**4. Discussion**

362 Remote Sensing – Applications

and cloud (Zhou et al., 2005). In this part using calculated apparent temperature for band 31 and applying 2830K threshold, heat masking is performed after new cloud masking

Images resulted from snow map algorithm before and after applying Liberal cloud mask related to February and March are illustrated in Figure 3. Right column shows images before liberal cloud masking and middle column show them after masking. Left column shows false color images which are made by combining visual and infrared channels of MODIS according to method introduced by Miller et al. (2004). Lands without snow cover, with snow cover, low height clouds and higher clouds appear as green, white, yellow and violet tones, respectively. False color image help to recognize cloudy regions on image as

Fig. 3. Snow area before applying the liberal cloud mask (right column) and after applying

the Liberal cloud mask (middle column) and false color composite (left column).

algorithm and before snow map algorithm.

**3. Results** 

well as cloud height.

In this part, using NDSI, topographic data and data gathered from snow measurement stations, snow map algorithm alone and together with Liberal cloud masking were separately interpreted. Ground-based snow measurement data and their corresponding points on images resulted from snow map algorithm as well as images resulted from snow map algorithm together with Liberal cloud masking in different dates are illustrated in table 2. In fact error matrix is drawn for each image and results have been surveyed.


Table 2. Evaluation of accuracy of snow gauge obtained from snow map algorithm and snow map algorithm with attending the Liberal cloud mask using earth data in 6th of February, 13th and 15th of March

Results demonstrate that in both images of 13th and 15th March in which snow map was applied, the number of points classified as snow is more than the time when applying snow map algorithm; adding cloud masking to snow map algorithm reduces this number. It means that regions which are incorrectly classified as snow by snow map algorithm can be categorized as cloud after adding cloud masking. Furthermore, no snow regions identified as snow in snow-gauging station and snow map algorithm with Liberal cloud masking are more than those no snow regions that are not classified as snow without applying Liberal cloud masking. So it can be concluded that snow map algorithm shows some regions as snow despite the fact that they are clouds. However, cloud masking can detect them and classify as cloud. Error matrix demonstrates that accuracy of snow map algorithm increases by applying cloud mask (Riggs and Hall, 2002; Ault et al., 2006; Hall and Riggs, 2007). Overall, results from snow map algorithm together with Liberal cloud masking are more compatible with data gathered from ground-based stations.

One of the factors affecting accuracy of snow detection is clouds which cover snow surface. These clouds are distinguishable by Liberal cloud masking provided that they are transparent and thin (Riggs and Hall, 2002; Ault et al., 2006). In images related to 21st February and 8th March, the observed cloud is thick and far from the Earth. False color images show that clouds are far from the Earth surface in both mentioned images so they can be detected and classified correctly by Liberal cloud masking. However, there is snow under these clouds and should be considered as snow. Data from ground-based snow

Predictability of Water Sources Using Snow

regions (Figure 4).

8th March

Maps Extracted from the Modis Imagery in Central Alborz, Iran 365

In order to show NDSI ability in isolation of cloud from snow, variation range of NDSI in regions which are identified as cloud using new cloud masking is compared with variation in regions where classified as snow by use of snow map algorithm. As it can be found in diagram NDSI variation in regions where identified as cloud and regions where classified as snow has some overlaps so NDSI cannot distinguish between snow and cloud in these

Fig. 4. The variation range of DSI in cloudy and snowy area on images of 25th February and

Figure 5 shows comparison of NDSI variation for image taken in 4th March which has only low height clouds in regions which are identified as cloud using new cloud masking with variation in regions where classified as snow using snow map algorithm. As it can be seen in diagram NDSI variation in regions where identified as cloud and regions where classified as snow are absolutely separable so NDSI can distinguish between snow and cloud in these

As a general rule, the amount of snow increases in higher elevations so if classification of snow and cloud is done perfectly, percentage of pixels related to snow should increase in higher elevation. This rule can be used to evaluate the accuracy of outputs resulted from snow map algorithm alone and together with Liberal mask algorithm. Figure 6 shows the relative frequency of snow pixels in each altitudinal zone. As it can be seen in this Figure, ascending trend occur whenever new cloud mask is applied together with snow map algorithm. In fact, cloud masking leads to the better identification of cloud pixels and prevents these pixels to be classified as snow. However ascending trend will not happen in mentioned diagram when snow map algorithm is used alone because some cloud pixels are

regions and act similar to new cloud mask (Figure 5).

categorized as snow incorrectly.

measurement and their corresponding points on images related to 21st February and 8th March resulted from snow map algorithm before and after applying Liberal cloud masking is shown in Table 3. As is shown in Table 3, field survey data are different from results obtained as a result of snow map algorithm together with Liberal cloud masking. In this situation, considering neighborhood effect, topographic factors and false color images, clouds over snow can be distinguishable and classify them as snow. Of course, neighborhood and topographic factors can be helpful when the cloud is smaller that total area of snow.


