Classifying coastal resources by integrating optical and ... - Springer Link

Mangroves and Salt Marshes 2: 109–119, 1998. © 1998 Kluwer Academic Publishers. Printed in the Netherlands.

109

Classifying coastal resources by integrating optical and radar imagery and color infrared photography Elijah W. Ramsey III, Gene A. Nelson1 and Sijan K. Sapkota1 U.S. Geological Survey, National Wetlands Research Center, U.S.A. 1 Johnson Controls World Services Inc., 700 Cajundome Blvd., Lafayette, LA 70506, U.S.A. (Received 20 August 1997; accepted 8 January 1998)

Key words: TM, SAR, CIR, progressive classification, DFA, Juncus roemerianus

Abstract A progressive classification of a marsh and forest system using Landsat Thematic Mapper (TM), color infrared (CIR) photograph, and ERS-1 synthetic aperture radar (SAR) data improved classification accuracy when compared to classification using solely TM reflective band data. The classification resulted in a detailed identification of differences within a nearly monotypic black needlerush marsh. Accuracy percentages of these classes were surprisingly high given the complexities of classification. The detailed classification resulted in a more accurate portrayal of the marsh transgressive sequence than was obtainable with TM data alone. Individual sensor contribution to the improved classification was compared to that using only the six reflective TM bands. Individually, the green reflective CIR and SAR data identified broad categories of water, marsh, and forest. In combination with TM, SAR and the green CIR band each improved overall accuracy by about 3% and 15% respectively. The SAR data improved the TM classification accuracy mostly in the marsh classes. The green CIR data also improved the marsh classification accuracy and accuracies in some water classes. The final combination of all sensor data improved almost all class accuracies from 2% to 70% with an overall improvement of about 20% over TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m in some areas.

Introduction Wetlands are diverse systems that can exhibit extreme variations in areal extent, temporal duration, and spatial complexity (Ramsey and Laine, 1997). Monitoring tools are needed that can discriminate wetland types and identify alterations by either natural (e.g., global climate change, sea-level rise, flooding) or socioeconomic forces (e.g., protection from coastal erosion, development, fire management) (Ramsey et al., 1994; Ramsey, 1995; Ramsey and Jensen, 1996). Monitoring tools must also be able to discriminate differences in what may appear at first glance to be fairly uniform and homogeneous stands of coastal vegetation. In wide expanses of monotypic coastal marshes, for example, differences in hydrology (dominated by tidal flooding extent, frequency, and duration) and salinity linked to the marsh micro topography alters the

plants’ biophysical characteristics (e.g., height, density, sexual reproduction). These sometimes subtle differences must also be recognized when evaluating species functions, stress impacts, and management in these marshes (Stout, 1984). The National Wetlands Inventory uses aircraft color infrared photography (CIR) to provide detailed biophysical, high quality information about the distribution of wetland types (Peters, 1994). Unfortunately, there is a nearly 10-year turnaround for new map production (Wilen and Frayer, 1990). To be more effective, the data must not only be detailed but timely and cost effective (Teuber, 1990). Programs such as the National Oceanic and Atmospheric Administration’s Coastal Change and Analysis Program use the Landsat Thematic Mapper (TM) sensor to provide a more timely and cost effective national system of classified coastal landcover maps (Klemas et al., 1993).

110 In aggregate, these maps provide a standard baseline and analysis approach for detecting and monitoring change on a regional scale. On a local scale and in certain instances, however, the spatial scale and classification detail may not fit the project requirements (e.g., where one species dominates a coastal wetland zone). Further, all optical systems are limited to daytime collections and are constrained by cloud cover and their usefulness is diminished by atmospheric haze, especially problematic in subtropical to tropical environments (Ramsey and Laine, 1997). Operational monitoring requires tools that can collect data consistently and with predetermination. Today, numerous satellites carry a variety of remote sensing instruments necessary for accurate mapping of coastal vegetation. Of these, optical sensors such as the TM and, more recently, SPOT sensors have the longest history of mapping wetland types and monitoring wetland changes (e.g., Weismiller et al., 1977; Klemas et al., 1980; Hardisky et al., 1986; Jensen et al., 1987). However, with the launch of Seasat in 1978, radar has slowly gained importance in wetland mapping (Lyon and McCarthy, 1981). Radar sensors could extend the capabilities of optical sensors in mapping coastal wetlands by adding a potential for higher canopy penetration, more detailed canopy orientation and density information, and 24-hour-a-day collections nearly independent of weather conditions (Ormsby et al., 1985; Dobson et al.,1992). The primary objective of this project was to prepare a timely and detailed physiographic and species map of the coastal marsh and adjacent forest in St. Marks National Wildlife Refuge, Florida. Juncus roemerianus (black needlerush) is the dominant vegetation of and accounts for the bulk of biomass in most marshes on the northeastern gulf coast from the mangrove coastline of south-central Florida to the Spartina alterniflora (smooth cordgrass) coastline of Louisiana (Stout, 1984). Progressing inland beyond the black neddlerush marsh, dominant vegetation types are fresh marsh (Cladium jamaicense; sawgrass), pine-palmetto fringe forest, pine, hardwoods, and scrub shrub dependending upon local drainage and the influences of human activity. Achieving the desired level of detail required that changes in biophysical character (i.e., primary canopy structure within a single species) defining low to high marsh zones in the dominantly black needlerush marsh be identified and also that the narrow fresh marsh, marsh-to-forest transition zones, and a majority of the pine-cabbage palm hummocks within the marsh be defined. To provide

