Topographic Normalization of Landsat Thematic Mapper Data in Three Mountain Environments Thomas R. Allen Department of Political Science and Geography, Old Dominion University Norfolk, Virginia 23529-0088, U.S.A. E-mail:
[email protected]
Abstract A comparative analysis is presented for empirical topographic normalization of Landsat Thematic Mapper (TM) data in varied forest and topographic settings. The paired study included rugged areas of Glacier National Park, Montana, Linville Gorge Wilderness, North Carolina, and Green Mountains, Vermont, U.S.A. Empirical models of topographic bias achieved significant corrections in the Montana and Vermont sites. Relative homogeneity of forest structure offset rugged topography in Montana to yield the highest success of normalization. Significant models could not be derived for Linville Gorge. Topographic normalization is most successful when canopy complexity and altitudinal zonation are low to moderate. Atmospheric pollution and geologic control in ridge-valley alignment are important considerations when undertaking topographic normalization in mountain environments.
Introduction While techniques for atmospheric and radiometric correction have advanced, differential illumination of slopes remains a controlling factor in remote sensing of mountain environments. Although several studies have addressed normalization of topographic bias in satellite imagery, little basis exists for broad applicability of the technique. A variety of techniques for reducing topographic bias have been evaluated, ranging from band ratios (Holben and Justice, 1981), spectral vegetation indices (Walsh et al., 1990), and modeling for correction of topographic effects (c.f., Colby, 1991; Meyer et al., 1993). Slope angle and aspect effect differential illumination, yielding variable spectral responses for perhaps otherwise homogeneous cover types. This reliefinduced brightness variation across similar surface conditions is commonly found in multispectral satellite imagery (Colby, 1991). Topographic normalization has been shown to reduce these effects and increase image classification accuracies using non-Lambertian simulation of terrain illumination and statistical correction (Meyer et al., 1993). Assumption of Lambertian reflectance in radiometric correction or topographic normalizations tends to result in over-correction (Civco, 1989) or errors in image classifications (Naugle, 1992; Pons and Solé-Sugrañes, 1994). ln synthetic aperture radar (SAR) imagery collected from satellites, topographic effects also limit the utility of soil moisture estimation (Goering et al., 1995). Slope aspect stratification (Ricketts et al., 1993) or integration of digital elevation models (e.g., Franklin, 1990) for image classification are alternative techniques for possible reduction of topographic effects but Geocarto International, Vol. 15, No. 2, June 2000 Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong.
may require substantial processing efforts. Further, few such techniques have been evaluated for their general applicability to mountain environments. To benefit land cover change, monitoring of biophysical features, and modeling processes in mountain environments, remote sensing requires understanding of the geographic applicability and universality of processing techniques. For example, the application of seasonal multitemporal imagery to land cover and ecological classification has proven prone to differential illumination. Conese et al. (1993) illustrate an approach to reducing topographic bias in multitemporal data for the Tuscany Mountains, Italy. However, a focused study of topographic normalization is needed to compare results across topographic and ecologic settings. Differential topographic effects among land cover types may reduce the potential for expedient algorithms. Hall-Könyves (1987), for example, could detect topographic effects in pasture but only found weak relationships in forest and cultivated lands. Prior investigations of topographic bias have primarily been restricted to single area studies (Civco, 1989; Colby 1991; Meyer et al. 1993) and have not sought replication of techniques nor utilization of control data sets. Appropriate selection and implementation of these techniques may result in significant benefits such as improved accuracy of thematic maps (Meyer et al. 1993), remote assessment of forest characteristics (e.g., Colby 1991), and greater potential for robust multitemporal analysis.
Objectives The central objective of this project is to assess the extensibility of empirical topographic normalization of 15
multispectral satellite imagery using paired regional studies in montane forests having contrasting topographic and floristic structure. The study could provide geographic synthesis by examining multiple locations in mountains of the conterminous United States including Glacier National Park, Montana; Green Mountains, Vermont; and Linville Gorge Wilderness, North Carolina. A secondary objective was to determine the relative influence of canopy and topographic complexity upon topographic normalization. As a paired study, the project would also be able to develop new hypotheses considering external factors such as geologic setting and relative climate in the form of atmospheric haze in spectral responses.
