1. Feature Analysis of Groundwater Discharge Points in Coastal Regions around Mt. Chokaisan, Japan by Using ALOS PALSAR DATA FR1.T09.5 - GIS and Agro-Geoinformatics Applications Yoichi KAGEYAMA, Hikaru SHIRAI, and Makoto NISHIDA Department of Computer Science and Engineering, Graduate School of Engineering and Resource Science, Akita University, JAPAN

2. 2 Table of Contents 1.Motivation 2.Study area 3.Data analysis 4.Results and Discussion 5.Summary

3. Submarine groundwater discharge Rain or Snow Groundwater flows mountain Sea Submarine groundwater discharge -A key role in linking land and sea water circulation -Collecting water directly -Water quality, amount of discharge, and discharge location are quite different.

4. previously presented study Use ALOS AVNIR-2 data †1 Y. Kageyama, C. Shibata, and M. Nishida, “Feature Analysis of Groundwater Discharge Points in Coastal Regions around Mt. Chokaisan by Using ALOS AVNIR-2 Data”, IEEJ Trans. EIS, Vol.131, No.10 (in press) properties of the AVNIR-2 data acquired in different seasons were well able to retrieval the sea surface information †1. spreading of the groundwater discharge

5. ・ ALOS AVNIR-2 (Advances Visible and Near Infrared Radiometer type 2)are passive sensors - the data will be affected by clouds - the limited data are available. ・ ALOS PALSAR (Phased Array type L-band Synthetic Aperture Radar) are active sensor - we use the data regardless of the weather conditions. Analyzes features of the groundwater discharge points in coastal regions by using the ALOS PALSAR data as well as the AVNIR-2 data ⇒ use of textures calculated from co-occurrence matrix ⇒ classification maps regarding the textures were obtained with k-means. ⇒ comparison the PALSAR classification maps with the AVNIR-2 ones. Purpose

6. 6 Table of Contents 1.Motivation 2.Data used and study area 3.Data analysis 4.Results and Discussion 5.Summary

7. Coastal region in Japan Sea Around the Mt.Chokaisan Groundwater discharge at Kamaiso (Aug. 3, 2010) Study area Well known as the origin of Crassostrea nippona ⇒ Groundwater discharge can affect the Its growth

8. ALOS PALSAR data Winter data (Jan. 30, 2010) Autumn data (Oct. 7, 2009) ALOS AVNIR-2 Autumn data (Sep. 20, 2009) Winter data (Feb. 25, 2010) (R,G,B:band3,2,1) Band 1 0.42 ～ 0.50 blue Band 3 0.61 ～ 0.69 red Band 2 0.52 ～ 0.60 green Band 4 0.76 ～ 0.89 NIR (μm)(μm) 1270 MHz(L-band)

9. Survey points ・ Kisakata beach(2 points) ・ Fukuden(3points) ・ Kosagawa beach(3points) ・ Kosagawa fishing port (1point) ・ Misaki(3points) ・ Kamaiso(1point) ・ Gakko River(2points) Ground survey Date: Aug 3, 2010

10. Comparison of sea and spring water in each water quality Sea water Spring water pH8.097.37 Dissolved oxygen 6.85mg/L10.2mg/L Electric conductivity 4.21S/m0.002S/m Salinity27.6%0% Total Dissolved Solids 45.6g/L0.1g/L Sea water specific gravity 1.023sg1.002sg Water temperature 26.0 ℃ 13.3 ℃ Turbidity7.78NTU5.05NTU ● :Sea Water ●:Spring water ●:Sea and spring water

11. 11 Table of Contents 1.Motivation 2.Data used and study area 3.Data analysis 4.Results and Discussion 5.Summary

12. Preprosessing -Geometric correction -Masking Grayscale conversion -16,32,64,128,256,512 For PALSAR data Textures computed from co- occurrence matrix k-means algorithm to create the resulting classification - second order conformal transformation -cubic convolution ⇒ average RMS error was 0.41 吹浦 Winter data (Jan. 30, 2010) Autumn data (Oct. 7, 2009) Geometric correction