this detail, we integrated aircraft CIR, TM, and ERS1 synthetic aperture radar (SAR) data. We hoped that the higher spatial resolution of the CIR and the increased potential of the SAR to provide canopy structure information would result in proportionally less mixed boundary pixels in transitional classes and allow greater separation of zonation within the marsh. A secondary objective was to estimate the contribution of each sensor in producing the categorized map. To accomplish this objective, a progressive discriminant analysis scheme was devised and descriptive statistics were generated at each step for comparison.

Methods CIR, TM, and ERS-1 SAR images were combined to create a classified map of the marsh and upland areas of the refuge. To decrease the computation effort but retain most of the CIR landcover information, only the green CIR band (0.5–0.6 µm; Sabins, 1997), related visually to the highest contrast, was used in the classification procedure. The NIR band (0.7–0.8 µm; Sabins, 1997) usually containing the highest information content of the three CIR bands was highly saturated during initial processing, diminishing the information content and usefulness compared to that of the green CIR band. The six reflective TM channels, centered at 0.48 µm, 0.57µm, 0.66 µm, 0.80 µm, 1.68 µm, and 2.22 µm (Markham and Barker, 1985) and nominally at a 30-m spatial resolution, were entered into the classification procedure. The thermal channel was excluded because of the lower spatial resolution (about 120 m) and because the signal is from emission, not from scattering, the primary mechanism for return in the optical region. The TM data were not converted to radiance values, but during post processing, data were normalized to comparable ranges, and thus the data between channels were comparable. The ERS-1 SAR data (C band VV polarization) were radiometrically calibrated, corrected for antennae falloff, and transformed to a ground range representation (Ramsey, 1995). Even though the shorter wavelength of the ERS-1 SAR system is normally less penetrating than longer wavelength systems (e.g., Shuttle Imaging Radar L band SAR), the steep incidence angle (about 23◦ ) of the sensor increases the probability of returns from the lower canopy or ground surface. Further, like polarizations (VV, HH), although less likely than cross polarizations (V send H return or H send V return) to be from the canopy volume, can

111 be preferentially influenced by the specific orientation of the canopy. The effects of either system parameter on the SAR contribution to the classification detail and accuracy are not directly examined in this study; however, this SAR is widely used and has been useful in other classifications (even if general or descriptive; Dobson et al., 1996; Kasischke and Bourgeau-Chavez, 1997). The CIR, originally scanned and mosaiced to a 3-m spatial resolution, was resampled with a nearest neighbor algorithm to a 10-m spatial resolution. The TM and SAR images, standardized to similar planar geometries during initial processing, were resampled from 25-m to 10-m spatial resolution. Any residual spatial error in the standardized geometries between image types was aggregated into the mean error of the georegistration. Root-mean-square-errors associated with the georegistration of the CIR and SAR images to the TM images were ± 23 m and ± 25 m, respectively. A K-means clustering algorithm was used to classify the TM, SAR, and CIR images (Tou and Gonzalez, 1974; PCI, 1993). Instead of classifying each image separately (e.g., Schriever and Congalton, 1993; Wolter et al., 1995), the green CIR, TM reflective, and SAR C band VV polarization data were combined into a single classification analysis. To further improve separation between classes, a progressive classification scheme was used (Jensen et al., 1987; Ramsey and Laine, 1997). This method allowed the progressive separation of mixed clusters until no further spectral separation was possible. A class-stratified, random sampling technique was used to generate classification error estimates for the study area (Congalton, 1988). The original 3-m CIR photographs were used as reference data. Results were detailed in a matrix format (e.g., Congalton, 1991; Janssen and van der Wel, 1994). Even with the use of progressive classification and multiple image types, however, some confusion still existed between classes. In these cases, final class determination was based on retaining the landscape pattern while minimizing the classification error. This was especially necessary in transition classes where areal coverage was either comparatively small (e.g., fresh marsh to pine/palmetto, pine/palmetto to pine or hardwood) or where changes in the landscape were not distinct, but gradational (e.g., low to medium marsh, high to fresh marsh). In both transitional cases, increased error resulted from a somewhat arbitrary assignment of mixed boundary pixels to one class or another (more than one class within the sensor reso-

