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Watershed assessment along a segment of the Rio Conchos in Northern Mexico using satellite images
ARTICLE IN PRESS Journal of Arid Environments Journal of Arid Environments 56 (2004) 395–412 www.elsevier.com/locate/jnlabr/yjare
Watershed assessment along a segment of the Rio Conchos in Northern Mexico using satellite images Me! lida Gutie! rrez*, Elias Johnson, Kevin Mickus Department of Geography, Geology and Planning, Southwest Missouri State University, 901 S. National, Springfield, MO 65804, USA Received 28 June 2002; received in revised form 5 November 2002; accepted 2 April 2003
Abstract Satellite images were used to illustrate the usefulness of such data in evaluating the ecological impacts of precipitation and land use on selected segments of the lower Rio Conchos in northern Mexico. Variations in the size and turbidity of impounded reservoirs, riparian vegetation, soil salinity and land use within the Rio Conchos basin were analysed using four Landsat TM images over a period of 10 years. A variety of image enhancements were applied to determine subtle changes between the images. These, when combined with precipitation and historical land-use data as well as one time water quality and soil EC, provided useful interpretation of the images, and therefore, in the monitoring of the basin. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Chihuahuan desert; Remote sensing; Rio Conchos; Rio Grande basin; Riparian vegetation; Soil salinity
1. Introduction Arid areas in northern Mexico as well as most arid lands elsewhere, pose a challenge to hydrological and solute balance determinations due to the extensive spatial and temporal variability in their hydrological properties and major limitations in road accessibility and availability of quality climate, hydrologic and water quality data (Scanlon et al., 1997; Gleick, 1998). Fortunately, satellite images have proved to be a powerful tool in the monitoring of certain landscape variations *Corresponding author. E-mail address: [email protected] (M. Guti!errez). 0140-1963/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0140-1963(03)00060-0
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(soil salinity, soil erosion, water quality/transparency, land cover) in arid environments (Sharma, 1989; Asner et al., 1999; Havstad et al., 1999; Kalra and Joshi, 1996; Diouf and Lambin, 2001; Kahn et al., 2001). In order to evaluate landscape and river variations in northern Mexico we conducted a multi-temporal study of a series of Landsat Thematic Mapper (TM) scenes spanning over approximately 10 years (1986–1997) along a portion of the lower Rio Conchos basin (Fig. 1). The analysis of the images was combined with the available data on precipitation, river flow, water quality, and land use to verify the results obtained from the images. There have been few remote-sensing studies in northern Mexico (Pulido et al., 1997, 1998; Unland et al., 1998) and none within the Rio Conchos basin. This, together with lack of detailed ground data (historic or monitoring) precluded a detailed analysis, but a general overview of ecological variations within the watershed was nevertheless obtainable. The purpose of the study was to determine if and how satellite images can be used to evaluate the ecological impacts of precipitation (including droughts) and land use onto the riparian area and water quality of a segment of the lower Rio Conchos. To do this task, we selected specific areas of the lower Rio Conchos basin that illustrate the usefulness of satellite images in making such evaluations. A variety of satellite image enhancement techniques were used to determine variations in water levels and surface changes of reservoirs along the Rio Conchos, their water quality (e.g. turbidity), riparian vegetation abundance, soil salinity and land use.
2. Hydrologic and geologic setting of the study area The Rio Conchos is a major river in northern Mexico, a large portion of it flowing through the Chihuahuan Desert. The Rio Conchos basin is part of the Rio Grande basin, which is shared by Mexico and the United States of America. Variations in the amount of water flowing from the Rio Conchos into the Rio Grande have been identified as the most important potential impact to the water supply of the lower Rio Grande basin (HARC, 2000). Although the flow of the Rio Conchos is determined at several gauging stations along its course, historical discharge information available to the public is restricted to a gauging station near the border city of Ojinaga. A hydrological report of the State of Chihuahua (INEGI, 1999), a 1:250,000 hydrological map of the area (INEGI, 1983) and a report by Gutie! rrez and Borrego (1999) provide background water quality information on the lower Rio Conchos. Kelly (2001) reports an overview of water availability and land uses in the Rio Conchos basin and Rodriguez et al. (1999) assess ground-water resources in central Chihuahua. Field measurements of water flow and evaporation (e.g. by lysimeters) and water quality data are scarce. In contrast with the Rio Conchos basin, extensive work has been performed in the United States’ portion of the Rio Grande basin (Popp and Laquer, 1980; Ellis et al., 1993; IWBC, 1994, 1997; TNRCC, 1994; HARC, 2000; Cole and James, 2001). Work involving satellite imagery within the Rio Grande basin has been conducted in a natural resources inventory (Richardson et al., 1996), monitoring the responses of
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Fig. 1. A June 1992, Landsat TM band 4 image showing the study area and features mentioned in the text within the state of Chihuahua, northern Mexico. The Rio Conchos is flowing north and joins the Rio Grande approximately 183 km downstream from the Luis L. Leon reservoir.
vegetation to hydrological fluxes (Havstad et al., 1999), monitoring cotton stalk elimination (Richardson et al., 1992), native riparian forest vegetation (Leonard et al., 1998; Elmore et al., 1999), grassland (Langley et al., 2001) and the effects of soil salinity on crops (Wiegand et al., 1992).
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The study area includes a stretch of the Rio Conchos approximately 100 km long (Fig. 1) with little to no human disturbances affecting it. However, this stretch of the river goes through self-cleaning processes after it receives large waste loads (irrigation return drains and domestic wastes) upstream at the Rio San Pedro and Rio Chuviscar confluences (INEGI, 1999). The surrounding topography and climate are relatively unchanged along the first 75 km of flow, with the surface geology changing from mainly alluvium and Tertiary igneous rocks to a suite of limestones and shales during the last 25 km of flow before reaching the Luis L. Leon reservoir (INEGI, 1981).
3. Precipitation data Precipitation data available from www.sequia.edu.mx consist of monthly average rainfall collected by one or more of seven meteorological stations within the study area from 1983–1995. The monsoon pattern is evident for precipitation, with June, July and August being the wet months of the year. After 1995, the only precipitation data available were a 5-year average for the whole basin. Daily flow of the Rio Conchos measured at a gauging station near Ojinaga (183 river kilometers downstream from study area) as well as water quality (reported as electrical conductivity (EC)) are available from the International Boundary and Water Commission annual reports at www.ibwc.state.gov/. Although this station is not within the study area, the amount of precipitation received in the central part of the basin and the flow at this gauging station positively correlated (correlation coefficient +0.72). Fig. 2 shows the average monthly precipitation for the study area for 1983–1995 (1996–1998 data is the 5-year average for the whole Conchos basin) and Rio Conchos flow at the gauging station near Ojinaga, in m3/s.
Fig. 2. Average monthly precipitation for study area and Rio Conchos flow at gauging station near Ojinaga (source: CAN and IBWC).
