Remote sensing of environment and disaster laboratory	

Institute of Industrial Science, The University of Tokyo, Japan	

FLOOD EVENT DETECTION BY AMSR-E
AND PALSAR ON GLOBAL SCALE
LI Xi1) and Wataru Takeuchi1)1) IIS, The University of Tokyo, Japan
ABSTRACT： In this paper, we investigated the potential of AMSR-E for flood detection. The daily land surface water coverage (LSWC) database, which was derived from AMSR-E intime series from 2002 to 2011, was built and by analysing LSWC database the anomaly was extracted which showed a good identity with the actual flood events. Finally, PALSAR data(ScanSAR mode) was used to validate several flood events, it is indicated that the availability and importance of LSWC database for flooding detection, moreover, by using PALSAR data(Fine mode) to map water coverage, showed its superiority in the accurate flood evaluation based on a known flood event.	
PALSARPALSAR
Introduction	
Methodology	 The International
AMSR-E
ScanSAR mode
Fine mode（10km)
Due to the global warming combined with excessive human activities, the flood have been one of the mostCharter(100m)(6.25m)catastrophic hazard among various natural disasters. It was not only a threat to human life, but also related losses
in infrastructure and economy. Therefore, it’s necessary to grasp the accurate information and right extent of theflood for the strategy of government and BCP of enterprises. AMSR-E presents a potential for flood monitoring
LSWC database
Flood（2002-2011）
because of the ability of the microwave signal to penetrate through cloud and provide an all-day data and its
event
sensitivity to surface water (Temimi M, et al., 2007). It can fast reveal large-scale flood patterns (Zheng W et al., 2008).
Detection	
Validation
PALSAR, which is also an active microwave sensor, has 3 observation modes providing higher range resolution
and is suitable to flexibly correspond to wide-ranging natural disasters like flooding.
Anomaly
Evaluation
The objective of this paper is to detect flood event on global scale by analysing LSWC database from 2002 to2011 derived from AMSR-E and to preliminary investigate the superiority of PALSAR in flood evaluation.	

Results and discussion	!

!!!!!!!!!!(a)!Jan!!!!!!!!!!!!!!!!!!!!!!!!!!!(b)!Feb!!!!!!!!!!!!!!!!!!!!!!!!!!(c)!Mar!!!!!!!!!!!!!!!!!!!!!!!!!!!!(d)!Apr!!!!!!!!!!!!!!!!!!!!!!!!!(e)!May!!!!!!!!!!!!!!!!!!!!!!!!!(f)!Jun!

!

!!!!!!!!!!!(g)!Jul!!!!!!!!!!!!!!!!!!!!!!!!!!!(h)!Aug!!!!!!!!!!!!!!!!!!!!!!!!!(i)!Sep!!!!!!!!!!!!!!!!!!!!!!!!!!!!!(j)!Oct!!!!!!!!!!!!!!!!!!!!!!!!!!!!(k)!Nov!!!!!!!!!!!!!!!!!!!!!!!!!(l)!Dec!

!

Figure 1. A flowchart of this study		Table 1. Basic informationof selected flood events occurred in the worldwide	
Date of Charter Central location
Country
Location of Event
Activation
Lat.
Lon.
China
Anhui province
2007-7-19
32.5N
115.8E
Vietnam
North and Central provinces
2008-11-5
20.9N
105.8E
Pakistan
North West Pakistan
2010-8-19
28.2N
69.4E
ThailandCentral Thailand
2011-9-30
14.9N
100.3E
SenegalSenegal2007-9-18
16.1N
13.8W
Namibian
Northern Namibia
2011-4-5
18.2S
15.7E
Argentina
Santa Fe and Entre Rios provinces 2007-3-30
31.2S
60.6W
Bolivia
Cochabamba, Santa Cruz
2008-2-9
14.6S65.1W
Colombia
Bolivar province
2011-5-23
8.3N
73.9W
MexicoTabasco
2007-11-3
18.1N
92.7W
USAIowa
2008-6-13
42.5N
93.2W
Australia
Queensland
2011-1-9
27.3S
151.3E

Figure 2: Monthly changes of LSWC in Anhui province in China (32.5N, 115.8E) in 2007. Brighter area indicates highabundance of water coverage at that pixel.	
!

