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Chapter 18 Land-cover Classification from Landsat Imagery for Mapping Dynamic Wet and Saline Soils
Developments in Soil Science, volume 31 P. Lagacherie, A.B. McBratney and M. Voltz (Editors) r 2007 Elsevier B.V. All rights reserved
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Chapter 18
LAND-COVER CLASSIFICATION FROM LANDSAT IMAGERY FOR MAPPING DYNAMIC WET AND SALINE SOILS S. Kienast-Brown and J.L. Boettinger
Abstract Wet and saline soils have been recognized as an important and complex component of wetland ecosystems in arid environments. Analysis and classification of remotely sensed spectral data is an effective method for discerning the spatial and temporal variability of soils. The East Shore Area (ESA) of the Great Salt Lake soil survey update is focused on updating soil map units containing wet and saline soils. The ESA provides a unique environment for the use of remotely sensed spectral data for map unit refinement because of low relief and a large extent of soils that are wet and saline to various degrees. Map units in the ESA containing wet and saline soils were updated and refined using Landsat 7 imagery. Five land-cover classes are related to dominant soil types that vary in soil wetness, salinity, calcium carbonate concentration and vegetation cover type. Supervised classification of the imagery was performed using the five land cover classes. The final classification resulted in 14 land cover classes, including nine additional classes that help describe the variability in the original five classes. The classification results were validated using visual inspection in the field, a priori knowledge of the area and an error matrix. The results of the classification were used to enhance original soil map units and calculate map unit composition in the final soil mapping process. This information was then incorporated into the updated soil map. Temporal variation in land cover classes has the potential to be considered in map-unit refinement to reflect the dynamic nature of the margins of the Great Salt Lake, Utah.
18.1 Introduction Satellite data have been used to examine the Earth’s surface for several decades. Remote sensing techniques have provided scientists with a more efficient method of conducting large-scale studies of the Earth’s surface. Landsat thematic mapper (TM) and Landsat multispectral scanner (MSS) spectral data have been the most widely used satellite data for remote sensing research. Much information about the Earth’s surface, relating to vegetation, rocks, minerals and soils can be extracted from spectral data.
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Remote sensing imagery has also facilitated the study and mapping of salt- and sodium-affected soils. Metternicht and Zinck, (1997) used image classification of Landsat TM data, combined with field observations and laboratory data, to discriminate salt-affected versus sodium-affected soils in Bolivia. Transformed divergence analysis was used to determine the best band combination for maximum separability between salt- and sodium-affected soil classes, and resulted in accuracies from 64 to 100% in the classification. The spectral behaviour of salt-affected soils was examined using Landsat TM false color composite (FCC) images and in situ radiometric measurements (Rao et al., 1995). The middle infrared region (MIR) bands were added to the FCC image and greatly improved the ability to identify salt-affected soils. Singh (1994) used Landsat TM FCC imagery to successfully monitor the increase or decrease in the extent of salt-affected soils over time, and to delineate moderately versus severely salt-affected soils. The ability to distinguish soil properties from spectral data has led to the use of remote sensing for soil mapping. Landsat MSS digital number data was used to delineate soil associations in Nebraska (Lewis et al., 1975). Soil associations mapped by conventional field techniques were compared to soil associations mapped on the satellite image by photo-interpretation techniques. Soil associations were successfully delineated in areas where topography and vegetation could be discriminated on the satellite image, and related to field data. In an Arizona soil survey, Landsat MSS digital number data was clustered according to soil parent material and landform (Roudabush et al., 1985). This information was used to refine map unit boundaries and composition, and increase the understanding of map unit variability. They also concluded the creation of a soil map from spectral data to be a valuable prefield mapping tool. An update soil survey of the East Shore Area (ESA) of the Great Salt Lake, Utah, USA, headed by the USDA Natural Resources Conservation Service (NRCS), began in 1999. A soil survey of the ESA was published in 1975 as part of the East Box Elder County soil survey (Chadwick et al., 1975). The ESA update is focused on refining soil map units containing wet and saline soils. The ESA is heavily influenced by fluctuations in precipitation and lake level, and anthropogenic controls on fresh water flow from diked areas. The interaction between these factors affects local groundwater level, soil moisture and soil salinity. Therefore, the ESA is a dynamic and complex system, resulting in a challenge for map unit refinement. The ESA provides a unique environment for the use of remotely sensed spectral data for map unit refinement because of low relief and a large extent of soils that are wet and saline to various degrees. Interpretation of remotely sensed spectral data is an effective method for mapping wet and saline surfaces at large scales. Five major land cover types exist in the ESA and are related to
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soil moisture, salinity, calcium carbonate concentration and vegetation cover type. These land cover types are salt flat, pickleweed flat, upland, saltgrass, and sedges-rushes. A supervised classification of Landsat 7 imagery was conducted to determine spatial extent and variability of the five major land cover types so that mapping of the dominant associated soil types can be refined. 18.2 Methods and materials 18.2.1 Study area The ESA is located in northern Utah, USA, between 411 41’18’’ N, 1121 14’ 46’’ W and 401 44’ 46’’ N, 1121 5’ 14’’ W. The ESA encompasses 166,905 ha on the northeastern shore of the Great Salt Lake (Fig. 18.1). There is very little elevation change over the ESA, with elevations ranging from 1280 m in the southern part to 1368 m in the northern part. The mean annual temperature in the ESA is 8–101C, and the mean annual precipitation is 300–380 mm (Chadwick et al., 1975). The soil climate is mesic and xeric or xeric/aquic. The ESA contains natural and managed waters. Freshwater inputs include Blue Creek, West Canal and Sulfur Creek, which flow into the ESA from the northwest, north and northeast, respectively. Runoff from the Promontory Mountains flows into the ESA from the west. The majority of these freshwater sources are managed through dikes by private land owners in the area. The Bear River drains into the ESA from the east, and is heavily managed by the Bear River Migratory Bird Refuge. All the freshwater that enters the ESA eventually flows south into the Great Salt Lake. Lake levels fluctuate radically as a function of precipitation and evaporation. As the lake level rises, highly saline water from the Great Salt Lake moves north and mixes with the freshwater. As the lake level lowers, the lake water retreats and evaporation concentrates salts in the soil and on the soil surface. In both scenarios, the salinity of the freshwater increases dramatically as it flows through the ESA to the Great Salt Lake.
