Advances in Hydro-Science and Engineering, vol. VIII
Proc. of the 8th International Conference on Hydro-Science and Engineering, Nagoya, Japan, Sept. 9-12, 2008

VULNERABILITY ANALYSIS OF REGIONAL FLOOD HAZARD
BASED ON MODIS IMAGERY AND DEMOGRAPHIC DATA
IN THE HUAIHE RIVER BASIN, CHINA
Nanshan Zheng1, Kaoru Takara2, Yasuto Tachikawa3 and Osamu Kozan4
1Student, Department of Urban and Environmental Engineering, Kyoto University
Kyoto, 615-8540, Japan, e-mail: zhengnanshan@z05rcl.mbox.media.kyoto-u.ac.jp
2
Professor, Disaster Prevention Research Institute, Kyoto University, Uji, 611-0011, Japan
3
Associate Professor, Department of Urban and Environmental Engineering, Kyoto University
Kyoto, 615-8540, Japan
4
Assistant Professor, Center for Southeast Asian Studies, Kyoto University, Japan

ABSTRACT
Information on spatial distribution of potential flood extent, land use and land cover aswell as population is significant to analyze vulnerability to flood hazard. Taking the typicalflood event of the year 2003 occurred in the Huaihe River basin, China, this paper analyses
the spatial extent and temporal pattern of flood inundation with the application of time-series
Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference
Vegetation Index (NDVI) composite data products with the spatial resolution of 250m.
Meanwhile land use classification generated by multi-temporal MODIS NDVI 16-daycomposite data is presented. Based on the resultant MODIS-derived flood inundation
mapping, a method to identify the people at risk vulnerable to flooding is proposed, and the
distribution of population affected by flooding is derived. Finally the flood hazard
vulnerability is analyzed by a spatial analysis method, which is helpful to assess flood hazard
vulnerability in the further study.
Keywords: vulnerability, flood hazard, MODIS, NDVI, spatial analysis
1.

INTRODUCTION

Floods are known as frequent and most devastating events worldwide. Asia continent
is much affected by floods and the counties like China, India, and Bangladesh are extremely
vulnerable (WWAP, 2006). The impacts of hazardous events are often exacerbated by theinteractions with other hazards or by occurring in areas with a high density population and/or
social vulnerability. Thus the need for a spatially oriented vulnerability assessment ishighlighted. For example, Birkmann (2006) stressed the need for a paradigm shift from thequantification and analysis of the hazard to the identification, assessment and ranking ofvulnerability. In this regard, vulnerability has emerged as the most critical research field in
disaster studies. Although vulnerability is a multidimensional and multivariate concept
associated with high uncertainty in measurement and classification, land use and population
are the key components of exposure for flood hazard vulnerability analysis. Large scale
flooding due to heavy rainfall and drainage congestion has been regularly experiencing in thefloodplain area of the Huaihe River basin, China. The summer dominant rainfall and special
topographic conditions make the region highly vulnerable to flooding. In this research, taking
the Huaihe River basin as case study, the main objective is to study the vulnerability based onland use and land cover as well as demographic data.
Geo-referenced land use and land cover (LULC) data sets are primary inputs forenvironmental modeling and monitoring, natural resource management, and policy
development. Over the past decade, the science of large-area LULC mapping has madeconsiderable strides as remotely sensed data and computing resources have improved andadvanced classification techniques have emerged (DeFries and Belward, 2000).
Furthermore the identification of potential hazard is essential in describing biophysicalvulnerability. Thus a typical historical flood event is taken to supply the prerequisite
information. For detecting large flooded areas, satellite remote sensing provides powerful
techniques. Many studies have been undertaken in these research fields with the application ofa range of satellite data sources, such as Landsat, IKONOS, SPOT, NOAA-AVHRR and
RADARSAT SAR data. However large-area mapping using for example Landsat data has
been limited by the considerable costs of acquiring and processing the large data volumes thatare required. For a severe flood occurring in a large area, MODIS data products (Huete et al.,1999) offer a great opportunity to acquire the expected information.
In the paper, some research points are addressed: (1) The identification of regional
scale flood and the classification of land use and land cover with the application of time-series
MODIS NDVI composite imagery; (2) Estimation of the population distribution at flood risk
based on flood event analysis; and (3) Brief analysis of flood hazard vulnerability. It ishypothesized that regional differences in flood hazard vulnerability are best explained by the
exposure components such as land use and population.

2.