Study Area and Data Study area selection sought a range of forest diversity (coniferous, mixed, and deciduous forests) and topographic ruggedness (moderately undulating slopes to precipitous ranges). Among the forest vegetation in selected mountain environments, the Green Mountains of Vermont contain extensive deciduous and human-modified landscapes, a useful analog for land use mapping programs and topographic normalization. Subalpine forests found in the Rocky Mountains are analyzed in Glacier National Park, Montana (high relief-minimal vegetation complexity.) Finally, the Linville Gorge Wilderness Area of the Southern Appalachians in western North Carolina typifies an environment of moderate relief and high vegetation complexity. Landsat TM data were acquired for Glacier National Park (GNP), Montana, the Green Mountains (GM), Vermont, and Linville Gorge Wilderness Area (LGWA), North Carolina (Figure 1.) All study area data coincide with similar conditions of summer insolation and illumination, cloud-free imagery, and full leaf-on conditions. Further, color-infrared aerial photography was available within 1-3 years of image sensing for each study area. Field visitation to each site also occurred within three years of each image date. Further, image digital data were all collected by the Landsat-5 platform TM sensor.
Figure 1
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Study site locations in three mountain regions of the conterminous United States.
Site 1: Glacier National Park, Montana Situated in the northwest of Montana, Glacier National Park (GNP) characterizes a relatively pristine subalpine environment of the northern Rocky Mountains, having abundant spruce-fir forests within an extremely rugged, alpine glacial landscape with approximately 1700 m local relief (Figure 2a). GNP’s landscape provides for analysis of extremely rugged topography and a relatively homogeneous coniferous forest. The selected site is located in the Two Medicine Area of GNP east of the Continental Divide in a typical u-shaped alpine valley. The relief consists of approximately 1000 m having steep slopes to 45 degrees. The expansive forest community of the valley floor consists of Abies lasiocarpa, Pseudotsuga menziessii, and Pinus flexilis canopy dominants grading into krummholz and tree island forms toward treeline elevations composed of Picea engelmanni, Pinus flexilis, and Pin us albicaulis. Dendrochronology of the Two Medicine area indicates a mixture of stand ages regenerating from fires, with stand ages increasing toward the isolated canyons near the Divide (Barrett, 1993.) Field data were collected for related alpine treeline studies during July and August 1991 and 1993 (Allen and Walsh, 1996). A cloud-free Landsat TM scene for September 3, 1990, was available for analysis. Site 2: Green Mountains, Vermont The Green Mountains, Vermont (GMV) provide data for a comparatively less rugged, but more diverse forest landscape composed of several forest cover types and associated vegetation communities (Figure 2b). The area of Camel’s Hump in the northern portion of Green Mountain National Forest was selected for its accessible trails and availability of aerial photography and digital satellite data. In addition, prior published remote sensing analyses of forests at this location provided useful documentation of habitat quality (Vogelmann and Rock, 1986). The altitudinal gradient of vegetation across 1300 m local relief includes mixed northern hardwoods and upper elevations having Picea rubens and Abies balsamea forests (Marchand, 1987). Landsat TM data sensed on August 29, 1993 (path 14, row 34) were acquired for the GMV analysis. Site 3: Linville Gorge Wilderness, North Carolina In contrast to sites 1 and 2, the Linville Gorge Wilderness (LOW) exhibits both high topographic and vegetative complexity. Extending approximately 30 km and 700 m in depth, the Linville Gorge is a managed wilderness area of the U.S. Forest Service’s Pisgah National Forest. The Gorge has extensive stands of old growth hemlock (Tsuga canadensis) and poplar (Liriodendron tulipifera) as well as mixed deciduous and pine-dominant stands. Dense hardwoods cover lower elevations but are interspersed with a variety of rich cove forests or xeric pine stands on rocky south-facing slopes. Figure 2c. (after Whitaker, 1956) graphically illustrates the forest community diversity that can be found in the Linville Gorge area in relationship to elevational gradients. Owing to its ruggedness and isolation, much of the gorge is considered
Figure 2
Topographic gradients and vegetation structure in three study areas: (a) subalpine spruce-fir forest and treeline, Glacier National Park, Montana, (b) nothern hardwoods and spruce-fir forest, Green Mountains, Vermont (after Marchand, 1987), and (c) Linville Gorge Wilderness, example of Southern Appalachians after Whittaker (1956) (H= hemlock, BG= beech gap, P=pine, F=Fraser fir, S=spruce, OCH=chestnut oak, OH=oak-hickory, and CF=cove forest).
virgin forest. Wide variations in forest composition arise from topographic gradients such as river and stream banks, concave slope “coves,” convex toe slopes and divides, upper slopes, ridge tops and outcrops (Greeley, 1990). A Landsat TM image sensed June 6, 1993 was used for study of the Linville Gorge.