Figure 18.1. Location of the ESA study area in northern Utah, USA.
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The ESA is composed entirely of lacustrine sediments from Pleistocene pluvial Lake Bonneville (13,000–25,000 years ago) and from the present-day Great Salt Lake. The Great Salt Lake is the existing remnant of Lake Bonneville. The lake sediments in the ESA are mainly fine-grained, stratified silts and clays (Stokes, 1988). The entire ESA study area is located on a lake plain. The area is essentially flat, with an elevation gradient of about 90 m over a 16-km distance, resulting in an average slope of 0.5%, dipping to the south. The only topographic features occurring in the ESA are upland ‘‘islands’’ located throughout the lake plain. These islands are approximately 9 m above the lake plain and are composed of stratified coarse silt lake sediments. 18.2.2 Major land cover over types and associated soil properties The ESA contains five major land cover types that are related to dominant soil types that vary with respect to moisture, salinity (reported as saturated paste electrical conductivity (EC) to a depth of 150 cm), calcium carbonate concentration (reported to a depth of 150 cm) and vegetation cover type. Table 18.1 shows selected characteristics of the land cover types. The land cover types in the order of increasing wetness are upland, pickleweed flat, salt flat, saltgrass and sedgesrushes. The land cover types in the order of increasing calcium carbonate concentration are upland, sedges-rushes, saltgrass, pickleweed flat and salt flat. The land cover types in the order of increasing salinity are upland, saltgrass, sedgesrushes, pickleweed flat and salt flat. Figure 18.2 shows the representative landscape, calcium carbonate profile and EC profile for the salt flat and upland land cover types as examples of the two extremes in the area. 18.2.3 Imagery and analysis Landsat 7 imagery from May 21, 2003, path 38 rows 31 and 32, was used for all analyses. The two images were mosaiced and then a subset of the ESA study area was made. ERDAS Imagine 8.6 was used for all image processing, analysis and classification. Training sites for the supervised classification were identified using GPS points from field data collection, and from digital ortho-photo quads (DOQs) and Landsat imagery using a priori knowledge of the area. A minimum area equivalent to a 3 3 Landsat pixel area (8100 m2) was used for training site selection. Originally, 90 training sites were selected in the following 10 classes: water, shallow water, very shallow water, sedges-rushes, sedges-rushessaltgrass, saltgrass, upland, agriculture, salt flat and pickleweed flat. A spectral signature set containing a spectral signature for each of the 90 training sites was created in Imagine. Optimum index factor (OIF) was calculated to determine the optimum three-band combination for the subset Landsat 7
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Table 18.1. Selected characteristics of major land cover types and associated soils. Land cover type
Sedges/ rushes Saltgrass Salt flat
Percent of total area
Max Min EC Ave EC Max CaCO3 Min CaCO3 Ave CaCO3 EC (dS/m) (dS/m) concentration concentration concentration (dS/m) (%) (%) (%)
9
Typic Endoaquepts
38
29
30
26
21
23
4 36
Typic Halaquepts Typic or Calcic Aquisalids Typic or Calcic Aquisalids Typic or Aquic Natrixeralfs Typic or Aquic Natrixerolls Sodic or Typic Calcixerepts Typic Haploxeroll
52 107
11 64
29 87
25 30
22 14
24 26
34
12
30
33
17
25
15
9
12
15
10
13
Pickleweed flat Upland
19
Agricultureb Waterb
o1 27
a
Associated soil classificationa
5
Soil Survey Staff, 2003. Not considered a major land cover type for the purposes of this study, but did occur in the study area.
b
image (Jensen, 1996). Transformed divergence analysis was used to evaluate the separability of the spectral signatures and determine the band combination that would result in maximum separability of the signatures (Metternicht and Zinck, 1997). The OIF-calculated three-band combination was evaluated against other combinations of the six Landsat 7 bands in the transformed divergence analysis. Mean spectral signature plots for the 90 classes were generated, and like signatures were merged to achieve maximum separability with the minimum number of classes. Spectral signature histograms were evaluated for each of the resulting 14 classes to determine appropriate classification algorithm based on data distribution, normal versus multimodal.
