Synthesis of ground and remote sensing data for

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Remote Sensing of Environment 113 (2009) 1473–1485
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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 e v i e r. c o m / l o c a t e / r s e
Synthesis of ground and remote sensing data for monitoring ecosystem functions
in the Colorado River Delta, Mexico
Pamela L. Nagler a,⁎,1, Edward P. Glenn a,1, Osvel Hinojosa-Huerta b
a
b
Environmental Research Laboratory, Department of Soil, Water, and Environmental Science, University of Arizona, Tucson, AZ 85721, USA
Pronatura Noroeste, San Luis, Sonora, Mexico
a r t i c l e
i n f o
Article history:
Received 29 November 2007
Received in revised form 7 June 2008
Accepted 23 June 2008
Keywords:
Riparian zone
Saltcedar
Native plants
Tamarix
Populus
Salix
Environmental monitoring
Evapotranspiration
MODIS
ETM+
TM imagery
a b s t r a c t
The delta of the Colorado River in Mexico supports a rich mix of estuarine, wetland and riparian ecosystems
that provide habitat for over 350 species of birds as well as fish, marine mammals, and other wildlife. An
important part of the delta ecosystem is the riparian corridor, which is supported by agricultural return flows
and waste spills of water originating in the U.S. and Mexico. These flows may be curtailed in the future due to
climate change and changing land use practices (out-of-basin water transfers, increased agricultural
efficiency, and more optimal management of dams) in the U.S. and Mexico, and resource managers need to
monitor the effects of their water management practices on these ecosystems. We developed groundvalidated, remote sensing methods to monitor the vegetation status, habitat value, and water use of wetland
and riparian ecosystems using multi-temporal, multi-resolution images. The integrated methodology
allowed us to project species composition, leaf area index, fractional cover, habitat value, and
evapotranspiration over seasons and years throughout the delta, in response to variable water flows from
the U.S. to Mexico. Waste spills of water from the U.S. have regenerated native cottonwood and willow trees
in the riparian corridor and created backwater and marsh areas that support birds and other wildlife.
However, the main source of water supporting the riparian vegetation is the regional aquifer recharged by
underflow from U.S. and Mexico irrigation districts. Native trees have a short half-life in the riparian zone due
to human-set fires and harvesting for timber. Active management, monitoring, and restoration programs are
needed to maintain the habitat value of this ecosystem for the future.
Published by Elsevier Inc.
1. Introduction
1.1. Background of the study
Many protected ecosystems are difficult to monitor because they
are in remote or poorly accessible areas of the world. Remote sensing
offers a means to unobtrusively monitor these ecosystems (McDermid
et al., 2005; Melesse et al., 2007). We present a case study of the
Colorado River delta (CRD) in Mexico. The CRD contains approximately 170,000 ha of wetland, estuarine and riparian ecosystems
(Zamora-Arroyo et al., 2005), and is an internationally important
habitat for birds, with over 350 species cited (Hinojosa-Huerta, 2006;
Hinojosa-Huerta et al., 2006, 2008). Portions of the delta have been
designated as a Ramsar Wetland site under the Ramsar International
Convention on Wetlands, and much of the lower delta is in the United
Nations designated, Biosphere Reserve of the Upper Gulf of California
and Delta of the Colorado River (Zamora-Arroyo et al., 2005). The
⁎ Corresponding author. U.S. Geological Survey, Southwest Biological Science Center,
Sonoran Desert Research Station; 1110 E. South Campus Drive, Room 123; Tucson, AZ
85721; Tel.: 520 626 1472; fax: 520 670 5001.
E-mail addresses: [email protected], [email protected] (P.L. Nagler).
1
Tel.: 520 626 2664
0034-4257/$ – see front matter. Published by Elsevier Inc.
doi:10.1016/j.rse.2008.06.018
delta also supports species listed as endangered in the U.S. For
example, it supports over 80% of the remaining Yuma clapper rails,
and another eight threatened or endangered bird species, two
endangered fish species, and the endangered vaquita porpoise
(Zamora-Arroyo et al., 2005).
We developed monitoring and assessment protocols for the riparian
corridor in the delta (Fig. 1). The riparian corridor covers approximately
30,000 ha and extends from the U.S–Mexico border to the intertidal
portion of the river where it enters the Gulf of California (Zamora-Arroyo
et al., 2005; Nagler et al., 2008a). It provides an important migration
route for birds moving up the Sonoran Coast to summer breeding
grounds in the U.S. and Canada, as well as habitat for numerous species
of resident birds, reptiles, and mammals (Zamora-Arroyo et al., 2005;
Hinojosa-Huerta, 2006; Hinojosa-Huerta et al., 2008).
1.2. Sources of water and ecosystem dynamics in the CRD
The vegetation in the riparian zone is no longer supported by
natural water flows of water, but by agricultural return flows, waste
spills, and flood releases of water from the U.S. to Mexico (Nagler et al.,
2008a). Since Lake Powell, behind Glen Canyon Dam on the Lower
Colorado River, first filled in 1981, large releases of water from the U.S.
to Mexico have occurred during El Nino cycles, when winter snowfall
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Fig. 1. Location map for the riparian corridor of the Colorado River in Mexico, based on a July, 2002 ETM+ image. The Limitrophe is the portion of river that forms the boundary
between the U.S. and Mexico. The star (located at 32o 11′ 36.9″ N, 115o 9″ 41.8″ W), shows location of GIS details shown in Fig. 5.
in the Colorado River watershed exceeded the storage capacity of
the dam-and-reservoir system (Medellin-Azuara et al., 2007). These
occasional pulse flows have regenerated cottonwood (Populus
fremontii) and willow (Salix gooddingii) trees amidst dominant
saltcedar (Tamarix ramosissima) stands in the riparian corridor
(Nagler et al., 2005b). Saltcedar is an introduced, salt-tolerant shrub
that is now widely distributed in the western U.S. and Mexico
(Zavaleta, 2000), whereas cottonwood and willow are native trees
that are required for high-quality avian habitat (Anderson & Ohmart,
1976; Hunter et al., 1988). Smaller “administrative spills” (i.e., water
ordered but not used by irrigators), have created a nearly permanent
base flow of 2–5 m3 s− 1 in the channel of the river in Mexico (Nagler
et al., 2008a). These low flows support marsh and open water habitat,
which are also important in supporting avian habitat (Anderson
and Ohmart, 1976; Hinojosa-Huerta, 2006; Hinojosa-Huerta et al.,
2008).
In addition to surface flows, the riparian corridor is supported by a
high (0.5–2 m depth), mildly saline (1000–3000 mg l− 1 Total
Dissolved Salts, TDS) regional aquifer that originates from irrigation
water applied to fields throughout the Mexicali and San Luis Valleys
surrounding the riparian corridor (Portugal et al., 2005; Nagler et al.,
2008a). The trees and shrubs populating the riparian corridor are
phreatophytes, capable of tapping groundwater to support growth.