STUDY AREAThe Huaihe River basin (HRB) is situated in eastern China, which is one of the sevenmajor river basins in China (Fig. 1). Geographically, it locates between the latitude 31°N36°N and longitude 112°E -121°E, covering an area of 270,000km2. HRB is composed of two
water systems, which are the Huaihe River to the south of the old Yellow River and the
Yishusi River to the north. The catchment area of the Huaihe River water system is 190,000
km2, accounting for about 70% of the total area of the basin. The Huaihe River originatesfrom the Tongbo Mountain, and flows into the Yangtze River with the length ofapproximately 1000 km. A large amount of area is plain area (Fujiyoshi and Yihui, 2006).

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Huainan!
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Zhumadian!

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Xuchang

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Kaifeng!

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Fig. 1 The location of the Huaihe River basin (The left small figure shows the location of
study area in China.)

Climatologically, it lies in the warm semi-humid monsoon region. Precipitation mainly
occurs in the period from mid-May to mid-October. Because of anomalies of the Meiyu front
during the rainy season, which is influenced by the South Asian monsoon and the uniquetopography, the basin has been known for its frequent disasters.
Moreover, this region is populous with the population density of 663 capita per km2 in
2003, where the population accounts for 13% of the nation’s population and there is 17% of
the country’s cultivated land (Anhui Statistical Yearbook, 2004). Therefore it is of greatsocio-economic importance.

3.

METHODOLOGY

The goal of this study is to analyze the vulnerability to flood hazard in terms of land
use and demographic data. In order to acquire the spatial information for large area about
flood inundation as well as land use and land cover, the MODIS composite products havebeen utilized. The fundamental methodology is shown in figure 2.
Input data

Extraction of water area

Classifier:Decision Tree

Identification of land use

Identification of flooded area
People at flood risk

Vulnerability analysis

Data preprocessing

Population
Fig. 2 The flowchart to study on the flood hazard vulnerability
MODIS image data
NASA’s Earth Observing System (EOS) has supplied a suite of standard MODIS data
products to the users, in this research the “MOD13Q1 V004” MODIS satellite products are
adopted, which are the 16-day composite 250-m Vegetation index (VI) data downloaded fromNASA MODIS website (http:// modis.gsfc.nasa.gov/data). The MODIS VIs provide a
consistent spatial and temporal coverage of vegetation conditions and complement each otherfor vegetation studies (Huete et al., 2002). The time series per year is composed of 23 images,
each of which covers an area of 1200 km by 1200 km. Two tiles (h27v05, h28v05) of the
MODIS data are required for the study area. The NDVI and EVI MODIS products weregeometrically, atmospherically and bidirectional reflectance distribution fraction (BRDF)
corrected, validated and quality assured through the EOS program (Huete et al. 2002). For
each composite data, the VI data are extracted by tile, mosaiched, and reprojected from theSinusoidal (SIN) to Albers Equal Area Conic projection according to a nearest neighbour resampling routine, and finally combined into a multilayer image stack with the spatial
resolution of 250 m.
Due to the lack of field observations, Google Earth images across the study area areadopted to supply ground truth points. Time-series VI data are sampled and analyzed from thesites of specific land use classifications in the study area.
Multi-temporal NDVI profiles
The VI profiles represent the temporal plant canopy responses to soil, plant and water
regime combinations within the study area for each period. The multi-temporal VI profile of a
specific land use and land cover type is applied to analyze and compare to each other in orderto determine the classification. In additions, regional variations in each kind of land use typeare also considered.
Thus, in this study the index such as NDVI, EVI and resultant NDWI sampleddispersedly from composite imagery data have been shown in figure 2, where six categories
of land use and land cover have been considered according to the research requirement andbiophysical conditions. Each general land use category has unique multi-temporal NDVI and
EVI profiles.
This study mainly relies on the NDVI profiles to detect the land use and land coverclassification because both NDVI and EVI show the similar characteristic for each land use as
demonstrated in Fig. 3.
Data pre-processing for NDVI time series
Many analysis methods have been developed to detect land use classification with theapplication of NDVI time-series data. After NASA launched the MODIS, a variety of timeseries data have been available. However, these time-series data inevitably containdisturbances caused by cloud presence, atmospheric variability and snow. Noise has bad
effect on the analysis. A couple of automated methods for de-noising have been proposed toreconstruct high quality NDVI time-series data, such as the best index slope extraction (BISE)
algorithm (Viovy et al., 1992; Kozan et al., 2004), the Savitzky-Golay filter approach(Savitzky et al., 1964), wavelet method and some other interpolation methods.
In this research, an adaptive Savitzky–Golay filter has been tested, which is one wayof smoothing data and suppressing disturbances. The filtering equation is shown as below(William et al., 1992).
nR

g i = ∑ cn f i + nn = − nL

(1)