18.2.4 Classification Supervised classification was performed using fuzzy classification with two best classes assigned to each pixel (Jensen, 1996). The fuzzy classification method was chosen because of the high variability over small distances in the ESA and the high likelihood of mixed pixels, especially in the salt flat and pickleweed flat classes. The fuzzy classification image and associated distance image were processed using a fuzzy convolution filter to reduce speckle, or ‘‘salt and pepper,’’ in the classification. The fuzzy convolution filter uses the fuzzy classification image and the associated distance file to calculate a total weighted distance for all classes within the filter window. Then, the class with the largest total inverse distance, summed over the entire set of fuzzy classification layers
S. Kienast-Brown and J.L. Boettinger
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(a)
Salt Rat Carbonates % carbonates 20
40
0
0
0
20
20
40
40
60
60
depth (cm)
depth (cm)
0
Salt Rat EC
80 100
80
120
140
140
160
160
(f)
Upland Carbonates
(d)
Upland EC
% carbonates 0
10
EC (ds/m) 20
0
0
0
20
20
40
40
60
60
80 100
depth (cm)
depth (cm)
150
100
120
(e)
EC (ds/m) 50 100
10
20
80 100
120
120
140
140
160
160
Figure 18.2. Representative landscape, calcium carbonate profile and EC profile for the salt flat (a), (b), (c) and upland (d), (e), (f) land cover types.
(in this case, two), is assigned to the central pixel of the filter window. A 3 3 pixel neighbourhood was used for the filter window. The fuzzy classification was processed using both the minimum distance to means and the maximum likelihood algorithms. The resulting images from the two different classification algorithms were compared to determine which algorithm best represented the variation in the classes across the ESA. The classified image was validated by visual field inspection and a priori knowledge of the field area. An accuracy assessment was conducted using 135 random points and an error matrix (Congalton and Green, 1999). The land cover type class composition for the ESA study area was also calculated (Table 18.1). These data will be used in the final survey updating process to refine soil map-unit composition throughout the ESA survey area. Spatial distribution of the map units was also updated based on the results of the classification.
Land-cover classification from landsat imagery
241
Mean Spectral Signature by Class
200
moist salt flat salt flat/pickleweed flat shallow water saltgrass/sedges/rushes salt flat dry salt flat pickleweed flat upland agriculture sedges/rushes/saltgrass saltgrass water very shallow water sedges/rushes
Mean Brightness Value
180 160 140 120 100
80 60 40 20 0 1
2
3 4 Landsat 7 Band
5
7
Figure 18.3. Mean spectral signatures of the final 14 classes resulting from original 90 training sites. 18.3 Results and discussion 18.3.1 Spectral signature analysis The OIF calculation suggested that the combination of Landsat 7 bands 3, 5 and 7 would provide the most information for the ESA image. When the spectral signature separability was evaluated for the OIF-band combination, a transformed divergence value of 1860 resulted. When compared to the transformed divergence value calculated for all six bands, 1982, the OIF combination value was lower. When compared to other 2-, 3-, 4- and 5-band combinations with transformed divergence values ranging from 1860 to 1890, the OIF band combination value was very similar. The combination of all six bands resulted in the greatest separability, as indicated by the highest value of transformed divergence. Therefore, all six Landsat bands were used for the supervised classification. After evaluating class statistics and mean reflectance in each band for the original 90 training sites, like signatures were merged. This process resulted in a final signature set containing 14 classes (Fig. 18.3) that would be used for the image classification. The final 14 classes were salt flat, moist salt flat, dry salt flat, salt flat-pickleweed flat, pickleweed flat, saltgrass, sedges-rushes-saltgrass, saltgrass-sedges-rushes, sedges-rushes, upland, agriculture, water, shallow water and very shallow water. Compound classes, such as saltgrass-sedges-rushes, indicate that the majority of the cover in the class is the first cover type listed (i.e. saltgrass). The evaluation of the histograms for each of the resulting 14 classes showed a mixture of normal and multimodal distributions. Within a single class, variation
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in data distribution existed between the six Landsat 7 bands. In some bands, the data distribution was normal, and in other bands, it was multimodal. 18.3.2 Classification Given the mixture of normal and multimodal data distributions among the classes, the fuzzy classification was processed using both the minimum distance to means and the maximum likelihood classification algorithms, to determine which would yield the best results. Both classification algorithms produced reasonable results; however, the results of the minimum distance to means classifier seemed to represent the variation on the landscape better than the maximum likelihood classifier. This assessment is based purely on the field experience and knowledge of the soil scientist performing the classification, and a bona fide accuracy assessment would be the preferred method to determine the optimal classification algorithm. Plate 18 (see Colour Plate Section) shows the thematic map resulting from the supervised fuzzy classification and the application of the fuzzy convolution filter. Identifying the extent and variability of the salt-flat and pickleweed-flat classes is a top priority, as together they cover a significant portion of the ESA, are highly variable, and least accessible (Table 18.1). Validation of the final classified image was completed by visual field inspection, a priori knowledge of the ESA and an error matrix. The error matrix used 135 points randomly stratified over the 14 final classes. Each point was visited in the field, and land cover type recorded. The error matrix determined the overall map accuracy to be 88%, which indicates strong agreement between predicted and observed classes. 