Remote Sensing of Environment 112 (2008) 4107–4119
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Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Using MODIS data to characterize seasonal inundation patterns in the
Florida Everglades
Callan Ordoyne, Mark A. Friedl ⁎
Department of Geography and Environment, Center for Remote Sensing, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, United States
A R T I C L E
I N F O
Article history:
Received 2 April 2007
Received in revised form 13 July 2007
Accepted 22 August 2007
Keywords:
MODIS
Wetlands
Florida evergaldes
Hydrology
A B S T R A C T
Information regarding the spatial extent and timing of flooding in the world's major wetlands is important to a
wide range of research questions including global methane models, water management, and biodiversity
assessments. The Florida Everglades is one of the largest wetlands in the US, and is subject to substantial
development and pressures that require intensive hydrological modeling and monitoring. The Moderate
Resolution Imaging Spectrometer (MODIS) is a global sensor with high frequency repeat coverage and
significant potential for mapping wetland extent and dynamics at moderate spatial resolutions. In this study,
empirical models to predict surface inundation in the Everglades were estimated using MODIS data calibrated
to water stage data from the South Florida Water Management District for the calendar year 2004. The results
show that hydropatterns in the Florida Everglades are strongly correlated to a Tasseled Cap wetness index
derived from MODIS Nadir Bidirectional Reflectance Function Adjusted Reflectance data. Several indices were
tested, including the Normalized Difference Wetness Index and the diurnal land surface temperature
difference, but the Tasseled Cap wetness index showed the strongest correlation to water stage data across a
range of surface vegetation types. Other variables included in the analysis were elevation and percent tree
cover present within a pixel. Using logistic regression and ensemble regression trees, maps of water depth and
flooding likelihood were produced for each 16-day MODIS data period in 2004. The results suggest that MODIS
is useful for dynamic monitoring of flooding, particularly in wetlands with sparse tree cover.
© 2008 Elsevier Inc. All rights reserved.
1. Introduction
The wetlands of the world possess enormous ecological and
economic value. Wetlands are home to many endemic species, provide
crucial nurseries for aquatic and amphibious species (Postel & Richter,
2003; Pringle et al., 2000), and are often oases for terrestrial species in
areas with harsh dry seasons (e.g. Barbier & Thompson, 1998). Wetlands
absorb floodwaters, recharge aquifers (e.g. Acreman et al., 2001), and
filter contaminants and sediments from runoff to rivers, streams, and
groundwater (Rykiel, 1997; Wilen & Bates, 1995).
Wetlands are also an important emerging element of global climate
change research. Because they limit decomposition of organic matter,
flooded soils contain about 1/3 of all organic matter stored worldwide
(Schlesinger,1997) and thus are assumed to be strong net sinks of carbon.
Globally, wetlands emit 72% of naturally generated methane (Schlesinger,
1997), and between 19 and 40% of all methane emitted (IPCC, 2001).
Wetlands may also provide a positive feedback mechanism to climate
change because the amount of methane released by wetlands is
influenced by climate (Prigent et al., 2001).
⁎ Corresponding author.
E-mail address: [email protected] (M.A. Friedl).
0034-4257/$ – see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2007.08.027
Because of increasing human requirements for land and water,
wetlands are threatened worldwide. Hydropower, irrigation, and
dependable water supplies can all be provided by damming, diverting or draining water that currently sustains wetlands. Further, many
of the world's most important wetlands are located in developing
countries that are likely to view their water resources in terms of
untapped economic potential (Barbier & Thompson, 1998). Despite
substantial efforts to quantify the ecological and economic value of
wetlands (e.g. Baron et al., 2002; Wilson & Carpenter, 1999), the
benefits and requirements of wetlands are often ignored. This
situation is exacerbated by a lack of basic information on wetland
area and distribution. For example, Finlayson and Davidson (1999)
reviewed wetland inventories on local to global scales and found that
it was impossible to assess the extent and condition of wetlands
worldwide at that time due to insufficient data. They argued that
identifying the location, distribution and status of wetlands is a basic
prerequisite for water management and policy-making. With the
advent of new global-scale remote sensing data sets in the last
decade, this type of analysis is now more feasible.
