Download Flood inundation mapping- An application of remote sensing

Flood inundation mapping- An application of remote sensing techniques
Devendra Singh
Department of Science and Technology
Technology Bhawan, New Mehrauli Road
New Delhi-110016, India
Address:
Room no.10, Main Administrative Block
Technology Bhawan, New Mehrauli Road,
New Delhi-110016, India
e-mail: [email protected]
Phone:+91-11-26510490
Fax: +91-11-26510490
Flood inundation mapping- An application of remote sensing techniques
Devendra Singh
Department of Science and Technology
Technology Bhawan, New Mehrauli Road
New Delhi-110016, India
Abstract
This paper describes two applications of remote sensing on a near-real time
mapping of flood inundation and crop health in the light of natural disaster and crop
monitoring. The mapping of flood inundation was carried out over Bihar state of India
during monsoon 2008. The moderate spatial resolution of the MODIS sensor (250m up to
1km pixel size), along with its frequent acquisition of twice daily, make it theoretically
ideal for mapping flood inundation, however it is often subject to cloud cover particularly
during flood events. Due to the slow nature of the flood event, and the high proportion of
cloud cover, the remote sensing imagery was analyzed at weekly intervals. For MODIS,
the 8-day Surface Reflectance (MOD09A1) with a pixel size of 500m was used for
mapping the flood inundation in this study. An Optical Water Index was used to map
water extent in the MODIS images. Simultaneously, health of the crop is also monitored
using Enhanced Vegetation Index (MOD13A1) over the same area for corresponding
weeks. The damaged crop area has been found to be in very good agreement with flood
inundated area and demonstrated the potential use of high temporal resolution MODIS
data for the monitoring of flood mapping on a near real time basis.
1. Introduction
Remote Sensing technologies have proved to be valuable tools to support
effective early warning for disasters. They enable the collection and monitoring of data
about atmospheric conditions and characteristics of the Earth’s surface leading to
processes, which may bring about natural disasters. Such information can be used to help
determine appropriate actions to reduce the disastrous effects of these processes. The
ability of remote sensing technologies to identify surface water has been utilized for
many tens of years. Mapping surface water from the air, or space, has allowed the
potential to monitor the dynamics of hydrological events through space and time at a
consistent level of accuracy. For mapping flood events, remote sensing imagery needs to
be frequent enough to capture critical flood stages. Both optical/infrared and microwave
remote sensing instruments have been used for mapping surface water through time.
Optical imagery operates in the visible portion of the wavelength spectrum, but also
includes the infrared and thermal regions. Microwave imagery operates in the longer
wavelengths from less than a centimeter up to a meter (or frequencies from 89 GHz to 0.3
GHz respectively).
Different wavelengths have different responses to water in the landscape. Much of
the literature shows that for optical imagery the infrared wavelength, particularly around
1.5-1.7 micrometers performs well for separating water from land (Overton et al., 2006).
Instruments such as Landsat have a relatively high spatial resolution (30m pixels)
allowing for the detection of fine water bodies. However such imagery is not routinely
collected on a global scale through time, making regular monitoring of flooding events
difficult. Many factors play a role in the occurrence of flooding, such as the intensity and
duration of rainfall, snow melt, deforestation, poor farming techniques, sedimentation in
river bed, and natural or man-made obstructions. In the evaluation of flood hazards, the
following parameters should be taken into account: depth of water during flood, the
duration of flood, the flow velocity, the rate of rise and decline, and the frequency of
occurrence.
Landsat and SPOT data have a high spatial (Landsat: 30m, SPOT: 20m), but low
temporal resolution (repetition rate: Landsat 16 days, SPOT 26 days). NOAA-AVHRR,
MODIS and SPOT-VÉGÉTATION offer medium to low spatial resolution (250m-1km)
but a high temporal resolution (almost daily global coverage). Due to the dependence of
these optical sensors on cloud conditions they are often not suitable to monitor floods as
they are normally accompanied by dense clouds. Radar data is therefore often more
suited. Depending on the requirements, temporal analysis of the flood requires high
temporal resolution, detailed spatial analysis high spatial resolution, the optimum sensors
or sensor combination should be used. Instruments such as the Advanced Very High
Resolution Radiometer (AVHRR) and the more recent MODIS do collect data at
daily/sub-daily intervals. Even with a poorer spatial resolution (1 km pixels), the AVHRR
has been used to map water with reasonable success (Tanaka et al., 2003). The MODIS
sensors have a higher spatial resolution than AVHRR, ranging from 250m up to 1km
with a twice daily overpass worldwide, which has also been used to map surface water
(Sakamoto et al., 2007).