The resulting ecosystem consists of a mix of 10–15% native trees
interspersed amidst saltcedar stands, and with a nearly continuous
flow of water in the river that supports freshwater marshes and
backwaters. This ecosystem provides a rich habitat for bird life, such
that bird density and diversity is ten times higher in the riparian
corridor in Mexico than on upstream reaches of the river in the U.S.
(Hinojosa-Huerta, 2006). The U.S. portion of the river is flowregulated and overbank flooding is rare, and the riverbanks are
populated mainly by saltcedar and other salt-tolerant shrubs tapping a
saline aquifer (5000–10,000 mg l− 1 TDS) at 3–4 m depth, with little
standing water present on the terraces away from the river (Nagler
et al., 2008a).
1.3. Need for monitoring and management programs
Excess flows from the U.S. to Mexico are not guaranteed, and are
likely to decrease in the future, as more water is diverted from
agricultural to industrial and urban uses (Medellin-Azuara et al.,
2007). Furthermore, Colorado River flows are inherently variable
(Thomas, 2007), and climate change is expected to decrease spills
from the dams due to an overall drying trend in the watershed
(Christensen and Lettenmaier, 2007). Additionally, the ecological
value of the riparian corridor has been degraded by tree cutting,
human-set fires, and vegetation clearing for flood control in Mexico
(Nagler et al., 2005b). It has become important to assess and monitor
this ecosystem and to develop management strategies to maintain
ecosystem functions over the next fifty years in the face of climate
change and increased human population in the region, which will
increasingly restrict the flow of water to the delta (Zamora-Arroyo
et al., 2005).
1.4. Study goals
Our studies had two main goals: 1) characterize the extent and
species composition of the riparian vegetation; and 2) determine
vegetation dynamics and the sources and volumes of water that
support the vegetation. Based on these analyses, we then determined
the principle threats to this ecosystem and developed monitoring
protocols to continue to assess the riparian corridor unobtrusively.
Finally, we made recommendations to resource managers in the U.S.
and Mexico as to how this ecosystem can be sustained into the future.
Our work required extensive use of remote sensing methods for
mapping and monitoring the ecsosytem, and for determining
biophysical functions such as evapotranspiration (ET) and fractional
vegetation cover (fc) over annual and interannual cycles and in
response to climate and river flows. We had the opportunity to
conduct extensive ground surveys of vegetation in the riparian
corridor and to extend these findings by aerial photography and
P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485
satellite imagery over a 14 year period (1992–2006). Our research
approach was to collect baseline ground data on the ecology of the
riparian corridor then to develop satellite-based monitoring methods
that could be repeated in the future to track changes in ecosystem
functions in response to environmental drivers through time.
This paper describes our methods for combining ground and
remote sensing data to develop a model of vegetation dynamics in
response to surface flows in the riparian corridor. It also presents the
main results of these studies, and recommendations for the management of this important ecosystem. The Materials and methods and
Results and discussion sections are organized around the two goals
listed above. In the Conclusions and recommendations section we
suggest protocols for continued monitoring and make recommendations for management of this ecosystem.
Throughout this paper, we compare our choice of remote sensing
methods with alternative methods used in other studies, and discuss
the sources of error and uncertainty inherent in the different
approaches. Themes that cut across the different goals are: (i) the
issue of scaling (i.e., how can point data collected on the ground be
aggregated to the ecosystem level of measurement) (Blöschl and
Sivapalan, 1995; Ludwig et al., 2007); (ii) cross-calibration of images
collected at different times (Song et al., 2001; Janzen et al., 2006) and
with different sensor systems (Fensholt et al., 2006); and (iii)
validation (how can we place error bars around our estimates so
that when future studies are conducted, resource managers can
distinguish real change from the normal variance expected among
studies) (McDermid et al., 2005).
2. Material and methods
2.1. Description of the study area
The Colorado River is an international river that forms the border
between the U.S. and Mexico from the Northerly International
Boundary (NIB) to the Southerly International Boundary (SIB)
25 km to the south (this stretch is called the Limitrophe Region as it
forms the limit between the two countries) (Fig. 1). Morelos Dam, at
the NIB, is the last diversion point for water on the river. Water that
passes Morelos Dam that is not evapotranspired ultimately reaches
the intertidal zone of the Gulf of California. Below the SIB, the river is
wholly within Mexico and is confined within flood-control levees as it
flows an additional 75 km through the Mexicali and San Luis del Rio
Colorado Irrigation Districts. The width of the corridor is b2 km in the
north but it widens to 30 km at the southern end of this stretch. The
river forms a series of braided channels and meanders between the
levees. Below the irrigation districts, the Colorado River joins the Rio
Hardy, which is maintained by agricultural return flows and is saline,
and flows to the Gulf of California.
2.2. Determining the areal extent and species composition of the riparian
vegetation
2.2.1. Ground surveys
Ground surveys of riparian vegetation were conducted along the
length of the riparian corridor in 1999 (Nagler et al., 2001; ZamoraArroyo et al., 2001) and 2002 (Nagler et al., 2005b); providing
baseline ecological data on the ecoystem. The 1999 survey (ZamoraArroyo et al., 2001) was conducted along nine transects perpendicular
to the river, 10 km apart starting 5 km below Morelos Dam and
extending to near the junction of the Colorado River and the Rio Hardy
(the beginning of the intertidal portion of the river). The 2002 surveys
(Nagler et al., 2005b) were coordinated with bird counts during the
spring migration (Hinojosa-Huerta, 2006; Hinojosa-Huerta et al.,
2006, 2008), hence the methodology was different from the 1999
survey. Land cover classes were estimated along 30, 2000 m,
perpendicular-to-the-river transects distributed at approximately
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equal distances from just below the Southerly International Boundary
between the U.S. and Mexico, to the junction of the Colorado and
Hardy rivers. Each transect contained eight circular survey stations
(50 m radius) 200 m apart, for a total of 240 stations. We used the
survey results to check the accuracy of the vegetation map
constructed from aerial photographs by overlaying 2002 ground
survey points on the map, and comparing estimates of water, soil,
shrubs, trees, and marsh between the image and the ground survey.
To more thoroughly survey the cottonwood and willow populations in 1999 and 2002, we also conducted age class surveys near three
of the 1999 transect sites that had well-developed stands of trees
(Nagler et al., 2005b). We measured tree height, canopy width and
trunk diameter at breast height of 100 randomly-chosen trees at each
site. Trunk diameter was related to tree age by coring a subsample of
30 trees to correlate number of tree annual tree rings with trunk
diameter (Zamora-Arroyo et al., 2001).
