RESEARCH ARTICLE

Floodplain ecosystem response to climate variabilityand land-cover and land-use change in Lower MissouriRiver basin

Yuyan C. Jordan • Abduwasit Ghulam •

Robert B. Herrmann

Received: 17 November 2011 / Accepted: 17 April 2012 / Published online: 3 May 2012

� Springer Science+Business Media B.V. 2012

Abstract This contribution aims at characterizing the

extreme responses of Lower Missouri River basin

ecosystems to land use modification and climate change

over a 30-year temporal extent, using long term Landsat

data archives spanning from 1975 to 2010. The inter-

annual coefficient of variation (CoV) of normalized

difference vegetation index was used as a measure of

vegetation dynamics to address ecological conse-

quences associated with climate change and the impact

of land-cover/land-use change. The slope of a linear

regression of inter-annual CoV over the entire time span

was used as a sustainability indicator to assess the trend

of vegetation dynamics from 1975 to 2010. Deduced

vegetation dynamics were then associated with precip-

itation patterns, land surface temperature, and the

impact of levees on alluvial hydrologic partitioning

and river channelization reflecting the links between

society and natural systems. The results show, a higher

inter-annual accumulated vegetation index, and lower

inter-annual CoV distributed over the uplands remain-

ing virtually stable over the time frame investigated;

relatively low vegetation index with larger CoV was

observed over lowlands, indicating that climate change

was not the only factor affecting ecosystem alterations

in the Missouri River floodplain. We cautiously

conclude that river channelization, suburbanization and

agricultural activities were the possible potential driving

forces behind vegetation cover alteration and habitat

fragmentation on the Lower Missouri River floodplain.

Keywords Remote sensing � Coefficient of variation �Normalized difference vegetation index �Land-cover and land-use change

Introduction

Habitat degradation is a process by which natural

habitat is rendered less functionally able to support an

originally present species (Groom et al. 2006), and

habitat fragmentation that is closely associated with

climate change and human activity is the most

important cause of species extinction worldwide

(Barbault and Sastrapradja 1995). As characterized

by increasing water temperature and changes in

precipitation patterns that manifest themselves as

changes to stream flow regimes, and terrestrial and

oceanographic hydrologic cycles, climate change may

alter available environmental conditions, and thus

cause observable shifts in geographic distribution of

plant and animal species (Malmqvist and Rundle

2002; Dudgeon et al. 2006). Urbanization and agri-

cultural expansion are primary human activities at

landscape scale that account for degraded water

Y. C. Jordan � A. Ghulam (&) � R. B. Herrmann

Department of Earth and Atmospheric Sciences, Center

for Environmental Sciences, Saint Louis University,

St. Louis, MO 63103, USA

e-mail: ghulam@eas.slu.edu

123

Landscape Ecol (2012) 27:843–857

DOI 10.1007/s10980-012-9748-x

quality and natural resource over-exploitation, and

eventually will exacerbate the ecological impacts of

global climate changes (IPCC 2001; Allan 2004).

The floodplain of the Lower Missouri River, once

the habitat of supporting abundant and diverse aquatic

fauna and flora, has experienced dramatic urbanization

and river channelization in the past century. The

federal endangered species list for Missouri State

indicates that most of the habitats of the endangered

and threatened species are associated with alluvial

wetland and river floodplains (U.S. Fish & Wildlife

Service office 2009). With the dramatic expansion of

human population, the areas as suitable habitat for

wildlife have been converted into agriculture and

urban area, inducing a loss of biodiversity (Sechrest

and Brooks 2002).

The impacts of human activities on the Mississippi

River system have been explored in number of studies.

For example, Belt (1975) demonstrated that the

engineering modifications on the upper Mississippi

River coincided with several marked shifts in flood

response; Pinter and Heine (2005) indicated that the

engineering construction at the Lower Missouri River

altered the hydrodynamic and morphodynamic pro-

cesses of the fluvial system driving flood magnifica-

tion. Such a flow regime reconfiguration has the

potential to have great impact on the ecosystem on the

floodplain. River regulation and channel construction

have been shown to decrease floodplain tree growth

and productivity on some rivers (Reily and Johnson

1982; Middleton and McKee 2005). River morphol-

ogy explained by channel patterns and channel forms

is the dominant controlling factor of the availability of

shallow and slow water habitat even in reaches where

the hydrograph is more intensively altered (Jacobson

and Galat 2006). Unfortunately, such an important

sanctuary for floodplain biodiversity has been lost on

many intensively engineered rivers in the United

States. The re-configurations to channels and flood-

plains have resulted in loss of biodiversity (Committee

on Missouri River Ecosystem Science et al. 2002).

