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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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slide1

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

slide2

Table of Contents

Motivation

Study area

Data analysis

Results and Discussion

Summary

slide3

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.

slide4

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)

slide5

・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.

slide6

Table of Contents

Motivation

Data used and study area

Data analysis

Results and Discussion

Summary

slide7

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)

slide8

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)

slide9

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)

slide10

Comparison of sea and spring water in each water quality

●:Sea Water

●:Spring water

●:Sea and spring water

slide11

Table of Contents

Motivation

Data used and study area

Data analysis

Results and Discussion

Summary

slide12

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)

slide13

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

slide14

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

slide15

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

slide16

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

slide17

Table of Contents

Motivation

Data used and study area

Data analysis

Results and Discussion

Summary

slide18

Filter size (e.g., mean)

3×3

9×9

7×7

11×11

5×5

slide19

Select of feature

(a)mean

(b)entropy

(c)second moment

(d)variance

slide20

Select of feature

(e)contrast

(f)homogeneity

(g)dissimilarity

(h)correlation

slide21

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)

slide22

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.

slide23

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/

slide24

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.

slide25

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

slide26

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.