Most global maps of wetlands (Cogley, 2003, Matthews & Fung, 1987,
Stillwell-Soller et al., 1995) are derived from field-based mapping that
emphasizes hydrophytic plant species and hydric soils rather than using
remotely sensed methods. Recently, however, remote sensing is
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increasingly being used for this purpose, particularly in the context of
global change studies. Prigent et al. (2001) merged 1992–1993 AVHRR,
SSM/I, and ERS-1 data to map global wetland area at 0.25° spatial
resolution. At finer resolutions, global land cover maps (Loveland et al.,
1999; Friedl et al., 2002; Global Land Cover 2000, 2003) include wetlands
as one of many categories, but estimates of global wetland area in these
maps are significantly lower than estimates provided by available global
wetland databases (Cogley 2003; Matthews & Fung,1987; Stillwell-Soller
et al., 1995), which have also been criticized (Lehner & Döll, 2004). For
example, Finlayson and Davidson (1999) note that global estimates of
wetland area run from 5.6–9.7 million km2, but summing the minimum
continental estimates of wetland areas yields a global figure of
12.7 million km2, which suggests that global data sets omit large numbers of small wetlands in their inventories.
While any moderate resolution remotely sensed map will
unavoidably underestimate wetland area due to the small and
fragmented nature of many wetlands, substantial improvement over
current broad-scale remotely sensed maps is urgently needed. To
address this need, substantial research has been directed to using
remote sensing to monitor and map wetlands and flooded areas at
local and regional scales. Of all the strategies employed, methods
using Synthetic Aperture Radar (SAR) have been most widely used
(e.g. Basist et al., 2001; Costa, 2004; Hess et al., 1995; Melack & Hess,
1998; Mertes et al., 2004; Smith, 1997). Passive microwave sensors
possess much coarser resolution than active SAR systems, but have
been used for hydroperiod monitoring in wetlands with some success
(Hamilton et al., 1996; Hamilton et al., 2002), and researchers have
also documented the utility of optical and SAR data in conjunction
with Digital Elevation Models (DEM) for prediction and monitoring of
wetland inundation, river stage, storm runoff depth, and evapotranspiration (e.g. Chen et al., 2002; Frazier et al., 2003; Hamilton et al.,
1996; Hudson & Colditz, 2003; Melesse & Shih, 2002; Nagler et al.,
2005; Smith, 1997; Townsend & Foster, 2002, Townsend & Walsh,
1998; Töyrä & Pietroniro, 2005). Multispectral and hyperspectral data
are also frequently used to map wetlands at local scales, often elucidating detailed information about species types, human impacts, and
geomorphological changes (e.g. Bachmann et al., 2002; Blasco et al.,
1998; Frihy et al., 1998; Harvey & Hill, 2001; Henderson et al., 1999;
Munro & Touron, 1997).
Frequent temporal coverage makes moderate resolution remotely
sensed data from instruments such as AVHRR, SPOT and MODIS wellsuited to monitoring flood duration, flooding probability, or
ephemeral wetlands (Gumbricht et al., 2004; Ringrose et al., 2003;
Roshier & Rumbachs, 2004). Indeed, the high frequency temporal
acquisition strategy for moderate resolution sensors provides unique
and complementary capability for monitoring large wetland complexes relative to radar and other remote sensing data types. To date,
however, moderate resolution data has not been widely used for this
purpose. With this issue in mind, the objective of this paper is to
evaluate the utility of MODIS for mapping the extent and timing of
inundation in large wetland complexes, ideally at a scale useful for
regional ecology and water management. As a test case, we examined
the Florida Everglades.
2. Data and methods
2.1. Study area: the Florida Everglades
The Everglades has a strongly seasonal hydrologic regime with
three-quarters of annual precipitation concentrated in tropical storms
in the May–October rainy season (Lodge, 2005). During the dry season
water levels recede throughout the area, with surface water
completely retreating from large portions of the basin. The dominant
vegetation type is graminaceous marshland, with areas of mangroves,
sparse pine uplands, evergreen broadleaf tree islands, mixed forest
strands (broad shallow streams), and floating vegetation in deeper
waters. Spatial and temporal heterogeneity in vegetation cover can be
substantial.
During the past 100 years, the Everglades have been substantially
altered by humans to support commerce and flood control (McPherson &
Halley, 1996). Today, the natural southward flow is diverted and
controlled by a complex series of major canals, levees, and other control
structures, resulting in three major Water Conservation Areas (WCAs)
(Fig. 1). These WCA's are intensively managed by the South Florida Water
Management District (SFWMD) for three competing purposes: irrigation,
urban water requirements, and ecosystem maintenance. Human use
decreases the amount of water available (disproportionately so in drier
years) and often affects the timing of water releases to the ecosystem.
Because of the climate of the region, a large flood pulse through the
Everglades is expected in all years during the wet season. Minor, irregular
water releases add some variability to the hydrocycle, but do not obscure
the dominant annual hydroperiod. Comprehensive hydrologic models of
the Everglades have been developed (e.g., the Everglades Landscape
Model or the Natural Systems Model; Fitz et al., 1996; Sklar et al., 2001;
SFWMD, 1998) to aid in the management of Everglades waters.