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4. Satellite imaging data and processing Four Landsat TM images (row 40, path 32) from April 4, 1986, May 20, 1989, June 21, 1992 and June 3, 1997, were acquired and used in our analysis. The Landsat TM data have spectral bands 1–5 and 7 that have spatial resolutions of 30 m. The June 1997 image only contains bands 1–4. The thermal band (6) was not used in our study because it represents emitted energy, or radiant temperature. Its large spatial resolution of 120 m does not allow for the identification of smaller-scale features. The TM bands represent different frequencies of the electromagnetic spectrum that allows for the identification of a wide range of geological, biological and hydrological features. TM bands 1–3 represent visible electromagnetic radiation with wavelengths of 0.45–0.52, 0.52–0.60 and 0.63–0.69 mm, respectively. Band 4 represents near infrared with wavelengths of 0.76–0.90 mm, and bands 5 and 7 represents mid-infrared with frequencies of 1.55–1.75 and 2.08–2.35 mm, respectively. The radiometric resolution of all the data was 8 bit with 256 levels of brightness. 4.1. Image preparation As the Landsat scenes were being acquired, ground control points (GCPs) from a number of recognizable sites easily identified on the images were collected throughout the study area using a global positioning system (GPS). The positional data were collected in a Universal Mercator Transverse (UTM) co-ordinate system which were used for vector data, such as roads and hydrologic features, obtained for this study. Because of the expansive area these Landsat images cover (about 100 100 nautical miles) and the inaccessibility of some of the more remote areas within these scenes, a minimal number of GPCs were collected. Furthermore, precise locational data could not be obtained at this time from other sources. Therefore, with only ten GCPs, the June, 1992 Landsat image was rectified. Errors at some of these points were quite large as we first attempted a linear transformation; however, with a quadratic polynomial, the largest RMS error was essentially a pixel. The remaining images were rectified by an image-to-image method from the 1992 scene with RMS errors less than 0.6 pixels. Because of our concerns of obtaining a fairly accurate rectification for this change detection study, other preprocessing techniques followed. A nearest-neighbor resampling procedure was used in geoprocessing so that the digital numbers (Dns) that represent the brightness values of the pixels for each band were not altered. The next step in processing the data was to attempt to remove the atmospheric effects that tend to attenuate the signal. This is usually a difficult procedure with older data, as a model of the atmosphere at the image location should be obtained (Jensen, 1996). This could not be accomplished for our data sets, so we used a haze removal algorithm (Jensen, 1996) in which the histogram of brightness values is evaluated relative to a reference target, such as deep, clear water that should have Dns at or near zero. This procedure was completed for all four Landsat scenes.
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4.2. Image processing The TM data were analysed using the ERMAPPER image processing software (ERMAPPER, 1995). There are a wide variety of techniques that can be used to enhance and analyse remote-sensing data that contain different spectral bands (Jensen, 1996). These include contrast stretching, band ratioing, principal component analysis (PCA), spatial filtering, image classification and indices for specific mineral/vegetation types derived from field reflectance measurements, e.g. gypsum, by Neville et al. (2000). We applied several of these techniques in order to emphasize different features including water turbidity, soil salinity and land-use change in our images. As shown by the analysis below there is no one best technique in determining the various parameters used in analyzing a watershed. The techniques we used represent a start in determining regions and parameters that might be further investigated using more detailed field studies. As mentioned earlier, the limited amount of field data was a major limitation to our study; as with most remote-sensing image enhancement techniques, field data are required for quantifying the results.
5. Results The extent of the Rio Conchos basin and the approximate location of the areas that were analysed for water levels and turbidity of reservoirs, riparian vegetation, soil salinity, and land-use variations are shown in Fig. 3. 5.1. Reservoirs To determine potential changes in water levels and turbidity of the reservoirs within the Rio Conchos basin, we looked at the Luis L. Leon reservoir (Fig. 4). To emphasize any turbidity in the water, we performed density slicing of the average of bands 1 and 2 as described below. We chose bands 1 and 2 as according to Campbell (1996) electromagnetic energy with a wavelength of 0.48 mm has a peak transmittance in clear water; at longer wavelengths, absorption of sunlight increases until most energy is absorbed in the near-infrared and longer wavelengths. With increased turbidity, this spectral characteristic relative to a water body changes, i.e. background energy increases and the peak wavelength becomes longer. To reveal those areas of the reservoir with the greatest amount of turbidity, an average of the blue-green (band 1) and green (band 2) bands were averaged for the purpose of displaying brightness values for water where some impurities, especially sediment, exist. These averaged Dns for bands one and two were saved as a subset for the reservoir and immediate land area. Because some clouds covered a portion of the reservoir for the 1989 scene, a threshold value for the cloud’s higher brightness values were sought. From a close inspection of Dns over the reservoir for the four scenes, Dns of over 60 represented cloud reflectance. Therefore, a threshold of brightness values from zero to 60 was used to display turbidity over the reservoir and
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Fig. 3. Location of the Rio Conchos basin (dashed line) and the sites selected for analysis using the remote-sensing images. These include: (1) water levels and turbidity of reservoirs, (2) riparian vegetation, (3) soil salinity and (4) land use.
to mask out most of the effects of cloud cover along with most land features. To more clearly quantify the Dns that characterize turbidity, density slicing of brightness values using six steps was performed on the images and a linear contrast stretch with a 99% clip was added to each subscene. From inspection of the density sliced images little turbidity is observed in the 1986 image. This scene was captured earlier than the other scenes during droughty conditions in April and the two preceding months. With little precipitation falling during these months little turbidity is observed. For the 1989 image, turbidity values were apparently quite high. However, this was the scene with scattered clouds covering portions of the reservoir. Even with the attempt to mask these clouds with the use of a threshold Dn
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Fig. 4. Landsat TM images of a small region surrounding the Rio Conchos that includes the Luis L. Leon reservoir. The images are averages of bands 1 and 2 using a density slice of the brightness values in six steps for: (a) April 1986, (b) May 1989, (c) June 1992 and (d) June 1997. Dn values greater than 60 were masked out. The brighter areas within the reservoir approximates the amount of turbidity. This figure emphasizes the change in reservoir size and turbidity over the years. The river is flowing in a northeast direction.