Table 3. Evaluation of accuracy of snow gauge obtained from snow map algorithm

There is a negligible difference before and after applying Liberal masking images covered by low height clouds (e.g. image of 4th March) (Table 4). It means that in this situation snow map algorithm with and without liberal cloud masking has the same result. So it can be concluded that snow map algorithm is able to detect low height clouds because the spectral diagram of low height clouds are different from that of snow in visual and infrared spectrum range. Data gathered from ground-based snow measurement and its corresponding points on images resulted from snow map algorithm as well as images resulted from snow map algorithm together with liberal cloud masking is shown in Table 4.


Table 4. Evaluation of accuracy of snow gauge obtained from snow map algorithm and snow map algorithm with attending the Liberal cloud mask using earth data in 4th of March

measurement and their corresponding points on images related to 21st February and 8th March resulted from snow map algorithm before and after applying Liberal cloud masking is shown in Table 3. As is shown in Table 3, field survey data are different from results obtained as a result of snow map algorithm together with Liberal cloud masking. In this situation, considering neighborhood effect, topographic factors and false color images, clouds over snow can be distinguishable and classify them as snow. Of course, neighborhood and topographic factors can be helpful when the cloud is smaller that total

> Total Accuracy %

21/2/2006 Snow 15 2 17 77 Snow 12 0 12 100 No snow 0 1 1 100 No snow 3 3 6 50 Total 15 3 18 Total 15 3 18

100 66 88 Accuracy

100 66 88 Accuracy

Table 3. Evaluation of accuracy of snow gauge obtained from snow map algorithm

There is a negligible difference before and after applying Liberal masking images covered by low height clouds (e.g. image of 4th March) (Table 4). It means that in this situation snow map algorithm with and without liberal cloud masking has the same result. So it can be concluded that snow map algorithm is able to detect low height clouds because the spectral diagram of low height clouds are different from that of snow in visual and infrared spectrum range. Data gathered from ground-based snow measurement and its corresponding points on images resulted from snow map algorithm as well as images resulted from snow map algorithm together with liberal cloud masking is shown in Table 4.

> Total Accuracy %

4/3/2006 Snow 11 0 11 100 Snow 11 0 11 100 No snow 1 6 7 85 No snow 1 6 7 85 Total 12 6 18 Total 12 6 18

91 100 94 Accuracy

Table 4. Evaluation of accuracy of snow gauge obtained from snow map algorithm and snow map algorithm with attending the Liberal cloud mask using earth data in 4th of March

8/3/2006 Snow 12 2 14 85 Snow 8 0 8 100 No snow 0 4 4 100 No snow 4 6 10 55 Total 12 6 18 Total 1 6 18

Snow map (masking)

%

%

Snow map (masking)

%

Snow No snow

91 100 94

Snow No snow

83 16 83

66 100 77

Total Accuracy %

Total Accuracy %

area of snow.

Date of acquisition

Date of acquisition Snow map

 Accuracy %

Snow No snow

Snow map

 Accuracy %

 Accuracy %

Snow No snow In order to show NDSI ability in isolation of cloud from snow, variation range of NDSI in regions which are identified as cloud using new cloud masking is compared with variation in regions where classified as snow by use of snow map algorithm. As it can be found in diagram NDSI variation in regions where identified as cloud and regions where classified as snow has some overlaps so NDSI cannot distinguish between snow and cloud in these regions (Figure 4).

Fig. 4. The variation range of DSI in cloudy and snowy area on images of 25th February and 8th March

Figure 5 shows comparison of NDSI variation for image taken in 4th March which has only low height clouds in regions which are identified as cloud using new cloud masking with variation in regions where classified as snow using snow map algorithm. As it can be seen in diagram NDSI variation in regions where identified as cloud and regions where classified as snow are absolutely separable so NDSI can distinguish between snow and cloud in these regions and act similar to new cloud mask (Figure 5).