Several previous studies have explored mapping the hydrology of
the Everglades via remote sensing. Welch et al. (1999) mapped the
vegetation of the Everglades from field surveys and visual interpretation of color-infrared photography, resulting in the most detailed
vegetation map of the Everglades currently available. Kasischke et al.
(2003) examined the possibility of using SAR data to map surface
inundation in the Everglades, and concluded that SAR has the
potential to detect flooding levels in both forested and herbaceous
areas. Similarly, Wdowinski et al. (2004) characterized flooding
patterns and relative water level changes for water conservation
areas in the northern Everglades quite accurately through radar
altimetry techniques using three acquisitions of JERS-1 L-band data.
2.2. Data sources
2.2.1. Remotely sensed data: MODIS
As we described above, the goal of this study was to explore the utility
of MODIS data for mapping the spatial and temporal dynamics of
inundation in the Florida Everglades. With a nominal 1-km spatial
resolution and a revisit period of once every two days (daily above 30° N),
MODIS provides synoptic observations at spatial and temporal resolutions
that are well-suited for characterizing the hydrodynamics of a large
wetland complex such as the Everglades. The MODIS sensor possesses
seven bands designed specifically for land remote sensing, and provides
substantial technical improvements over other multispectral sensors (e.g.,
AVHRR) in regard to its geometric, radiometric and calibration properties
(Justice et al., 1998). Melack (2004) observed that MODIS data offers
untapped potential for advancements in the field of wetland mapping.
All MODIS data used for this study were Collection 4 products
acquired in 2004. Specifically, we used the Nadir BRDF-Adjusted
Reflectance (NBAR) product, which is produced at 16-day intervals
and corrected for sun and view angle effects (MCD43B4; Schaaf et al.,
2002), and the 8-day land surface temperature product (MYD11A2; Wan
et al., 2002). A full year of NBAR data provided 23 16-day periods, while
land surface temperature data provided 46 8-day periods. These latter
data were averaged to produce 16-day values to be consistent with the
NBAR data.
All of the data were screened to eliminate outliers: i.e., data points
likely to represent errors in acquisition or processing. Because the
input data was approximately Gaussian, outliers were identified as
data values falling more than three standard deviations from the
mean (Table 1). Missing data presented a significant challenge
because of cloud cover. Only 3.6% of pixels in the Everglades region
contained valid data for all 23 dates, and missing data are unevenly
distributed by date (Fig. 1). In the results presented below, model
predictions were not generated for pixels on dates when input data
was unavailable.
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Fig. 1. Proportion of data missing due to clouds for each date for the MODIS NBAR and LST datasets. NBAR data are represented by TC wetness, as this index incorporates all seven
bands (see Section 2.3.2).
2.2.2. SFWMD hydrography and elevation data
A key source of data was the water stage monitoring stations in the
Everglades basin (DBHydro, 2005; Kotun, 2005; Turcotte, 2005). The 31
sites used in this study are spatially distributed throughout the
Everglades basin (Fig. 2) and are managed by the Everglades National
Park, the SFWMD and the USGS. Sites were selected to represent as many
of the diverse cover types and regions in the Everglades basin as possible.
Data were provided in units of water stage: water height above sea
level in the NAVD29 datum, rather than water level height above
ground surface. Some sites were located in areas covered by a highaccuracy (15-cm vertical resolution, 30–500 m spacing) GPS-sampled
DEM of Southern Florida (Desmond et al., 2000). Areas not covered by
the DEM were supplemented by the elevation data used as inputs for
the SFWMD hydrological models, which are currently the best
available despite being compiled from several sources and having
variable accuracy (Jeffrey Sullivan, pers. comm.).
Using these surface elevation data sets, water levels relative to the
local land surface elevation were derived from water stage data at each
station. Water stage data were provided as daily mean values. For the
purpose of this study, we averaged the hydrographs to 16-day periods
corresponding to the MODIS data. The scale mismatch between point
measurements and 1-km MODIS pixels can be significant, and thus
only sites that were representative of their surrounding area were
used (areas with clear anomalies such as canals, roads, or large tree
islands were excluded). The resulting set of hydrographs provided a
time series of water stage data and was used to calibrate and validate
statistical models predicting inundation.
