are similar but NDSI gives high values in shadows, we got problems by using NDSI directly in shadowed areas. Then we applied topographic correction using slope matching method. S3 gives high percent of accuracy and well suited for snow under vegetation area and it can identify snow-covered area without using references data, while NDSI and NDCI both need vegetation data to identified snow area under dense vegetation. S3 is combination of NDSI and NDCI so S3 is most suited for automatic identification of snow covered area with effect of topographic using slope matching technique.

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

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ERROR 15 17 12 13 13 10

Accuracy 92.50 91.50 94.00 93.50 93.50 95.00

SCR % 50 50 60 60 55 55

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Devesh Khosla* et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 7, Issue No. 1, 042 - 047

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