lution) or overlaying discrete boundaries on a fairly continuous surface (same species but subtle changes in the canopy structure), for example, in high to low marsh. Even though higher classification accuracy could have been obtained by aggregating these transitional classes, an objective of this study was to provide the highest detail possible of the changes in the landscape, both within vegetation type (e.g., marsh canopy structure) and between vegetation types (e.g., fresh and saline marshes, marshes and fringe forests). Field experience showed that the canopy structure of the nearly monotypic black needlerush salt marsh transgressed from tall, dense low marsh to short, less dense high marsh. Consequently, five classes within this coastal marsh were chosen to best represent these changes in structure. These included fresh marsh, high marsh, medium high marsh, medium marsh, and low marsh. Identification of these marsh classes was based on field experience, photography collaboration, and distances from flooding influences. Including these five marsh classes, a final 13-class classification was designed that consisted of water and forest classes as well. To estimate the value of each sensor in predicting the variation in landcover types, these 13 classes were entered into a discriminant function analysis (DFA) (Johnson and Wichern, 1988; SAS, 1989). The DFA progressively compared different combinations of the TM, CIR, and SAR data with the 991 points (between 33 to 139 points per class) used in the classification accuracy assessment based on the original photographic verification (Table 1). For example, the first DFA analysis tested only the ability of SAR to predict the 13 classes correctly. Before applying the progressive DFA, however, multivariate normality and equality of the population covariance matrices were investigated for the TM, CIR, and SAR data set in order to be used as classifying variables. A nearly 1-to-1 line of the exact percentile of chi-square distribution against the ordered squared distances of sample data suggested multivariate normality of the data set (Johnson and Wichern, 1988). The test for equality within covariance matrices, however, showed a significant difference among the matrices (p < 0.01). Therefore, quadratic, instead of linear, classification rules were applied (Johnson and Wichern, 1988).

112 Table 1. Accuracy assessment of progressive classification

Progressive classification

Color infrared photography (reference data) 4 5 6 7 8 9 10

11

12

13

Total

Correctly classified (%)

3 2 43 8 9 3 0 1 1 0 0 9 0

4 3 6 36 1 0 0 0 0 0 7 3 0

0 0 0 3 75 2 0 0 1 0 6 11 0

0 0 0 0 3 109 3 0 6 0 1 4 0

0 0 1 1 2 16 104 4 10 0 0 1 0

0 0 0 0 1 12 15 75 10 0 0 1 0

0 0 0 0 0 2 11 13 66 1 2 0 0

0 0 0 0 1 1 2 1 0 49 2 0 1

0 3 0 0 4 2 10 3 4 0 28 0 0

0 0 0 0 0 0 0 0 0 0 0 31 2

0 0 0 0 0 0 0 0 0 0 0 0 47

48 50 50 50 98 147 146 97 98 50 46 61 50

85.42 84.00 86.00 72.00 76.53 74.15 71.23 77.32 67.35 98.00 60.87 50.82 94.00

79

60

98

126

139

114

95

57

54

33

47

991

75.28

1

2

3

1. Hardwood 2. Pine 3. Scrub shrub/pine palmetto 4. Fresh marsh 5. High marsh 6. Medium high marsh 7. Medium marsh 8. Low marsh 9. Channel water and mud 10. Shallow coastal water 11. Water 12. Sand flats 13. Oyster bars/sand bars

41 0 0 0 0 0 0 0 0 0 0 1 0

0 42 0 2 2 0 1 0 0 0 0 0 0

Total

42

47

Figure 1a.