To estimate logistic regression models predicting inundated v. dry
areas, the stage data were recoded as a binary variable where water
levels greater than 6 in. above the surface were coded as flooded and
water levels more than 6 in. below the surface were coded as not
flooded. Due to topographic variability within the pixel, a water stage
Table 1
Outliers screened from each variable included in the model: see descriptions of
composited variables in Section 2.3.2
TC wetness
TC greenness
NDVI
LST difference
Elevation range
Elevation mean
Outliers
%
outliers
Original
min.
Screened
min.
Original
max.
Screened
max.
2231
1697
2404
2708
109
16
0.006
0.005
0.007
0.008
0.007
0.001
−3890
−3697
− 0.54
−70.46
0.00
− 7.17
−2406
−163
0.15
−6.74
0.00
−7.17
4784
3605
0.99
67.58
170.32
32.06
425
2771
0.99
26.48
10.33
21.27
reading close to the surface has a high likelihood of being located in a
partially flooded pixel. Over the entire year, 234 observations were
within 6 in. of the surface, leaving n = 277 pixels that we coded as either
inundated or dry.
2.2.3. Ancillary model inputs: sub-pixel elevation and tree cover
Given the importance of topography in wetland ecosystems, we
sought to incorporate a measure of sub-pixel topography in our
models. Anecdotal observations suggested that topography would
have a substantial influence on the duration of flooding as well as subpixel flooding, and thus would likely influence the relationship
between spectral response and water level. Using the SFWMD DEM
at approximately 30-m resolution, we calculated the mean and range
of elevation within each MODIS pixel.
Tree cover can also have a significant influence on the spectral response of the surface to flooding. To determine the percentage of tree
cover within each MODIS pixel we developed a high-resolution forested/
unforested mask based on training sites covering the range of cover
types in each category. These training sites were used to estimate a
supervised classification (an ensemble decision tree; Friedl et al., 1999),
which was applied to a set of Landsat images of southern Florida
(comprising Landsat ETM images p015r042, 1/9/02; p15r043, 11/6/01;
and p016r042,11/13/01). The resulting mask possessed an accuracy of 91
percent, which was verified by visual inspection. In addition, a few small
areas that were deforested between 2001 and 2004 were manually
edited to reflect 2004 tree cover status.
2.3. Mapping and analysis methods
2.3.1. Delineating the Florida Everglades
As a first step, we used (Quinlan, 1993; Friedl et al., 1999; McIver &
Friedl, 2002) to distinguish between three classes: (1) permanent
water, (2) seasonally or permanently flooded wetlands, and (3)
uplands. The training sites were selected using a series of four Landsat
ETM images, acquired on April 4, June 23, August 26, and September
27 of 2004; all wetland training sites were flooded on at least one date,
permanent water sites lacked emergent vegetation, and uplands were
located in areas that were developed or never flooded. The classifier
inputs were 16-day NBAR data for each of the seven MODIS “land”
bands (Schaaf et al., 2002), the Enhanced Vegetation Index (EVI)
calculated from NBAR data (Huete et al., 2002), and MODIS land
surface temperature (LST; Wan et al., 2002). Using a full year (2004) of
these input data (consisting of 23 observations for each pixel), a mask
delineating the Everglades basin wetlands was derived. The crossvalidated accuracy of the wetlands mask was 94.5 percent. Note that
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Fig. 2. SFWMD and USGS water stage gauges (yellow dots). Sites were screened to be representative of their surrounding area, and to correlate to surface elevation data.
extensive wetlands outside the primary Everglades region were
identified by the classifier; these areas were manually excluded
(Fig. 3). The resulting mask was used to restrict subsequent analyses to
the contiguous Everglades complex. The results presented below
apply only over the area within this mask.
2.3.2. Multispectral indices
In addition to the MODIS NBAR, EVI and LST inputs described above,
we also tested three spectral indices that are designed to be correlated
to surface wetness: the Normalized Difference Wetness Index (NDWI;
Gao, 1996), the Tasseled Cap wetness index (TC wetness; Kauth and
Thomas, 1976), and an index based on day–night land surface
temperature differences. The NDWI is computed as the normalized
difference between the near-infrared (0.86 µm) and the mid-infrared
spectral reflectances (1.24 or 1.6 µm; Gao, 1996; Xiao et al., 2002). For
this work we tested NDWI data using both the 1.24 µm and 1.6 µm
MODIS bands (bands 5 and 6, respectively). Coefficients for the TC
greenness, brightness and wetness indices used for this work were
derived by Zhang et al. (2002) for use with MODIS data (Table 2).
Although TC wetness was of primary interest for this study, we also
calculated TC brightness and greenness to supplement the information
available to the model.
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4111
Fig. 3. The final Everglades mask overlaid on mosaiced Landsat imagery. Areas filled with red hatches are classified as uplands or permanent water, and were not included in the
statistical models.
