Co-registration issue M. Shimada

Current operation

Procedures for making the mosaic

• 1) Path process (Produce the slope corrected ortho-product for each) • SRTM + geoid(EGM96)+GRS80 (6738.137 km+ 6356.7523141km • ), no-datum

shift.(Sentinel:WGS84:6.378137000000000e+06/6.356752314245000e+06)

• 2) After collecting all the data • 3) Mosaic for 500kmx500km • 4) Extract 1x1 degree product

• ? <terrainHeight>2.953564895454545e+02</terrainHeight> • In sentinel file?

This position

Equal-lat.-lon. coordinate

2010

ALOS

2015

ALOS-2

Forest change

2010-2015

Forest Change in Borneo

Forest

Deforestation

Non-forest

2010: 34929.8[1000ha] 2015: 32010.4[1000ha] -8.36%/5 years -1.67%/year

PALSAR and PALSAR-2 are aligned with.

Geometric issue

Shift variation: 200~100~0m ? at different areas:

• 25m mosaic: connection • iteration 1:

• ScanSAR mosaic • Iteration: 1:

• JJ-FAST: • iteration 0:

Correlation between the SAR radar backscatter and the OPS intensity for FNF

M. Shimada, I. Sekine, K. Komuro, and S. Miyako, Kc-final meeting

Feb. 5-8, 2019 RESTEC meeting room

contents

•Forest classification threshold consideration

• InSAR observation of the wetland on water level variation

•ATI

•Book

Threshold determination using the SAR • JAXA FNF depends on the threshold of the gamma-

zero distribution for F and NF. Simply the medium value of the F and NF is selected.

• Forest cover by the Landsat depends on the forest cover over 10%.

• PALSAR Forest is estimated lower than the FRA and Landsat forest cover.

• Relationship between the gamma-zero and relative intensity for F-NF were evaluated.

FF x( ) =1- FNF x( )

FF x( ) º fF x '( )x

¥

ò dx '

FNF x( ) º fNFx '( )

-¥

x

ò dx '

Determination of the threshold

1) Measure the DF of “Forest” & “Non-F”

2) Calculate the Cumulative DFs and measure

the “threshold” that maximizes the both.

3) Threshold is region dependent.

Sumatra Case

Histograms for Forest & Non-forest (Acacia)

Cumulative Distribution Fn.

Threshold

1. FNF map generation

HV HH

HV

Amazon areas

Target areas

1990.12 2016.12

70km四方における森林面積の減少が矩形的に多く確認できた地域

ブラジル･アマゾン 緯度：-9°46′15.2″

経度：-61°0′38.4″

(左上座標)

(Ⅰ)Forest

(Ⅱ)Non-forest

(Ⅲ)Tangent Non-forest region

研究対象領域内で15箇所の評価領域を設定し、Three target

Gamma-zero measurement Google earth image (Landsat)

後方散乱係数：SARから発射されたマイクロ波が地表で散乱しレーダに戻ってくる強度を数値化したもの

2007.6.21 2010.9.29

相対輝度：簡易的な明るさの指標

グレースケールに変換

2007 2010

2007 2010

評価領域 HV HH

1 -0.680 -0.764

2 -0.743 -0.772

3 -0.137 -0.391

4 -0.186 -0.336

5 0.080 -0.172

6 0.119 -0.180

7 -0.762 -0.771

8 -0.779 -0.819

9 -0.551 -0.632

10 -0.645 -0.642

11 -0.459 -0.735

12 -0.583 -0.624

13 -0.935 -0.929

14 -0.920 -0.925

15 -0.764 -0.820

相関係数

(Ⅰ)定常森林域

(Ⅱ)定常非森林

(Ⅲ)遷移森林・

非森林域

-30

-25

-20

-15

-10

-5

0

38 40 42 44 46 48

後方散乱係数

(dB

)

相対輝度

定常森林域 定常非森林 遷移森林・非森林

後方散乱係数と相対輝度の関係(HV偏波)

-20

-15

-10

-5

0

38 40 42 44 46 48

後方散乱係数

(dB

)

相対輝度

定常森林域 定常非森林域 遷移森林・非森林域

後方散乱係数と相対輝度の関係(HH偏波)

SAR and optical shows the good correlation on F and NF.

Correlation between gamma-zero and the intensity

Correlation Coefficients

Forest

Forest

Non-forest

Non-forest

Transient

Transient

Relative Intensity

Relative Intensity

Gam

ma-

zero

G

amm

a-ze

ro

Forest

Non-forest

Summary

SAR and Optical show a very good correlation on the FNF identification.

