Detecting Floodplain Inundation Frequency

Using MODIS Time-series Imagery

Chang Huang

Key laboratory of Geographic Information Science

East China Normal University

Shanghai, China

Chang.huang@csiro.au

Yun Chen

Land and Water

CSIRO

Canberra, Australia

Yun.chen@csiro.au

Jianping Wu

Key laboratory of Geographic Information Science

East China Normal University

Shanghai, China

jpwu@geo.ecnu.edu.cn

Jia Yu

Department of Geography

Shanghai Normal University

Shanghai, China

yujia@shnu.edu.cn

Abstract—Floodplains play an important role in riverine

hydrological and ecological environments. Accurate estimate of

the spatial and temporal inundation frequency patterns is an

critical step to understand how the inundation conditions affect

local hydrology and floodplain ecosystems. This study presents a

methodology to detect spatial-temporal changes in inundation

extent associated with flood inundation frequency based on

MODIS 8-day composite time-series imagery (MOD09A1) from

2000 to 2010. It takes Chowilla floodplain located in the

northwestern Murray-Darling Basin of Australia as a study area.

The GIS-based framework includes extraction of flow peaks from

time-series of observed flow data, inundation detection from

MODIS using the Open Water Likelihood index, inundation

validation using modified Normalized Difference Water Index on

Landsat TM image, and flood extent and frequency mapping.

The study shows that the maximum inundated areas that have

been inundated at least once are 12.5% of the total study area.

The areas with different inundated frequencies were calculated

and linked with ecological significant flood return periods. The

areas that were inundated twice in the 11 years, which is

equivalent to a 1-in-5 Average Recurrence Interval, occupies

27.8% of total inundated area. The results of this study provide

an important indicator for the health condition and spatial

distribution of some dominant species in the floodplain, such as

river red gum. This, in turn, improves knowledge of eco-

hydrology characteristics of floodplain ecosystem.

Keywords-inundation extent; flow; OWL; mNDWI; remote

sensing; GIS

I. INTRODUCTION

Floodplains play an important role in riverine hydrological and ecological environments. Hydrologically, floodplains receive and retain water from over-bank, which attenuates the effect of floods. Ecologically, floodplains act as sources or sinks of organic matter and nutrients, breeding grounds and nurseries for aquatic biota. They provide habitat for a diversity range of aquatic and terrestrial plants and animals [1]. For example, plants are very sensitive to the water environment

they are living in. As a result, land with different flood inundation frequencies suits different plants, which maintains the diversity of plant communities in floodplains. Thus, an accurate estimation in the spatial and temporal patterns of inundation frequency is an important step to understand how the inundation conditions affect local hydrology and floodplain ecosystems.

Satellite remote sensing provides powerful techniques for delineating inundated areas. Actually, flood detecting is one of the classical applications of remote sensing. Numerous studies have been carried out to map flood inundation using multi-spectral, multi-temporal, multi-scale images and different methods. Landsat TM is one of the most accurate sensors in interpreting flooded areas because of its high resolution (30m) [2] [3]. However, its temporal resolution of 16 days is a limitation for time series inundation analysis. Since Terra/MODIS was launched in December 1999, MODIS instruments have provided daily measurements for the entire globe. The high-frequency coverage and medium resolution make them particularly suitable for both short-term and long-term inundation change study over large areas. Several pioneering studies have been conducted in deriving flood inundation extent using MODIS imagery. Huete et al. used the most common index Normalized Difference Vegetation Index (NDVI) for water/land delineation [4]. McFeeters used Normalized Difference Water Index (NDWI) to detect inundation [5]. A modified Normalized Difference Water Index (mNDWI) was also applied in MODIS image to delineate water by Ordoyne and Friedl [6]. Sakamoto et al. identified inundated pixels from the difference between the Land Surface Water Index (LSWI) and Vegetation Indices (NDVI or EVI) [7]. Guerschman et al. introduced an Open Water Likelihood (OWL) index to map inundation areas [8]. Among all these methods, OWL method is likely to provide the most consistent representation of inundation throughout time series without the need to extract wet pixels from individual image by applying different thresholds.

The aim of this study is to apply a new methodology to detect spatial-temporal changes in OWL-derived inundation extent associated with inundation frequency using observed flow data and MODIS imagery. We took Chowilla floodplain in the Murray-Darling Basin (MDB) of Australia as a study area and develop a framework to map flood inundation frequency for underpinning future ecological analysis.