The third index that we used was based on the hypothesis that
because the thermal inertia of water is much higher than that of most
land surface materials, day–night differences in land surface temperature should provide information about inundation status of the
land surface. Intuitively we expected the difference between day and
night temperatures to be smaller over wet regions because water has a
much greater heat capacity than dry land, and the diurnal range in
surface temperature should be smaller in inundated areas relative to
upland (dry) areas. Accordingly, we calculated the diurnal temperature difference for each pixel from daytime and nighttime MODIS LST
data and included these data in our analyses.
To determine which remotely sensed index was most highly
correlated with seasonal patterns in the water stage data, we normalized
the water stage data and the remotely sensed indices to have zero mean
and equal range for each date at each site. Inspection of the indexhydrograph correlation plots revealed that all the indices except TC
wetness were inversely related to the water stage data, so we inverted
them for analysis purposes (Fig. 4). To quantify the relative utility of the
six indices, we compared the root mean squared error (RMSE) and the
mean absolute deviation (MAD) between each rescaled index and the
corresponding hydrograph data.
2.3.3. Statistical models
We tested several different kinds of statistical models to map both
the extent of flooding and the height of water. Note that the goal of
this analysis was not to compare the relative utility of different
statistical approaches. Rather, the goal was to assess utility of MODIS
data for mapping the timing and extent of flooding in the Everglades.
We therefore tested several different statistical approaches, using both
binary (flooded, not flooded) and continuous response (local water
height) variables. To do this we used the MODIS, elevation, and tree
Table 2
MODIS NBAR Tasseled Cap coefficients
Band
TM (nm)
Red
630–
690
MODIS
620–
(nm)
670
Brightness 0.3956
Greenness −0.3399
Wetness
0.10839
Near-IR
Blue
Green
760–
900
841–
876
0.4718
0.5952
0.0912
450–
520
459–
479
0.3354
−0.2129
0.5065
520–
600
545–
565
0.3834
−0.2222
0.4040
M-IR
M-IR
M-IR
1230–
1250
0.3946
0.4617
− 0.2410
1550–
1750
1628–
1652
0.3434
−0.1037
−0.4658
2080–
2350
2105–
2155
0.2964
−0.4600
−0.5306
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Fig. 4. Index comparison data for two randomly selected sites. Solid horizontal line represents ground surface elevation. Solid varying line represents true hydrographs; symbols represent index values for the corresponding dates. All indices
except TC wetness are shown inverted.
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Table 3
Index comparison similarity to hydrograph data: means across 31 sites
TC wetness
TC brightness
TC greenness
NDWI-5
NDWI-6
LST difference
RMSE
AbsMeanDev
0.240
0.213
0.328
0.477
0.328
0.279
0.183
0.163
0.259
0.397
0.259
0.221
cover data described above, co-registered with the SFWMD hydrologic
and elevation data. Using these data we explored multiple linear
regression, logistic regression, linear discriminant analysis, and
ensemble classification and regression models (Random Forests).
Assessment of model results was performed using both crossvalidation and an independent validation data set that was held out
from the training data. Specifically, we tested the ability of each model
to predict whether or not each site was flooded or not, using the
SFWMD hydrologic and elevation data. Based on the results from this
analysis, we determined that logistic regression predicted the best
binary results, and Random Forests regression trees predicted the best
continuous response variable results. Thus, only results from these
two models are presented.
3. Results and discussion
3.1. Assessment of remotely sensed indices
Table 3 shows that TC brightness is most highly correlated with the
seasonal pattern, with TC wetness a close second. However, TC
brightness and wetness are very closely correlated (r = −0.87) and
produced nearly identical results. For consistency with the intended
application of these indices, we chose to use TC wetness as the
primary remote sensing input for subsequent analyses. LST day–night
difference and TC greenness are also significantly correlated with the
hydrograph data, and are weakly correlated to TC wetness (−0.567 and
−0.336), so we used these in the statistical models as well. The NDWI
was only weakly correlated with seasonal patterns in water stage, and
thus was not considered further.
3.2. Statistical models
3.2.1. Logistic regression
We constructed a null generalized linear model to predict flooded v.
dry areas using logistic regression (Fig. 5). To estimate this model we
determined which variables to include by the drop-in-deviance test and
the Bayesian Information Criteria, both of which optimize the tradeoff
between the amount of variance explained and the number of predictor
variables (Table 4). Both metrics selected the same set of predictors:
TC wetness, sub-pixel elevation range, TC greenness, mean elevation
per pixel, LST difference, and NDVI. To assess the overall quality of the
model, we calculated the deviance statistic, which compares the loglikelihood for the fitted model to the log-likelihood of a model that
specifies every data point precisely (dev. = 52.53, p = 0.000).