Here n L is the number of points used “to the left” of a data point i , i.e., earlier than it, whilen R is the number used to the right, i.e., later. f i is the time series. c n is the filter coefficient.
g i is the result.
After sampling VI data from some positions, the quality analysis of multi-temporal
MODIS composite imagery has been performed, and some noise in the data at the period of
33 and 353 has been found. With the application of the Savitzky–Golay filtering method,
the filtered time profiles of NDVI, EVI and some other derived data are obtained from
MOD13 16-day composite time-series data with 250-m spatial resolution. The smoothed
results indicate that the method is effective, especially can keep the peak value of NDVI in
the growing season (Fig. 3). It also is helpful to select reasonable threshold for classifying
land use.

NDVI

10000*NDVI

Water

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paddy

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Fig. 3 Multi-temporal NDVI, EVI and NDWI profiles of the major land use types
NDVI
Sampled-Paddy

Smoothed-Paddy

Sampled-Water1

Smoothed-Water1

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NDVI*10000

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Fig. 4 The comparison of smoothed NDVI data and sampled NDVI data

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The identification of flooded area
Flooding is equivalent to an increase in water-covered area through time. As such, it
can be detected as a change in the water covered area. To study the extent of flood inundation,
the extraction of spatial information based on the multi-temporal images is conducted by theDecision Tree classifier, which is defined as a classification procedure that recursively
partitions a data set into more uniform subdivisions based on tests defined at each node in thetree (Chandra and Clinton, 2005). It is obvious that the change of index value sampled fromwater area is very gentle and the value is the lowest compared with that sampled from otherkind of land use (Fig. 3). Therefore to extract the water area, the key is to determine thedynamic threshold. Because it is hard to distinguish between water areas and urban, thebackground data of lake and reservoirs have been taken as auxiliary data. According to multitemporal data analysis, the water distribution in the Huaihe River basin is identified in termsof the flood event in the year 2003. The flood occurred from the end of June to the beginningof August, around that time the temporal change of water information is demonstrated, where
the blue color area stands for water area (Fig. 5). With the application of spatial analysis, thewater covered area is synthesized. After the elimination of background water body, theflooded area marked by red color is mapped (Fig. 6). The total area affected is 2575.75 km2,
which mainly spreads around flood detention areas.
115°30'0"E

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DOY 161-176
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DOY 177-192

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Fig. 5118°0'0"E

32°0'0"N

115°30'0"E

116°0'0"E

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The temporal change of flood inundation area

117°30'0"E

118°0'0"E

115°30'0"E

33°30'0"N

116°0'0"E

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117°0'0"E

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118°0'0"E

118°30'0"E:

33°0'0"N

33°0'0"N

MengwaTangduo

32°30'0"N

32°30'0"N

32°0'0"N

LegendBackground water body
Water area

115°30'0"E

116°0'0"E

116°30'0"E

117°0'0"E

117°30'0"E

118°0'0"E

Fig. 6 The identification of flood distribution along the Huaihe River
114°0'0"E

115°0'0"E

116°0'0"E

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118°0'0"E

32°0'0"N

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34°0'0"N

113°0'0"E

Legend32°0'0"N

1-water2-Non:
113°0'0"E

3-Urban4-Village
5-Forest
6-Paddy
7-Crop114°0'0"E

115°0'0"E

116°0'0"E

117°0'0"E

118°0'0"E

Fig. 7 The extraction of land use and land cover in the year 2003
Land use classification
Due to the occurrence of great land use and land cover change in developing country,
the MODIS composite data are adopted to extract land use and land cover, which have higher
spatial resolution than AVHRR.
To implement the extraction of land use and land cover in larger area is challenging,as many factors could have effects on the dynamics of VI data. However each general land
use has clear multi-temporal NDVI and EVI profiles, the Decision Tree method is still applied
to classify the land use. Firstly with the application of quality control flags for cloud, somemasks for cloud cover have been generated to eliminate the area affected by cloud. Thenbased on the features of land use, the dynamic thresholds to determine the land use

classification are specified elaborately. The distribution of different land use is shown infigure 7.
The distribution of population at flood risk
The population distribution is the key factor to make mitigation plan as well as hazard
analysis. To obtain the potential people at flood risk, the census (2002) is applied. In GIS
platform, the affected population distribution is mapped (Fig. 8). Accordingly the populations
at flood risk in the whole basin are estimated.
115°30'0"E

