FR1.T09.5 - GIS and Agro-Geoinformatics Applications<br />Feature Analysis of Groundwater Discharge Points in Coastal Regi...
2<br />Table of Contents <br />Motivation<br />Study area<br />Data analysis<br />Results and Discussion<br />Summary<br />
Submarine groundwater discharge<br />Rain or Snow<br />mountain<br />Submarine groundwater <br />discharge<br />Sea<br />G...
previously presented study<br />spreading of the<br />groundwater discharge<br />Use ALOS AVNIR-2 data<br />properties of ...
・ALOS AVNIR-2 (Advances Visible and Near Infrared Radiometer type 2)are passive sensors<br />- the data will be affected b...
6<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br ...
Study area<br />Coastal region in Japan Sea<br />Around the Mt.Chokaisan<br />Well known as the origin of Crassostreanippo...
ALOS PALSAR data<br />ALOS AVNIR-2<br />Winter data<br />(Feb. 25, 2010)<br />Autumn data<br />(Sep. 20, 2009)<br />Winter...
Ground survey<br />Date: Aug 3, 2010<br />Survey points<br />・Kisakata beach(2 points)<br />・Fukuden(3points)<br />・Kosaga...
Comparison of sea and spring water in each water quality<br />●:Sea Water<br />●:Spring water<br />●:Sea and spring water<...
11<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br...
For PALSARdata<br />Geometric correction<br />- second order conformal transformation <br /><ul><li>cubic convolution </li...
Masking<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-Masking<br />A hydrology expert’s comment<...
16<br />32<br />64<br />128<br />256<br />512<br />For PALSARdata<br />Grayscale conversion<br />-Noise reduction<br />PAL...
Textures computed from <br />co-occurrence matrix<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-...
Homogeneity,
Dissimilarity,
Correspond</li></ul>小砂川<br />小砂川<br />Grayscale conversion<br />-16,32,64,128,256,512<br />Textures computed from co-occur...
k-means<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-Masking<br />The processing was ended: <br...
17<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br...
Filter size (e.g., mean)<br />3×3<br />9×9<br />7×7<br />11×11<br />5×5<br />
Select of feature<br />(a)mean<br />(d)variance<br />(b)entropy<br />(c)second moment<br />
Select of feature<br />(f)homogeneity<br />(e)contrast<br />(g)dissimilarity<br />(h)correlation<br />
Autumn PALSAR results<br />The red clusters exist in Kosagawa, Misaki, Kamaiso.<br />The green and blue clusters are also ...
Autumn and winter PLASAR results<br />the red clusters are <br />decreasing in winter <br />Winter data<br />(16 gray leve...
Autumn and winter PLASAR results<br />the difference of temperature between Sea and spring water<br /> in the winter data ...
PLASAR and AVNIR-2 results in Autumn<br />PALSAR data<br />(16 gray levels; mean; K=7)<br />AVNIR-2 data<br />(band1,2,3; ...
