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
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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
Table of Contents
1.Motivation2.Study area3.Data analysis4.Results and Discussion5.Summary
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.
previously presented study
Use ALOS AVNIR-2 data
†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)
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
・ 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
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Table of Contents
1.Motivation2.Data used and study area3.Data analysis4.Results and Discussion5.Summary
Coastal region in Japan SeaAround the Mt.Chokaisan
Comparison of sea and spring water in each water quality
Sea water Spring water
pH 8.09 7.37
Dissolved oxygen
6.85mg/L 10.2mg/L
Electric conductivity 4.21S/m 0.002S/m
Salinity 27.6% 0%
Total Dissolved Solids 45.6g/L 0.1g/L
Sea water specific gravity 1.023sg 1.002sg
Water temperature 26.0℃ 13.3℃
Turbidity 7.78NTU 5.05NTU
●:Sea Water●:Spring water●:Sea and spring water
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Table of Contents
1.Motivation2.Data used and study area3.Data analysis4.Results and Discussion5.Summary
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
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 commentjudged from the scale of Mt. Chokaisan,the submarine groundwater discharge exist ranging from land regions to 500 meters offing.
500m
For PALSAR data
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 reductionPALSAR data (2bytes)
⇒ 16,32,64,128,256,512
gray levels16
3264
128256
512
For PALSAR data Grayscale conversion
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 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.
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Table of Contents
1.Motivation2.Data used and study area3.Data analysis4.Results and Discussion5.Summary
large difference of temperature between spring water and air
Kosagawa
Misaki
Kamaiso
The red clusters exist in Kosagawa, Misaki, Kamaiso.The green and blue clusters are also formed⇒a spread of spring water.
8.2 ℃
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)
Kosagawa
Misaki
Kamaiso
Kosagawa
Misaki
Kamaiso
Winter data(16 gray levels; mean; K=7)
the red clusters are decreasing in winter
Autumn data
Kosagawa
Misaki
Kamaiso
Kosagawa
Misaki
Kamaiso
Winter data
Autumn data Winter data
air 18.7℃ 2.4℃
Sea water About 21℃ About 12℃
Spring water About 10.5℃ About 10.5℃†1http://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 ℃
PLASAR and AVNIR-2 results in Autumn
Kosagawa
Misaki
Kamaiso
AVNIR-2 data(band1,2,3; k=7)
Kosagawa
Misaki
Kamaiso
The red clusters exist in Kosagawa, Misaki, and Kamaiso as well as the PALSAR classification results.
PALSAR data(16 gray levels; mean;
K=7)
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
Kosagawa
Misaki
Kamaiso
PALSAR data(16 gray levels, mean, K=7)
Misaki
Kamaiso
Kosagawa
The conditions consistent with a decrease in the amount of submarine groundwater discharge in winter
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.
* 塩分濃度:「 Assessing the potential of remotely sensed data for water quality monitoring of coastal and inland waters」 , 高知工科大学紀要 ,Vol.5,No.1,pp.201-207(2008)