2.2.2. Vegetation mapping
As ground surveys were designed to provide detailed information
on species composition only, we supplemented these with a mapping
program to spatially distribute vegetation in the riparian corridor. We
used aerial photography, satellite images, and GIS analyses to prepare
vegetation maps of the riparian zone. Remote sensing and GIS have
become key tools for habitat mapping and other wide-area ecological
assessments (e.g., Melesse et al., 2007). However, standard methods
for integrating remote sensing and GIS methods with traditional
vegetation mapping techniques do not yet exist, due partly to
historical disagreement within the ecological community on what
elements should form the base units of a habitat map (e.g. vegetation
units, ecosystem units, or landscapes units, Muller, 1997), and partly to
the difficulty of applying relatively new, quantitative remote sensing
tools to ecological problems that have traditionally been approached
from ground-based observations, which are often qualitative in nature
(McDermid et al., 2005; Ludwig et al., 2007).
Vegetation maps for western U.S. rivers up to now have been based
on a qualitative, polygon approach (e.g., Anderson and Ohmart, 1976;
CH2MHILL, 1999; Watts, 2000). Using high-resolution aerial photographs, photo interpreters divide the riparian zone into a mosaic of
polygons, corresponding to patches of natural vegetation, such as
trees, shrubs or marshes. In Anderson and Ohmart's (1976) system for
the Lower Colorado River, polygons are classified according to the
dominant vegetation type and then are assigned a vertical structure
class based on the height of different plant types within the polygon.
This system was developed based on field surveys that defined the
avian habitat values of different riparian plant associations (Anderson
and Ohmart, 1976), and mapping has been repeated at approximately
five year intervals to present (e.g., CH2MHILL, 1999; Lower Colorado
River Multi-Species Conservation Program, 2004). However, these
maps have not been used for quantitative change detection because
classification criteria are qualitative and ambiguous, such that a given
polygon can be classified different ways without violating the
classification rules (Lower Colorado River Multi-Species Conservation
Program, 2004; Nagler et al., 2005a). Furthermore, the minimum
mapping unit is 2.5 ha, which nearly always encompasses mixed plant
types as well as areas of bare soil, and the amount of vegetation and
bare soil within a polygon are not quantified.
We developed a vegetation mapping system for the CRD that
contained quantitative information at several resolutions. The cover of
cottonwood and willow trees and of emergent marsh habitat in the
riparian corridor were of primary interest, as these high-quality
habitat features have become rare on the U.S. portion of the river.
Hence, these features were quantitatively digitized from aerial
photographs. In July, 2000, and June, 2002, we acquired high-level
(3000 m above ground level, AGL) and low-level (1000 m AGL) digital
aerial photographs of the riparian corridor, with 1.5 m and 0.5 m
resolutions, respectively (Nagler et al., 2005a,b).
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The 2002 aerial photographs were used to create a base map of the
riparian corridor (Nagler et al., 2005b). A mosaic of 333 high-level
images and 562 low-level images was produced using a computerassisted linear transformation method in AdobePhotoshop (San Jose,
CA), in which 3 control points on adjacent photographs were visually
aligned with each other. The photomosaic was then split into nine
sections (to minimize file size), which were separately georectified to
an Enhanced Thematic Mapper (ETM+) image acquired in June, 2002,
using control points such as road intersections, stands of trees, or river
bends that were visible on both the ETM+ and the photographs.
Georectification used a linear transformation method (ERDAS Imagine, Atlanta, GE). The root mean square error of 27 additional control
points (3 per section) co-located on the satellite image and
photomosaic was determined to be 25.5 m across all nine map
sections, about the same magnitude as the resolution of the ETM+
image (28.5 m per pixel).
Vegetation and other landscape features were digitized manually
using ArcInfo, ArcView and ArcMapper software (ESRI, Inc., Redlands,
CA) from the aerial photomosaics and the ETM+. The seven land cover
classes that could be unambiguously separated on the photomosaics
were: (i) areas of 1–2 year old fire scars; (ii) roads and levees; (iii) open
water; (iv) emergent marsh vegetation; (v) shrub vegetation (saltcedar, arrowweed and other shrub species); (vi) live willow or
cottonwood trees N6 m height; and (vii) dead cottonwood or willow
trees. Dead trees were distinguished by their white crown of leafless
branches. Live trees were distinguished by the area and color of their
canopies and by their characteristic “lollipop” shadows. However, we
could not distinguish willow from cottonwood trees and trees under
6 m height could not be distinguished from shrubs (Nagler et al.,
2005a).
The ETM+ image served as the base layer for the GIS. Pixels were
converted to reflectance-based NDVI values (see Section 2.3.1), then
subjected to an unsupervised classification procedure in ERDAS
Imagine to identify areas of open water (negative NDVIs), bare soil
(NDVI 0.0—0.15), and four qualitative classes of vegetation density.
2.3. Vegetation dynamics and sources and volumes of water supporting
riparian habitats
2.3.1. Estimating fractional vegetation cover and LAI from satellite
imagery
We correlated VI values on time-series satellite imagery with flows
measured by the International Boundary and Water Commission at
the SIB (International Boundary and Water Commission, 2008) to
determine how surface flows affected vegetation density in the
riparian corridor from 1992–2006.
In vegetation change-detection studies, NDVI or other VIs are used
as proxies for specific biophysical properties of the landscape, and
NDVI values must be calibrated against ground measurements of
biophysical attributes to be meaningful (e.g., McDermid et al., 2005).
Leaf Area Index (LAI) is often the preferred biophysical attribute to use
in vegetation studies, because it allows the scaling of plant
physiological processes (Asner et al., 2003). However, in partially
vegetated landscapes, satellite NDVI values are more closely related to
fractional vegetation cover (fc) than to LAI, because the relationship
between LAI and NDVI is non-linear (Lu et al., 2003) and saturates at
LAIs above about 3 (Carlson and Ripley, 1997). In this study, we
attempted to calibrate remotely sensed values of NDVI and other VIs
with ground-based measurements of both LAI and fc.
In 1999, photographs were obtained from a light aircraft equipped
with the MQUALS package of sensors (Huete et al., 1999; Nagler et al.,
2001), which were developed to provide validation of the MODIS
sensors aboard the Terra satellite (Gao et al., 2003), launched in 1999
and operational in 2000. The aircraft was equipped with a multiband
(blue, red and NIR) digital camera (DyCam, Inc., Chatsworth, CA) with
0.15 m resolution at the flight altitude of 150 m AGL. Digital Numbers
(DN) band values on DyCam images were converted to reflectance
values with data obtained from twin Exotech radiometers (Exotech,
Inc., Gaithersberg, MD), one on the aircraft and the other recording
data from a white reflectance calibration plate (Spectralon Diffuse
Reference Panel, Labsphere, Inc., North Sutton, NH) on the ground
during the overflight (Huete et al., 1999).
The DyCam images had sufficient resolution to distinguish
individual shrubs and trees, which made up most of the vegetation.