II. STUDY AREA AND DATA

A. Study Area

The Chowilla Riverland Floodplain is located on the Murray River within MDB, in south eastern South Australia. It covers 30,640 ha (hectare). The area consists of a series of anabranch creeks, wetlands, lakes and floodplains, as well as the main Murray River channel. It is an important region for native fauna and flora and was listed as a Riverland Wetland of International Importance in 1987 under UNESCO Ramsar Convention [9]. The dominant species in this region are the black box, river red gum, shrub lignum and large areas of annual grass. Situated in a semi-arid environment, it has a mean annual rainfall of approximately 260 mm and an average evaporation of 1,960 mm [10]. Our study is focused on a selected rectangle area which is 460,550 ha as shown in Fig.1.

Figure 1. Location of Chowilla floodplain

B. Materials

We used two major data sources in this study, flow data and remote sensing images.

Observed daily flow data were obtained from Gauge 426510 (Murray River flow to SA). They were used to identify “peak flow rates” (as daily mean discharge in GL/d (Giga Liters per day)).

Remote sensing images were used to detect flood inundation extent. We used MODIS for inundation detection and Landsat TM for result validation. The MODIS data are distributed through the Earth Observation System Data Gateway [11]. This study acquired time series images of MODIS Terra product “MOD09A1” from year 2000 to 2010. These images are 8-day composite data at a 500 m resolution. Compositing involves compiling daily images over an eight day period and selecting pixels of the highest quality based on a combination of low view angle, the absence of clouds or cloud shadow, and aerosol loading [12]. Landsat TM images were acquired from the U.S. Geological Survey's Earth Resources Observation and Science (EROS) Center.

III. METHODS

River flow is assumed to reflect correctly the magnitude of flood events, thus river discharge is considered as a key parameter for defining inundations in floodplains [13]. As a result, we used observed gauge flow data to define peak flows and then selected MODIS images for inundation detecting using OWL based on the dates of these peaks. A validation using Landsat TM images was performed to investigate that MODIS images can be used to derive reasonable inundation extent. Corresponding to each flow peak, flood inundation extent was mapped by overlaying inundated extent derived by selected MODIS images using OWL. All inundation maps were aggregated to produce the inundation extent map from 2000 to 2010. Flood inundation frequency map over that period was finally derived. The framework of is outlined in Fig. 2. It was implemented in a Python script.

Figure 2. Flowchart of study methodology

A. Flow data Analysis

Observed flow data collected from the gauge station were plotted in Fig. 3. 11 peaks were chosen to represent an average of one peak per year from 2000 to 2010. Each peak was assumed to represent a flood event.

Figure 3. Flow data and peaks of gauge 426510 from 2000 to 2010

33.8

10.8

5.06

12.4

6.42

12.2

4.54 4.19 3.02

4.23

29.8

0

5

10

15

20

25

30

35

40

1/1/2000 1/12/2001 1/11/2003 1/10/2005 1/9/2007 1/8/2009 2/7/2011

Flo

w (

GL

/d)

Date

Flow (GL/day) Peaks (GL/day)

Observed

gauge data

Peaks MODIS images

Inundation

extent

Flood inundation

extent maps

Flood inundation

frequency maps

TM images

OWL

Aggregation

Overlay

Peak selection

mNDWI

images

Validation

selection

Image mNDWI

MODIS images were selected based on the flow peaks and their related dates. For each peak, we selected five images which were closest to the occurrence date. These images were then used to detect inundation extent.

B. Inundation Detecting

An OWL index [8] was used to map water in MODIS imagery (Fig. 4(a)). It uses four parameters to estimate the proportion of water within a pixel. They are: 1) Short-Wave Infrared (SWIR) band which is highly sensitive to moisture content in the soil and vegetation canopy; 2) NDVI; 3) NDWI; 4) Multi-resolution Valley Bottom Flatness (MrVBF; [14]). The OWL is calculated as

(1)

where

(2)

and a0 = -3.41375620,

a1 = -0.959735270,

a2 = 4.17955330,

a3 = 14.1927990,

a4 = -0.430407140,

a5 = -0.0961932990,

x1 = SWIR band 6,

x2 = SWIR band 7,

x3 = NDVI (NDVI = (band2-band1)/(band2+band1)),

x4 = NDWI (NDWI = (band2-band6)/(band2+band6)), and x5 = MrVBF.

OWL values range from 0 to 100 representing the likelihood of the presence of water within a MODIS 500 m pixel [8] [15]. With a reasonable cut-off threshold, inundation extent can be derived from OWL images. Based on visual inspection, we used 1 as the cut-off threshold. Pixels with an OWL value great than 1 are identified as inundated pixels (Fig. 4(b)).

We then used inundation extent derived by Landsat TM image as the “ground truth” to validate OWL method. In this study, we acquired TM image of 13/12/2000 (Fig. 5(a)) which is closest to the date of a peak flow (11/12/2000). We then used it to validate two MODIS OWL-derived inundation maps that have near dates (10/12/2000 and 18/12/2000).