18.4 Discussion Supervised classification of Landsat 7 imagery for the ESA produced a reasonable thematic representation of the 14 land cover type classes across the landscape. This classification also provided much information regarding the spatial distribution of the classes and the variability within the major classes of salt flat, pickleweed flat, sedges-rushes and saltgrass. The salt flat and pickleweed flat classes were of particular interest because together they cover a significant proportion of the area and are least accessible; also, their spatial variability was not well understood. The classification gave new information about the spatial variability of these land cover types, and the information was presented in a context that can be interpreted with greater ease than aerial photos or DOQs. This information enhanced the understanding of soil-landscape relationships in the ESA and allowed for refinement of existing map units and polygon lines (Plate 18, see Colour Plate Section). Supervised classification also provided a method for refining wet and saline map units based on the spectral characteristics of the area. Examining the spectral characteristics and relating them to
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existing knowledge of the area produced quantitative, physical data on which to base map unit refinement decisions. The quantitative data can then be archived and enhanced as other data become available. Another interesting application of remotely sensed data is the ability to examine the temporal variability of an area. This is of particular interest in the ESA due to the dynamic response of soil moisture and salinity to fluctuations in local water table caused by natural and anthropogenic controls. Figure 18.4 shows four Landsat 7 images that highlight changes in the landscape over one season (April and June, 2000) and over several years of drought (2000–2003). The dynamic nature of the ESA landscape presents an interesting challenge for map unit refinement. However, temporal variation in the landscape has the potential to be considered in map unit refinement by comparing Landsat 7 images and classifications of spectral characteristics. Changes in the proportional area for each class can be calculated and presented as documentation for map unit
Figure 18.4. Temporal variation of the ESA due to natural and anthropogenic controls.
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refinement. Ultimately, a single pixel and its classification changes related to soil moisture and salinity can be tracked through time. 18.5 Conclusions Supervised classification of Landsat 7 imagery for the ESA provided insight into the spatial variability and extent of major land cover types across the landscape. Information gained from the classification was used to enhance and refine the original soil map units by establishing the relationship between the new set of land cover classes and soil properties. The final soil map resulting from this process will provide users with more complete information regarding the wet and saline resources in the ESA than did the original soil survey. The use of remotely sensed data and techniques, such as supervised classification, provides soil scientists with tools to create robust soil maps that present soil-landscape relationships based on quantitative, physical data. Acknowledgments 1. Utah Agriculture Experiment Station, Journal paper Number 7739. 2. USDA Natural Resources Conservation Service.
References Chadwick, R.S., Barney, M.L., Beckstrand, D., Campbell, L., Carley, J.A., Jensen, E.H., McKinlay, C.R., Stock, S.S., Stokes, H.A., 1975. Soil Survey of Box Elder County, Utah, Eastern Part. U.S. Government Printing Office, Washington D.C. Congalton, R.G., Green, K., 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton, Florida. Jensen, J.R., 1996. Introductory to Digital Image Processing; A Remote Sensing Perspective, 2nd edition. Prentice-Hall, Englewood Cliffs, New Jersey. Lewis, D.T., Seevers, P.M., Drew, J.V., 1975. Use of satellite imagery to delineate soil associations in the Sand Hills region of Nebraska. Soil Sci. Soc. Amer. Proc. 39, 330–335. Metternicht, G., Zinck, J.A., 1997. Spatial discrimination of salt- and sodium-affected soil surfaces. Int. J. Remote Sens. 18, 2571–2586. Rao, B.R.M., Ravi Sankar, T., Dwivedi, R.S., Thammappa, S.S., Venkataratnam, L., Sharma, R.C., Das, S.N., 1995. Spectral behaviour of salt-affected soils. Int. J. Remote Sens. 16, 2125–2136. Roudabush, R.D., Herriman, R.C., Barmore, R.L., Schellentrager, G.W., 1985. Use of Landsat multispectral scanning data for soil surveys on Arizona rangeland. J. Soil and Water Conser. 40, 242–245. Singh, A.H., 1994. Monitoring change in the extent of salt-affected soils in northern India. Int. J. Remote Sens. 15, 3173–3182. Stokes, W.L., 1988. Geology of Utah. Utah Museum of Natural History and Utah Geological Survey. Salt Lake City, Utah. Soil Survey Staff, 2003. Keys to Soil Taxonomy, 9th edition. U.S. Government Printing Office, Washington, D.C.