value, the bright areas in this figure could be the result of some cloud reflection, especially since this was a droughty year. The 1992 scene shows considerable turbidity in the reservoir except near its center. The brightest areas of the image is where the Rio Conchos enters the reservoir. This was a rather wet spring in this area and should highly correlate with the greater turbidity levels observed. Finally, the 1997 scene shows high amounts of turbidity where the Rio Conchos enters the reservoir and along the eastern shore. There was no precipitation data available for
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this year to relate to the observed reflectance values. Similar patterns as those seen in Figs. 4a–d can be determined from band ratioing of 1 and 7; however, we did not use this method as the June 1997 image does not contain band 7. The above technique also shows the variation in shape and size of the Luis L. Leon reservoir. The shape of the reservoir varies considerably from image to image and this is especially evident on the 1997 image (Fig. 4d) when the reservoir clearly has the smallest size of the four images. Although this fact correlates well with the small amount of rainfall that normally occurs in June, the relationship between rainfall, river flow and reservoir size and turbidity is not always straightforward. For the 1986 and 1992 images (Figs. 4a and c), the reservoir sizes are larger than in 1989 (Fig. 4b) but the amount of rainfall and river flow is relatively high in 1986 and low in both 1992 and 1989. Clearly, other factors are influencing these results with variations in water withdrawal for irrigation within the Delicias region probably accounting for most of the differences seen in the images. The reduction in its cultivated acreage throughout the 1990s (see Section 5.4) may explain the higher reservoir level seen in the 1992 image. The size, shape and turbidity of the other impounded reservoir within the study area, the Francisco I. Madero also varied in similar manner as the Luis L. Leon reservoir, so we will only show images of the Luis L. Leon reservoir. The changes on the shallower southern end of the Luis L. Leon reservoir can be seen in Figs. 4a and d. The swollen area of the reservoir, as can be observed in the 1986 (Fig. 4a) and 1992 (Fig. 4c) images, is likely associated with rain that fell in the Delicias area within the previous weeks (3.7 and 52.5 mm respectively) (Fig. 2). Yet, effects on the surrounding vegetation (caused either by water quality or water table variations) and soil salinity are expected to give more insight into ongoing hydrological processes. 5.2. Riparian vegetation In arid regions, riparian areas are narrow but nevertheless vital to the establishment of wildlife habitat. Besides direct removal by individuals, e.g. as a source of wood, disappearance of riparian vegetation has been associated with lowering of water tables (Unland et al., 1998). Riparian communities within the Chihuahuan desert are complex and not well understood (Unland et al., 1998; Leonard et al., 1998). However, Leonard et al. were able to discriminate among the vegetative cover and found the dominant shrub and ground species using color infrared photography for an area near the Falcon Dam on the Rio Grande. There are a number of image enhancement techniques that can determine the regions of vigorously growing vegetation. Two such techniques are the tasseled cap transformation (Crist and Cicone, 1984) and the normalized difference vegetation index (NDVI) (Larsson, 1993). We used both of these techniques to demonstrate and compare their usefulness as a means to develop ‘‘greenness indices’’. A comparison could be made for all but the 1997 scene because of the absence of band 7 that is necessary for the tasseled cap transformation. Nevertheless, the tasseled cap transformation was employed to three scenes (Figs. 5a–c) and NDVI to all four images (Figs. 6a–d) to assess vegetative greenness. We applied these techniques to a
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Fig. 5. Landsat TM images of a portion of the Rio Conchos west of Luis L. Leon reservoir shown as the greenness of the tasseled cap transformation for: (a) April 1986, (b) May 1989 and (c) June 1992. Bright regions along the river represent vigorously growing vegetation.
segment of the Rio Conchos, namely as the river enters the Luis L. Leon reservoir, since we expected this area to have less human disturbances and show the response of native vegetation. Both techniques show vigorously growing vegetation appearing in bright white, contrasting with the darker background, as can be observed along the river in Figs. 5b and 6d. More riparian vegetation was expected to indicate a wet year. However, we observed the opposite. Riparian vegetation covered the riverbed during the drier years (1989 and 1997) as shown in Figs. 5 and 6. This indicated a more complex pattern than was expected and is similar to that reported by Diouf and Lambin (2001), who explained this pattern as a result of competition among species; those that respond favorably to wet conditions and those that succeed under drier conditions. While both techniques clearly show the vigorously growing vegetation along the river, the tasseled cap transformation records variations within the bright regions. This can be seen in the 1989 image, as the NDVI image (Fig. 6b) is basically one shade of bright gray while the tasseled cap transformation (Fig. 5b) shows a variation of grays implying the region has various stages of vegetation growth or different types of plants. This same trend can be seen in all the images and implies for our region the tasseled cap transformation works best in defining the vigor of the vegetation.
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Fig. 6. Landsat TM images of a portion of the Rio Conchos west of Luis L. Leon reservoir shown as a NDVI transformation for: (a) April 1986, (b) May 1989, (c) June 1992 and (d) June 1997. Bright regions along the river represent vigorously growing vegetation.
5.3. Soil salinity Soil salinity is a common problem in arid, irrigated areas and has been the focus of extensive research, e.g. Kalra and Joshi (1996), Neville et al. (2000), Pulido et al. (1997, 1998), Rhoades et al. (1999, pp. 197–215), Taylor et al. (1996) and Wiegand et al. (1992). Use of EC has been used extensively in conjunction with satellite images in northern Mexico (Pulido et al., 1997) and arid areas elsewhere (Rhoades, 1982; Neville et al., 2000). Pulido et al. (1998) determined the spectral classes useful for defining soil salinity and wheat crop yield in Sinaloa, Mexico. The use of soil salinity derived from remote-sensing data determined that the crop yield was reduced with an increase of salinity above 4 dS/m (4000 mS/cm). Neville et al. (2000) derived an electromagnetic inductance index (EI) to determine increased areas of soil EC by collecting soil samples in known saline soil areas and determining their TM spectral response. Their EI equation was determined by applying a regression analysis to the field data. This is the best technique to determine an EI for a region as each region is unique due to the variety of minerals in the soil and a general EI equation probably cannot be determined. Since we did not determine the spectral response to our soil samples and Neville et al.’s equation used TM band 5, we decided to use the selective principal component (SPCA) technique of Chavez and Kwarteng (1989). Normal PCA uses all seven TM bands, however Chavez and Kwarteng (1989) showed that some information might be mapped into one of the unused components and how SPCA can be used to map the spectral contrast between specific spectral bands. According to these researchers, several
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Fig. 7. Landsat TM images of a region north of Meoqui shown as the first component of a SPCA using bands 2 and 4 for: (a) April 1986 and (b) June 1992. Bright areas within the test area represent regions of higher soil salinity.
bands can be relatively uncorrelated, for which using an SPCA with these bands might bring out unique information. This procedure is commonly a trial and error approach that must be used with the data statistics and known ground truth information. We found that the first principal component and of bands 4 and 7 highlighted known areas of soil salinity best for the 1986, 1989 and 1992 images. We used the first principal component of bands 2 and 4 for the 1997 image. On Figs. 7a–d, areas with high salinity are shown in white near the test area, although
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Table 1 Composition of soil solution (1:2 soil water ratio) and water in the vicinity of a patch of highly saline soils between Meoqui and Delicias pH EC (mS/cm) Alkalinity (mg/L CaCo3) Na (ppm) Mg (ppm) K (ppm) Ca (ppm) Soil—highly saline 8.68 248,000 Soil—regulara 8.36 1300 Surface waterb 8.24 1462 Irrigation canal water 8.40 2080 Groundwaterb 7.70 N/A a b
2500 175 N/A N/A N/A
8970 107 89 123 163
4 21 22 5 2
75 28 6 4 1
49 119 169 8 45
Non-cultivated, non-saline soil near Meoqui. Average of three samples.
one has to be careful in the interpretation as this color could also result from factors other than salinity. An area of known salinity was used as a reference as follows. Soil samples from an area affected with salinity near Meoqui, and within the irrigation district, were collected in November 2000 and analysed for EC, pH, alkalinity and major cations. A sample from a nearby non-cultivated, non-saline land parcel was also collected for comparison purposes. The results are shown in Table 1. The reflectance properties of this area of known high salinity (Figs. 7a–d) were the same as other areas of suspected salinity. The presence of similar patches of saline soil in nearby non-irrigated land within the basin and the fact that basins next to this are closed basins with extensive playa lakes suggest that these salts are naturally occurring, although intensive irrigation and being located over a shallow water table may have contributed some influences. Similarly to the yearly variations on riparian vegetation, the images did not show an increase of salinity during dry years, as we had expected. At the general level of this study, no significant changes were detected in the amount of land exhibiting soil salinity, which suggests that most of the salinity was formed prior to the 10 years span of our images. 5.4. Land use The main land use affecting the water quality and quantity of the area is irrigation (Kelly, 2001). The irrigated land within the study area is part of the Irrigation District 05 (Delicias), which comprises nearly 105,000 ha of land. A drought of 1983 and the 1990s (the latter extended from 1992 to 2002; CNA, 2000) caused a reduction in the amount of water allocated to farmers, and therefore in the amount of cultivated land from 107,000 ha in 1989 to 16,500 ha in 2002 (Fig. 8). As a test for using Landsat TM images in determining land-use changes, a small area within the Irrigation District was selected as representative of the district, which included an alluvial fan and agriculture parcels within the irrigation district by the Rio San Pedro northwest of Delicias (Figs. 3 and 9). The images were converted to
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Fig. 8. Area cultivated in the irrigation district 05 (Delicias) between 1983 and 2002 (source: Kelly (2001) and Diario de Delicias (2002).