Band values were used to calculate vegetation indices (VIs), including
the Normalized Difference Vegetation Index, the Soil Adjusted
Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI)
(see Nagler et al., 2001; Gao et al., 2003 for formulas and discussion of
VIs). VIs of whole DyCam scenes as well as individual plant types, bare
soil, and water were calculated. Calibration studies were conducted on
nine reference sites for which the image area was accessible on the
ground. Ground measurements included LAI of individual plant
species, and global LAI (GLAI) of the plots representing each image
area. LAI was measured with a Licor 2000 Leaf Area Index Meter (Licor,
Inc., Lincoln, NE) calibrated against leaf-harvesting methods for the
main riparian species (Nagler et al., 2004). GLAI for each image was
determined by multiplying LAI of each plant type on an image by the fc
of that plant type (Nagler et al., 2001). We used this information to
determine the relationship between LAI and GLAI and the different
VIs. For 84 images, we also determined fractional vegetation cover (fc)
on the images, using a point-intercept method in which bare soil and
vegetation cover on the image were scored at each of 100 grid points
overlain on the image. We then developed regression equations to
estimate fc from VIs on the images.
Ground-calibrated DyCam VIs were then used to calibrate NDVI to
fc on a May, 1999, Landsat 5 TM image acquired within three weeks of
the overflight. Nine DyCam images (each covering a scene of
67 m × 100 m) representing a range of fc values were co-located
with 4–6 pixels on the TM image using visual cues to align the areas of
interest; five additional scenes of bare soil, open water, and full-cover
riparian vegetation were also co-located on the two sets of images,
and a simple linear regression equation was developed between
DyCam NDVI and DyCam NDVI for scenes with known fc.
2.3.2. Intercalibrating satellite images for change detection
Images collected by the same satellite sensors on different dates
can differ in DN values for the same (assumed invariant) ground
object due to differences in illumination and observation angles,
sensor sensitivity, and variation in atmospheric effects (e.g., Song
et al., 2001; Janzen et al., 2006). Comparison across different satellite
sensor systems adds additional sources of error (Fensholt et al., 2006).
Several methods are available to intercalibrate images to be used for
change detection, and the choice of method depends on the purpose
of the change analysis and the amount of ancillary data available
(Song et al., 2001). Absolute radiometric correction converts DNs on
each image in a series to reflectance values using atmospheric models,
and requires ancillary data such as atmospheric and sensor properties
determined at the time of image acquisition (Song et al., 2001).
However, when using archived imagery for remote regions, as in the
present study, the data needed for absolute radiometric correction are
often unavailable. Relative radiometric correction normalizes multiple
satellite scenes to each other based on the manual selection of
pseudo-invariant features (PIF) on the images themselves, without
the need to convert DNs to actual reflectance values (Song et al.,
2001). PIFs can include areas of deep, clear water; bare soil, rock or
concrete; areas of dense vegetation; and maximum, mean and
minimum pixel values from each scene (Janzen et al., 2006). Although
PIFs are assumed to be invariant, even carefully chosen sites do vary
through time (Schmidt et al., 2008), necessitating the use of multiple
PIFs representing different land cover types (Janzen et al., 2006).
Not all change-detection studies require radiometric correction of
images (Song et al., 2001; Van Niel and McVicar, 2003). In some cases,
P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485
atmospheric correction either for a single image or a time-series of
images amounts to subtracting a near-constant from pixel values in a
spectrum band, in which case no gain in accuracy of change detection
is achieved. However, when NDVI or other band ratios are used in
change detection, atmospheric effects must be considered if they
differentially effect DN values recorded at the sensors for different
bands. Contributions to NDVI from the atmosphere can change values
by 50% or more from incomplete vegetation canopies (Song et al.,
2001; Janzen et al., 2006). In this study, the stability of NDVI values
was assessed on Landsat 5 TM images acquired in May, June or July in
1992, 1994, 1996, 1998, and 2006, and on Landsat 7 ETM+ images
collected in July 2000 and 2002. The PIF approach of Janzen et al.
(2006), in which multiple PIFs are compared was used; yet instead of
comparing individual bands, reflectance-based NDVI values were
compared, as the objective was to use NDVI to detect changes in fc
over time.
NDVI values for water, bare soil, mean NDVI, maximum NDVI, and
minimum NDVI for each image were compared; in addition, NDVI
values calculated from DN values to NDVI values calculated from exoatmospheric reflectance values for the same images were compared.
This allowed use of archived TM scenes for which only DN values were
available in our time series.
TM and ETM+ images were used to track changes in annual fc from
1992–2006 and MODIS NDVI and EVI to track seasonal changes from
2000–2004. MODIS NDVI values are supplied as pre-processed,
radiometrically corrected data composited over 16 day periods
(Huete et al., 2002). MODIS VI values closely match ground
measurements of VIs (Gao et al., 2003; Fensholt et al., 2006) but
they are not necessarily equivalent to VI values obtained from Landsat
images. Regression equations between NDVI and EVI (independent
variables) and fc determined by MQUALS aerial analysis (dependent
variable) were developed to calculate fc by MODIS pixel values for the
riparian corridor.
2.3.3. Estimating water consumption by riparian vegetation
Constructing a water budget for the riparian corridor required an
estimate of water consumption by riparian vegetation via evapotranspiration (ET). Water use by riparian vegetation is an important
yet poorly known part of the overall water budget of riparian
corridors (Tabacchi et al., 2000; Shafroth et al., 2005; Nagler et al.,
2008a). Remote sensing methods to estimate ET fall into two broad
classes (reviewed in Glenn et al., 2007; Verstraeten et al., 2008).
Firstly, Surface Energy Balance (SEB) methods use thermal IR bands
to estimate sensible heat flux from the surface by the difference
between air temperature and surface temperature; they then
calculate latent heat flux (related to ET) as a residual in the SEB
equation. These methods are physically-based and can be applied
across different ecosystems, however, they provide only a snapshot
of ET at the time of satellite overpass, and error is introduced when
extrapolating them over daily or longer time periods (e.g., McVicar
& Jupp, 1999).
The other class of ET methods is based on correlating time-series
VIs with ground-based measurements of either actual or potential ET
(reviewed in Glenn et al., 2007). These methods are generally not
valid outside the area for which they were calibrated, yet they can
accurately project ground measurements of ET over the specific range
of conditions for which they were calibrated. In this, they are similar to
locally-calibrated, correlative approaches to estimating potential
evaporation that were developed in the 1950 to 1980s (discussed in
McVicar et al., 2007), yet taking advantage of the plot-level
measurements of ET from flux towers now available for calibration.
They are useful in tracking changes in ET over time because they are
based on time-series images. We estimated ET in the CRD riparian
corridor with the algorithm for ET from Nagler et al. (2005c), which
regressed ET measured at nine riparian flux tower sites against MODIS
EVI values and maximum daily air temperature (Ta).