ALOS PALSAR level-1.5 product can be converted to NRCS
by the following formula:	

Jul.10 ---- Aug. 1	
(DOY=191)	 (DOY=213)

NRCS = 10 × log10 (DN 2 ) + CF

where：
DN: digital number of the amplitude image, CF: scaling factor	

!

!
a. AMSR-E LSWC (10km)!

Figure 3: Daily changes of LSWC in China (32.5N, 115.8E) in 2007 	Table 2. Comparison of the period of real flood events and anomaly extracted from AMSR-E	

China
Pakistan
VietnamThailand
MexicoUSA
Australia
Namibia
Senegal
Bolivia
Argentina
Colombia

Date of International
Charter Activation
2007/07/19
2010/08/19
2008/11/5
2011/09/30
2007/11/3
2008/06/13
2011/01/9
2011/04/5
2007/09/18
2008/02/9
2007/03/30
2011/05/23

Resize
DataAnomaly extracted by AMSR-E
07/10-07/17,07/19-08/1
07/29-09/1703/30,07/1,10/26-11/5
05/17-06/4,09/17-09/30
01/24,10/27-12/6
06/5-06/13
01/6-01/14,03/18-03/21
03/20-05/1
08/31-09/23
02/8-05/3
03/28-03/31
03/29-03/31,04/14-07/5,07/26-07/28

50	

LSWC database(AMSR-E)25	0	
－22.0	

－16.5	!

－11.0	

－5.5	

0.0	

flood events 	

dB （PALSAR）	

!

Figure 5: Scatter plot between dB and WC(Colombia )	
Figure 4:! Two kinds of remote sensing image in Colombia (8.3N, 73.9W)	
c. PALSAR water map (100m)!

d. PALSAR LSWC (10km)!

Masking out!

!
a. AMSR-E LSWC (10km)

b. PALSAR water map (6.25m)

08/19(231) 	

PALSAR FBS(Fine mode,HH,6.25m)
can better
detect and evaluate	flood events 	

－24	

The anomalies which extracted from AMSR-E by debugging in
Matlab basically coincided with the actual period of flooding
accordingto International Charter.		

75	

Can detect	* absolute error: 20 relative error: 100%	

07/29-09/17(210-260)

4(b), 4(d)were matched	

water coverage（PALSAR）	

Country

Masking out

b. AMSR-E LSWC (10km)!

water coverage（AMSR－E）	

100	!
c. PALSAR water map (100m)!

d. zoom in (100m)!

!

－16	

ー8	dB（PALSAR ）	

0	Figure 7: Scatter plot between dB and WC(Vietnam)	

8		Comparing
Figure 5，7	Ø The relative low range resolution
Figure! 6: Two kinds of remote sensing image in Vietnam (20.9N, 105.8E)	

References:	
Temimi M, R. Leconte R and F. Brissette, 2007. Flood and soil wetness monitoring over theMackenzie River Basin using AMSR-E 37 GHz brightness temperature. Journal of Hydrology,
333(24): 317-328.	
Zheng, W, Liu, C, Xin, ZB, Wang, ZX, 2008. Flood and waterlogging monitoring over HuaiheRiver Basin by AMSR-E data analysis, Chinese Geographical Science 18(3): 262-267.	
W. Takeuchi and Louis Gonzalez, 2009, Blending MODIS and AMSR-E to predict daily land
surface water coverage, International Remote Sensing Symposium.	

Future work 	

AMSR-Edata
slightlyØ Influence of surface conditions(eg.vegetation and surface roughness) 	 overestimated flood 	l To calculate the threshold value in order to clearly distinguish the flood areas.
l To analysis the differences on flood detection based on different mechanism of AMSR-E and PALSAR.
l To carry out precise flood detection by combining other data in complex environments such as impact of buildings andvegetation in order to supply accurate information for BCP of enterprises.	

For further details, contact: LI XI, Ce-505, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/E-mail: lixi@iis.u-tokyo.ac.jp)