2. Objectives of the study
The objective of this study is to establish a strong relationship between crop
health damage due flood extent with two different ways using remote sensing data like
EVI for crop health and optical index for flood mapping. These two different methods are
supposed to map flood extent on different time-scales will be suited to the nature of their
respective flood events. The area of interest is north of Bihar state of India, where
flooding mostly occurs due to upstream rainfall events. Water in this area is heavily
regulated, with water extracted from the river.
3. Study area
Bihar is mainly a vast stretch of very fertile flat land shown in fig-1. It is drained
by the Ganges River, including northern tributaries Gandak and Koshi originating in the
Nepal Himalayas and the Bagmati originating in the Kathmandu Valley that regularly
flood parts of the Bihar plains. The total area covered by the state of Bihar is 94,163 km².
the state is located between 21°-58'-10" N ~ 27°-31'-15" N latitude and between 82°-19'50" E ~ 88°-17'-40" E longitude. Its average elevation above sea level is 173 feet (53 m).
The Bihar plain is divided into two unequal halves by the river Ganga which flows
through the middle from west to east. Other Ganges tributaries are the Son, Budhi
Gandak, Chandan, Orhani and Falgu. The Himalayas begin at foothills a short distance
inside Nepal but influence Bihar's landforms, climate, hydrology and culture. Central
parts of Bihar have some small hills, for example the Rajgir hills. The Himalayan
Mountains are to the north of Bihar, in Nepal. To the south is the Chota Nagpur plateau,
which was part of Bihar until 2000 but now is part of a separate state called Jharkhand.
Bihar has notified forest area of 6,764.14 km², which is 7.1 percent of its geographical
area.
Fig-1 the location of worst affected districts due to flood as shown in red circles.
4. Data and Methodology
MODIS, the 8-day Surface Reflectance (MOD09A1) with a pixel size of 500m
was used to compute Enhanced Vegetation Index (EVI) and Global Vegetation Moisture
Index (GVMI). The EVI and GVMI are as follows;
where NIR/red/blue are atmospherically-corrected or partially atmosphere corrected
(Rayleigh and ozone absorption) surface reflectances, L is the canopy background
adjustment that addresses non-linear, differential NIR and red radiant transfer through a
canopy, and C1, C2 are the coefficients of the aerosol resistance term, which uses the
blue band to correct for aerosol influences in the red band. The coefficients adopted in the
MODIS-EVI algorithm are; L=1, C1 = 6, C2 = 7.5, and G (gain factor) = 2.5.
where NIR/swir2 are atmospherically-corrected or partially atmosphere corrected
(Rayleigh and ozone absorption) surface reflectances.
An Optical Water Index (OWI) (Guerschman et al., 2008) was then used to map water
extent in the MODIS images. The OWI is based on the relationship between the EVI
(equation 1) (Huete et al., 2002) and GVMI (equation 2) (Ceccato et al., 2002). For
example, the OWI=0 when EVI=>0.2 and OWI=GVMI-EVI when EVI<0.2. In this
study, an OWI of >=0.2 was adopted as water.
5. Results and Discussions
Kosi river flows north to south, from Himalayas in Nepal and Tibet to Ganges in
Bihar state of India. Usually due to heavy silt deposition, the river keeps taking longer
curvilinear path to drain into Ganges. Due to this, the river had shifted its course in the
last 300 years. After the breach, the river has shifted its course nearly 100 kilometers
eastward from its earlier course (which use to pass through the districts of Supaul,
Saharsa, Madhubani, Darbhanga and Khagaria( Sitrep – Bihar Floods- 26 August 2008).
The 2008 Bihar flood, which is one of the worst floods in the history of the Indian state
of Bihar, occurred due to a breach in the Kosi embankment near Indo-Nepal border (at a
place called Kusha in Nepal) on August 18, 2008. The river changed its course and
inundated areas which had not experienced floods in last many decades. The flood
affected over 2.3 million people in the northern part of Bihar. Due to very heavy rain on
18th August 2008 as shown in fig-1(e), there was a breach in the embankments of kosi
river. About 1,29,800 cusec water was discharged through the breach inundating
hundreds of villages in north Bihar districts of Supaul, Madhepura and Saharsa. The
flood submerged most of the Kosi alluvial fan area, which is very fertile and has dense
agrarian population. The breach caused colossal loss of life and property in the south east
tarai region of Nepal and in six districts of north Bihar, affecting more than 2.5 million
human lives.