3.2.2. Random Forests regression
The Random Forests model was estimated using water level as the
response variable. The Random Forests algorithm developed by
Breiman (2001) extends the standard tree-based modeling paradigm
(Breiman et al., 1984) by estimating multiple trees using randomly
selected subsets of predictor variables to estimate nodes in each tree.
The prediction for each case is then computed as a weighted sum of
predictions from the individual trees. Random Forests can be used to
estimate both classification and regression-type models; for this work
we used the latter approach. The predictor variables supplied to
Fig. 5. Logistic regression predictions v. observations. Actual water height plotted on the
x-axis, probability of flooding (on a zero-one scale) plotted on the y-axis. Solid
horizontal bar corresponds to 0.5 probability of flooding, while solid vertical bars
corresponds to a water height of zero, or ground surface level +/- 6 in.
Random Forests were TC wetness and greenness, land surface
temperature, NDVI, elevation range and mean, and tree cover percent.
Fig. 6A shows that predictions from Random Forest have good
overall correlation with water height observations (R2 = 0.82). However, inspection of model residuals provides useful insight to model
performance. Specifically, Fig. 6B and C reveals a perceptible bias in
the model results, in which the highest observed water levels are
under-predicted and the lowest water levels are over-predicted. This
bias reflects a shortcoming of the Random Forests algorithm, which
computes averages over a large number of model predictions and
therefore tends to reduce the range and variance of predictions
relative to observations. Low spectral sensitivity at the tails of the
distribution (flooded or very dry) also probably contributes to this
problem. This is illustrated in Fig. 6D where the largest outliers are
from sites 3 and 18, both of which are herbaceous permanently
flooded sites in the south of WCA3A that have water stage heights that
are 1.5 to 4.5 ft above the surface. Similarly, the extreme negative
outliers in Fig. 6D are associated with sites 30, 35, 17, and 12 where
water stage at each fell further than 2 ft below the ground surface in
the dry season.
Finally, it is important to note that inspection of model residuals
(Fig. 6E) shows evidence of temporal autocorrelation; i.e., residual
values are slightly correlated with low and high water seasonality.
Likewise, when the residuals are labeled by date (Fig. 6F) we see the
dry season dates (periods 9–12) tend to have the smallest outliers,
with the wet season dates (periods 17–19) in the highest ranges. The
Random Forests model does not require the assumptions of standard
linear regression models. Thus, this pattern does not call into question
the validity of the model. It does, however, point to variance that
Random Forests is not able to capture.
Table 4
Logistic regression model selection: drop-in-deviance test results
Variables
NULL
TC wet
Elevation range
TC green
Elevation mean
LST
NDVI
Tree %
Dates
Burning
Deviance resid
df
Resid dev
Pr(Chi)
BIC
212.2
51.0
23.0
18.5
12.2
9.7
3.3
0.9
0.0
276
275
274
273
272
271
270
269
268
267
379.0
166.9
115.9
92.9
74.4
62.2
52.5
49.2
48.3
48.3
0.000
0.000
0.000
0.000
0.000
0.002
0.068
0.349
0.994
178.1
132.8
115.4
102.5
95.9
91.9
94.2
99.0
104.6
Variables are listed sequentially, first to last from top to bottom.
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Fig. 6. Residual assessment for the Random Forest regression model: A) RF predictions v. observed values, B) a residuals v. fitted values plot, C) Square root of absolute values of residuals
v. fitted values plot, D) a residuals v. fitted values plot labeled by training site (each training site contributed up to 23 observations, one for each satellite observation date), E) a boxplot of
residuals aggregated into 23 16-day periods, representing a temporal trace of the residuals through time, and F) a residuals v. fitted values plot labeled by observation period (1–23).
3.3. Mapping results
Both models were applied to MODIS data for the Everglades
area, producing 23 maps that characterize seasonal variation in
flood regimes. To assess map quality, we stratified each set of model
predictions by date and produced boxplots of the model predictions
over the course of the year (Fig. 7). Both models show similar
seasonal patterns of flooding, with marked dry and wet seasons at
the appropriate times of year. Spatial patterns are also realistic,
predicting that 95% of the Everglades area was flooded at the peak
Fig. 7. Model predictions applied to the full Everglades area, aggregated by date. Mean for each date is indicated by the white bar, 25th–75th percentile limits are enclosed by the gray
box, and 5th–95th percentile limits are enclosed by the dashed braces. Outliers are individual lines. Each 16-day period is a separate box, and is presented in order by date — letters
indicate the month in which that period began.