N
Land Cover Classes Agriculture Upland Dry salt flat (mudflat) Moist salt flat Salt flat (saltcrust) Pickleweed flat Salt flat/pickleweed flat Saltgrass Saltgrass/sedges/rushes Sedges/rushes Sedges/rushes/saltgrass Shallow water Deep water Very shallow water
0
5
10
20
30
40 Kilometers
Plate 18. East Shore Area of the Great Salt Lake, Utah USA. Classified image using supervised fuzzy classification, the minimum distance to means classifier, a fuzzy convolution filter and all six Landsat 7 bands. Inset box shows an example of the original soil survey lines (black, ca. 1975) and the revised lines (yellow) created using the classified image as a guide.
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LAND-COVER CLASSIFICATION FROM LANDSAT IMAGERY FOR MAPPING DYNAMIC WET AND SALINE SOILS S. Kienast-Brown and J.L. Boettinger
Abstract Wet and saline soils have been recognized as an important and complex component of wetland ecosystems in arid environments. Analysis and classification of remotely sensed spectral data is an effective method for discerning the spatial and temporal variability of soils. The East Shore Area (ESA) of the Great Salt Lake soil survey update is focused on updating soil map units containing wet and saline soils. The ESA provides a unique environment for the use of remotely sensed spectral data for map unit refinement because of low relief and a large extent of soils that are wet and saline to various degrees. Map units in the ESA containing wet and saline soils were updated and refined using Landsat 7 imagery. Five land-cover classes are related to dominant soil types that vary in soil wetness, salinity, calcium carbonate concentration and vegetation cover type. Supervised classification of the imagery was performed using the five land cover classes. The final classification resulted in 14 land cover classes, including nine additional classes that help describe the variability in the original five classes. The classification results were validated using visual inspection in the field, a priori knowledge of the area and an error matrix. The results of the classification were used to enhance original soil map units and calculate map unit composition in the final soil mapping process. This information was then incorporated into the updated soil map. Temporal variation in land cover classes has the potential to be considered in map-unit refinement to reflect the dynamic nature of the margins of the Great Salt Lake, Utah.
18.1 Introduction Satellite data have been used to examine the Earth’s surface for several decades. Remote sensing techniques have provided scientists with a more efficient method of conducting large-scale studies of the Earth’s surface. Landsat thematic mapper (TM) and Landsat multispectral scanner (MSS) spectral data have been the most widely used satellite data for remote sensing research. Much information about the Earth’s surface, relating to vegetation, rocks, minerals and soils can be extracted from spectral data.
236
S. Kienast-Brown and J.L. Boettinger
Remote sensing imagery has also facilitated the study and mapping of salt- and sodium-affected soils. Metternicht and Zinck, (1997) used image classification of Landsat TM data, combined with field observations and laboratory data, to discriminate salt-affected versus sodium-affected soils in Bolivia. Transformed divergence analysis was used to determine the best band combination for maximum separability between salt- and sodium-affected soil classes, and resulted in accuracies from 64 to 100% in the classification. The spectral behaviour of salt-affected soils was examined using Landsat TM false color composite (FCC) images and in situ radiometric measurements (Rao et al., 1995). The middle infrared region (MIR) bands were added to the FCC image and greatly improved the ability to identify salt-affected soils. Singh (1994) used Landsat TM FCC imagery to successfully monitor the increase or decrease in the extent of salt-affected soils over time, and to delineate moderately versus severely salt-affected soils. The ability to distinguish soil properties from spectral data has led to the use of remote sensing for soil mapping. Landsat MSS digital number data was used to delineate soil associations in Nebraska (Lewis et al., 1975). Soil associations mapped by conventional field techniques were compared to soil associations mapped on the satellite image by photo-interpretation techniques. Soil associations were successfully delineated in areas where topography and vegetation could be discriminated on the satellite image, and related to field data. In an Arizona soil survey, Landsat MSS digital number data was clustered according to soil parent material and landform (Roudabush et al., 1985). This information was used to refine map unit boundaries and composition, and increase the understanding of map unit variability. They also concluded the creation of a soil map from spectral data to be a valuable prefield mapping tool. An update soil survey of the East Shore Area (ESA) of the Great Salt Lake, Utah, USA, headed by the USDA Natural Resources Conservation Service (NRCS), began in 1999. A soil survey of the ESA was published in 1975 as part of the East Box Elder County soil survey (Chadwick et al., 1975). The ESA update is focused on refining soil map units containing wet and saline soils. The ESA is heavily influenced by fluctuations in precipitation and lake level, and anthropogenic controls on fresh water flow from diked areas. The interaction between these factors affects local groundwater level, soil moisture and soil salinity. Therefore, the ESA is a dynamic and complex system, resulting in a challenge for map unit refinement. The ESA provides a unique environment for the use of remotely sensed spectral data for map unit refinement because of low relief and a large extent of soils that are wet and saline to various degrees. Interpretation of remotely sensed spectral data is an effective method for mapping wet and saline surfaces at large scales. Five major land cover types exist in the ESA and are related to