Results Progressive classification Final classification accuracy using the six TM bands, the green CIR band, the SAR data, and the progressive classification methodology was about 75% (Table 1). In general, class definition and spatial detail were greatly improved compared to the use of the TM data

alone (i.e., Figures 1a–c, 2, and 3a–b). Multiple marsh and forest types were identified with only minor to moderate overlap in some classes and some small scale features (∼ 10 m) were detected. The ability to provide a highly detailed classified map was linked to the spectral differentiation of the landcover classes, but it was also related to the ability to spatially resolve many small features in this hetero-

113

Figure 1b.

Figure 1. Comparison of different data sources. Spatial detail as well as vegetation discrimination were improved by combining the three data sources: (a) green band (0.5–0.6 µm) CIR photography at 10-m spatial resolution; (b) ERS-1 SAR (C-band VV polarization); and (c) Landsat Thematic Mapper band 4 (0.8–0.9 µm). The area lies within St. Marks National Wildlife Refuge south of Tallahassee, Florida. The box defines the subset enlarged at the bottom of Figure 2.

geneous landscape. Spatial definition in the original TM classification was inadequate, especially in defining features such as hummocks, transition zones, small channels, and heterogeneous and mixed interior forest areas (on the order of 10 m to 20 m). The merger of the three data sources produced a classified map with

an improved spatial detail that defined many features that were not separable with solely TM data (Figure 2). However, even though classification assessment quantified the map accuracy and visual assessment verified the spatial distribution of classes (e.g., general progradation of wetland types), these assessments could not

114

Figure 2. Classified map generated from a combination of green band CIR photography, Landsat Thematic Mapper imagery, and ERS-1 SAR imagery at 10-m spatial resolution. For visual clarity sand flats and oyster bars/sand bars are combined.

quantify the importance of each sensor’s data in classifying the wetland to upland landcovers. To better understand the contribution of each sensor in identifying the landcover classes, a comparison of each sensor and all combinations of sensors was performed by using the DFA and t-test statistics. DFA classification Singularly, the green CIR data explained about 31% of the landcover classes while SAR data explained 16% and TM data (reflective bands) attained 44% expla-

nation accuracy (Table 2). Thematic Mapper and the green CIR data combined explained 60%, and TM and SAR combined explained 47% of the landcover distribution. The SAR and green CIR band together correctly explained about 34% of the class distribution. The final combination of TM, SAR, and the green CIR data resulted in an overall 63% correct DFA classification. Because the TM, CIR, and SAR variables were progressively introduced in the DFA, the results depended on the introduction of extra variables to the

115

Figure 3. A comparison of the progressive classification results for class 5 – high marsh – with (a) solely TM reflective data (simulated with the discriminant analysis) and (b) the progressive classification scheme using all three sensor data. This figure also shows how the distribution of misclassifications was centered mainly around the target classes (high marsh in this case). Table 2. Result of discriminant analysis using SAR, CIR (green) and TM as classifying variables. Vegetation class

Frequencya

SARb

CIRb

SAR+CIRb

TMb

SAR+TMb

CIR+TMb

SAR+CIR+TMb

1. Hardwood 2. Pine 3. Scrub shrub/pine palmetto 4. Fresh marsh 5. High marsh 6. Medium high marsh 7. Medium marsh 8. Low marsh 9. Channel water and mud 10. Shallow coastal water 11. Water 12. Sand flats 13. Oyster bars and sand bars

42 47 79 60 98 126 139 114 95 57 54 33 47

47.62 53.19 0.00 0.00 0.00 21.43 0.00 0.00 0.00 40.35 3.70 0.00 42.55

73.81 0.00 1.27 0.00 11.22 0.00 0.00 0.00 0.00 85.96 87.04 75.76 63.83

71.43 27.66 12.66 0.00 2.04 31.75 5.04 0.88 12.63 68.42 66.67 78.79 61.70

73.81 72.34 7.59 60.00 41.84 37.30 58.27 17.54 38.95 84.21 11.11 57.58 12.77

73.81 80.85 10.13 66.67 45.92 43.65 58.27 28.07 38.95 82.46 11.11 60.61 14.89

80.95 76.60 18.99 61.67 55.10 43.65 65.47 16.67 45.26 84.21 57.41 93.94 82.98

76.19 85.11 21.52 65.00 57.14 48.41 66.19 32.46 52.63 80.70 57.41 93.94 82.98

Total

991

16.07

30.68

33.82

44.10

47.34

60.22

63.05

Note: a True frequency from color infrared photography. b Percentage of correct classification obtained from the discriminant functional analyses.