Climate change studies indicate that the central

U.S. has cooled by 0.2–0.8 K while most other major

land regions experienced a summer warming trend

over the last 25 years (Pan et al. 2004). The local

reduction of warming predicted for the next several

decades over the central U.S., including the Lower

Missouri River flood plain, is associated with changes

in low-level atmospheric circulation that leads to

replenishment of seasonally depleted soil moisture,

thereby increasing late-summer evapotranspiration

and suppressing daytime maximum temperatures

(Pan et al. 2004). Causal attribution of recent biolog-

ical trends to global climate change is complicated

since non-climatic influences dominate local, short-

term biological changes (Parmesan and Yohe 2003).

Vegetation changes, as an important component

of terrestrial ecosystems, can provide informative

index for understanding land-cover/land-use (LCLU)

dynamics, and interactions between human activities

environment and climate change via coupling the

phenologic effects and the anthropogenic impacts on a

long basis (Edwards and Richardson 2004). Remote

sensing has obvious advantages in monitoring spatio-

temporal dynamics of vegetation, water and energy

cycles at terrestrial scales. Vegetation indices using

spectral measurements have been developed to qual-

itatively and quantitatively assess vegetation cover

(Bannari et al. 1995). Chlorophyll in live plant leaves

strongly absorbs the red radiation for use in photo-

synthesis, and on the other hand the cell structure of

live green leaves strongly reflects near-infrared (NIR)

radiation. Hence, the healthy plants with more leaves

will have less reflectance for the red light and more

reflectance for the NIR light. The normalized ratio of

these two spectral regions is the well-known normal-

ized difference vegetation index (NDVI), which had

been successfully utilized to monitor and assess

regional to global-scale vegetation covers (Weier

and Herring 2011).

The goal of this paper is to evaluate the potential

causes of habitat quality alteration in the Lower

Missouri River floodplain by assessing the association

between presumed independent variables of climate,

land-use, and river engineering that may contribute to

the observed habitat degradation, and response vari-

ables NDVI and land surface temperature (LST).

Following description of the study area in second

section, data acquisitions and processing methods will

be discussed in ‘‘Data and methodology’’ section.

Terrestrial vegetation dynamics and temporal trends

will be associated with precipitation patterns, LST

changes, river channelization and urban expansion,

and the driving forces of vegetation changes on

the Lower Missouri River floodplain area, and the

subsequent potential environmental impacts to the

habitat quality alteration will be discussed in ‘‘Results

and discussions’’ section.

844 Landscape Ecol (2012) 27:843–857

123

Study area

The study area focuses on the floodplain from the town

of Portland, Missouri, downstream to St. Charles,

Missouri, a distance of 138 km along the Lower

Missouri River, and Portland is 183 km upstream of

the confluence of the Missouri and Mississippi River

(Fig. 1). The Missouri River flows from the northern

Rocky Mountains along the continental divide, and

flows generally south and eastwardly to join the

Mississippi River. The Missouri River descends at a

steady slope of about 17.16 cm/km from west to east

until it joins the Mississippi River.

The climate of Missouri is continental type with

distinct alteration of seasons characterized by wide

ranges in temperature, and irregular annual and

seasonal precipitation. In the summer time, the moist

and warm air masses blown from the Gulf of Mexico

bring abundant rainfall for this region. The elevation

in this area ranges from 93 to 300 m. The highest

elevation is located at the north of the Missouri River

channel which is primarily covered by deciduous oak-

hickory forest (upland). The southern part of the study

site is lowland agricultural fields predominantly

covered by corn and soybean cropland, growing

period ranging from April to October.

Fig. 1 Study area from Portland to St. Charles, MO (See the online version for the color version of this figure)

Landscape Ecol (2012) 27:843–857 845

123

By the late 1970s the Missouri had been totally

channelized, both federal and non-federal levees were

established along the river channel from Portland to

St. Charles, to provide protection from 20 to 5 %

probability floods to the agricultural lands (U.S. Army

Corps of Engineers 2003). The river and the floodplain

in this area are subjected to the natural physiographic

and geological character of the river, and the human

modifications.