Inundation in TM image was detected using mNDWI which is one of the most popular indices for inundation mapping. It was developed based on a combination of

Figure 4. (a) Colour composite (R7G2B4) MODIS image (10/12/2000);

(b) inundation map with a threshold=1 (OWL>1)

reflectance in the green band (band 2) and Short-Wave Infrared (SWIR) band (band 5) [16]. The mNDWI is calculated as

(3)

The pixel values of mNDWI images derived from Landsat TM images using (3) range from -1 to 1. mNDWI pixel values greater than 0 represent permanent water. Generally, the spatial distributions of the mNDWI images were evaluated to determine the cut-off points to distinguish water bodies and inundated areas from the others. Each individual image may have specific threshold. For this TM image we are using, inundated pixels are identified where the mNDWI value is greater than or equal to -0.15 (Fig. 5(b)).

Two MODIS OWL-derived inundation maps were resampled to 30m using nearest method to match the TM-derived inundation map. Then we made comparison on them with TM inundation map respectively (Fig. 6).

We used four indices to indicate the delineation accuracy of OWL comparing mNDWI on TM. They are overall accuracy, omission error, commission error and Kappa coefficient (Table I). Overall accuracy is the percentage of total correct classified pixels. Omission error represents the percentage of inundated pixels incorrectly classified as non-inundated pixels. Commission error is the percentage of non-inundated pixels classified as inundated pixels. Kappa coefficient is an index that estimates the agreement between two classifications taking into account the agreement occurring by chance. It is generally thought to be a more robust measure than simple percentage agreement calculation. Kappa coefficient between 0.6 and 0.8 represents substantial agreement [17].

Figure 5. (a) Colour composite (R7G4B2) TM image (13/12/2000); (b)

inundation map with a threshold=-0.15 (mNDWI >-0.15)

Figure 6. Spatial validation for OWL inundation: (a) MODIS image (

10/12/2000) vs. TM (13/12/2000); (b) MODIS image (18/10/2000) vs. TM(13/12/2000)

TABLE I. VALIDATION RESULTS

MODIS Overall

Accuracy

(%)

Omission

Error (%) Commission

Error (%) Kappa

10/12/2000 95.45 2.55 2.00 0.62

18/12/2000 94.86 2.16 2.98 0.62

We can see from Table I and Fig. 6 that MODIS OWL index is able to detect inundation at an acceptable accuracy. Therefore, it is reasonable to use it for inundation mapping.

C. Flood Inundation Extent mapping

For those images corresponding to each flow peaks, OWL was applied with a cut-off threshold equals 1 to derive the inundation extent. We used binary images to represent the inundated extent, 0 is not inundated and 1 is inundated. A “Maximum” overlay statistics method is applied to aggregate the five binary images for each peak respectively. The method is based on pixels, which means that the result map will take the maximum pixel value among five pixels. Finally, 11 flood inundation extents for each peak were mapped. These maps are also binary maps with 0 for non-inundation and 1 for inundation. The peak flow, selected images, and inundated area were listed in Table II.

TABLE II. PEAKS, SELECTED IMAGES AND INUNDATED AREA

Flow

peak

(GL/d)

Date of

peak

MODIS Date

Inundated

area (ha)

Total

% a

33.8 11/12/2000 24/11/2000-26/12/2000 34,700 7.53

10.8 13/11/2001 01/11/2001-03/12/2001 35,050 7.61

5.06 07/12/2002 25/11/2002-27/12/2002 29,975 6.51

12.4 24/09/2003 06/09/2003-08/10/2003 29,700 6.45

6.42 30/01/2005 17/01/2005-18/02/2005 27,275 5.92

12.2 13/11/2005 01/11/2005-03/12/2005 32,975 7.16

4.54 15/02/2007 02/02/2007-06/03/2007 27,075 5.88

4.19 19/01/2008 01/01/2008-02/02/2008 24,950 5.42

3.02 06/02/2009 25/01/2009-26/02/2009 26,450 5.74

4.23 30/01/2010 17/01/2010-18/02/2010 25,675 5.57

29.8 13/11/2010 01/11/2010-03/12/2010 37,000 8.03

a. Percentage of inundated area in total study area (460,550 ha)

D. Flood Inundation Frequency Analysis

We integrated the 11 flood inundation extent maps into two resultant maps. The first one is the maximum inundation extent map which represents the biggest possible inundated area from 2000 to 2010. It was derived by applying the “Maximum” overlay statistics to the 11 individual maps (Fig. 7). The second one is the map of inundation times. The inundated times ranges from 1 to 11 (Fig. 8), which indicates the inundation frequency of these years.