The flood mapping has been carried out for Areria, Supaul, Sharsha and most
affected district Madhepura. However, in this study, the results from most affected
district (Madhepura) have been presented. The fig-1 (a and e) depicts the crop health
status and rainfall before the event of flood occurrence while fig-1(b,c,d,f,g and h) show
crop damaged areas and flood inundated areas during the flood period. It has been
noticed that crop damaged areas very well matched with flood inundated areas during the
flood period almost over two months period. The damaged area due to flood event on
different dates is presented in table-1. Although, these two products are different in
nature but both products showed the common damaged areas and therefore, demonstrated
the potential use of OWI for flood mapping.
Fig-1 Madhepura district of Bihar; Left panel shows crop health (EVI)(green color
= very good crop health, yellow color poor crop health and red color = sparse
vegetation and black color= damaged area) and right panel shows Flood inundated
area using Optical Water Index (OWI) from 12 August to 9 October 2008 Blue color
shows flood inundated area in second and fourth vertical panels.
Table-1 Statistics of the flood affected area on different dates
Dates of flood events
Area Affected (in Ha) Area Affected(in Ha) Area
Affected(in
using Optical Index
using Crop health index Ha) Surveyed
25th August 2008
23872
23919
23403
4th September 2008
22638
22795
22184
30th September 2008
15329
15683
15139
9th October 2008
9687
9837
9372
Source for surveyed area : EHA-India_Sunsari_Flooding_.pdf
Tactical decision making on land management can benefit from immediate access
to the latest MODIS data. Public land managers must reevaluate various schedules as
seasonal weather changes the foraging conditions. Under the crop insurance, insurers
need an objective means of evaluating whether an area meets some threshold of crop
damage from flood. The MODIS 8-day derived OWI may provide one means of arriving
at decisions as to which farms qualify for insurance payments or for disaster relief from
the federal government. At this stage, however, the MODIS OWI data should be
considered only a general index of crop development.
6. Conclusions
The very high temporal and moderate spatial resolution of Terra/Aqua MODIS
observations is found to be very useful for the mapping of flood inundation. Since,
Landsat and other high spatial resolution satellite data are unfortunately limited by 16day repeat cycling and cloud contaminations. The present study has demonstrated the
potential application of derived product Optical Water Index using MODIS 250m
reflectances for the mapping of flood inundated areas over a period of two months time.
The mapped area using OWI matches very well with the crop damage areas at
corresponding places all over the district. The mapping of flood inundated area is found
to be in good agreement with field surveyed reports.
7. Acknowledgements
Author would like to thank Dr. T Ramasamy, Secretary, Department of Science
and Technology, Ministry of Science and Technology, Government of India for their
encouragement and support during the course of this study. Author is also thankful to
NASA DAAC for providing MODIS data.
References
Ceccato, P, Gobron, N., Flasse, S., Pinty, B., Tarantola, S. (2002). Designing a spectral
index to estimate vegetation water content from remote sensing data: Part 1 Theoretical
Approach. Remote Sensing of Environment, 82,188-197.
Guerschman, J.P., Van Dijk, A.I.J.M., McVicar, T.R., Van Niel, T.G., Lingtao, L., Lui,
Y., Peña-Arancibia, J. (2008), Actual evapotranspiration and water balance estimates
from satellite observations over the Murray-Darling Basin. Canberra, CSIRO Science
Report.
Huete, A., Didan, K., Muira, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. (2002),
Overview of the radiometric and biophysical performance of the MODIS vegetation
indices. Remote Sensing of Environment, 83, 195-213.
Overton, I.C., McEwan, K., Gabrovsek, C., Sherrah, J.R. (2006). The River Murray
Floodplain Inundation Model – Hume Dam to Lower Lakes. CSIRO Water for a Healthy
Country Technical Report 2006. CSIRO: Canberra.
Tanaka, M., Sugimura, T., Tanaka, S., Tamai, N. (2003), Flood-drought cycle of Tonle
Sap and Mekong Delta area observed by DMSP-SSM/I, International Journal of Remote
Sensing, 24(7), 1487-1504.
Townshend JRG, Justice CO. 2002. Towards operational monitoring of terrestrial
systems by moderate resolution remote sensing. Remote Sensing of Environment 83:
351–359.
Sakamoto, T., Nguyen, N.V., Kotera, A., Ohno, H., Ishitsuka, N., Yokozawa, M. (2007),
Detecting temporal changes in the extent of annual flooding within the Cambodia and the
Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sensing of
Environment, 109, 295-313.