Data and methodology

Data collection

Clear sky Landsat images including the Multispec-

tral Scanner (MSS), Thematic Mapper (TM) and

Enhanced Thematic Mapper Plus (ETM?) spanning

from 1975 to 2010 were collected. Geometric correc-

tion was performed for the MSS images collected

before 1990. The MSS data were re-sampled to have

30 m spatial resolution that is consistent with the

TM/ETM? data. The MSS, TM/ETM? sensors have

different radiometric resolutions, hence their respec-

tive digital numbers (DNs) carry different levels of

information that cannot be directly compared. Con-

verting the images to surface reflectance as described

later in this section will eliminate the problems of

comparing data with different levels of quantization.

An instrument malfunction occurred onboard ETM?

on May 31, 2003 due to the failure of the Scan Line

Corrector (SLC) designed to correct the undersam-

pling of the primary scan mirror. Consequently, the

ETM? data collected after May, 2003 has been

subject to an increased scan gap. Gap filling was

performed for the images collected after 2003 follow-

ing Scaramuzza et al. (2004) to correct the missing

strips in the data. Satisfactory results were achieved

after the gap filling processes. Radiometric calibra-

tions and atmospheric corrections were performed to

derive surface reflectance using QUick Atmospheric

Correction (QUAC) (Bernstein et al. 2005) available

with ENVI� image processing and analysis software,

from EXELIS Visual Information Solutions. Spatio-

temporal dynamics of annual precipitation were used

in this study to separate the contributions of natural

forcing from anthropogenic disturbances. Precipita-

tion data obtained from the Tropical Rainfall Measur-

ing Mission (TRMM), a joint mission between NASA

and the Japanese Space Exploration Agency (JAXA)

launched on November 27, 1997, were used in this

study. These measurements are on 0.25� 9 0.25� cell

between 50� south and 50� north of latitude (Huffman

2007). The original files in HDF format were down-

loaded from NASA’s TRMM ftp site (disc2.nas-

com.nasa.gov, product 3B43). These data showed

monthly average precipitation rate in mm/h, and were

converted to yearly total precipitation in mm in the

IDL programming environment.

The flow frequency data were obtained from The

USACE Upper Mississippi River System Flow Fre-

quency Study (U.S. Army Corps of Engineers 2003),

which calculated flood frequencies for each of the

USGS streamflow-gaging stations using standardized

methods. These calculations provided flood profiles for

the 2, 5, 10, 20, 50, 100, 200, and 500-year recurrence

floods under the 2007 channel condition. Kriging

interpolation was used to predict the potential area

subjected to flood inundation along the river channel.

We also collected field measured temperature and

precipitation data over six standard weather stations

across the study area (Fig. 1) from 1975 to 2010. The

station-average method was used to interpolate data

missing in the annual record. The purpose of these

ground data was to examine the driving forces behind

the prospective environmental change over a longer

period when TRMM data were not available, and

calibrate any existing bias or offset contained in the

TRMM derived precipitation.

Temporal changes of vegetation indices

The inter-annual coefficient of variation (CoV) of

NDVI and precipitation were used to measure the

vegetation and precipitation dynamics to understand

the potential influences that contribute to environ-

mental change. The statistics of CoV had been widely

used to determine the spatial difference of temporal

variability of the vegetation activity of the world

(Weiss et al. 2001; Sun et al. 2010). The changes in the

value of the pixel level CoV over time can be

interpreted as a measure of vegetation dynamics over

the time period. In statistics, CoV is a value calculated

from the average, or mean and the standard deviation

of the NDVI series in each pixel (Milich and Weiss

2000), that gives the slope of year by NDVI value on a

pixel by pixel basis, as shown in the following

formula.