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kageyama-2550-0728min (1).pptx

  1. 1. FR1.T09.5 - GIS and Agro-Geoinformatics Applications<br />Feature Analysis of Groundwater Discharge Points in Coastal Regions around Mt. Chokaisan, Japan by Using ALOS PALSAR DATA<br /> <br />Yoichi KAGEYAMA, Hikaru SHIRAI, <br />and Makoto NISHIDA<br />Department of Computer Science and Engineering, <br />Graduate School of Engineering and Resource Science, <br />Akita University, JAPAN<br />
  2. 2. 2<br />Table of Contents <br />Motivation<br />Study area<br />Data analysis<br />Results and Discussion<br />Summary<br />
  3. 3. Submarine groundwater discharge<br />Rain or Snow<br />mountain<br />Submarine groundwater <br />discharge<br />Sea<br />Groundwater flows<br />-A key role in linking land and sea water circulation<br />-Collecting water directly<br />-Water quality, amount of discharge, and discharge location are quite different.<br />
  4. 4. previously presented study<br />spreading of the<br />groundwater discharge<br />Use ALOS AVNIR-2 data<br />properties of the AVNIR-2 data acquired in different seasons<br /> were well able to retrieval the sea surface information†1.<br />†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) <br />
  5. 5. ・ALOS AVNIR-2 (Advances Visible and Near Infrared Radiometer type 2)are passive sensors<br />- the data will be affected by clouds<br /><ul><li> the limited data are available. </li></ul>・ALOS PALSAR (Phased Array type <br />L-band Synthetic Aperture Radar) are active sensor <br />- we use the data regardless of the weather conditions. <br />Purpose<br />Analyzes features of the groundwater <br />discharge points in coastal regions by using the ALOS PALSAR data as well as the AVNIR-2 data<br />⇒ use of textures calculated from co-occurrence matrix<br />⇒ classification maps regarding the textures were obtained with k-means. <br />⇒ comparison the PALSAR classification maps with the AVNIR-2 ones.<br />
  6. 6. 6<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br />Summary<br />
  7. 7. Study area<br />Coastal region in Japan Sea<br />Around the Mt.Chokaisan<br />Well known as the origin of Crassostreanippona<br />⇒ Groundwater discharge can affect the Its growth<br />Groundwater dischargeat Kamaiso<br />(Aug. 3, 2010)<br />
  8. 8. ALOS PALSAR data<br />ALOS AVNIR-2<br />Winter data<br />(Feb. 25, 2010)<br />Autumn data<br />(Sep. 20, 2009)<br />Winter data<br />(Jan. 30, 2010)<br />Autumn data<br />(Oct. 7, 2009)<br />(R,G,B:band3,2,1)<br />1270 MHz(L-band)<br />(μm)<br />
  9. 9. Ground survey<br />Date: Aug 3, 2010<br />Survey points<br />・Kisakata beach(2 points)<br />・Fukuden(3points)<br />・Kosagawa beach(3points)<br />・Kosagawa fishing port(1point)<br />・Misaki(3points)<br />・Kamaiso(1point)<br />・Gakko River(2points)<br />
  10. 10. Comparison of sea and spring water in each water quality<br />●:Sea Water<br />●:Spring water<br />●:Sea and spring water<br />
  11. 11. 11<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br />Summary<br />
  12. 12. For PALSARdata<br />Geometric correction<br />- second order conformal transformation <br /><ul><li>cubic convolution </li></ul>⇒average RMS error was 0.41<br />Preprosessing<br />-Geometric correction<br />-Masking<br />Grayscale conversion<br />-16,32,64,128,256,512<br />Textures computed from co-occurrence matrix<br />吹浦<br />k-means algorithm to create the resulting classification<br />Winter data<br />(Jan. 30, 2010)<br />Autumn data<br />(Oct. 7, 2009)<br />
  13. 13. Masking<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-Masking<br />A hydrology expert’s comment<br />judged from the scale of Mt. Chokaisan,<br />the submarine groundwater discharge <br />exist ranging from land regions to 500 <br />meters offing. <br />500m<br />Grayscale conversion<br />-16,32,64,128,256,512<br />+<br />Textures computed from co-occurrence matrix<br />Masked images<br />k-means algorithm to create the resulting classification<br />Land area<br />-Various DNs<br />-DNs are larger<br />