Land-cover classification from landsat imagery
237
soil moisture, salinity, calcium carbonate concentration and vegetation cover type. These land cover types are salt flat, pickleweed flat, upland, saltgrass, and sedges-rushes. A supervised classification of Landsat 7 imagery was conducted to determine spatial extent and variability of the five major land cover types so that mapping of the dominant associated soil types can be refined. 18.2 Methods and materials 18.2.1 Study area The ESA is located in northern Utah, USA, between 411 41’18’’ N, 1121 14’ 46’’ W and 401 44’ 46’’ N, 1121 5’ 14’’ W. The ESA encompasses 166,905 ha on the northeastern shore of the Great Salt Lake (Fig. 18.1). There is very little elevation change over the ESA, with elevations ranging from 1280 m in the southern part to 1368 m in the northern part. The mean annual temperature in the ESA is 8–101C, and the mean annual precipitation is 300–380 mm (Chadwick et al., 1975). The soil climate is mesic and xeric or xeric/aquic. The ESA contains natural and managed waters. Freshwater inputs include Blue Creek, West Canal and Sulfur Creek, which flow into the ESA from the northwest, north and northeast, respectively. Runoff from the Promontory Mountains flows into the ESA from the west. The majority of these freshwater sources are managed through dikes by private land owners in the area. The Bear River drains into the ESA from the east, and is heavily managed by the Bear River Migratory Bird Refuge. All the freshwater that enters the ESA eventually flows south into the Great Salt Lake. Lake levels fluctuate radically as a function of precipitation and evaporation. As the lake level rises, highly saline water from the Great Salt Lake moves north and mixes with the freshwater. As the lake level lowers, the lake water retreats and evaporation concentrates salts in the soil and on the soil surface. In both scenarios, the salinity of the freshwater increases dramatically as it flows through the ESA to the Great Salt Lake.
Figure 18.1. Location of the ESA study area in northern Utah, USA.
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The ESA is composed entirely of lacustrine sediments from Pleistocene pluvial Lake Bonneville (13,000–25,000 years ago) and from the present-day Great Salt Lake. The Great Salt Lake is the existing remnant of Lake Bonneville. The lake sediments in the ESA are mainly fine-grained, stratified silts and clays (Stokes, 1988). The entire ESA study area is located on a lake plain. The area is essentially flat, with an elevation gradient of about 90 m over a 16-km distance, resulting in an average slope of 0.5%, dipping to the south. The only topographic features occurring in the ESA are upland ‘‘islands’’ located throughout the lake plain. These islands are approximately 9 m above the lake plain and are composed of stratified coarse silt lake sediments. 18.2.2 Major land cover over types and associated soil properties The ESA contains five major land cover types that are related to dominant soil types that vary with respect to moisture, salinity (reported as saturated paste electrical conductivity (EC) to a depth of 150 cm), calcium carbonate concentration (reported to a depth of 150 cm) and vegetation cover type. Table 18.1 shows selected characteristics of the land cover types. The land cover types in the order of increasing wetness are upland, pickleweed flat, salt flat, saltgrass and sedgesrushes. The land cover types in the order of increasing calcium carbonate concentration are upland, sedges-rushes, saltgrass, pickleweed flat and salt flat. The land cover types in the order of increasing salinity are upland, saltgrass, sedgesrushes, pickleweed flat and salt flat. Figure 18.2 shows the representative landscape, calcium carbonate profile and EC profile for the salt flat and upland land cover types as examples of the two extremes in the area. 18.2.3 Imagery and analysis Landsat 7 imagery from May 21, 2003, path 38 rows 31 and 32, was used for all analyses. The two images were mosaiced and then a subset of the ESA study area was made. ERDAS Imagine 8.6 was used for all image processing, analysis and classification. Training sites for the supervised classification were identified using GPS points from field data collection, and from digital ortho-photo quads (DOQs) and Landsat imagery using a priori knowledge of the area. A minimum area equivalent to a 3 3 Landsat pixel area (8100 m2) was used for training site selection. Originally, 90 training sites were selected in the following 10 classes: water, shallow water, very shallow water, sedges-rushes, sedges-rushessaltgrass, saltgrass, upland, agriculture, salt flat and pickleweed flat. A spectral signature set containing a spectral signature for each of the 90 training sites was created in Imagine. Optimum index factor (OIF) was calculated to determine the optimum three-band combination for the subset Landsat 7
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Table 18.1. Selected characteristics of major land cover types and associated soils. Land cover type
Sedges/ rushes Saltgrass Salt flat
Percent of total area
Max Min EC Ave EC Max CaCO3 Min CaCO3 Ave CaCO3 EC (dS/m) (dS/m) concentration concentration concentration (dS/m) (%) (%) (%)
9
Typic Endoaquepts
38
29
30
26
21
23
4 36
Typic Halaquepts Typic or Calcic Aquisalids Typic or Calcic Aquisalids Typic or Aquic Natrixeralfs Typic or Aquic Natrixerolls Sodic or Typic Calcixerepts Typic Haploxeroll
52 107
11 64
29 87
25 30
22 14
24 26
34
12
30
33
17
25
15
9
12
15
10
13
Pickleweed flat Upland
19
Agricultureb Waterb
o1 27
a
Associated soil classificationa
5
Soil Survey Staff, 2003. Not considered a major land cover type for the purposes of this study, but did occur in the study area.