846 Landscape Ecol (2012) 27:843–857

123

CoV ¼ rl¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1n

Pni9¼1 xi� lð Þ2

q

1n

Pni¼1 xi

where l is the temporal average or mean NDVI value

for each pixel, r is the standard deviation of the NDVI

time series from 1975 to 2010 and Xi is the NDVI

value in each pixel for a specific year. The seasonality

and phenology of vegetation and agricultural activities

may affect observed NDVI values and trends. Such

impact may be too substantial to neglect at a pixel

level measurements, especially over land cover types

subject to seasonal manipulation by agricultural

activities. Therefore, one may expect that remote

sensing images are collected at least in the same month

of the year to deduce an inter-annual times series

analysis. However, optical remote sensing data, par-

ticularly those collected during rainy season over wet

climate, are often contaminated by clouds. In our case,

there is at least one Landsat image collected during the

summer months from June to August for the period of

1975–2010. Care was taken to select the images of the

same months of the years. If more than one image were

available for the summer months, then the average

NDVI was employed as a seasonal average data. To

minimize the effects of vegetation phenology and

human activities on the temporal time-series analysis,

we employed spatially mean of total NDVI, denoted as

mean-total NDVI hereafter, which is defined as the

ratio of the sum of NDVI values for all pixels over

the total number of pixels of the whole study region. The

linear regression slope of time series mean-total NDVI

over observed time frame is used as an indicator to

assess the spatio-temporal trend of vegetation dynamics

from 1975 to 2010 (Bai et al. 2005; Sun et al. 2010). The

slope a can be derived from linear regression model

Y = aX ? b, where, Y is the yearly accumulated

NDVI, X is the time array series, and a is the slope, and

b is regression coefficient. Such vegetation dynamics

(temporal trend) then can be associated with natural

growing forest and farmland or urban areas, reflecting

the correlation between natural precipitation watering

pattern and human activity (Sun et al. 2010).

Urban expansion estimation

To explore additional impacts of human activity, on

floodplain eco-systems, spatio-temporal dynamics of

urban areas were extracted for different years. Unlike

vegetation growth that showed great fluctuation by

month and year, urbanization was a slow process over

time. Therefore, we selected a limited number of years

with almost a decade interval, namely, 1976, 1991,

2001, and 2010 for urban area extraction. Examining a

number of supervised classification techniques with

different classifiers, we found that maximum likeli-

hood classification based on the Principle Component

Transform (PCT) of original spectral dataset was the

most suitable one for extracting urban areas. PCT was

an image enhancement technique used to highlight

spectral signatures, and successfully allied in mineral

exploration (Gabr et al. 2010), burn change detection

(Koutsias et al. 2009). PCT reduces the dimensionality

of the data while retaining as much as possible of the

variation present in the dataset (Pechenizkiy et al.

2004). We subsequently used first three components of

PCT to extract the urban areas from Landsat imagery

with maximum likelihood classification.

Channelization possible influence to floodplain

Landscape elevations were used to assess how water

stages interact with the ground surface. Two landscape

elevations were used in this study. The first one was the

National Elevation Dataset (NED; U.S. Geological

Survey 1999) derived from Light Detection and Rang-

ing (LIDAR) point cloud data, which had 3 m horizontal

resolution and 15 cm vertical resolution. This model

reflected the regulated current channel form and flood-

plain landscape condition after channelization.

We also synthesized a digital elevation model

(DEM) for the floodplain prior to channelization. We

obtained levee shape file data from Missouri Spatial

Data Information Service (MSDIS); these data con-

sisted of the geo-coordinates of the levees. We

registered the levees on the current DEM based on

the levees geo-coordinate. From the MSDIS levee data

we knew that the levee heights ranged from 2 to 6 m,

so we assumed that all levees were two meters high for

modeling the no levee channel condition. Using GIS

software, we subtracted the elevation at the levee

location on the current DEM to synthesize a pre-

channelization DEM of the river channel condition

without the levees. Because this project considered

only floodplain inundation changes, we ignored the

channel depth shown on these DEM data. In this step,

we had not routed the water or we had not hydrauli-

cally modeled based on conservation of mass and

Landscape Ecol (2012) 27:843–857 847

123

energy, hence the distributions of inundation were

simply the intersections of water surface elevations

that would exist under current conditions and how they

would intersect the floodplain topography under

current and pre-channelization conditions. This sys-

tematically over-maps inundation area in the pre-

channelization condition for discharges that did not

overtop the current levees (that is for 2- to 50-year

recurrence floods peak discharges).

The flood inundation map for different recurrence

intervals and floodplain topographic DEM were

overlain separately, providing different interval flood

stages for the current channel condition and for the

synthetic pre-channelization condition. As a result of

this comparison, we observed different inundation

frequencies on the floodplain at the study area under

two groups of floodplain conditions. The detailed

results of this part are shown in the discussion section.