Figure 7. Maximum inundated area

Figure 8. Inundation frequency

IV. RESULTS AND DISCUSSIONS

A. Spatial and Temporal Patterns of inundation

Flow peak values and corresponding inundated area in Table II were plotted in Fig. 9. An obvious correlation can be seen from the chart with a Pearson’s correlation coefficient of 0.81. This means that at a significant level, we can estimate the inundated area based on the flow data. Also, it indicates that the spatial inundation extent can be delineated from MODIS images with OWL index. Moreover, the graph clearly illustrates the similarity between the pattern of flow and inundated area, except for that the variation range of inundation area is much smaller than that of flow value. This can be explained by the floodplain’s attenuation effect on floods.

According to Table II and Fig. 9, most of the flood inundation happens in later spring or summer (November to February). The inundated area in 2010 is the largest among the year 2000 to 2010, while that in 2008 is the smallest. The maximum inundated extent from 2000 to 2010 has an area of 57,525 ha, which is 12.5% of the total study area.

Figure 9. Flow value and inundated area of peaks

B. Inundation Frequency

Fig. 8 shows the inundation frequency over Chowilla floodplain from 2000 to 2010. Obviously, the high frequency areas are those that are near lake or river channel. That is because floods always begin from the high discharge of rivers to floodplains.

Figure 10. Inundated area of different inundated times (percentage is based on

total inundation area (57,525 ha))

We calculate the area of different inundation times and their percentage against the total inundated area (57,525 ha) (Fig. 10) based on the inundation frequency map (Fig. 8). An area of 14,900 ha (25.9%) was inundated 11 times, which means these areas were inundated at least once a year, note that this figure includes the permanent water like river channels and lakes.

The inundation times among these 11 years can be linked with the flood Average Recurrence Intervals (ARIs). Areas that were inundated only once can be considered as the flood inundation extent of 1-in-10, they have an area of 15,975 ha (27.8%). Being inundated twice may equal to 1-in-5 ARI, which has an area of 5,275 ha (9.2% of total inundation area), and has a distribution as shown in Blue color in Fig. 8. Five and six times in 11 years are approximately 1-in-2 flood. They have an area of 5,075 ha, and their distribution is in Green color in Fig. 8. 1-in-1 flood inundation extent should match those areas that were inundated 11 times in Fig. 8.

The results from this study will contribute to ecological study. For example, river red gum forests, one of the dominant plants in ecosystem of Chowilla floodplain, historically received flooding about every five years [18]. The floods dispersed the seeds and deposited fertile soil around their roots, which is critical for their growth and reproduction. In other words, water requirement of river red gum is dependent on the inundation extent corresponding to 1-in-5 ARI. Thus, the distribution of river red gum forests should be included within the areas that are inundated more than two times in Fig. 8. Once we overlay the vegetation distribution map with the inundation frequency map, we can easily find out those red gums that have not received enough water over these years. Thus, if we want to protect them from possible declination, new water regime is needed to make sure they receive additional water for vigorous growth.

V. CONCLUSIONS

This study introduces a simple but efficient method to analysis floodplain inundation frequency using MODIS imagery. The methodology involves the flow peak analysis, OWL inundation extraction and flood frequency mapping. The framework was implemented via Python programming.

The resultant inundation extent and frequency maps from linking hydrological data with remote sensing imagery contribute to a better understanding of the spatial and temporal patterns of flood inundation. This, in turn, improves knowledge of eco-hydrology characteristics of floodplain ecosystem.

Spatial analysis of the time-series satellite imagery is a powerful way of quantifying changes in inundation. Time-series MODIS imagery makes it possible and efficient to compile a long-term record of inundation over large spatial scale. However, the accuracy is affected by the coarse resolution of MODIS. Besides, flood inundation is highly correlated with terrain. Combining high resolution Digital Elevation Model (DEM), such as LiDAR, with MODIS in inundation characterization will be the future focus of on-going study.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

0

5

10

15

20

25

30

35

40

01/2000 12/2001 11/2003 10/2005 09/2007 08/2009 07/2011

Inu

nd

ated

are

a(h

a)

Flo

w v

alu

e (G

L/d

)

Date

Flow (GL/d) Inundated area (ha)

27.8%

9.2%

5.7% 3.5% 4.1% 4.7%

5.3% 3.0%

5.3% 5.6%

25.9%

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1 2 3 4 5 6 7 8 9 10 11

Inu

nd

ated

are

a (h

a)

Inundated times

ACKNOWLEDGMENT

This work has been conducted under the auspices of the CSIRO Land and Water (CLW) and Water for a Healthy Country National Research Flagship. The authors are grateful to our colleagues in CLW: Linda Merrin, for her job on collecting flow data; Garth Warren and Juan Pablo Guerschman, for their help in deriving OWL images; Susan Cuddy, for the initial review of the manuscript.

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