RGBs using bands 4, 3 and 1 to show growing vegetation as bright red, which corresponded almost entirely to cultivated land. There are small patches of red in the nearby hill probably due to vegetation growing in a narrow arroyo. Next, the images were classified using a maximum-likelihood unsupervised classification technique (Jensen, 1996) to determine which areas of cultivated vigorously growing land with vegetation could be identified, isolated and the acreage measured. Figs. 9a and c show the images as band 4 for June 1992 and 1997 and the corresponding classified images (Figs. 9b and d). These images depict the drastic reduction in cultivated land reported by CNA (2000) and Kelly (2001). The area selected (with a total area of 8183 ha) showed a reduction in hectares from 18.6% in 1986, 19.6% in 1989 and 18.4% in 1992, to 9.6% in 1997.
6. Discussion Remote-sensing analysis of satellite images is a highly effective technique in assessing watersheds where the lack of data and remoteness is an obstacle. A variety of remote-sensing image enhancement and interpretation techniques allow for the user to emphasize particular aspects in the image. In our study, changes in soil salinity and riparian vegetation as determined by selective principal component and tasseled cap transformation, respectively, can be related to changes in precipitation and/or land use; however, the relationship is complex. Ground data are especially important when interpreting satellite images and as shown above were necessary in interpreting areas of soil salinities. A complete watershed analysis requires the interpretation of multiple aspects of the basin as shown by our preliminary study of the lower Rio Conchos. Such an analysis is required for good management of the basin and to estimate the impacts of different natural phenomena, e.g. droughts, and human activities such as the removal of riparian vegetation and increase in agricultural land use. The techniques utilized in this study were successful in showing the variations in several watershed parameters that are usually used in monitoring a basin. Several
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Fig. 9. Landsat TM images of an agricultural region along Rio San Pedro northwest of Delicias shown and band 4 for: (a) June 1992 and (c) June 1997. Figures (b) June 1992 and (d) June 1997 represent an unsupervised classification for cultivated land with growing vegetation (dark areas). The area northwest of the agricultural region are Tertiary volcanic rocks cropping out in low mountains and an alluvial fan. The classified region within this area represents growing vegetation. The Rio San Pedro is flowing in a northeasterly direction and it is shown in the southeast corner of each image.
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unexpected results were found including the increased abundance of riparian vegetation in a dry year as opposed to a wet year and the relative unchanged surface area affected by saline soils in either wet or dry years. An interesting observation determined from the unsupervised classification of the images was the proliferation of ‘‘abandoned’’ irrigation plots as the drought conditions intensified towards the latter half of the 1990s. This result was obtained in a small region, approximately 5% of the entire agricultural area of the Rio Conchos basin, northwest of Delicias and shows the usefulness of interpreting the various images. The next step would be to conduct a detailed supervised classification of the entire agricultural region of the lower Rio Conchos basin, which can be seen on Fig. 1, to determine if these phenomena are more widespread. When combined with a soil salinity analysis of the abandoned plots, a more complete estimation of the extent of the drought conditions could be determined for the lower Rio Conchos basin. Such a study would require additional images and ground data, including soil analyses and ground truthing of the classified abandoned plots.
Acknowledgements We would like to acknowledge the Pan American Center of Environmental and Earth Studies (PACES) at the University of Texas at El Paso who kindly provided us with several Landsat TM images. A faculty development grant from Southwest Missouri State University partly supported this study.
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ERMAPPER, 1995. Reference Manual for ERMAPPER Software, v. 5.0. Earth Resource Mapping Pty Ltd., San Diego, California. Gleick, P.H., 1998. The World’s Water. The Biennial Report on Freshwater Resources. Island Press, Washington, DC., 307pp. Guti!errez, M., Borrego, P., 1999. Water quality assessment of the Rio Conchos, Chihuahua, Mexico. Environment International 25, 573–583. HARC, 2000. Houston Advance Research Center, and Instituto Tecnologico y de Estudios Superiores de Monterrey. Water and Sustainable Development in the Binational Lower Rio Grande/Rio Bravo Basin. Final Report. Center for Global Studies. HARC, Woodland, TX, 1045pp. Havstad, K., Kustas, W., Rango, A., Ritchie, J., Schmugge, T., 1999. Jornada experimental range: a unique arid land location for experiments to validate satellite systems. Remote Sensing of the Environment 60, 13–25. ! INEGI, Instituto Nacional de Estad!ıstica, Geograf!ıa e Inform!atica, 1981. Carta Geologica: Chihuahua, Mexico. Escala 1:1,000,000, Mexico, D.F. ! INEGI, 1983. Carta Hidrologica de Aguas Superficiales: Delicias, Chihuahua. Escala 1:250,000 Mexico, D.F. ! INEGI, 1999. Estudio Hidrologico del Estado de Chihuahua. Instituto Nacional de Estadistica, Geografia ! Coahuila, Mexico. e Informatica. Torreon, IWBC, 1994. Binational Study Regarding the Presence of Toxic Substances in the Rio Grande/R!ıo Bravo and its Tributaries along the Boundary portion between the United States and Mexico. International Water and Boundary Commission, United States and Mexico, El Paso, TX, 246pp. IWBC, 1997. Second Phase of the Binational Study Regarding the Presence of Toxic Substances in the Rio Grande/R!ıo Bravo and its Tributaries along the Boundary Portion between the United States and Mexico. International Water and Boundary Commission, United States and Mexico, El Paso, TX, 138pp. Jensen, J.R., 1996. Introductory Digital Imaging Processing: A remote sensing perspective. Prentice-Hall, Upper Saddle River, NJ, 316pp. Kahn, M.A., Gupta, V.P., Moharana, P.C., 2001. Watershed prioritization using remote sensing and geographical information system: a case study from Guhiya, India. Journal of Arid Environments 49, 465–475. Kalra, N.K., Joshi, D.C., 1996. Potentiality of Landsat, SPOT and IRS satellite imagery, for recognition of salt affected soils in Indian Arid Zone. International Journal of Remote Sensing 17, 3001–3014. Kelly, M.E., 2001. The Rio Conchos: A Preliminary Overview. Texas Center for Policy Studies, Austin, TX, 27pp. Langley, S., Cheshire, H., Humes, K., 2001. A comparison of single dataset and multitemporal satellite image classifications in a semi-arid grassland. Journal of Arid Environments 49, 401–411. Larsson, H., 1993. Regression for canopy cover estimation in Acacia Woodlands using Landsat TM, MSS, and SPOT HRV XS data. International Journal of Remote Sensing 14, 2129–2136. Leonard, R.I., Judd, F.W., Everitt, J.H., Escobar, D.E., Davis, R., Crawford, M.M., Desai, M.D., 1998. Monitoring riparian forest vegetation. Earth Observation Magazine 7, 32–34. Neville, P., Coward, R., Watson, R., Inglis, M., Morain, S., 2000. The application of TM imagery and GIS data in the assessment of arid lands, water and land resources in west Texas. Photogrammetric Engineering and Remote Sensing 66, 1373–1379. Popp, C.J., Laquer, F., 1980. Trace metal transport and partitioning in the suspended sediments of the Rio Grande and tributaries in central New Mexico. Chemosphere 9, 89–98. Pulido, L., Wiegand, C.L., Rodriguez, C., Neck, S., 1997. Satellite images to identify soil salinity of irrigation districts. Terra 15, 201–210. Pulido, L., Robles, B., Gonzalez, J., Wiegand, C.L., Sanvicente, H., 1998. Advances in the use of remote sensors to detect soil salinity in Mexican irrigation districts. Proceedings, of the 16th World Congress of Soil Science. Montpellier, France, June 1998. Rhoades, J.D., 1982. Soluble Salts. In: Page, A.L. (Ed.), Methods of Soil Analysis, Part 2, Chemical and microbiological properties. American Society of Agronomy, Madison, WI, pp. 167–176.