Results and discussions

Spatial patterns of inter-annual mean NDVI

Initial inspection of the inter-annual mean NDVI data

showed a negative skew distribution with a long tail in

the negative direction of the data histogram. We used

Natural Breaks (Jenks 1967) classification scheme for

grouping the data into different categories. Our choice

of using this classification was based on the fact that it

places the class breaks on a histogram such that breaks

fall in the troughs, and classes represent natural

clumps inherent in the data. Therefore, the features are

divided into classes that best group similar values and

maximize the differences between classes.

The inter-annual mean NDVI values ranged from

-0.3 to 0.76 during 1975 to 2010 at the study area

(Fig. 2); the spatial distribution showed obvious varia-

tions, and the NDVI values were divided into five

categories manually based on the rough land cover

types. The values \0.15 were observed in the river

channel. The relatively higher values of the inter-annual

mean NDVI (0.16–0.4) mostly occurred in the flood-

plain and urban area, whose land cover was dominated

by sporadic vegetation. The NDVI values of land cover

type dominated by farmland range from 0.41 to 0.62.

The highest values of the inter-annual mean NDVI

appeared in the upland (forested area) with indices

0.63–0.76. The spatial patterns of the vegetation cover

not only reflected the spatial characteristics in climate,

topography, but also revealed the differences in alloca-

tion of water resources in human society, i.e., reduced

vegetation over rapidly developing urban areas and

altered flood plains, and larger values over hilly areas.

Spatial patterns of inter-annual CoV

Figure 3 presented the inter-annual CoV values

divided by equal intervals method. The values reflected

the salient spatial patterns of overall vegetation

Fig. 2 Spatial distribution of inter-annual mean NDVI over the last three decades (See the online version for the color version of this

figure)

848 Landscape Ecol (2012) 27:843–857

123

dynamics from 1975 to 2010. It exhibited an obviously

different distribution when compared to the inter-

annual mean NDVI. The areas with the lowest inter-

annual CoV values from 0 to 0.2 scattered in the high

elevation hilly area, indicating the least temporal

changes in vegetation with time. Inter-annual CoV

values increased in the lowland from 0.4 to 0.6. The

areas with the highest inter-annual CoV values were

mainly located in the floodplain and urban areas with

value of 0.6–1.

Comparing Figs. 2 and 3, the spatial distribution of

inter-annual mean NDVI and inter-annual CoV val-

ues, we found that in general the areas with inter-

annual mean NDVI less than 0.52 coincides with the

inter-annual CoV values between 0.4 and 1.0; these

regions were more related to cultivated land use and

urban area. When the inter-annual mean NDVI values

were higher than 0.53, the variation of vegetation

covering over years becomes smaller, these areas were

mainly dominated by forest area. The larger CoV

observed over urban and flood plain implied that the

human activity (e.g., agricultural and housing devel-

opment) may have been the driving factor behind these

changes.

Temporal variability of inter-annual mean-total

NDVI

In order to further demonstrate the annual trend of

NDVI dynamics in the study area, the inter-annual

mean-total NDVI values from 1975 to 2010 of the

whole study area were used to reflect the overall

vegetation dynamics. The results (Fig. 4) revealed

that there had been a slightly descending trend of

Fig. 3 The spatial distribution of inter-annual CoV of NDVI (See the online version for the color version of this figure)

Fig. 4 Inter-annual mean-

total NDVI trajectory from

1975 to 2010

Landscape Ecol (2012) 27:843–857 849

123

inter-annual mean-total mean NDVI for the last

35 years with minimum value of 0.41 in 2007 and a

maximum of 0.67 in 1999. During the period of

1975–1991, there were small fluctuations in NDVI

values, ranging from 0.5 to 0.64. From 1992 to 1995

the values decreased from 0.61 to 0.48 and the values

climbed up again from 1995 to 2000. After 2000, there

were 3 years of especially low NDVI values mainly

occured in 2006, 2007 and 2010. The decreasing trend

(slope = -0.002) was noticeable, and 11.2 % of the

variation of the NDVI could be explained by time series

although the regression was marginally not statistically

significant at a = 0.05 (F1, 31 = 3.926, p = 0.056).