C. Ordoyne, M.A. Friedl / Remote Sensing of Environment 112 (2008) 4107–4119
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Fig. 8. Model predictions for the Random Forest regression model (top row) and logistic regression (bottom row). Both models are extracted for 3 relatively-cloud-free 16-day periods
corresponding to the intermediate dry-down phases, peak dry season, and peak wet season. Blank areas on both sets of maps correspond to missing data for that date. Map
boundaries correspond to the Everglades region mask, including Everglades National Park, Big Cypress National Preserve, and the Water Conservation Areas (Section 2.1).
of the wet season in September to an average depth of around 1 ft,
while about 80% of the area was dry in May of 2004 (a drought
year).
Encouragingly, the boxplots also show remarkable similarities
between models. If we consider a water height above zero and a
probability of flooding above 0.5 to be comparable, the predicted
Fig. 9. Histograms of errors: Random Forest (all errors, and mean error by site) and logistic regression (percentage of correct predictions by site).
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Table 5
Logistic regression leave-one-out cross-validation results
Observed unflooded
Observed flooded
Predicted unflooded
Predicted flooded
192
51
46
306
Each observation was categorized as flooded or unflooded (above or below the surface)
as was each prediction (greater or less than 50% likelihood flooded). 83.7% of all cases
were correctly predicted.
means and 25th–75th percentile boxes show close date-by-date
correspondence. For example, the second 16-day period in March is
the first for which the water height and mean probability both drop
below the ground surface. Likewise, the 75th percentile water heights
are below the nominal ground surface for both models in May,
although not before or after. Such close tracking of results across very
different models suggests that the underlying remotely sensed data
provide a consistent and reliable indicator of water level.
In Fig. 8 we plot predictions from the two models for three 16-day
periods corresponding to the wet season, the dry season, and in the
intermediate dry-down phase. The images show a realistic progression
in the spatial pattern of flooding. In the transitional season, water
height hovers near zero, although the probability of flooding is high
over much of the basin. In the dry season, the water table has fallen
below the ground surface in most of the basin, except in coastal areas
and the southeast edge of WCA3A. In the wet season, the probability of
Fig. 10. Water stage monitoring stations shown in Fig. 11 (hydrograph validation). Sites represent a wide range of vegetation cover types.
C. Ordoyne, M.A. Friedl / Remote Sensing of Environment 112 (2008) 4107–4119
flooding is high across the basin, with the exception of the northernmost edge of the Big Cypress area. Water levels accurately show higher
depths in WCA3A and along the coastal areas.
3.4. Cross-validation statistics
Cross-validation assessment of model quality was obtained by
omitting one water monitoring site at a time and predicting water
levels at the unseen site using a model estimated based on the
remaining cases (i.e., leave-one-out cross-validation). When iterated
over all training sites, we obtain a set of predictions that are assumed
to be independent.
The Random Forests model shows a squared correlation of 0.72
between the leave-one-out predictions and observations. The RMSE was
0.0092 ft, with a standard deviation of 0.755 ft, and the errors were
normally distributed when aggregated across all sites (Fig. 9). The
distribution of model errors for each site, however indicates that one site
had especially large negative errors, while many sites were slightly overpredicted.
For the logistic model, the leave-one-out predictions show good
correspondence with the observed values. Using the logistic regression
model to predict the presence or absence of flooding (i.e., greater or less
than 0.5 probability flooded), 83.7% of the cases were correctly classified
(Table 5). Examination of misclassified cases, reveals that most of the
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incorrectly classified cases fall in a narrow band (see Fig. 5), within 6 in.
of the surface — only 3.4% of the incorrect predictions fell outside this
band. Given the uncertainty in our elevation data, errors within 6 in. of
the surface are not surprising. Closer inspection reveals that three sites
in particular were classified correctly less than 50% of the time (Fig. 9).
3.5. Model evaluation using independent sites
To further assess the quality of the models and their limitations, six
additional sites were selected that represent the range of cover types in
the Everglades (Fig. 10). Using these independent sites, we compared
model predictions against observations (Fig. 11). Visual assessment of
these results suggests that the overall modeled patterns are realistic.
Model errors at some sites, however, were substantial (e.g., sites 1–7,
Fig. 11) and may indicate model weaknesses. The largest model errors
may also be associated with inaccuracies in the DEM (DEM errors are not
spatially consistent over the Everglades basin), or situations when the
water gauge was located at an elevation that substantially differs from
the mean pixel elevation. This scenario likely explains at least part of the
errors at sites 1–7, which is located in an area with ridge-and-slough
morphology and tree islands. Water gauges are typically located in the
lower sloughs, and it is not surprising that the hydrograph records water
levels higher than our predictions; thus, these predictions may still be
relatively accurate for the overall pixel.