Methods Reference Data and Preprocessing A combination of field data and aerial photo-interpretation provided reference data for topographic normalization. Field collection utilized the Global Positioning System (GPS) with subsequent differential correction within 15 m or less to collect positional location on vegetation characteristics (tree
and ground cover and density) observed on line-intercept samples. National High Altitude Program (NHAP) colorinfrared photography was used as the principal source of identifying homogeneous areas of forest cover across slope and altitudinal sites. Level three Digital Elevation Models (DEMs) derived from digitized 1:24,000 USGS quadrangles were acquired and integrated in a raster geographic information system (GIS) with UTM earth coordinates and 30m x 30m pixels for each site. McNab’s (1993) topographic index was calculated to guide the selection of sites in aerial photographs, aiding coverage of the range of topographic settings for a given cover type. In the absence of detailed meteorologic information for the dates of image sensing, a relative correction technique was used to remove haze and atmospheric scattering (Jensen, 1996.) Given availability of detailed atmospheric conditions, a model atmosphere could be deduced for radiometric correction for each scene and time period (Cavayas, 1987). This detailed data was not available for these time periods and study sites. However, since empirical topographic normalization does not require absolute radiances, a relative atmospheric correction using the histogram subtraction method (Jensen, 1996) prior to normalization was justified. This technique is common to the evaluation of multitemporal remote sensing where detailed meteorological data are unavailable. Once atmospheric corrections were completed, bad scanlines found in the GNP image were replaced by averaging the immediate pixels above and below rows of the bad scanline band. Original TM band 1 data for the Linville Gorge site was missing, limiting this site analysis to TM bands 2-5 and 7. Geometric corrections were applied to each image. The Landsat TM images were georeferenced in ERDAS to hardcopy 1:24,000 topographic quadrangles. Approximately 30 ground control points (GCP’s) were collected in quadrangles distributed throughout each study area. Using these points, linear geometric transformations were computed. The resulting root mean square error (RMSE) was less than one-half pixel (0.5) for each image. Nearest neighbor resampling algorithm was used to place original spectral data into new georeferenced earth coordinates in output images. Topographic Normalization The normalization procedure is based on removing the influence of topography (slope and aspect) from the observed spectral reflectance values in the satellite images. Simple cosine correction overcompensates for differential illumination (Jensen, 1996.) This trigonometric technique causes spectral reflectance at low incidence angles (cos(i) scales the angle between incident solar radiation and the surface normal) to become saturated in 8-bit scaled output images as a result of over-correction on shaded slopes. Shaded and weakly illuminated slopes may appear white and almost all northerly facing slopes became over-illuminated in this model. The Minnaert correction (Colby, 1991; Hodgson and Shelley, 1994) proved difficult to establish model parameters. Much more intensive field sampling would be required to estimate the form of this transformation. In the logarithmic form of a 17
regression model, Hodgson and Shelley ( 1994) recognized that a value for k may be required for each landcover type. Since no existing vegetation maps were available for the each study area in nearly comparable spatial or thematic resolution, estimation of the k parameters could only be empirically derived through intensive multiple study area field sampling. Thus, a statistical method with moderate field data collection needs and greater potential extensibility was adopted and tested in the remainder of the evaluation study. The adopted normalization method models solar illumination using information on digital elevation model (DEM) derived slope angle and aspect and the altitude and azimuth of the sun at the time of satellite sensing. This information is used to build an illumination model simulating direct beam solar radiation incident upon the surface using Erdas IMAGINE Spatial Modeler. Using this measure, a linear model is constructed which predicts reflectance at a pixel as a function of illumination. By regression analysis, the influence of illumination can be removed, allowing for standard assumptions of linear models. Meyer et al. ( 1993) provide a detailed account of this regression approach to topographic normalization applicable to this research. The general method is also given in Jensen (1996.) The approach assumes that differential illumination is the same for all cover types. Thus, the sampling strategy is confined to sites with similar canopy-cover types within each scene determined by aerial photointerpretation and field reconnaissance. Further, the regression equation assumes a linear relationship between illumination and spectral reflectance and seeks to remove this confounding effect for subsequent image analysis. The linear regression model takes the form: LH = LT -cos(i) m -b + Lµ + e