b
image (Jensen, 1996). Transformed divergence analysis was used to evaluate the separability of the spectral signatures and determine the band combination that would result in maximum separability of the signatures (Metternicht and Zinck, 1997). The OIF-calculated three-band combination was evaluated against other combinations of the six Landsat 7 bands in the transformed divergence analysis. Mean spectral signature plots for the 90 classes were generated, and like signatures were merged to achieve maximum separability with the minimum number of classes. Spectral signature histograms were evaluated for each of the resulting 14 classes to determine appropriate classification algorithm based on data distribution, normal versus multimodal.
18.2.4 Classification Supervised classification was performed using fuzzy classification with two best classes assigned to each pixel (Jensen, 1996). The fuzzy classification method was chosen because of the high variability over small distances in the ESA and the high likelihood of mixed pixels, especially in the salt flat and pickleweed flat classes. The fuzzy classification image and associated distance image were processed using a fuzzy convolution filter to reduce speckle, or ‘‘salt and pepper,’’ in the classification. The fuzzy convolution filter uses the fuzzy classification image and the associated distance file to calculate a total weighted distance for all classes within the filter window. Then, the class with the largest total inverse distance, summed over the entire set of fuzzy classification layers
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(in this case, two), is assigned to the central pixel of the filter window. A 3 3 pixel neighbourhood was used for the filter window. The fuzzy classification was processed using both the minimum distance to means and the maximum likelihood algorithms. The resulting images from the two different classification algorithms were compared to determine which algorithm best represented the variation in the classes across the ESA. The classified image was validated by visual field inspection and a priori knowledge of the field area. An accuracy assessment was conducted using 135 random points and an error matrix (Congalton and Green, 1999). The land cover type class composition for the ESA study area was also calculated (Table 18.1). These data will be used in the final survey updating process to refine soil map-unit composition throughout the ESA survey area. Spatial distribution of the map units was also updated based on the results of the classification.
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Figure 18.3. Mean spectral signatures of the final 14 classes resulting from original 90 training sites. 18.3 Results and discussion 18.3.1 Spectral signature analysis The OIF calculation suggested that the combination of Landsat 7 bands 3, 5 and 7 would provide the most information for the ESA image. When the spectral signature separability was evaluated for the OIF-band combination, a transformed divergence value of 1860 resulted. When compared to the transformed divergence value calculated for all six bands, 1982, the OIF combination value was lower. When compared to other 2-, 3-, 4- and 5-band combinations with transformed divergence values ranging from 1860 to 1890, the OIF band combination value was very similar. The combination of all six bands resulted in the greatest separability, as indicated by the highest value of transformed divergence. Therefore, all six Landsat bands were used for the supervised classification. After evaluating class statistics and mean reflectance in each band for the original 90 training sites, like signatures were merged. This process resulted in a final signature set containing 14 classes (Fig. 18.3) that would be used for the image classification. The final 14 classes were salt flat, moist salt flat, dry salt flat, salt flat-pickleweed flat, pickleweed flat, saltgrass, sedges-rushes-saltgrass, saltgrass-sedges-rushes, sedges-rushes, upland, agriculture, water, shallow water and very shallow water. Compound classes, such as saltgrass-sedges-rushes, indicate that the majority of the cover in the class is the first cover type listed (i.e. saltgrass). The evaluation of the histograms for each of the resulting 14 classes showed a mixture of normal and multimodal distributions. Within a single class, variation
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in data distribution existed between the six Landsat 7 bands. In some bands, the data distribution was normal, and in other bands, it was multimodal. 18.3.2 Classification Given the mixture of normal and multimodal data distributions among the classes, the fuzzy classification was processed using both the minimum distance to means and the maximum likelihood classification algorithms, to determine which would yield the best results. Both classification algorithms produced reasonable results; however, the results of the minimum distance to means classifier seemed to represent the variation on the landscape better than the maximum likelihood classifier. This assessment is based purely on the field experience and knowledge of the soil scientist performing the classification, and a bona fide accuracy assessment would be the preferred method to determine the optimal classification algorithm. Plate 18 (see Colour Plate Section) shows the thematic map resulting from the supervised fuzzy classification and the application of the fuzzy convolution filter. Identifying the extent and variability of the salt-flat and pickleweed-flat classes is a top priority, as together they cover a significant portion of the ESA, are highly variable, and least accessible (Table 18.1). Validation of the final classified image was completed by visual field inspection, a priori knowledge of the ESA and an error matrix. The error matrix used 135 points randomly stratified over the 14 final classes. Each point was visited in the field, and land cover type recorded. The error matrix determined the overall map accuracy to be 88%, which indicates strong agreement between predicted and observed classes. 