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3. Results and discussion
3.1. Extent and species composition of the riparian vegetation
3.1.1. Ground survey results
The species composition of the floodplain based on the 1999 and
2002 ground surveys is in Table 1 (from data in Zamora-Arroyo et al.,
2001; Nagler et al., 2005b). The two surveys gave similar results,
although the 2002 survey had more survey plots and detected more
species. Saltcedar (mean height 3 m) was the most common plant,
accounting for over half of the vegetation cover, followed by arrowweed (1.4 m height), a salt-tolerant native shrub. However, mesic
trees and shrubs (willow, seepwillow and cottonwood) were also
abundant, constituting 18–20% of the vegetation cover. These species
constitute less than 2% of the vegetation cover on the U.S. stretch of
the Lower Colorado River, as the floodplain and aquifer have become
saline in most places from lack of overbank flooding (Zamora-Arroyo
et al., 2001). Fractional cover of vegetation was 65–70% in these
surveys. The 2002 survey extended into the river to the middle of the
active channel, hence it also quantified aquatic habitat, whereas the
1999 survey stopped at the river bank. At the time of the 2002 survey,
the river was running at only 2 m3 s− 1, yet emergent plants (cattail,
bulrushes and common reed) and open water accounted for greater
than 10% of the floodplain. Aquatic ecosystems provide important
habitat for water birds (Hinojosa-Huerta, 2006), and the extent of this
habitat has declined on the U.S. portion of the river (Anderson and
Ohmart, 1976; Hunter et al., 1988; van Riper et al., 2008; Sooge et al.,
2008).
While the two surveys produced similar results with respect to
species composition, a detailed survey of cottonwood and willow trees
produced a much more dynamic view of the tree populations (Nagler
et al., 2005b). In both surveys willows were more numerous than
cottonwoods, and willows are considered pioneer species. In the 1999
survey, most of the trees dated from the 1993 flood event, as
determined by tree rings, but a 1997 cohort was also present, as were
some trees started by floods in the 1980s (Fig. 2). By 2002, however,
the largest age class of trees was only two years old, started by the
2000 floods (Fig. 2). The 1980s and 1993 age classes had largely
disappeared. Based on these two surveys, the average age of willow
and cottonwood trees was only 3–7 years, suggesting a very rapid
turnover of trees on the floodplain from 1999 to 2002.
3.1.2. Vegetation mapping and GIS analyses
As expected, the vegetation map prepared from the aerial
photographs provided less detail than the ground data, yet they
provided a spatially complete survey that revealed some of the
dynamics of the tree populations in response to environmental
Table 1
Species composition and land cover classes for ground surveys conducted in the Native
Plant Zone of the Colorado River delta in 1999 and 2002.
Year
1999
2002
Species
Saltcedar (S)
Arrowweed (S)
Willow (T)
Seepwillow (S)
Cottonwood (T)
Quailbush (S)
Mesquite (T)
Common reed (E)
Bulrushes (E)
Land cover classes
Vegetation
Bare soil/open water
% of Vegetation
62.2 (2.2)
16.0 (2.1)
16.9 (1.4)
2.9 (0.4)
0.9 (0.1)
Not detected
Not detected
1.1 (0.2)
Not detected
% of Land cover
64.5 (3.0))
35.5 (1.5)
% of Vegetation
49.4 (1.6)
15.9 (1.1)
8.8 (0.7)
6.8 (0.6)
2.4 (0.3)
1.8 (0.5)
1.6 (0.5)
2.8 (0.4)
0.6 (0.1)
% of Land cover
70.3 (3.8)
29.7 (1.9)
Values are means and standard errors of means in parentheses. T = tree; S = shrub;
E = emergent wetland species.
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Fig. 2. Histograms showing the age class distribution of cottonwood and willow trees surveyed in the Colorado River delta riparian zone in 1999 (A, B) and 2002 (C, D). Arrows show
the year of the floods that started each cohort of trees.
drivers. Map estimates of fc, marsh areas and shrubs were generally
within 10% of ground estimates, but the vegetation map underestimated the amount of trees, because it could only detect trees N6 m
tall, whereas about half the trees were juveniles less than this height.
The detailed vegetation map prepared in 2002 and repeat aerial
photography were used to determine the cause of high tree mortality
and replacement. The vegetation map revealed that the ratio of live to
dead trees was 2.2:1 in 2002, supporting the conclusion of the tree
surveys of high tree mortality from 1999 to 2002. The map also
showed that fresh fire scars covered 12% of the floodplain in that year,
and that 82% of the dead trees were in burned areas (an example of
the GIS showing fire scars and live and dead trees is in Fig. 3) (from
Nagler et al., 2005b). We then selected 20 paired images from repeat
aerial photography from 1999 and 2002 to compare burned and
unburned areas (Fig. 4) (from Nagler et al., 2005b). Of the 20 images, 8
showed significant burn areas in 2002 that were not present in 1999.
Unburned plots had 59 trees per ha, compared with only 20 trees per
ha on plots with burns. Unburned plots measured in 2002 had 131% of
the trees present in 1999, showing that the 2000 floods had recruited
new trees to those plots. On the other hand, burned plots measured in
2002 had only 49% of trees present in 1999.
Fires appeared to be mainly of human origin, although lightning
strikes are also a possible cause. Household refuse is burned in the
riparian corridor, and these fires spread. Other fires are deliberately
set to burn out underbrush to provide access to the river, and others
are set by embers from wheat fields set on fire after harvest to burn
off the stubble. Furthermore, older trees are harvested by local
residents for firewood and lumber. Over the course of our surveys,
tree cover was stable at about 10–15% of vegetation cover, but this
was the result of new recruitment from flood events balanced
against high tree mortality from fire and other causes. Hence, any
actions that reduce flood frequency will rapidly reduce tree
populations, assuming that fire regimes and other modes of tree
mortality remain constant. Conversely, conservation measures that
improve tree longevity by reducing burning and harvesting will
enhance tree populations.
P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485
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Fig. 3. GIS of a section of the Colorado River riparian zone showing an area of extensive burns (dark areas) containing shrubs, both live and dead cottonwood and willow trees (CW).
The underlying aerial photomosaic is displayed in burned areas. Based on aerial photomosaics with pixel resolutions of 0.5–1.5 m.
3.2. Vegetation dynamics as affected by river flows
3.2.1. Estimating fc and LAI from remote sensing data
The four-band, DyCam images produced a sharp delineation of
open water, bare soil and vegetation. Hence, it was feasible to quantify
fc on the images using a point-intercept sampling method. All three
VIs tested were strongly, linearly related to fc, with fc being slightly
better correlated with NDVI (r = 0.91) than SAVI (r = 0.90) or EVI
(r = 0.89). In the DyCam series of images, fc ranged from 0.03 to 1.0
(mean = 0.46). The equation relating fc to NDVI (Fig. 5A) (from Nagler
et al., 2001) was:
f c = 1:8 NDVI − 0:08
isolated individuals or small clumps in this ecosystem, seriously
violated the assumption of a uniform overhead canopy built into the
Licor (Nagler et al., 2004). Second, VIs are strongly correlated with
ð1Þ
while the equation for EVI (also used in this study) was:
fc = 2:7 RVI − 0:01
ð2Þ
LAIs ranged from 1.8–2.6 among species. GLAI ranged from 0.3–2.2
and was less well predicted by VIs than fc, with r = 0.85, 0.81 and 0.80
for NDVI, SAVI and EVI, respectively.