Slope trend in Vegetation dynamics from 1975

to 2010

The slope factor of the linear regression of the inter-

annual mean NDVI over time (i.e., from 1975 to 2010)

was shown in Fig. 5. The slope value ranged from

-0.042 to 0.042 corresponding with red to green color

showed on the map, and the slope value was divided

into four categories manually based on the land-cover

types. The slope map presented those areas with

increasing vegetation cover in green color, and areas

with declining vegetation cover in orange and red

color. Those areas with orange color represent no

vegetation change and the regression line slopes were

close to zero. Positive slope values indicating vege-

tation increase was mainly distributed at the south side

of the river channel. Over the hilly area to the north of

the Missouri River, the vegetation cover showed

virtually no change, implying a stable natural habitat

less affected by human alterations. An obvious decline

in the amount of vegetation as demonstrated by

negative slope values was found at the northeast

corner of the study area which covers St. Louis county

and St. Charles County. Other notable vegetation

slope declining areas were distributed on the flood-

plain along the river channel. In St. Charles County

there have been extensive changes since 2000.

Comparing the Figs. 2, 3 and 5, a salient phenom-

enon could be noticed that those areas with low

accumulated NDVI values, high CoV and declining

trend of vegetation were all occurred on the floodplain

and urban areas. There were some corresponding

trends between these three distribution patterns, the

lower the total accumulated NDVI values, the higher

the CoV of total accumulation NDVI and the lower the

slope trends appeared, or vice versa. What were the

possible scenarios associated with the spatio-temporal

patterns of NDVI? The driving forces behind these

observations including the climate change and human

interference will be discussed in next section.

Observational connection to climate change

We collected temperature and precipitation data from

the Hermann, Warrenton, Weldon, St. Charles, Free-

dom and Union weather stations to examine the

Fig. 5 The temporal slope of NDVI derived from a linear regression analysis of data over 1975–2010. A positive slope value represents

increasing vegetation trend or vice versa (See the online version for the color version of this figure)

850 Landscape Ecol (2012) 27:843–857

123

climatic influence to the NDVI dynamics. These

stations are distributed following a stratified sampling

in the study area (Fig. 1). Unfortunately, we had no

temperature record for the Hermann station.

Figure 6 showed the temperature trajectories of the

five weather stations for the 35 years period between

1975 and 2010. The mean annual temperatures from

the five stations were different from each other, such

as in 2008 the temperature ranged from 52.46 �F in

Freedom to 63.52 �F in Warrenton. The results indi-

cated that the temperature trajectories of Warrenton,

St. Charles and Union had slightly positive slopes

(means increasing trend), while the Weldon and

Freedom presented slightly deceasing trends. The

temperature slope values were shown in Table 1,

ranging from -0.0375 to 0.082.

Figure 7 shows the precipitation trajectories at the

six weather stations from 1975 to 2010. One could be

seen from this figure that five stations had slightly

increasing trends of the annual precipitation while

Hermann had a negative precipitation slope, therefore,

a decreasing trend. Weldon had a highest precipita-

tion slope value, 0.3375 and the slope of Hermann

precipitation was -0.0169 (Table 1). The results of

statistical significance test for all the trend lines

showed that two trend lines of precipitation and one

trend line of temperature had significance value

(Table 1).

Linear regression (OLS) was used to detect tem-

poral trend of temperature and precipitation for each

location, where Kolmogorov–Smirnov tests were used

to test the normality (a = 0.05) and Durbin Waston

tests were used to check the presence of temporal

autocorrelation (Durbin and Watson 1950, 1951;

Sargan and Bhargava 1983). Only temperature data

over St. Charles station exhibited autocorrelation, and

most of datasets follows normal distribution with

exception of Precipitation in Warrenton (p = 0.04).

Comparing the trends and spatial distributions of

temperature and precipitation with the accumulated

NDVI, NDVI CoV, and NDVI slope map, we found

that the NDVI, CoV and NDVI slope distribution

patterns did not correlate with the temperature and

precipitation patterns. The lower inter-annual mean

NDVI, the higher NDVI CoV and declining NDVI

slope trends were main observations over the urban

and floodplain areas. The stable growing NDVI areas

were mainly distributed at relatively higher elevations,

for example, hilly areas. The temperatures had been

decreasing slightly in the Weldon and Freedom areas,

but the NDVI slope map showed that the vegetation

cover condition was quite stable in these two areas

from 1975 to 2010. From the Table 1, we could also

see that the precipitation pattern in this area was

dominated by an increasing trend.