As a general rule, the amount of snow increases in higher elevations so if classification of snow and cloud is done perfectly, percentage of pixels related to snow should increase in higher elevation. This rule can be used to evaluate the accuracy of outputs resulted from snow map algorithm alone and together with Liberal mask algorithm. Figure 6 shows the relative frequency of snow pixels in each altitudinal zone. As it can be seen in this Figure, ascending trend occur whenever new cloud mask is applied together with snow map algorithm. In fact, cloud masking leads to the better identification of cloud pixels and prevents these pixels to be classified as snow. However ascending trend will not happen in mentioned diagram when snow map algorithm is used alone because some cloud pixels are categorized as snow incorrectly.

Predictability of Water Sources Using Snow

**5. Conclusion** 

Taghvakish, 2005).

**6. References** 

103(D24):32,141-32,157.

*Processes* 21:1534-154.

Maps Extracted from the Modis Imagery in Central Alborz, Iran 367

Reviewing data resulted from ground-based snow measurements in addition to results from snow map algorithm and Liberal cloud mask, it can be concluded that snow map algorithm cannot detect some types of cloud and classify them as snow (Zhou et al., 2005; Riggs and Hall, 2002; Ault et al., 2006; Hall and Riggs, 2007), reducing the accuracy of maps produced for snow detection. Clouds which are not detected by snow map algorithm are those include ice particles in high elevations (Taghvakish, 2005). Using Liberal cloud masking can largely solve this problem and prevent some types of clouds to be categorized as snow. The accuracy of maps is increased approximately 10% in comparison with other methods. In images including only low elevation clouds, cloud masking cannot make better results; therefore it can be concluded that these kinds of clouds can be detected by snow map algorithm alone. Also, results from applying NDSI shows that some types of clouds are categorized in the same class as snow, so NDSI cannot distinguish between snow and cloud.

Altitudinal parameter is another tool in order to evaluate the accuracy of snow map algorithm and Liberal cloud masking. An ascending trend in frequency of snow pixels is

In summary, it can be said that although low height clouds are separable by snow map algorithm, some types of clouds cannot be detected by snow map algorithm alone and thus, application of cloud masking is inevitable. These are clouds which are in high elevation and include ice particles (Taghvakish, 2005). Finally, in some cases even cloud masking cannot distinguish between snow and ice particles (Ault et al., 2006, Riggs and Hall, 2002 ,

Ackerman S A, Strabala K I, Menzel P W P, Frey R A, Moeller C C and Gumley L E, 1998:

Adhami S, 2005. Application of remote sensing and geographic information system in snow cover (Agichay). Unpublished MSc Thesis, University of Tabriz (In Persian). Ault T W, Czajkowski K P, Benko T, et al., 2006. Validation of the MODIS snow product and

Dadashi Khanegha S, 2008. Appointment of snow cover using image processing techniques. Unpublished MSc Thesis. University of Shahid Beheshti (In Persian). Hall D K, Tait A B, Riggs G A, Salomonson V V, Chien J, Andrew Y L, and Klein G. 1998.

Hall D K, Tait A B, Foster J L, Change A T C, and Allen M, 2000. Intercomparison of

Great Lakes Region. *Remote sensing of Environment* 105: 341-353.

Number ATBD-MOD-10, NASA Goddard Space Flight Center, 1998*.* Hall D K and Riggs G, 2007. Accuracy assessment of the MODIS snow products. *Hydrological* 

satellite-derived snow-cover maps. *Annals of Glaciology* 31:396-376.

Discriminating clear sky from clouds with MODIS, *Journal of Geophysical Research*,

cloud mask using student and NWS cooperative station observations in the Lower

Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Iceand Sea Ice-Mapping Algorithms, MODIS Algorithm Theoretical Basis Document

evident whenever cloud masking is used in addition to the snow map algorithm.

However, those clouds in low elevation can be detected from snow.

Fig. 5. The variation range of NDSI in cloudy and snowy area on images of 4th March

Fig. 6. Relative frequency percentage of snow pixels in each altitude class; right: the obtained images of snow map algorithm; left: the obtained images of snow map algorithm accompanying the Liberal clod mask