Several problems emerged in using LAI as a primary biophysical
parameter in this ecosystem (Nagler et al., 2004). First, the Licor LAI2000 did not successfully measure LAI for all plant species when
results were compared to LAI determined by hand-harvesting leaves
on plants. Saltcedar and arrowweed gave similar results by both
methods, but cottonwood and willow trees, which tend to grow as
Fig. 4. Tree recruitment and survivorship in 20 plots in the riparian corridor of the
Colorado River delta from 1999 to 2002. Some plots experienced fire between sample
dates (B = burned plots) while others were unburned (UB). The bars show mean and
standard errors of total trees, surviving trees and new trees in 2002, as percents of the
trees present in 1999. Error bars are standard errors of means.
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Fig. 5. Calibration curves for estimating fractional vegetation cover (fc) from NDVI in the riparian corridor of the Colorado River delta. (A) shows fc vs. NDVI for 1999 DyCam aerial
images with 0.15 m resolution. (B) shows the relationship between DyCam and 1999 TM NDVIs measured on co-located areas of water, soil, partial riparian vegetation cover, and full
riparian vegetation cover (numbers in parentheses are sample size; plotted values are the mean and standard error of each cover class). (C) shows mean values and standard errors of
NDVI values for different cover classes and for image minimum, mean, and maximum values for four Landsat 5 TM and two Landsat 7 ETM+ images (Path 38, Row 38), 1992–2002.
(D) plots digital number (DN) NDVI values against calculated reflectance-based values for the same set of images as used in (C).
light reflection (and therefore absorption) from the canopy, whereas
LAI is only moderately correlated with light absorption by the canopy
(Monteith & Unsworth, 1990; Lu et al., 2003). In addition to LAI, light
absorption is also determined by leaf angles within the canopy and by
spectral properties of the leaves (Monteith & Unsworth, 1990; Nagler
et al., 2004; also reviewed in Glenn et al., 2007). Lu et al. (2003)
reported that NDVI was non-linearly related to LAI, whereas the
simple ratio (ρNIR/ρRed) was linearly related to LAI yet with
substantial scatter in the data for woody cover in Queensland,
Australia. In our ecosystem, the species ranged from planophiles
(cottonwood and willow) to extreme erectophiles (arrowweed), and
light absorption was poorly correlated with LAI across species (Nagler
et al., 2004). Since light absorption by the canopy is a determining
factor in physiological process such as primary productivity and ET, we
used fc rather than LAI as the primary biophysical variable for delta
vegetation.
Our findings support a theoretical analysis by Carlson and Ripley
(1997). They used a radiation transfer model to analyze the
contribution of local LAI (LAI of individual plants) and fc to GLAI in
less than full canopies with GLAI in the range of 2–3. Nearly all of the
variation in GLAI was due to fc rather than local LAI, hence they
concluded that for partially vegetated landscapes, VIs are most simply
related to fc rather than LAI, which in any case is difficult to measure
on-ground or by remote sensing methods (Asner et al., 2003). On the
other hand, fc was well predicted by VIs in this ecosystem.
3.2.2. Intercalibrating VIs across image acquisition dates and sensor
systems
A strong, near 1:1 relationship between DyCam and TM NDVI for
DyCam scenes co-located on a Landsat 5 TM image was found (Fig. 5B)
(Nagler et al., 2001). Furthermore, a series of summer TM and ETM+
images from 1992–2002 gave nearly identical mean values for soil,
P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485
1481
Fig. 6. Mean daily instantaneous flow rates (m3 s− 1) in the Colorado River at the SIB. Spikes of flows are related to water releases of excess flows to Mexico during El Nino cycles. The
period from 1964–1981 when little excess water was released was when Lake Powell behind Glen Canyon Dam was still filling. Dotted lines show the mean flow at Lee's Ferry (before
water is withdrawn for the U.S. or Mexico) and Mexico's allotment of water, diverted at Morelos Dam. The black circles show years in which TM images were acquired to compare
fractional vegetation cover with peak winter river flows.
water, and vegetation over the different images (Fig. 5C). Although
mean values can obscure differences among images, they can be used
in PIF analyses to determine if there are systematic differences in VIs
between sample dates (Janzen et al., 2006). DN values could be
converted to reflectance values through a simple linear relationship
(Fig. 5D) (from Nagler et al., 2001; Zamora-Arroyo et al., 2001). Since
NDVIs were highly stable over time, ground-calibrated NDVIs from TM
and ETM+ images could be used to estimate fc over different years.
3.2.3. Correlation of fc with river flows
We correlated fc determined on TM and ETM+ images from 1992
to 2006 with river flows crossing the SIB from the U.S. to Mexico
during the same period (Fig. 6) (Nagler et al., 2005b). Water releases
generally occurred in October through March, ceasing in summer
when irrigation demand was high. fc measured on TM images in
summer was moderately correlated with the volume of river flow in
the three years previous to the measurement period (r = 0.89–0.90);
however, the strongest predictor of fc (r = 0.97) was the number of
years of river flow prior to the measurement period rather than
volume of flow (Fig. 7). Each year of spring overbank flooding
produced new cohorts of plants (trees, saltcedar, and other shrubs) on
the floodplain that increased vegetation cover. On the other hand, in
years without flooding vegetation cover tended to decrease, due to
attrition of plants through fire and tree harvesting.
The results from Landsat images were checked against fc calculated
from MODIS imagery from 2000–2004 using Eqs. (1) and (2)
determined from the MQUALS study. Gao et al. (2003) reported a
near 1:1 relationship between NDVI and EVI values measured by
MODIS, ETM+, and MQUALS aerials over different landscape types in
the Jornada Experimental Grassland in New Mexico. MODIS estimates
of fc by NDVI and EVI produced similar trend lines, but NDVI estimates
were 20% higher than EVI estimates, and MODIS estimates were 20–
30% higher than the 2002 ETM+ estimate (Fig. 8A). By both sets of
satellite images, during the period from 2000 to 2006, in which there
were no large flood releases, fc decreased over time, and MODIS
imagery showed that the decrease was steeper in the Saltcedar Zone
than in the Mixed Vegetation Zone. The Saltcedar Zone is as much as
30 km wide, and without floods to bring water into the secondary
channels, large areas of vegetation burned or dried, whereas vegetation in the narrower Mixed Vegetation Zone deteriorated more slowly.