analysis. To examine the possibility that increases in the percent explanation of the different combinations were significant, paired t-tests were performed (Zar, 1984, Table 3). In all six cases the addition of the SAR and/or green CIR data to the TM data significantly (p < 0.1) improved all classifications. Four

of these six paired t-tests of the differences showed highly significant (p < 0.05) improvements. Analyzed per class and per sensor and excluding water-related and sand flat classes, the highest TM and green CIR band and the second to highest SAR classification percentages were with the hardwood class (Table 2). Singularly and in order of highest accu-

116 Table 3. Result of t-tests using TM and combination of TM with SAR and green CIR as classifying variables. Difference between the correctly classified percents using

Mean difference

SE

T-statistic

p-value

TM+SAR vs. TM TM+CIR vs. TM TM+SAR+CIR vs. TM

3.23 16.12 18.95

1.05 5.95 5.64

3.09 2.71 3.36

0.0094 0.0190 0.0057

TM+CIR vs. TM+SAR TM+SAR+CIR vs. TM+SAR TM+SAR+CIR vs. TM+CIR

12.89 15.71 2.83

6.31 5.81 1.50

2.03 2.71 1.88

0.0638 0.0191 0.0840

racy (above 50%), TM data were best in defining shallow coastal water, hardwoods, pines, fresh marsh, medium marsh, and sand flats. The lowest percentages were with scrub shrub/pine palmetto, low marsh, water, and oyster and sand bars. After pine forest, the highest percentages related to the SAR data were with hardwoods, shallow coastal water, oyster and sand bars, and medium high marsh. The green CIR band classification accuracy of water was a high 87%. The next highest classifications in decreasing order were with shallow coastal water, sand flats, hardwoods, oyster and sand bars, and high marsh. All other classifications were nearly 0% using the green CIR data. Combining data from the different sensors improved the classification results in all but a few cases of no change to slight decreases in percent accuracies. TM data combined with SAR improved the TM classification accuracy by up to 11% (low marsh) and with the green CIR band by up to 70% (oyster and sand bars). Compared to the individual SAR and green CIR band classifications, the combination of these data resulted in a decrease in the SAR pine class percent accuracy and in the green CIR high marsh and water classes accuracies. Slight-to-moderate increases were associated with scrub shrub/pine palmetto, medium high marsh, medium marsh, and channel water and mud. Up to 67% increase in SAR classification accuracy was obtained with the combination of SAR and TM versus SAR only. Similarly, up to a 77% classification accuracy increase was obtained by combining the green CIR band and TM as classifying variables versus the green CIR band only. Except for the shallow coastal water class, the three-sensor combination increased classification accuracy of oyster and sand bars the highest (70%) over the TM classification. The

next highest percent increases were with water (46%) and sand flats (36%) classes. All other classes had an increase in accuracy ranging from hardwoods at 2% to high and low marsh classes at 15% with an overall increase of about 20% over TM only as the classifying variable (e. g., Figures 4a–d).

Discussion The progressive classification of the coastal marsh and adjacent forest areas with TM, CIR, and SAR data resulted in an overall improved classification accuracy when compared to that using solely TM band data. As compared to the DFA results, the progressive classification increased accuracies from about 3% in the water class to 64% in the scrub shrub/pine palmetto class, with an overall average increase of about 12%. The progressive classification had a slight decrease (1%) in accuracy for pine and considerably large decrease (43%) in the sand flats class over DFA accuracy. Based on the progressive classification scheme, the highly detailed segregation of marsh classes within the nearly monotypic black needlerush marsh and separation of the forest classes was associated with classification accuracies ranging from about 71% to 77%. Assessment of the misclassification distributions indicates that confusion was mainly centered around the target class (e.g., Figures 4a and b). Along the transgressive sequence from low to medium high marsh, adjacent or nearly adjacent lower marsh types were most often confused with the target marsh type, while in high and fresh marsh classes the opposite was true. As stated earlier, other classification schemes differentiate coastal vegetation by species type, but do not dif-

117

Figure 4. An example of the successive change in classification accuracy using the results of DFA on medium high marsh class (class 6). Classification distributions with (a) TM reflective data only, (b) TM and SAR data, (c) TM and the green CIR data, and (d) all three data sources combined. This figure shows how the percent classification of the target class progressively increased whereas the misclassified class (mostly class 7) progressively decreased.