18.4 Discussion Supervised classification of Landsat 7 imagery for the ESA produced a reasonable thematic representation of the 14 land cover type classes across the landscape. This classification also provided much information regarding the spatial distribution of the classes and the variability within the major classes of salt flat, pickleweed flat, sedges-rushes and saltgrass. The salt flat and pickleweed flat classes were of particular interest because together they cover a significant proportion of the area and are least accessible; also, their spatial variability was not well understood. The classification gave new information about the spatial variability of these land cover types, and the information was presented in a context that can be interpreted with greater ease than aerial photos or DOQs. This information enhanced the understanding of soil-landscape relationships in the ESA and allowed for refinement of existing map units and polygon lines (Plate 18, see Colour Plate Section). Supervised classification also provided a method for refining wet and saline map units based on the spectral characteristics of the area. Examining the spectral characteristics and relating them to
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existing knowledge of the area produced quantitative, physical data on which to base map unit refinement decisions. The quantitative data can then be archived and enhanced as other data become available. Another interesting application of remotely sensed data is the ability to examine the temporal variability of an area. This is of particular interest in the ESA due to the dynamic response of soil moisture and salinity to fluctuations in local water table caused by natural and anthropogenic controls. Figure 18.4 shows four Landsat 7 images that highlight changes in the landscape over one season (April and June, 2000) and over several years of drought (2000–2003). The dynamic nature of the ESA landscape presents an interesting challenge for map unit refinement. However, temporal variation in the landscape has the potential to be considered in map unit refinement by comparing Landsat 7 images and classifications of spectral characteristics. Changes in the proportional area for each class can be calculated and presented as documentation for map unit
Figure 18.4. Temporal variation of the ESA due to natural and anthropogenic controls.
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refinement. Ultimately, a single pixel and its classification changes related to soil moisture and salinity can be tracked through time. 18.5 Conclusions Supervised classification of Landsat 7 imagery for the ESA provided insight into the spatial variability and extent of major land cover types across the landscape. Information gained from the classification was used to enhance and refine the original soil map units by establishing the relationship between the new set of land cover classes and soil properties. The final soil map resulting from this process will provide users with more complete information regarding the wet and saline resources in the ESA than did the original soil survey. The use of remotely sensed data and techniques, such as supervised classification, provides soil scientists with tools to create robust soil maps that present soil-landscape relationships based on quantitative, physical data. Acknowledgments 1. Utah Agriculture Experiment Station, Journal paper Number 7739. 2. USDA Natural Resources Conservation Service.
References Chadwick, R.S., Barney, M.L., Beckstrand, D., Campbell, L., Carley, J.A., Jensen, E.H., McKinlay, C.R., Stock, S.S., Stokes, H.A., 1975. Soil Survey of Box Elder County, Utah, Eastern Part. U.S. Government Printing Office, Washington D.C. Congalton, R.G., Green, K., 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press, Boca Raton, Florida. Jensen, J.R., 1996. Introductory to Digital Image Processing; A Remote Sensing Perspective, 2nd edition. Prentice-Hall, Englewood Cliffs, New Jersey. Lewis, D.T., Seevers, P.M., Drew, J.V., 1975. Use of satellite imagery to delineate soil associations in the Sand Hills region of Nebraska. Soil Sci. Soc. Amer. Proc. 39, 330–335. Metternicht, G., Zinck, J.A., 1997. Spatial discrimination of salt- and sodium-affected soil surfaces. Int. J. Remote Sens. 18, 2571–2586. Rao, B.R.M., Ravi Sankar, T., Dwivedi, R.S., Thammappa, S.S., Venkataratnam, L., Sharma, R.C., Das, S.N., 1995. Spectral behaviour of salt-affected soils. Int. J. Remote Sens. 16, 2125–2136. Roudabush, R.D., Herriman, R.C., Barmore, R.L., Schellentrager, G.W., 1985. Use of Landsat multispectral scanning data for soil surveys on Arizona rangeland. J. Soil and Water Conser. 40, 242–245. Singh, A.H., 1994. Monitoring change in the extent of salt-affected soils in northern India. Int. J. Remote Sens. 15, 3173–3182. Stokes, W.L., 1988. Geology of Utah. Utah Museum of Natural History and Utah Geological Survey. Salt Lake City, Utah. Soil Survey Staff, 2003. Keys to Soil Taxonomy, 9th edition. U.S. Government Printing Office, Washington, D.C.
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Plate 18. East Shore Area of the Great Salt Lake, Utah USA. Classified image using supervised fuzzy classification, the minimum distance to means classifier, a fuzzy convolution filter and all six Landsat 7 bands. Inset box shows an example of the original soil survey lines (black, ca. 1975) and the revised lines (yellow) created using the classified image as a guide.