3.2.4. ET by riparian vegetation
The ground and satellite analyses show that occasional large floods
are required to germinate new cohorts of native trees and to maintain
vegetation cover in the CRD. However, the flood flows were not
necessarily the main source of water supporting ET, as these plants use
water from the aquifer to support growth.
The general approach of predicting ET from MODIS VIs and flux
tower data has been applied to riparian (Nagler et al., 2005c; Scott
et al., 2008) and other ecosystems at the regional (Cleugh et al., 2007;
Wang et al., 2007) and continental (Yang et al., 2006) levels of
measurement. The algorithm calibrated with riparian flux tower data
Fig. 7. Fractional vegetation cover in the riparian corridor of the Colorado River delta in
response to years of winter–spring flood releases prior to the summer in which TM
images were acquired to calculate fc. Zero years of flow indicates no winter flows in the
previous year; one year of flow indicates flows the previous winter only; and multiple
years of flow indicate two to four years of consecutive winter flows prior to the summer
of image acquisition.
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(SEM = 0.3) while predicted ET was 3.6 mm d− 1 (SEM = 0.2),
indicating very good agreement. However, the absolute ET estimates
are subject to an error or uncertainty of 20–30%, due to errors and
uncertainties in the flux tower measurements and in the simplifying
assumptions in the MODIS algorithim (reviewed in Glenn et al., 2007).
ET is driven by net radiation, wind speed and vapor pressure deficit
(Monteith and Unsworth, 1990), but net radiation and vapor pressure
deficit are highly correlated with Ta, and the atmospheric conditions
are water limited in this ecosystem, hence Ta provides an adequate
simplification of controls on actual ET for western U.S. riparian
corridors. The magnitude of errors is similar to those inherent in SEB
methods (e.g., McVicar & Jupp, 1999).
Fig. 8. (A) Fractional vegetation cover in the Mixed Vegetation Zone (closed circles) and
Saltcedar Zone (open circles) in the riparian corridor of the Colorado River delta,
estimated by MODIS NDVI (solid lines) and EVI (broken line); fractional cover
estimated for the Mixed Vegetation Zone by Landsat images are shown as stars. (B) ET
in the riparian corridor estimated by MODIS EVI and maximum daily air temperature.
(Nagler et al., 2005c) produced peak ET rates of about 5–6 mm d− 1
during midsummer (Fig. 8B) (from Nagler et al., 2008b), about half
the value of potential ET (e.g., 9–12 mm d− 1 elsewhere on the Lower
Colorado River; AZMET, 2008). These correspond to annual rates of
1.15 m yr− 1 and 1.11 m yr− 1 in the Mixed Vegetation and Saltcedar
Zones, respectively, similar to estimates of saltcedar ET on other
western U.S. river systems (0.8–1.4 m yr− 1) (see Cleverly et al., 2002,
2006; Devitt et al., 1998; Shafroth et al., 2005; Owens and Moore,
2007; Westenburg et al., 2006). These river systems have potential ET
rates of ranging from 1.8–2.0 m yr− 1, similar to the CRD.
In the original calibration study, the correlation coefficient (r) of
measured vs. predicted ET was 0.86 (Nagler et al., 2005c). Since
saltcedar is the dominant vegetation in the delta, the accuracy of the ET
estimate will depend on the accuracy with which it predicts saltcedar
ET. Saltcedar was the dominant vegetation at three of the flux tower
sites (one on the Lower Colorado River and two on the Middle Rio
Grande). Measured mean annual saltcedar ET was 3.7 mm d− 1
3.2.5. Water budget for the riparian corridor
Actual ET rates were multiplied by the area of the riparian corridor
to estimate annual water discharge due to ET (Nagler et al., 2008b).
We then compared this value to the amount available from surface
flows. The annual rate of ET from 2000–2004, projected over the
29,750 ha of riparian vegetation, amounted to 3.34 × 108 m3 yr− 1,
considerably more than could have been provided by the surface flows
in the river during this period (mean flow = 1.48 × 108 m3 yr− 1, see
Fig. 9). Water stored in the vadose zone (ca. 2–3 m above the aquifer)
could have supplied an additional small amount of water to the
vegetation, but most of the water for ET must have originated from the
aquifer. The reduction in ET in 2002, estimated by MODIS EVI, is also
shown in the low value of fc in that year, estimated by ETM+ NDVI as
well as MODIS (see Fig. 8A). However, ET rates recovered somewhat
after 2002 despite the absence of high-volume surface flows.
Even at low flows (2–5 m3 s− 1) at the SIB, the GIS of the river
channel showed that a continuous flow was maintained from the SIB
to the confluence of rivers (Nagler et al., 2005b), and at high flows,
most of the water enters the Gulf of California, as the residence time of
water in the riparian corridor is only 2 days (Cohen and Henges-Jeck,
2001). Therefore, we conclude that surface flows could not be the
main source of water for the riparian vegetation. On the other hand,
the Mexicali and San Luis Rio Colorado irrigation districts support
207,000 ha of irrigated cropland, receiving 2.0 × 109 m3 of water per
year. Irrigation efficiency in the valley is low, and has created a
mounded aquifer under the valley which approaches the surface in
the riparian corridor (Portugal et al., 2005). Surface drains convey
2.7 × 108 m3 (about 13% of applied water) to either the Gulf of
California or the Salton Sea for disposal (Cohen and Henges-Jeck,
2001). We conclude that the riparian corridor discharges an additional
15% of water applied to fields as ET, using water from the aquifer to do
Fig. 9. Comparison of annual flows in the Colorado River below the SIB and annual ET
rates in the riparian corridor in Mexico, 2000–2004. Values are means and standard
errors of 16-day (ET) or daily (flows) values.
P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485
so (Nagler et al., 2008b). This must be the main source of water
supporting the riparian vegetation.
4. Conclusions and recommendations
4.1. Ecohydrology of the riparian corridor
Occasional high-flow events (80 m3 s− 1 and above) produce
overbank floods within the levees that play two important functions
in the riparian ecology. First, they help maintain fc on the floodplain,
by germinating seeds to reestablish stands of vegetation lost to fire
and other sources of mortality between flood events. Rainfall is less
than 8 cm yr− 1, and without floods, the floodplain is dry and seedlings
cannot establish. Second, the floods specifically favor the establishment of new cohorts of cottonwood and willow trees amidst saltcedar
and arrowweed stands (Nagler et al., 2005b). A pulse flood regime
washes salts from the surface soils and scours out new areas of bare
soil, allowing new cohorts of trees and shrubs to germinate on the
floodplain (Poff et al., 1997; Shafroth et al., 2002). Cottonwood and
willow trees require only one year of flood to establish, after which
they derive their water from the aquifer (Mahoney and Rood, 1998;
Nagler et al., 2005b). The trees quickly overtop the surrounding shrub
layer and become the locally-dominant vegetation. Without these
occasional flood flows, the river bank would become increasingly
saline, and eventually would not support mesic cottonwood and
willow trees, similar to most flow-regulated portions of the river in
the U.S. (Shafroth et al., 2002; Nagler et al., 2008a).