The spatial distribution of yearly total precipitation

slope over the last 11 years derived from TRMM data

also showed a lightly increasing trend overall (Fig. 8).

However, some local decreasing in the south-east part

of the study area was observed. We investigated the

trajectory of spatially averaged yearly total precipita-

tion over the time period. As shown in Fig. 9, the

Fig. 6 Temperature

(1975–2010) trajectories

from five weather stations

(See the online version for

the color version of this

figure)

Landscape Ecol (2012) 27:843–857 851

123

overall increasing trend in precipitation was further

confirmed.

Based on these observations, increasing tempera-

ture and precipitation, it is expect that total NDVI

should have increased over time. However, the total

mean NDVI demonstrated a declining trend as shown

in Fig. 4. Therefore, we deduced that temperature and

precipitation were not the primary reasons causing the

vegetation quality alteration in the study area.

Urban expansion

Since 1822, the first settlers began heading west out of

Franklin, Missouri, people started to develop and

cultivate on the fertile land. The cultivated farmland

was increased dramatically since the 1940s due to the

stimulus policies of U.S. Department of Agriculture

programs. The most important factor in farming the

floodplain was the bank stabilization and navigation

program and subsequent levee construction (Ferrell

1996). With the population increase, the floodplain

had been further developed and populated. New

communities, such as New Town in St. Charles

County Missouri, built on raised mounds surrounded

by levees, were examples of recent urbanization built

on the floodplains, which consumed large amount of

previously permeable farmland with impervious sur-

faces (Kusky et al. 2008). Figure 10a shows the urban

area in 1976, 1991, 2001, and 2010. The urban area in

1991 was 0.54 km2 less than in 1976, however,

increased by 43 % from 303.44 km2 in 1991 to

434 km2 in 2010 (Fig. 10b). Comparing the urban

expansion with the total mean NDVI trajectory, we

came to a cautious conclusion that documented trend

in decreased NDVI is related mostly to urbanization.

Therefore, ecosystem quality alteration in the study

area may be attributed to human activity, in particular,

the urbanization the last two decades.

Channelization of Missouri River

Variation in NDVI in agriculture fields may not

necessarily be an indicator of environmental quality

degradation, high variation could be due to seasonal

harvests of crops. However, temporal variation (i.e.

biomass or overage stability) is one of important

functional properties of plant ecosystems (Hooper et al.

2005), and our datasets primarily reflect the naturally

occurred vegetation at annual scale, therefore our studyTa

ble

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852 Landscape Ecol (2012) 27:843–857

123

exhibit a long-term pattern of vegetation condition in

the floodplain coupled with channelization and agri-

cultural activities. Despite that trends over time were

difficult to demonstrate given the high within-year

variation that accompanies agriculture practices river

channelization may have potential environmental

Fig. 7 Precipitation

(1975–2010) trajectories

from six weather stations

(See the online version for

the color version of this

figure)

Fig. 8 Temporal slope of precipitation derived from a linear

regression analysis of time series Tropical Rainfall Measuring

Mission (TRMM) data for each pixel. For the time period

of 2000–2010, a positive slope value represents increasing

precipitation while the negative slope value shows decreasing

precipitation trend (See the online version for the color version

of this figure)

Landscape Ecol (2012) 27:843–857 853

123

influence on the floodplain ecosystems; elsewhere this

impact have been identified (Junk et al. 1989; Poff et al.

1997; Thoms 2003; Liu and Wang 2010; Watkins et al.

2010).

During the past two centuries, the Missouri River,

along with its adjacent wetlands and floodplains, had

been dramatically modified in various attempts to

promote transportation, agriculture, and development

(Criss and Kusky 2008). From the discussions above,

we have seen that the floodplain along the river channel

was characterized as highly variable vegetation system

showing low NDVI values, high CoV and a declining

vegetation trend. In this study, we explored the potential

impact of channelization to vegetation quality alteration

on the floodplain by comparing inundation modeling

with current channel status with and without levee

construction, assuming the condition without levee was

the pre-regulation channel condition.