ferentiate wetland zonations within a single species. In this study we subdivided the dominantly single species marsh into zones related to hydrology, zones which are manifested in the vegetation as changes in canopy structure. These changes included variable canopy height and density, exposed soil background, and possibly live to dead biomass proportions. These changes show spatial heterogeneity, but in general the canopy structure changes are revealed as a gradational surface, which from a remote sensing perspective causes subtle

changes in the optical and microwave returns. Overlaying a discrete class structure on this more or less continuous surface results in increased classification confusion centered around the target class. Another source of classification error is related to ephemeral features, such as changes in water depths that expose a mud flat or sand bar in one image but not in another, or the presence and absence of seasonal algae growth in mud flats (e.g., confusion between marsh, water, and mud classes).

118 Given the complexity of the marsh classes used in this analysis and the complexity of assessing the accuracy, however, the per class and overall accuracy percentages were surprisingly high in most cases. Aggregation of some marsh classes may have improved the overall percent accuracy of the classification, but the simulated progression from low to high marsh would have been obscured. The highly detailed marsh delineation resulted in a more accurate portrayal of the marsh transgressive sequence of this convoluted and heterogeneous but monotypic marsh, thereby improving the usefulness of the classified map for resource management. To ascertain how the individual sensors contributed to the improved classification, a DFA procedure was used. The results of this procedure showed the improvements in classification accuracy as compared to using only the six reflective TM bands. Individual classification accuracies associated with the green CIR and SAR bands were about 31% and 16%, respectively. Individually, the green CIR and SAR data mostly discriminated broad vegetation types, such as forest, marsh, and water. Even though individual classification accuracies were low, in aggregate, the addition of the green CIR band and SAR data significantly increased the classification accuracy over that using only TM data in this study. Related to individual classes, combinations of TM and SAR and TM and the green CIR band improved some class accuracies by up to 11% and up to 70%, respectively, over the TM classification. In a few cases, class accuracies were not changed or decreased slightly. Results of the SAR and green CIR band combination were mixed. There were dramatic increases in percent accuracy in a couple of classes, but most class accuracies remained unchanged or changed only slightly. The final combination of TM, SAR, and the green CIR band data increased the accuracy of all classes with an overall average increment of about 20%, except in the shallow coastal water class where the percentage slightly decreased. The highest increases were linked to water, sand flats, and oyster and sand bars classes, while moderate increases were linked to transition classes, scrub shrub/pine palmetto, high marsh, and low marsh. The spatial detail of the final classified map was also improved over the original TM spatial resolution. Many small hummocks, streams, and narrow transition regions were delineated in the classified map derived from the combined sensor data sources. Final spatial resolution was estimated between 10 m to 20 m

as compared to the original 25-m TM and SAR resolution limits. Improved spatial detail was a byproduct of inserting the CIR data into the classification. The higher spatial detail of the CIR data, combined with the higher spectral detail of the TM data and the added information of the SAR data, produced not only a higher spatial classification but also a more detailed vegetation characterization. In total, the combination of TM, SAR, and green CIR data improved the ability to identify coastal marsh and adjacent forest types. SAR data improvements to the TM classification accuracy were slight and mostly related to the pine and transition marsh classes. The green CIR data also improved the transition marsh classification accuracy, as well as accuracies in forest and water classes. The final combination of all sensor data improved almost all class accuracies with an overall improvement of about 20% compared to TM data alone. Not only was the identification of vegetation types improved, but the spatial detail of the classification approached 10 m. Both improvements are necessary parts of providing resource managers and regulatory agencies with a more accurate portrayal of the distribution of coastal resources and changes to those resources. Sensor contributions to the classification found in this study are related to the environment examined and the operational parameters of the different sensor systems. Results of this study, however, may be used for estimating the performance of CIR photography and similar SAR’s in classifying coastal resources and in contributing to integrated sensor approaches to landscape classification. Future work will examine the capabilities of different SAR systems (bands and polarizations) in separately classifying coastal environments.

Acknowledgments We thank Joe White of the U.S. Fish and Wildlife Service for allowing access to the St. Marks National Wildlife Refuge, Florida and use of the Refuge office. We appreciate the work of U.S. Geological Survey personnel Ms Beth Vairin and Mr Daryl McGrath for editing this manuscript. We also thank the anonymous reviewers for their effort in improving this manuscript. Note: Mention of trade names or commercial products is not an endorsement or recommendation for use by the U.S. Government.

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