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FR1.T09.5 - GIS and Agro- Geoinformatics Applications

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

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FR1.T09.5 - GIS and Agro- Geoinformatics Applications

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

  2. Table of Contents Motivation Study area Data analysis Results and Discussion Summary

  3. Submarine groundwater discharge Rain or Snow Submarine groundwater discharge mountain Sea Groundwater flows -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 spreading of the groundwater discharge Use ALOS AVNIR-2 data properties of the AVNIR-2 data acquired in different seasons were well able to retrieval the sea surface information†1. †1Y. 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)

  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. Purpose 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.

  6. Table of Contents Motivation Data used and study area Data analysis Results and Discussion Summary

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

  8. ALOS PALSAR data ALOS AVNIR-2 Winter data (Feb. 25, 2010) Autumn data (Sep. 20, 2009) Autumn data (Oct. 7, 2009) Winter data (Jan. 30, 2010) (R,G,B:band3,2,1) 1270 MHz(L-band) (μm)

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

  10. Comparison of sea and spring water in each water quality ●:Sea Water ●:Spring water ●:Sea and spring water

  11. Table of Contents Motivation Data used and study area Data analysis Results and Discussion Summary

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

  13. For PALSARdata Masking Preprosessing -Geometric correction -Masking 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 Grayscale conversion -16,32,64,128,256,512 ＋ Textures computed from co-occurrence matrix Masked images k-means algorithm to create the resulting classification Land area -Various DNs -DNs are larger

  14. 16 32 64 128 256 512 For PALSARdata Grayscale conversion -Noise reduction PALSAR data(2bytes) ⇒　16,32,64,128,256,512gray levels 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

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

  16. For PALSARdata k-means Preprosessing -Geometric correction -Masking 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. 小砂川 小砂川 Grayscale conversion -16,32,64,128,256,512 Textures computed from co-occurrence matrix 吹浦 吹浦 k-means algorithm to create the resulting classification

  17. Table of Contents Motivation Data used and study area Data analysis Results and Discussion Summary

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

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

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

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

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

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

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

  25. PLASAR and AVNIR-2 results in Winter Compared with the autumn data, the cluster of red is reduced PALSAR data (16 gray levels, mean, K=7) AVNIR-2 data (band1,2,3;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.

  27. Thank you for your attention!

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