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Rhoades, J.D., Corwin, D.L., Lesch, S.M., 1999. Geospatial measurements of soil electrical conductivity to assess soil salinity and diffuse salt loading from irrigation. In: Corwin, D.L., Loague, K., Ellsworth, T.R. (Eds.). Assessment of Non-point Source Pollution in the Vadose Zone, Geophysical Monograph Series, Vol. 108. American Geophysical Union, Washington, DC, 369pp. Richardson, A.J., Crawford, M.M., Everitt, J.H., Summy, K.R., 1992. Monitoring the Rio Grande Valley Cotton Stalk Destruction Program with satellite imaging. Geocarto International 2, 15–20. Richardson, A.J., Wiegand, C.L., Anderson, G.L., Gerberman, A.H., Bray, M., Summy, K.R., Sugden, E.A., 1996. Six exemplary applications of GIS technology to subtropical Texas agriculture and natural resources. Geocarto International 11, 49–60. Rodriguez, J.A., Pingitore, N.E., Keller, G.R., Perez, A., 1999. An integrated gravity and remote sensing assessment of ground-water resources in central Chihuahua, Mexico. Environmental and Engineering Geoscience 5, 73–85. Scanlon, B.R., Tyler, S.W., Wierenga, P.J., 1997. Hydrologic issues in arid, unsaturated systems and implications for contaminant transport. Reviews of Geophysics 35, 461–490. Sharma, K.D, 1989. Role of satellite remote sensing for monitoring of surface water resources in an arid environment. Hydrological Science 34, 531–538. Taylor, G.R., Hewson, R.D., Bennet, B.A., Mah, A.H, Kruse, F.A., Kierein-Young, K.S., 1996. Characterization of saline soils using airborne radar imagery. Remote Sensing of the Environment 57, 127–142. TNRCC, 1994. Water quality in the Rio Grande Basin. Texas Natural Resource Conservation Commission, Austin, TX, 377pp. Unland, H.E., Arain, A.M., Harlow, C., Houser, P.R., Garatuze-Payan, J., Scott, P., Sen, O.L., Shuttleworth, W.J., 1998. Evaporation from a riparian system in a semi-arid environment. Hydrological Processes 12, 527–542. Wiegand, C.L., Everitt, J.H., Richardson, A.J., 1992. Comparison of multispectral video and SPOT-1 HRV observations for cotton affected by soil salinity. International Journal of Remote Sensing 13, 1511–1525.
Watershed assessment along a segment of the Rio Conchos in Northern Mexico using satellite images Me! lida Gutie! rrez*, Elias Johnson, Kevin Mickus Department of Geography, Geology and Planning, Southwest Missouri State University, 901 S. National, Springfield, MO 65804, USA Received 28 June 2002; received in revised form 5 November 2002; accepted 2 April 2003
Abstract Satellite images were used to illustrate the usefulness of such data in evaluating the ecological impacts of precipitation and land use on selected segments of the lower Rio Conchos in northern Mexico. Variations in the size and turbidity of impounded reservoirs, riparian vegetation, soil salinity and land use within the Rio Conchos basin were analysed using four Landsat TM images over a period of 10 years. A variety of image enhancements were applied to determine subtle changes between the images. These, when combined with precipitation and historical land-use data as well as one time water quality and soil EC, provided useful interpretation of the images, and therefore, in the monitoring of the basin. r 2003 Elsevier Science Ltd. All rights reserved. Keywords: Chihuahuan desert; Remote sensing; Rio Conchos; Rio Grande basin; Riparian vegetation; Soil salinity
1. Introduction Arid areas in northern Mexico as well as most arid lands elsewhere, pose a challenge to hydrological and solute balance determinations due to the extensive spatial and temporal variability in their hydrological properties and major limitations in road accessibility and availability of quality climate, hydrologic and water quality data (Scanlon et al., 1997; Gleick, 1998). Fortunately, satellite images have proved to be a powerful tool in the monitoring of certain landscape variations *Corresponding author. E-mail address: [email protected] (M. Guti!errez). 0140-1963/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0140-1963(03)00060-0
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(soil salinity, soil erosion, water quality/transparency, land cover) in arid environments (Sharma, 1989; Asner et al., 1999; Havstad et al., 1999; Kalra and Joshi, 1996; Diouf and Lambin, 2001; Kahn et al., 2001). In order to evaluate landscape and river variations in northern Mexico we conducted a multi-temporal study of a series of Landsat Thematic Mapper (TM) scenes spanning over approximately 10 years (1986–1997) along a portion of the lower Rio Conchos basin (Fig. 1). The analysis of the images was combined with the available data on precipitation, river flow, water quality, and land use to verify the results obtained from the images. There have been few remote-sensing studies in northern Mexico (Pulido et al., 1997, 1998; Unland et al., 1998) and none within the Rio Conchos basin. This, together with lack of detailed ground data (historic or monitoring) precluded a detailed analysis, but a general overview of ecological variations within the watershed was nevertheless obtainable. The purpose of the study was to determine if and how satellite images can be used to evaluate the ecological impacts of precipitation (including droughts) and land use onto the riparian area and water quality of a segment of the lower Rio Conchos. To do this task, we selected specific areas of the lower Rio Conchos basin that illustrate the usefulness of satellite images in making such evaluations. A variety of satellite image enhancement techniques were used to determine variations in water levels and surface changes of reservoirs along the Rio Conchos, their water quality (e.g. turbidity), riparian vegetation abundance, soil salinity and land use.