On the other hand, smaller maintenance flows (2–5 m3 s− 1) are
needed to support high-quality avian habitat. Hinojosa-Huerta et al.
(2008) conducted a multivariate study of the factors leading to high
terrestrial bird density and diversity in the riparian corridor, and
concluded that the most important factor was proximity to standing
water (to support insects), followed by the presence of some native
trees (10–15%) to provide an overstory layer above the dominant
saltcedar. The maintenance flows also support marsh habitat,
important for water birds.
1483
The relationship between the size of objects under study and the
resolution of the scene can be described as either H-resolution (pixel
size is much smaller than the objects of interest) or L-resolution (pixel
size is larger than individual objects of interest) (Strahler et al., 1986;
McDermid et al., 2005). Aerial photographs were H-resolution with
respect to trees and shrubs, but fc scored on the DyCam images was Lresolution since the whole scene was used to calculate fc. Landsat 5
TM and Landsat 7 ETM+ images were H-resolution at the level of
classification of land cover types in the scene, but L-resolution with
respect to fc over the floodplain. MODIS images were L-resolution with
respect to ET and fc. VIs been found to be nearly scale-invariant across
different levels of measurement in vegetated ecosystems (Hall et al.,
1992; Sellers et al., 1992), facilitating their use across different spatial
scales. However, in cross-scalar vegetation studies such as this one,
discrepancies between sensor systems, VIs, and different acquisition
dates introduce errors on the order of 20–30% (e.g., Lu et al., 2003;
Fisher & Mustard, 2007). Therefore it is important to confirm the
major findings at several levels of measurement when possible.
In this study, we relied on empirical methods to intercalibrate sensor
systems and to estimate fc and ET. These methods were appropriate for
our purposes, but the tradeoff is that the methods cannot necessarily be
extrapolated to other study areas. Furthermore, our study area was
relatively small (ca. 30,000 ha), allowing us to digitize individual
landscape features such as trees and fire scars manually, whereas
ecosystem monitoring at larger scales requires automated classification
procedures (McDermid et al., 2005). The Colorado River delta habitats
are expected to face increasing challenges over the next 50 years due to
climate change, water management decisions, and changing land use
practices. The present data set can be used as baseline data on the state
of the riparian corridor in a relatively wet period. However, the methods
do not necessarily represent the optimal set of monitoring protocols for
the future. Monitoring protocols need to be updated to keep pace with
the continuing refinement of remote sensing and analyses technologies,
the addition of new earth-observing satellites and the loss of older ones.
Continued field data collection is needed as it provides validation of
remote sensing data, and observational data to develop conceptual
models of ecosystem functioning.
4.2. Utility of remote sensing methods for monitoring riparian habitats
4.3. Recommendations for resource management
Ground surveys provided a detailed snapshot of vegetation
conditions for a given time period. They were essential for determining the species composition of the riparian corridor, habitat
preferences of birds (Hinojosa-Huerta, 2006; Hinojosa-Huerta et al.,
2008) (the main species of conservation interest in this ecosystem),
and for validating the remote sensing methods, yet by themselves
could not reveal vegetation dynamics, consumptive water use, or be
used for routine monitoring.
Vegetation mapping and GIS analyses, combined with the use of
repeat aerial photography and satellite imagery, allowed us to explore
the vegetation dynamics in more detail than could be achieved by
ground surveys alone. An unexpectedly high turnover of native trees
on the floodplain was revealed, with recruitment of new trees keeping
pace with the high tree mortality rate due to fire and timber
harvesting. Cohorts of new trees were started with each major flow
event exceeding approximately 80 m3 s− 1, occurring in 1983–1988,
1993, 1997–2000. Overall vegetation density was also associated with
flow events, and with the diminution of flows since 2000, fc and ET in
the riparian corridor have decreased.
These patterns were made evident by correlating hydrological
information with vegetation patterns revealed by high-resolution aerial
photography and lower resolution, Landsat 5 TM, Landsat 7 ETM+, and
MODIS time-series satellite images. Although hyperpectral imagery can
be used to distinguish riparian species in some cases (e.g., Hamada et al.,
2007), we were not able to completely resolve individual species even
on high-resolution aerial photographs, hence the ground surveys were
critical to our interpretation of remote sensing imagery.
The importance of standing water in supporting bird habitat
argues strongly for preserving the small-volume flows that enter the
river channel from both the U.S. and Mexico. In both countries,
however, efforts are underway to recapture some of these flows for
human use. Their value in supporting biodiversity should be taken
into account and weighed against the relatively small amount of
additional water they would provide for human use if recaptured.
The occasional high-volume flows are important for starting new
cohorts of native trees and in washing salts from the riverbanks. The
trees, in turn, are important elements in supporting avian habitat,
even when saltcedar is the dominant plant species (Sooge et al., 2008;
van Riper et al., 2008). The flows are related to ENSO cycles that
increase the snowpack in the Colorado River watershed during El
Nin o events, resulting in water releases from the U.S. dams in spring
(Thomas, 2007). These releases are expected to diminish in the future
due to effects of climate change on the watershed (Christensen &
Lettenmaier, 2007). As an immediate step, implementing conservation measures to reduce tree mortality by suppressing fires and
discouraging tree cutting would benefit the ecosystem.
Active restoration programs (for example, planting of trees) could
also enhance the habitat value of the riparian corridor. This is feasible
in light of the findings that trees need only one year of flooding to root
into the aquifer, and only 10–15% of trees markedly improves the
habitat for birds (van Riper et al., 2008). Therefore, tree planting
would not require large quantities of surface water for establishment,
and they use about the same amount of ground water as saltcedar
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(Nagler et al., 2005c; Shafroth et al., 2005) so they would not have a
large effect on the regional water budget.
The small- and large–volume flows from the U.S. to Mexico are
important in maintaining aquatic habitat and establishing native trees,
but they are not the main source of water supporting riparian ET. The
water balance shows that most of the water supporting the riparian
vegetation must come from the regional aquifer, which is shallow and
only slightly saline at present. However, climate change and changing
land use patterns (including diversion of water out of the basin for urban
use), could lead to increased water depth and salinity of this aquifer over
the next 50 years. Hence, a binational conservation program for this
ecosystem, including explicit recognition of the role of waste flows and
ground water in supporting ecosystem functions, is needed to maintain
the present high habitat value of the riparian corridor for the future.
Without legally-binding laws regarding environmental water resources
(flood flows, agricultural return flows, groundwater), the long-term
sustainability of CRD ecosystems is in doubt.
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