  14. 14. 16<br />32<br />64<br />128<br />256<br />512<br />For PALSARdata<br />Grayscale conversion<br />-Noise reduction<br />PALSAR data(2bytes)<br />⇒ 16,32,64,128,256,512 gray levels<br />Preprosessing<br />-Geometric correction<br />-Masking<br />Grayscale conversion<br />-16,32,64,128,256,512<br />Textures computed from co-occurrence matrix<br />k-means algorithm to create the resulting classification<br />
  15. 15. Textures computed from <br />co-occurrence matrix<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-Masking<br />Eight features<br />-Mean, <br />-Entropy, <br />-Second moment, <br />-Variance,<br /><ul><li>Contrast,
  16. 16. Homogeneity,
  17. 17. Dissimilarity,
  18. 18. Correspond</li></ul>小砂川<br />小砂川<br />Grayscale conversion<br />-16,32,64,128,256,512<br />Textures computed from co-occurrence matrix<br />e.g., mean<br />Average the DNs of points around<br />吹浦<br />吹浦<br />k-means algorithm to create the resulting classification<br />
  19. 19. k-means<br />For PALSARdata<br />Preprosessing<br />-Geometric correction<br />-Masking<br />The processing was ended: <br />-the number of the maximum<br /> repetition amounted to 100 times,<br />-moved pixels between clusters <br />became 5% or less of the whole <br />pixels. <br />k was set from 2 to 20.<br />小砂川<br />小砂川<br />Grayscale conversion<br />-16,32,64,128,256,512<br />Textures computed from co-occurrence matrix<br />吹浦<br />吹浦<br />k-means algorithm to create the resulting classification<br />
  20. 20. 17<br />Table of Contents <br />Motivation<br />Data used and study area<br />Data analysis<br />Results and Discussion<br />Summary<br />
  21. 21. Filter size (e.g., mean)<br />3×3<br />9×9<br />7×7<br />11×11<br />5×5<br />
  22. 22. Select of feature<br />(a)mean<br />(d)variance<br />(b)entropy<br />(c)second moment<br />
  23. 23. Select of feature<br />(f)homogeneity<br />(e)contrast<br />(g)dissimilarity<br />(h)correlation<br />
  24. 24. Autumn PALSAR results<br />The red clusters exist in Kosagawa, Misaki, Kamaiso.<br />The green and blue clusters are also formed<br />⇒a spread of spring water.<br />large difference of temperature between spring water and air<br />Weather information during the data acquisition†1<br />8.2 ℃<br />†1http://www.jma.go.jp/jp/amedas/<br />(16 gray levels; mean; K=7)<br />
  25. 25. Autumn and winter PLASAR results<br />the red clusters are <br />decreasing in winter <br />Winter data<br />(16 gray levels; mean; K=7)<br />Autumn data<br />(16 gray levels; mean; K=7)<br />In kosagawa,Amount of submarine groundwater discharge has been reduced in January to March.<br />
  26. 26. Autumn and winter PLASAR results<br />the difference of temperature between Sea and spring water<br /> in the winter data is smaller.<br />Autumn data<br />Winter data<br />(16 gray levels; mean; K=7)<br />Weather information at the data acquisition†1<br />10.5 ℃<br />1.5 ℃<br />†1http://www.jma.go.jp/jp/amedas/<br />
  27. 27. PLASAR and AVNIR-2 results in Autumn<br />PALSAR data<br />(16 gray levels; mean; K=7)<br />AVNIR-2 data<br />(band1,2,3; k=7)<br />The red clusters exist in Kosagawa, Misaki, and Kamaiso as well as <br />the PALSAR classification results.<br />
  28. 28. PLASAR and AVNIR-2 results in Winter<br />Compared with the autumn data, <br />the cluster of red is reduced<br />PALSAR data<br />(16 gray levels, mean, K=7)<br />AVNIR-2 data<br />(band1,2,3;k=7)<br />The conditions consistent with a decrease in the amount of <br />submarine groundwater discharge in winter<br />
  29. 29. Summary<br />This study has analyzed the features regarding the groundwater <br />discharge points in the coastal regions around Mt. Chokaisan, Japan. <br /> <br />-The experimental results suggest that the Mean obtained from the <br />co-occurrence matrix was good in extraction of the features <br />of the groundwater discharge points from the ALOS PALSAR data. <br />-The ALOS PALSAR data has the possibility of extracting the <br />groundwater discharge points in the study area. <br />-The k-means clustering results in the PALSAR and AVNIR-2 data <br />agreed with the findings acquired by the ground survey.<br />
  30. 30. Thank you for your attention!<br />

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