2. Hydrologic and geologic setting of the study area The Rio Conchos is a major river in northern Mexico, a large portion of it flowing through the Chihuahuan Desert. The Rio Conchos basin is part of the Rio Grande basin, which is shared by Mexico and the United States of America. Variations in the amount of water flowing from the Rio Conchos into the Rio Grande have been identified as the most important potential impact to the water supply of the lower Rio Grande basin (HARC, 2000). Although the flow of the Rio Conchos is determined at several gauging stations along its course, historical discharge information available to the public is restricted to a gauging station near the border city of Ojinaga. A hydrological report of the State of Chihuahua (INEGI, 1999), a 1:250,000 hydrological map of the area (INEGI, 1983) and a report by Gutie! rrez and Borrego (1999) provide background water quality information on the lower Rio Conchos. Kelly (2001) reports an overview of water availability and land uses in the Rio Conchos basin and Rodriguez et al. (1999) assess ground-water resources in central Chihuahua. Field measurements of water flow and evaporation (e.g. by lysimeters) and water quality data are scarce. In contrast with the Rio Conchos basin, extensive work has been performed in the United States’ portion of the Rio Grande basin (Popp and Laquer, 1980; Ellis et al., 1993; IWBC, 1994, 1997; TNRCC, 1994; HARC, 2000; Cole and James, 2001). Work involving satellite imagery within the Rio Grande basin has been conducted in a natural resources inventory (Richardson et al., 1996), monitoring the responses of
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Fig. 1. A June 1992, Landsat TM band 4 image showing the study area and features mentioned in the text within the state of Chihuahua, northern Mexico. The Rio Conchos is flowing north and joins the Rio Grande approximately 183 km downstream from the Luis L. Leon reservoir.
vegetation to hydrological fluxes (Havstad et al., 1999), monitoring cotton stalk elimination (Richardson et al., 1992), native riparian forest vegetation (Leonard et al., 1998; Elmore et al., 1999), grassland (Langley et al., 2001) and the effects of soil salinity on crops (Wiegand et al., 1992).
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The study area includes a stretch of the Rio Conchos approximately 100 km long (Fig. 1) with little to no human disturbances affecting it. However, this stretch of the river goes through self-cleaning processes after it receives large waste loads (irrigation return drains and domestic wastes) upstream at the Rio San Pedro and Rio Chuviscar confluences (INEGI, 1999). The surrounding topography and climate are relatively unchanged along the first 75 km of flow, with the surface geology changing from mainly alluvium and Tertiary igneous rocks to a suite of limestones and shales during the last 25 km of flow before reaching the Luis L. Leon reservoir (INEGI, 1981).
3. Precipitation data Precipitation data available from www.sequia.edu.mx consist of monthly average rainfall collected by one or more of seven meteorological stations within the study area from 1983–1995. The monsoon pattern is evident for precipitation, with June, July and August being the wet months of the year. After 1995, the only precipitation data available were a 5-year average for the whole basin. Daily flow of the Rio Conchos measured at a gauging station near Ojinaga (183 river kilometers downstream from study area) as well as water quality (reported as electrical conductivity (EC)) are available from the International Boundary and Water Commission annual reports at www.ibwc.state.gov/. Although this station is not within the study area, the amount of precipitation received in the central part of the basin and the flow at this gauging station positively correlated (correlation coefficient +0.72). Fig. 2 shows the average monthly precipitation for the study area for 1983–1995 (1996–1998 data is the 5-year average for the whole Conchos basin) and Rio Conchos flow at the gauging station near Ojinaga, in m3/s.
Fig. 2. Average monthly precipitation for study area and Rio Conchos flow at gauging station near Ojinaga (source: CAN and IBWC).
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4. Satellite imaging data and processing Four Landsat TM images (row 40, path 32) from April 4, 1986, May 20, 1989, June 21, 1992 and June 3, 1997, were acquired and used in our analysis. The Landsat TM data have spectral bands 1–5 and 7 that have spatial resolutions of 30 m. The June 1997 image only contains bands 1–4. The thermal band (6) was not used in our study because it represents emitted energy, or radiant temperature. Its large spatial resolution of 120 m does not allow for the identification of smaller-scale features. The TM bands represent different frequencies of the electromagnetic spectrum that allows for the identification of a wide range of geological, biological and hydrological features. TM bands 1–3 represent visible electromagnetic radiation with wavelengths of 0.45–0.52, 0.52–0.60 and 0.63–0.69 mm, respectively. Band 4 represents near infrared with wavelengths of 0.76–0.90 mm, and bands 5 and 7 represents mid-infrared with frequencies of 1.55–1.75 and 2.08–2.35 mm, respectively. The radiometric resolution of all the data was 8 bit with 256 levels of brightness. 4.1. Image preparation As the Landsat scenes were being acquired, ground control points (GCPs) from a number of recognizable sites easily identified on the images were collected throughout the study area using a global positioning system (GPS). The positional data were collected in a Universal Mercator Transverse (UTM) co-ordinate system which were used for vector data, such as roads and hydrologic features, obtained for this study. Because of the expansive area these Landsat images cover (about 100 100 nautical miles) and the inaccessibility of some of the more remote areas within these scenes, a minimal number of GPCs were collected. Furthermore, precise locational data could not be obtained at this time from other sources. Therefore, with only ten GCPs, the June, 1992 Landsat image was rectified. Errors at some of these points were quite large as we first attempted a linear transformation; however, with a quadratic polynomial, the largest RMS error was essentially a pixel. The remaining images were rectified by an image-to-image method from the 1992 scene with RMS errors less than 0.6 pixels. Because of our concerns of obtaining a fairly accurate rectification for this change detection study, other preprocessing techniques followed. A nearest-neighbor resampling procedure was used in geoprocessing so that the digital numbers (Dns) that represent the brightness values of the pixels for each band were not altered. The next step in processing the data was to attempt to remove the atmospheric effects that tend to attenuate the signal. This is usually a difficult procedure with older data, as a model of the atmosphere at the image location should be obtained (Jensen, 1996). This could not be accomplished for our data sets, so we used a haze removal algorithm (Jensen, 1996) in which the histogram of brightness values is evaluated relative to a reference target, such as deep, clear water that should have Dns at or near zero. This procedure was completed for all four Landsat scenes.
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4.2. Image processing The TM data were analysed using the ERMAPPER image processing software (ERMAPPER, 1995). There are a wide variety of techniques that can be used to enhance and analyse remote-sensing data that contain different spectral bands (Jensen, 1996). These include contrast stretching, band ratioing, principal component analysis (PCA), spatial filtering, image classification and indices for specific mineral/vegetation types derived from field reflectance measurements, e.g. gypsum, by Neville et al. (2000). We applied several of these techniques in order to emphasize different features including water turbidity, soil salinity and land-use change in our images. As shown by the analysis below there is no one best technique in determining the various parameters used in analyzing a watershed. The techniques we used represent a start in determining regions and parameters that might be further investigated using more detailed field studies. As mentioned earlier, the limited amount of field data was a major limitation to our study; as with most remote-sensing image enhancement techniques, field data are required for quantifying the results.
