ensing in Flood Mapping and Risk As
over large and often inaccessible areas, provides the means for quick assessment of hy
ication of the duration, distribution and timing of flood events, water quality measurem
pecifically applied to hydrological cycles. “Remote Sensing technologies currently al
ironmental monitoring and management. Data recording and delivery systems, sensor
y are constantly improving and each year deliver better remote sensing products for a
mote sensing systems allow investigation and analysis of water resources at a range of
studied and mapped in their entirety (Benger, 2009).
Floods inflict massive property damage and loss of life
worldwide, affecting hundreds of thousands of people every
year.
http://www.actionaid.org/india/2013/06/uttarakhand
-floods-human-made-disaster-wake-warning
Below is an image from Dartmouth’s Flood Observatory indicating the centers of floods that
have occurred since 1985.
Analysis of remotely sensed images can aid in the mapping,
modeling and prediction of flood events; saving lives and
property
Flood Mapping Applications
Remote sensing is an incredibly useful tool for floodplain mapping as well as mapping
lood extent. Ground based mapping of floodplains is a tedious and expensive process
using remotely sensed images the flood plain of any river can be calculated with an
extremely high level of statistical precision in a relatively short amount of time.
Floodplain mapping - Below is an ASTER mosaic image of the Tonle
Sap Lake/River system in Cambodia.
http://www.intechopen.com/source/html/9506/media/image2.png
study of remote sensing applications in determining flood extent in 2005. The study
iques. With a passive flood mapping process, first the delineation of water from nones of soil and water as seen in the graph below-left. An image from before the flood e
close to peak flood level as possible, due to satellite orbit, is compared to the referenc
domly selected for data training and a DN cut-off value was determined for ‘water’ on
can be quite accurately determined. The study found that using Landsat TM 4+ and T
with a 96% accuracy rating for determination of flooded vs non-flooded open areas. A
Lawrence et al (2005)
mouth’s Flood Observatory provides a global active archive of large flood events as derived from governmental, news, instrumen
te sensing sources. Data is available in shapefiles, Mapinfo coverage, excel files and as downloadable extent maps. The map be
event in Northern Italy that began Nov 1, 2014. The areas in red show flood extent while areas in blue show permanent water b
http://floodobservatory.colorado.edu/Version3/2014Italy4205.html.
005 study, determined that active flood mapping systems were more useful and accura
at increase in cost of applying these flood mapping procedures. In this study, Synthet
everal hours after the peak of a flood event caused by Hurricane Lili in Southern Loui
the emitted microwave radiation to penetrate cloud cover and deep forest canopies an
the sun. Pre-flood images are again compared to post-flood images and differences in
ng a multi-temporal image enhancement technique to analyze the brightness for each
rect correlation of water level and median DN values from radar backscatter as seen in
Below is a multi-temporal color composite image with flooded sites. Th
indicates marsh flooding on 3 October 2002.
Lawrence et al (2005)
Flood Risk Assessment
Any area prone to floods will greatly benefit from the use of remote s
different sized floods based on available hydrological models. By co
of remotely sensed images and sensors with hydrologic models, flood
created relatively cheaply. Flood risk maps are invaluable to rescue w
planners and insurance companies.
Flood Risk Assessment
Researchers Skakun et al used a
set of remotely sensed images in
a case study to determine flood
hazard and flood risk assessment
in Namibia. They used Landsat
5 and 7 images acquired over a
period of 23 years to create a
time series analysis and
determine the RFI (relative
frequency of inundation) value
for the Katima Mulilo region of
Namibia. The RFI derived from
analysis of the images was
statistically determined to have
the same distribution as relative
water depth measurements. This
process allows maps for flood
risk and flood hazard assessment
to be made quickly and
inexpensively to assist decision
makers and rescue workers for
any flood event provided that
Skakun, Sergii, et al. "Flood Hazard and Flood Risk
Assessment Using a Time Series of Satellite Images:
A Case Study in Namibia." Risk Analysis:
An International Journal 34.8 (2014): 1521-37. Print.
The image above is an exampl
flood extent
nd Landsat-7 satellites.
03) used commercially available IKONOS-2 Satellite imagery to create a classified la
w-right in the Southern part of The Netherlands. Despite the very high spatial resoluti
p accuracy was only 74% in large part due to the difficulty of discerning residential ar
ap was used as the input for analysis of the area in the LISFLOOD-FP flood simulatio
ment maps were created.
IKONOS-2 Satellite Image
Van der Sande (2003)
Van der S
The image below is an estimated financial damage
graduated
red: high
damage,
light red:land
Below iscolor
the map;
errordark
matrix
of the
IKONOS-2
low damage, white: no damage or no data
Van der Sande (2003)
cover classification map.
Citations
g of the Ecology and Functioning of the Mekong River Basin with Special Reference to the Tonle Sap.” Geoscience and Remote Sen
licelli, F., De Groeve, T., and Cohen, S. (2014). “Current Conditions and Maximum Flood Extent, 2014 Flooding, Northern Italy, DF
rsion3/2014Italy4205.html
S., Babin, A., and Baras, J (2005). “Applications of Radarsat-1 synthetic aperture radar imagery to assess hurricane-related flooding
0
ood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia." Risk Analysis: An International Journal vo
oo. (2003). "A Segmentation And Classification Approach Of IKONOS-2 Imagery For Land Cover Mapping To Assist Flood Risk A
Geoinformation 4.3: 217.
©Jhanek Szypulski 2014