For both channel conditions modeled with and

without levee, all the floodplains were inundated with

the 500, 200, and 100 years flood stages. For a

50 years flood, most of the levees stand higher than the

predicted water level. No significant inundation was

observed over the flood plains. However, the levee

Fig. 9 Inter-annual mean precipitation trajectory from 2000 to

2010

Fig. 10 a Urban cover area in 1976, 1991, 2001 and 2010 (See the online version for the color version of this figure). b Urban area

comparison in 1976, 1991, 2001 and 2010

854 Landscape Ecol (2012) 27:843–857

123

system was not a connected feature all along the

channel, and, therefore, water may overflow parts of

the areas. A 20-years flood seemed completely

constrained in the channel, and showed no overflow.

In other words, under the current channel condition,

when the water stage was lower than the 50 years/2 %

chance flood stage, there was no water overflow from

the channel to the adjacent floodplain.

When the levee height was removed from the

current DEM and overlain by predicted water levels

from 50, 20 and 10 years floods, the floodplain was

completely inundated. The red circle in Fig. 11a was

the levee location. The red circle in Fig. 11b shows

that the levee was removed to construct the assuming

pre-regulation channel condition. The levees possibly

decreased the opportunity of water over-bank flow to

the floodplain, and the ecological values of hydrologic

connections between a river’s main channel, back-

waters, and floodplain was emphasized in prior

researches (Gore and Shields 1995; Ward and Stanford

1995; USGS 1999). Levees were sporadically pro-

truding out of the water surface with the 5 years flood,

and most of levees were higher than the water surface,

therefore, no water overflows the river bank when it

was overlaid with 2 years/50 % chance of flood stage.

From Fig. 11a, b we could see that the flood stage

overflows the channel bank elevation after 50 years

flood stage, but for the assuming pre-regulation

channel condition without levees, 5 years flood stage

over passed the river bank and cover the floodplain.

According to statistics, 80 % of floodplain profiles had

overflow with the 5 years/20 % chance flood, and the

most of the rest of 20 % were located at the upper area.

The modeled inundation frequency for the assum-

ing pre-channelization condition was four times more

than the frequency of inundation under the current

channel conditions. The levees possibly decreased the

frequency and duration of inundation of the flood

plain, reduced sediment and nutrient exchanges

between floodplain and main channel. Inadequate

exchange could be worse for the floodplain ecosystem

in a period of low water stage. In these areas, the

decrease of overbank flow reduces the source of water,

soil and nutrition from the main channel for the growth

of vegetation, and reduces the medium for aquatic

fauna to move into floodplain to spawn and feed. This

hypothesis is consistent to the prior research that many

native fish and avian species experienced substantial

reductions, while nonnative species—especially

fishes—thrived in some areas (Committee on Missouri

River Ecosystem Science et al. 2002).

We acknowledge that quantifying the effects of

climate change and human activity on environmental

quality is a complicated task that requires extensive

inventories of plant and animal species. However, this

is beyond the scope of this contribution, and further

studies are needed to meet a solid outcome assessment.

Conclusions

This paper investigated the vegetation cover variation

connected with the role of human activity on rapidly

altering floodplain environmental quality in the Lower

Fig. 11 a Current river channel and floodplain profile at RM

96. b Assuming pre-regulation river channel and floodplain

profile at RM 96 (See the online version for the color version of

this figure)

Landscape Ecol (2012) 27:843–857 855

123

Missouri River floodplain using vegetation variables

derived from satellite data, and predicted flood

inundation scenarios from GIS modeling.

In the Lower Missouri River basin, the Missouri

River ecosystem recovery would benefit from a better

understanding of the causes of the habitat quality

alteration from natural and social perspectives. The

results show that the there were larger CoV of NDVI

over the river channel and urban areas, indicating all of

the social and economic stressors that channelization

engineering, and all of the social and economic

stressors that created the agricultural economy, subur-

banization, and urbanization may contribute to the

unstable ecosystem of Lower Missouri River basin.

These observations were further validated by trajecto-

ries of long-term temperature, precipitation, and flood

stage modeling using GIS. Overall, the inter-annual

total mean NDVI had decreased while precipitation

and surface temperature showed noticeable increase

due to regional climate variation for the time period

investigated. We came to a conservative conclusion

that human activities including suburbanization, river

channelization and levee engineering were potential

causes of environmental degradation and habitat

quality alteration in the Lower Missouri River basin.

Acknowledgments Authors would like to thank anonymous

reviewers and Dr. Robert Jacobson from Columbia

Environmental Research Center of U.S. Geological Survey for

his constructive comments on the manuscript.

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