5. Results The extent of the Rio Conchos basin and the approximate location of the areas that were analysed for water levels and turbidity of reservoirs, riparian vegetation, soil salinity, and land-use variations are shown in Fig. 3. 5.1. Reservoirs To determine potential changes in water levels and turbidity of the reservoirs within the Rio Conchos basin, we looked at the Luis L. Leon reservoir (Fig. 4). To emphasize any turbidity in the water, we performed density slicing of the average of bands 1 and 2 as described below. We chose bands 1 and 2 as according to Campbell (1996) electromagnetic energy with a wavelength of 0.48 mm has a peak transmittance in clear water; at longer wavelengths, absorption of sunlight increases until most energy is absorbed in the near-infrared and longer wavelengths. With increased turbidity, this spectral characteristic relative to a water body changes, i.e. background energy increases and the peak wavelength becomes longer. To reveal those areas of the reservoir with the greatest amount of turbidity, an average of the blue-green (band 1) and green (band 2) bands were averaged for the purpose of displaying brightness values for water where some impurities, especially sediment, exist. These averaged Dns for bands one and two were saved as a subset for the reservoir and immediate land area. Because some clouds covered a portion of the reservoir for the 1989 scene, a threshold value for the cloud’s higher brightness values were sought. From a close inspection of Dns over the reservoir for the four scenes, Dns of over 60 represented cloud reflectance. Therefore, a threshold of brightness values from zero to 60 was used to display turbidity over the reservoir and
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Fig. 3. Location of the Rio Conchos basin (dashed line) and the sites selected for analysis using the remote-sensing images. These include: (1) water levels and turbidity of reservoirs, (2) riparian vegetation, (3) soil salinity and (4) land use.
to mask out most of the effects of cloud cover along with most land features. To more clearly quantify the Dns that characterize turbidity, density slicing of brightness values using six steps was performed on the images and a linear contrast stretch with a 99% clip was added to each subscene. From inspection of the density sliced images little turbidity is observed in the 1986 image. This scene was captured earlier than the other scenes during droughty conditions in April and the two preceding months. With little precipitation falling during these months little turbidity is observed. For the 1989 image, turbidity values were apparently quite high. However, this was the scene with scattered clouds covering portions of the reservoir. Even with the attempt to mask these clouds with the use of a threshold Dn
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Fig. 4. Landsat TM images of a small region surrounding the Rio Conchos that includes the Luis L. Leon reservoir. The images are averages of bands 1 and 2 using a density slice of the brightness values in six steps for: (a) April 1986, (b) May 1989, (c) June 1992 and (d) June 1997. Dn values greater than 60 were masked out. The brighter areas within the reservoir approximates the amount of turbidity. This figure emphasizes the change in reservoir size and turbidity over the years. The river is flowing in a northeast direction.
value, the bright areas in this figure could be the result of some cloud reflection, especially since this was a droughty year. The 1992 scene shows considerable turbidity in the reservoir except near its center. The brightest areas of the image is where the Rio Conchos enters the reservoir. This was a rather wet spring in this area and should highly correlate with the greater turbidity levels observed. Finally, the 1997 scene shows high amounts of turbidity where the Rio Conchos enters the reservoir and along the eastern shore. There was no precipitation data available for
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this year to relate to the observed reflectance values. Similar patterns as those seen in Figs. 4a–d can be determined from band ratioing of 1 and 7; however, we did not use this method as the June 1997 image does not contain band 7. The above technique also shows the variation in shape and size of the Luis L. Leon reservoir. The shape of the reservoir varies considerably from image to image and this is especially evident on the 1997 image (Fig. 4d) when the reservoir clearly has the smallest size of the four images. Although this fact correlates well with the small amount of rainfall that normally occurs in June, the relationship between rainfall, river flow and reservoir size and turbidity is not always straightforward. For the 1986 and 1992 images (Figs. 4a and c), the reservoir sizes are larger than in 1989 (Fig. 4b) but the amount of rainfall and river flow is relatively high in 1986 and low in both 1992 and 1989. Clearly, other factors are influencing these results with variations in water withdrawal for irrigation within the Delicias region probably accounting for most of the differences seen in the images. The reduction in its cultivated acreage throughout the 1990s (see Section 5.4) may explain the higher reservoir level seen in the 1992 image. The size, shape and turbidity of the other impounded reservoir within the study area, the Francisco I. Madero also varied in similar manner as the Luis L. Leon reservoir, so we will only show images of the Luis L. Leon reservoir. The changes on the shallower southern end of the Luis L. Leon reservoir can be seen in Figs. 4a and d. The swollen area of the reservoir, as can be observed in the 1986 (Fig. 4a) and 1992 (Fig. 4c) images, is likely associated with rain that fell in the Delicias area within the previous weeks (3.7 and 52.5 mm respectively) (Fig. 2). Yet, effects on the surrounding vegetation (caused either by water quality or water table variations) and soil salinity are expected to give more insight into ongoing hydrological processes. 5.2. Riparian vegetation In arid regions, riparian areas are narrow but nevertheless vital to the establishment of wildlife habitat. Besides direct removal by individuals, e.g. as a source of wood, disappearance of riparian vegetation has been associated with lowering of water tables (Unland et al., 1998). Riparian communities within the Chihuahuan desert are complex and not well understood (Unland et al., 1998; Leonard et al., 1998). However, Leonard et al. were able to discriminate among the vegetative cover and found the dominant shrub and ground species using color infrared photography for an area near the Falcon Dam on the Rio Grande. There are a number of image enhancement techniques that can determine the regions of vigorously growing vegetation. Two such techniques are the tasseled cap transformation (Crist and Cicone, 1984) and the normalized difference vegetation index (NDVI) (Larsson, 1993). We used both of these techniques to demonstrate and compare their usefulness as a means to develop ‘‘greenness indices’’. A comparison could be made for all but the 1997 scene because of the absence of band 7 that is necessary for the tasseled cap transformation. Nevertheless, the tasseled cap transformation was employed to three scenes (Figs. 5a–c) and NDVI to all four images (Figs. 6a–d) to assess vegetative greenness. We applied these techniques to a
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Fig. 5. Landsat TM images of a portion of the Rio Conchos west of Luis L. Leon reservoir shown as the greenness of the tasseled cap transformation for: (a) April 1986, (b) May 1989 and (c) June 1992. Bright regions along the river represent vigorously growing vegetation.
segment of the Rio Conchos, namely as the river enters the Luis L. Leon reservoir, since we expected this area to have less human disturbances and show the response of native vegetation. Both techniques show vigorously growing vegetation appearing in bright white, contrasting with the darker background, as can be observed along the river in Figs. 5b and 6d. More riparian vegetation was expected to indicate a wet year. However, we observed the opposite. Riparian vegetation covered the riverbed during the drier years (1989 and 1997) as shown in Figs. 5 and 6. This indicated a more complex pattern than was expected and is similar to that reported by Diouf and Lambin (2001), who explained this pattern as a result of competition among species; those that respond favorably to wet conditions and those that succeed under drier conditions. While both techniques clearly show the vigorously growing vegetation along the river, the tasseled cap transformation records variations within the bright regions. This can be seen in the 1989 image, as the NDVI image (Fig. 6b) is basically one shade of bright gray while the tasseled cap transformation (Fig. 5b) shows a variation of grays implying the region has various stages of vegetation growth or different types of plants. This same trend can be seen in all the images and implies for our region the tasseled cap transformation works best in defining the vigor of the vegetation.
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Fig. 6. Landsat TM images of a portion of the Rio Conchos west of Luis L. Leon reservoir shown as a NDVI transformation for: (a) April 1986, (b) May 1989, (c) June 1992 and (d) June 1997. Bright regions along the river represent vigorously growing vegetation.