116°0'0"E

116°30'0"E

117°0'0"E

117°30'0"E

118°0'0"E

118°30'0"E

33°30'0"N:

33°0'0"N

33°0'0"N

Tangduo
32°30'0"N

MengwaLegendBackground water body

32°30'0"N

Water area
Population (capita/km2)
0 - 81532°0'0"N

815 - 3,528
3,528 - 10,322
10,322 - 23,721
23,721 - 39,980

115°30'0"E

116°0'0"E

116°30'0"E

117°0'0"E

117°30'0"E

118°0'0"E

Fig. 8 The location of population affected by flood

4.

VULNERABILITY ANALYSISThe conceptual ‘‘hazards-of-place’’ model of vulnerability was presented by Cutter(1996), which combines biophysical and social determinants. It provides geographers with apowerful tool for manipulating numerous and varied demographic and physical data (Cutter
1996; Cutter et al., 2003; Boruff et al., 2005). The hazards of place framework focuses on the
integration of social and physical data. The social and biophysical elements interpenetrate andshape the overall vulnerability of the place.
Little work on vulnerability in general has been studied in the Huaihe River basin. In
the study a methodology is put forth, which combines aerial layers representing hazard zones
and layers to examine the distribution and relative concentration of groups who are vulnerable
to hazard. Simply place vulnerability can be viewed as biophysical vulnerability and social
vulnerability, in which biophysical vulnerability relies on hydro-geological characteristics. As
a result, the biophysical vulnerability can be shown by the vulnerable area (Fig. 6). Moreoversocial vulnerability relies on the exposure, the susceptibility of social groups to the impacts ofhazards as well as their resilience. The creation of the social layer of vulnerability is
demographically driven, relying on data from Census Bureau.
In a general sense, social vulnerability is determined by the presence of a humanpopulation at risk to a particular hazard. In this research the key determinants of exposure,
such as land use and population are taken into consideration. Combining with demographic

data, spatial distribution of people at risk to flood hazard is derived based on overlay analysis
and buffer analysis (Fig. 8). The result indicates that the people at flood detention areas suchas Mengwa, Lake Tangduo, etc. are much more vulnerable to flood. For example, at Mengwa
community, there are 152,598 people estimated to have been affected and 377.9 km2 land
vulnerable to flooding (except water area) (Table 1). Because this area locates at thefloodplain and is populous, it is relative vulnerable to flood hazard. The analysis result shows
the methodology is effective and applicable to identify the flood vulnerable area. Accordingly
in the whole basin, the amount of farmland and population vulnerable to waterlogging can be
estimated.
Table 1 The area of land vulnerable to flood at Mengwa
Land use
Area (km2)

Crop
298.2

Paddy
76.8

Village
2.3

Forest
0.3

Urban
0.3

Water
14.2

Vulnerability is conceptualized as a function of exposure to a stressor, sensitivity to
associated impacts, and the ability of a system to adapt to the stressor’s impacts (Turner et al.,2003). In exploring vulnerability as a concept, three determinants of vulnerability can be
identified that influence the spatial distribution and the level of impact, which can be listed as
exposure, susceptibility and resilience. Therefore besides the exposure, some other factors
should be taken into consideration.

5.

CONCLUSION AND REMARKSThis study attempts to analyze the flood hazard vulnerability in the Huaihe River basin,
China. The proposed methodology is applied to produce time-series inundation maps for the
analysis of typical flood event of the year 2003. Meanwhile integrated with demographic data,
spatial analysis of vulnerability to flood hazard at a regional scale is put forth. In this study, it
pays more attention on the method to estimate the flooded area, which is applicable to large
area flood with long duration.
(1) The proposed approach is effective to extract the flooded area for large scale
flooding, although the temporal resolution is relatively poor. The benefit of using this data is
the availability of water area observations. In addition, accuracy assessment should be taken
into consideration.
(2) Meanwhile taking advantage of the results from typical flood event, brief analysis
of flood vulnerability at a regional scale is introduced. In this study, it proposes the method toestimate the populations and land use vulnerable to flood hazard.
(3) However, the degree to which populations are vulnerable to flood hazard is not
only dependent upon proximity to the flood threat or the exposure determinant, social factors
related to susceptibility and resilience also play a significant role in vulnerability
measurement. In the further study the holistic vulnerability can be assessed in detail.

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