Fig. 11. Hydrograph validation plots. Solid dots and lines indicate true water level (averaged to 16-day periods). Random Forest models (+) are continuous predictions and directly
comparable to the true water level dots. The logistic predictions (x) are rescaled such that 100% probability is plotted on the graph at 0.5 ft, 0% at −0.5 ft., as these are the water levels
that the probability models were trained to predict (thus the x predictions will never exceed the −0.5 to 0.5 ft. range). All model predictions shown were jackknifed: models were
trained on all sites but the one shown.
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Tree cover also probably contributes to model errors, for example at
sites BCA18 and BCA19. Both are heterogeneous pixels with higher than
average tree cover. Comparing the model predictions to both hydrographs, we see a pattern in which the models are largely unresponsive to
changes in water level, probably because tree cover masks the signal. It is
worth noting that the logistic model performs better in these cases, as it
does not attempt to track water level but merely estimates whether the
site is flooded or dry.
On a more encouraging note, the sites that represent the most
common topographic morphologies of the Everglades generally show
good correspondence between model predictions and ground observations. For example, site 3A-12 is located in an area that is
representative of the Water Conservation Areas, CY3 is located in
the marl prairies of the southern Everglades, and site NP205 is located
in an area that is primarily marl prairie, but which has scattered
hardwood tree islands. These results suggest that in the absence of
significant tree cover, the estimated models are responsive to
Everglades hydrology across a range of conditions.
4. Conclusions
Wetlands are among the world's most diverse, productive, and unique
ecosystems. Methods and datasets that allow large wetland systems to be
monitored from remote sensing would therefore be valuable to ecologists
and resource managers. In this paper we explored the use of MODIS data
for this purpose. While data from MODIS is not useful for studying
dynamics in small wetlands (i.e., less than ∼25 km2), results from this
work suggest that it can capture the seasonal hydrological variation of
major wetlands and thus provides different and complementary
information to existing static wetland maps and in situ monitoring
stations. By including information on the timing, extent, and duration of
inundation this study demonstrates the utility of multitemporal MODIS
data for characterizing the hydrologic regime of the Everglades.
We presented results from two models. The first model predicts the
presence or absence of flooding and the second model predicts water
stage height. Both were estimated from in situ water stage data using
remote sensing, elevation, and tree cover as predictors. The two models
provide slightly different information, maximizing flexibility and utility
for a wide range of applications. The results from both indicate that the
dynamic hydrology of large wetland complexes (in this case the Florida
Everglades) can be accurately mapped using MODIS data.
The results from this work also suggest that TC wetness index
holds high potential for identifying wetlands and describing their
hydrology. Indeed our analyses indicate that TC wetness is correlated
to water stage even when the water table is well below the ground
surface. Thus, the TC Wetness index appears to quantify the relative
degree of moisture present in a pixel rather than flooding per se.
Undoubtedly this approach would need to be carefully tested and
tuned with ground information. However, our results indicate that TC
wetness is useful in support of wetland mapping.
Moving forward, the next challenge is to extend the results from
this work to broader scales and assess whether or not the general
strategies used in the Everglades can be used to monitor other large
wetlands such as the Pantanal wetlands and the Okavango Delta. More
generally, the wetlands of the world are extraordinarily diverse,
ranging from arctic peatlands that rarely experience surface flooding,
to inundated rainforests with a dense forest canopies. Thus, any
wetland mapping strategy applied to global datasets must address the
challenge of accommodating diverse flooding and vegetation cover
regimes. It is unlikely that an approach based on a single data source
(e.g., MODIS) will be sufficient for mapping and monitoring the
diversity of global wetlands. However, the results presented in this
paper strongly suggest that data multispectral optical data from
moderate spatial resolution sensors such as MODIS can provide useful
information for mapping both the extent and seasonal inundation
patterns in large wetlands.
Acknowledgements
This project would not have been possible without the invaluable
data, expertise, and guidance provided by the scientists of the South
Florida Water Management District, Everglades National Park, and United
States Geological Survey. In particular, the authors thank Chris McVoy,
Kevin Kotun, Brian Turcotte, Jeffrey Sullivan, Carl Fitz, Roy Sonenshine,
Zhikang Chen, Martha Nungesser, Scot Hagerthey, and Susan Horner. The
authors gratefully acknowledge funding from the Clare Booth Luce
Graduate Fellows Program, and MODIS Grant # NNG04HZ71C.
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