Remote Sensing of Environment 113 (2009) 1473–1485 Contents lists available at ScienceDirect 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 1474 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 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 1475 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). 1476 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 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). 1477 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. 1478 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 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 1479 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. 1480 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 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. 1482 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 (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 1484 P.L. Nagler et al. / Remote Sensing of Environment 113 (2009) 1473–1485 (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. 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