There are two definitions for the SAR and OPT thresholds.

To synchronize them, select the SAR threshold 40% lower than the the current one.

Target area

Target area：Oze Total area：760ha 特徴：本州最大の湿原 福島県・新潟県・群馬県の３県またがる

16

2016/5/24

2014/9/2 2015/8/30

2016/6/19 2016/11/20

カラースケール

17

〜

2016/8/2

2018/5/22 2016/6/19

2016/11/20 2017/6/4

〜

〜

〜

〜

-5.9 +5.9

Data processed

18

Period T baseline Bperp

2016年5月24日〜2016年8月2日 1359 58.3

2014年9月2日〜2018年5月22日 71 111.6

2015年8月30日〜2016年6月19日 295 76.9

2016年6月19日〜2016年11月20日 155 206.5

2016年11月20日〜2017年6月4日 197 121.1

変動量の算出

単位：cm

No.1〜No5の変動量と平均値の算出 変動量=位相差の平均値×λ/2 （λ=23.6)

19

2016/5/24 2014/9/2 2015/8/30 2016/6/19 2016/11/20

〜2016/8/2 〜2018/5/22 〜 2016/6/19 〜 2016/11/20 〜2017/6/4

No,1 -5.1212 -9.2394 -3.6108 5.3572 -1.298

No.2 -4.1536 -5.3572 -2.9854 5.6994 -2.124

No,3 -3.6226 -6.5844 -3.7406 6.6316 -1.534

No.4 -3.8586 -6.5254 -2.8084 5.8174 -0.9204

No.5 -2.9028 -5.5106 -3.5872 5.7466 -1.298

平均 -3.93176 -6.6434 -3.34648 5.85044 -1.43488

期間内の 平均降水量(mm) 109 97.2 69.5 139.5 60.3

Every time decease

Cross section plot a〜cの断面的変動量を算出

20

a〜cの断面図

a線 b線

c線

2014年9月2日〜2018年5月22日

21

MRI database for precipitation

2016/5/24〜2016/8/2 2014/9/2〜2018/5/22

2015/8/30〜2016/6/19 2016/6/19〜2016/11/20 2016/11/20〜2017/6/4 22

Conclusion

• Five image pair was used for the ALOS=2 InSAR sensitivity for the the water level change.

• Four pairs showed the decrease of the water level and one pair showed the rise.

• During 2014/9/2〜2018/5/22 in average -6.6cm decrease of the water level observed.

• One water level rise is related to the increase of the precipitation reported from the MRI for 2016/9/19 and 2016/11/20.

・It was told that the water level in oze area is in decrease probably due to

・Decrease of the snow

・decrease of the vegetation ・other causes.

23

移動体(大型航空機？）

-160m/s 160m/s

ALOS-2/PALSAR-2 Along Track InSAR

California, Hamilton city

28

Iguazu Fall, Brazil

amp corr 29

Iguazu Fall, Brasil

-p p 30

Imaging from Spaceborne and Airborne SARs, Calibration, and Applications (SAR Remote Sensing) (英語) ハードカバー – 2018/11/9 Masanobu Shimada (著)

ハードカバー: 391ページ 出版社: CRC Press; 1版 (2018/11/9) 言語: 英語 ISBN-10: 113819705X ISBN-13: 978-1138197053 発売日： 2018/11/9 商品パッケージの寸法: 17.8 x 25.4 cm

•Amazon 売れ筋ランキング: 洋書 - 118,884位 (洋書の売れ筋ランキングを見る) 26位 ─ 洋書 > Computers & Technology > Programming > Graphics & Multimedia > GIS •136位 ─ 洋書 > Computers & Technology > Programming > Algorithms > Pattern Recognition •263位 ─ 洋書 > Professional & Technical > Engineering > Telecommunications

￥ 21,155

1. Introduction 2. Introduction of the SAR System 3. SAR Imaging and Analysis 4. Radar Equation for SAR Correlation Power—Radiometry 5. ScanSAR Imaging 6. Polarimetric Calibration 7. SAR Elevation Antenna Pattern—Theory and Measured Pattern from the Natural Target Data 8. Geometry/Ortho-Rectification and Slope-Corrections 9. Calibration—Radiometry and Geometry 10. Defocusing and Image Shift due to the Moving Target 11. Mosaicking and Multi-Temporal SAR Imaging 12. SAR Interferometry 13. Irregularities (RFI and Ionosphere) 14. Applications 15. Forest Map Generation