13. Preprosessing -Geometric correction -Masking Grayscale conversion -16,32,64,128,256,512 Textures computed from co- occurrence matrix k-means algorithm to create the resulting classification ＋ Masked images Masking Land area -Various DNs -DNs are larger A hydrology expert’s comment judged from the scale of Mt. Chokaisan, the submarine groundwater discharge exist ranging from land regions to 500 meters offing. 500m For PALSAR data

14. Preprosessing -Geometric correction -Masking Grayscale conversion -16,32,64,128,256,512 Textures computed from co- occurrence matrix k-means algorithm to create the resulting classification -Noise reduction PALSAR data (2bytes) ⇒ 16,32,64,128,256,512 gray levels 16 32 64 128 256 512 For PALSAR dataGrayscale conversion

15. Preprosessing -Geometric correction -Masking Grayscale conversion -16,32,64,128,256,512 Textures computed from co- occurrence matrix k-means algorithm to create the resulting classification Textures computed from co-occurrence matrix 小砂川 吹浦 小砂川 吹浦 Eight features -Mean, -Entropy, -Second moment, -Variance, -Contrast, -Homogeneity, -Dissimilarity, -Correspond e.g., mean Average the DNs of points around For PALSAR data

16. Preprosessing -Geometric correction -Masking Grayscale conversion -16,32,64,128,256,512 Textures computed from co- occurrence matrix k-means algorithm to create the resulting classification k-means 小砂川 吹浦 小砂川 吹浦 For PALSAR data The processing was ended: -the number of the maximum repetition amounted to 100 times, -moved pixels between clusters became 5% or less of the whole pixels. k was set from 2 to 20.

17. 17 Table of Contents 1.Motivation 2.Data used and study area 3.Data analysis 4.Results and Discussion 5.Summary

18. 3×37×7 5×5 9×9 １１ × １１ Filter size (e.g., mean)

19. (a)mean(d)variance(b)entropy(c)second moment Select of feature

20. (f)homogeneity(e)contrast(g)dissimilarity(h)correlation Select of feature

21. (16 gray levels; mean; K=7) Autumn PALSAR results air 18.7 ℃ Wea water About 21 ℃ Spring water About 10.5 ℃ †1 http://www.jma.go.jp/jp/amedas/ Weather information during the data acquisition †1 large difference of temperature between spring water and air The red clusters exist in Kosagawa, Misaki, Kamaiso. The green and blue clusters are also formed ⇒ a spread of spring water. 8.2 ℃

22. Autumn and winter PLASAR results In kosagawa, Amount of submarine groundwater discharge has been reduced in January to March. Autumn data (16 gray levels; mean; K=7) Winter data (16 gray levels; mean; K=7) the red clusters are decreasing in winter the red clusters are decreasing in winter

23. Autumn data Winter data Autumn dataWinter data air 18.7 ℃ 2.4 ℃ Sea water About 21 ℃ About 12 ℃ Spring water About 10.5 ℃ †1 http://www.jma.go.jp/jp/amedas/ Weather information at the data acquisition †1 the difference of temperature between Sea and spring water in the winter data is smaller. Autumn and winter PLASAR results (16 gray levels; mean; K=7) 10.5 ℃ 1.5 ℃

24. PLASAR and AVNIR-2 results in Autumn AVNIR-2 data (band1,2,3; k=7) The red clusters exist in Kosagawa, Misaki, and Kamaiso as well as the PALSAR classification results. PALSAR data (16 gray levels; mean; K=7)

25. PLASAR and AVNIR-2 results in Winter AVNIR-2 data (band1,2,3;k=7) Compared with the autumn data, the cluster of red is reduced PALSAR data (16 gray levels, mean, K=7) The conditions consistent with a decrease in the amount of submarine groundwater discharge in winter

26. Summary This study has analyzed the features regarding the groundwater discharge points in the coastal regions around Mt. Chokaisan, Japan. -The experimental results suggest that the Mean obtained from the co-occurrence matrix was good in extraction of the features of the groundwater discharge points from the ALOS PALSAR data. -The ALOS PALSAR data has the possibility of extracting the groundwater discharge points in the study area. -The k-means clustering results in the PALSAR and AVNIR-2 data agreed with the findings acquired by the ground survey.

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