5.3. Soil salinity Soil salinity is a common problem in arid, irrigated areas and has been the focus of extensive research, e.g. Kalra and Joshi (1996), Neville et al. (2000), Pulido et al. (1997, 1998), Rhoades et al. (1999, pp. 197–215), Taylor et al. (1996) and Wiegand et al. (1992). Use of EC has been used extensively in conjunction with satellite images in northern Mexico (Pulido et al., 1997) and arid areas elsewhere (Rhoades, 1982; Neville et al., 2000). Pulido et al. (1998) determined the spectral classes useful for defining soil salinity and wheat crop yield in Sinaloa, Mexico. The use of soil salinity derived from remote-sensing data determined that the crop yield was reduced with an increase of salinity above 4 dS/m (4000 mS/cm). Neville et al. (2000) derived an electromagnetic inductance index (EI) to determine increased areas of soil EC by collecting soil samples in known saline soil areas and determining their TM spectral response. Their EI equation was determined by applying a regression analysis to the field data. This is the best technique to determine an EI for a region as each region is unique due to the variety of minerals in the soil and a general EI equation probably cannot be determined. Since we did not determine the spectral response to our soil samples and Neville et al.’s equation used TM band 5, we decided to use the selective principal component (SPCA) technique of Chavez and Kwarteng (1989). Normal PCA uses all seven TM bands, however Chavez and Kwarteng (1989) showed that some information might be mapped into one of the unused components and how SPCA can be used to map the spectral contrast between specific spectral bands. According to these researchers, several
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Fig. 7. Landsat TM images of a region north of Meoqui shown as the first component of a SPCA using bands 2 and 4 for: (a) April 1986 and (b) June 1992. Bright areas within the test area represent regions of higher soil salinity.
bands can be relatively uncorrelated, for which using an SPCA with these bands might bring out unique information. This procedure is commonly a trial and error approach that must be used with the data statistics and known ground truth information. We found that the first principal component and of bands 4 and 7 highlighted known areas of soil salinity best for the 1986, 1989 and 1992 images. We used the first principal component of bands 2 and 4 for the 1997 image. On Figs. 7a–d, areas with high salinity are shown in white near the test area, although
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Table 1 Composition of soil solution (1:2 soil water ratio) and water in the vicinity of a patch of highly saline soils between Meoqui and Delicias pH EC (mS/cm) Alkalinity (mg/L CaCo3) Na (ppm) Mg (ppm) K (ppm) Ca (ppm) Soil—highly saline 8.68 248,000 Soil—regulara 8.36 1300 Surface waterb 8.24 1462 Irrigation canal water 8.40 2080 Groundwaterb 7.70 N/A a b
2500 175 N/A N/A N/A
8970 107 89 123 163
4 21 22 5 2
75 28 6 4 1
49 119 169 8 45
Non-cultivated, non-saline soil near Meoqui. Average of three samples.
one has to be careful in the interpretation as this color could also result from factors other than salinity. An area of known salinity was used as a reference as follows. Soil samples from an area affected with salinity near Meoqui, and within the irrigation district, were collected in November 2000 and analysed for EC, pH, alkalinity and major cations. A sample from a nearby non-cultivated, non-saline land parcel was also collected for comparison purposes. The results are shown in Table 1. The reflectance properties of this area of known high salinity (Figs. 7a–d) were the same as other areas of suspected salinity. The presence of similar patches of saline soil in nearby non-irrigated land within the basin and the fact that basins next to this are closed basins with extensive playa lakes suggest that these salts are naturally occurring, although intensive irrigation and being located over a shallow water table may have contributed some influences. Similarly to the yearly variations on riparian vegetation, the images did not show an increase of salinity during dry years, as we had expected. At the general level of this study, no significant changes were detected in the amount of land exhibiting soil salinity, which suggests that most of the salinity was formed prior to the 10 years span of our images. 5.4. Land use The main land use affecting the water quality and quantity of the area is irrigation (Kelly, 2001). The irrigated land within the study area is part of the Irrigation District 05 (Delicias), which comprises nearly 105,000 ha of land. A drought of 1983 and the 1990s (the latter extended from 1992 to 2002; CNA, 2000) caused a reduction in the amount of water allocated to farmers, and therefore in the amount of cultivated land from 107,000 ha in 1989 to 16,500 ha in 2002 (Fig. 8). As a test for using Landsat TM images in determining land-use changes, a small area within the Irrigation District was selected as representative of the district, which included an alluvial fan and agriculture parcels within the irrigation district by the Rio San Pedro northwest of Delicias (Figs. 3 and 9). The images were converted to
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Fig. 8. Area cultivated in the irrigation district 05 (Delicias) between 1983 and 2002 (source: Kelly (2001) and Diario de Delicias (2002).
RGBs using bands 4, 3 and 1 to show growing vegetation as bright red, which corresponded almost entirely to cultivated land. There are small patches of red in the nearby hill probably due to vegetation growing in a narrow arroyo. Next, the images were classified using a maximum-likelihood unsupervised classification technique (Jensen, 1996) to determine which areas of cultivated vigorously growing land with vegetation could be identified, isolated and the acreage measured. Figs. 9a and c show the images as band 4 for June 1992 and 1997 and the corresponding classified images (Figs. 9b and d). These images depict the drastic reduction in cultivated land reported by CNA (2000) and Kelly (2001). The area selected (with a total area of 8183 ha) showed a reduction in hectares from 18.6% in 1986, 19.6% in 1989 and 18.4% in 1992, to 9.6% in 1997.
6. Discussion Remote-sensing analysis of satellite images is a highly effective technique in assessing watersheds where the lack of data and remoteness is an obstacle. A variety of remote-sensing image enhancement and interpretation techniques allow for the user to emphasize particular aspects in the image. In our study, changes in soil salinity and riparian vegetation as determined by selective principal component and tasseled cap transformation, respectively, can be related to changes in precipitation and/or land use; however, the relationship is complex. Ground data are especially important when interpreting satellite images and as shown above were necessary in interpreting areas of soil salinities. A complete watershed analysis requires the interpretation of multiple aspects of the basin as shown by our preliminary study of the lower Rio Conchos. Such an analysis is required for good management of the basin and to estimate the impacts of different natural phenomena, e.g. droughts, and human activities such as the removal of riparian vegetation and increase in agricultural land use. The techniques utilized in this study were successful in showing the variations in several watershed parameters that are usually used in monitoring a basin. Several
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Fig. 9. Landsat TM images of an agricultural region along Rio San Pedro northwest of Delicias shown and band 4 for: (a) June 1992 and (c) June 1997. Figures (b) June 1992 and (d) June 1997 represent an unsupervised classification for cultivated land with growing vegetation (dark areas). The area northwest of the agricultural region are Tertiary volcanic rocks cropping out in low mountains and an alluvial fan. The classified region within this area represents growing vegetation. The Rio San Pedro is flowing in a northeasterly direction and it is shown in the southeast corner of each image.
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unexpected results were found including the increased abundance of riparian vegetation in a dry year as opposed to a wet year and the relative unchanged surface area affected by saline soils in either wet or dry years. An interesting observation determined from the unsupervised classification of the images was the proliferation of ‘‘abandoned’’ irrigation plots as the drought conditions intensified towards the latter half of the 1990s. This result was obtained in a small region, approximately 5% of the entire agricultural area of the Rio Conchos basin, northwest of Delicias and shows the usefulness of interpreting the various images. The next step would be to conduct a detailed supervised classification of the entire agricultural region of the lower Rio Conchos basin, which can be seen on Fig. 1, to determine if these phenomena are more widespread. When combined with a soil salinity analysis of the abandoned plots, a more complete estimation of the extent of the drought conditions could be determined for the lower Rio Conchos basin. Such a study would require additional images and ground data, including soil analyses and ground truthing of the classified abandoned plots.
Acknowledgements We would like to acknowledge the Pan American Center of Environmental and Earth Studies (PACES) at the University of Texas at El Paso who kindly provided us with several Landsat TM images. A faculty development grant from Southwest Missouri State University partly supported this study.
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