(1)
where: LH = normalized reflectance observed for a horizontal surface; LT = observed reflectance on sloped terrain; Lµ,= the mean of LT for pixels determined by ground verification data; i = solar incidence angle in relation to the surface normal; m = slope; b = y-intercept of the linear model; and e = error. Regression models are derived for each band as topographic bias is wavelength dependent. The normalization procedure utilized Erdas IMAGINE for image algebra. Using the area of interest (AOI) interactive display tools, calibration areas were identified with seed pixels within each study area using photomorphic regions identified in the CIR and B/W photography and from homogeneous stands visited in the field. For these areas, a collection of pixels (GNP n = 223, GMV n= 256, LGW n= 235) provided a purposive sample of closed-canopy forest spectral responses. The air photo sites were identified by photomorphic regions of uniform texture and tone without indication of subcanopy cover. Pixel values were exported to separate data sets and loaded into statistical software. Histograms and descriptive statistics were plotted to evaluate regression assumptions. A spatial model was developed in ERDAS Imagine to apply these normalizations interactively. The model requires user specification of the solar elevation and azimuth, input spectral image, input DEM, a table of 18
slope coefficients, and a table of slope intercepts. The spatial model runs within Erdas and incorporates input and output rasters, coefficients, tables, and functions. The models were executed within IMAGINE to generate corrected output files for statistical and visual evaluation. Pre- and postnormalization images were visually displayed for intial assessment of topographic bias removal (e.g., Civco, 1989; Meyer et al., 1993).
Results and Discussion Findings focus on the evaluation of model parameters and visual comparison of normalization output for statistically significant models. Figure 3. shows the linear relationship derived for GNP l 990 TM band 4 (near-infrared) reflectance as a function of illumination intensity (cos(i) x 100). Two clusters of observations clearly represent differential illumination on north and south facing slopes. Using the regression parameters for each significant band-illumination linear relationship, topographic bias is removed from the TM data. The form of the point distribution and ordinary least squares estimate satisfy regression assumptions of homoscedasticity, existence of a positive correlation, and sufficiency linearity in the relationship. The distribution of two general clouds represents the distinctive difference in illumination-reflectance in the steeply opposed north- and south-facing slopes typical of subalpine valleys in GNP yet does not violate the assumption of normality across values of the independent variable (cos(i)). Tables 1, 2, and 3 detail the regression parameters derived for the GNP, GMV. and LGW TM images, respectively. P-values are reported while the a priori test for significance was chosen alpha = 0.05. This value was set as a threshold before any band-normalization could be applied to the original TM data. Correlation coefficients and R-squared values indicate strong linear relationships in all bands of the GNP data (Figure 3., Table 1.) Only the darkest shadows that exhibited almost no reflectance were unimproved by topographic normalization. Thus, the statistical-empirical approach proved very useful for removing topographic bias in the Landsat TM data for GNP site, increasing the sensitivity to actual land cover spectral characteristics across slope and aspect variations. Marginal but significant regressions were calculated for the Green Mountain TM bands 2, 4, and 5 (Figure 4., Table 2.) These results were deemed marginal. Further, regression modeling for the LGW southern Appalachian site proved more problematic. Table 3. shows that no significant models could be derived using linear models for LGW data. Even when reflectance values were transformed by squaring or applying log values, the correlations with cos(i) were low and insignificant. Even with such marginal results in the most complex of three mountain environments, slight trends in the data (Table 3.) indicate a common tendency with GNP and GMV data; the increased topographic bias in near- and middleinfrared TM bands exhibited by beta- I and it-squared parameters. The stronger relationships generally found in near- and
middle-IR bands and prompted use of near- and middle-IR composites for visual evaluation. Pre- and post-normalization false-color composites (4, 3, 2, and 5, 4, 2 = RGB) were displayed on workstation CRT displays. Figure 5 presents a normalized false-color subset area of GNP (TM bands 4,3,2 are red, green, and blue, respectively.) Individual pixels of 30 m x 30 m are visible for a kilometer-wide peak. The site includes subalpine forest, krummholz, tundra, and bare rock cover. The top image (Figure 5(a)) portrays the satellite data prior to topographic normalization; illumination differences
Figure 3
Linear relationship derived for GNP TM 1990 band 4 (nearinfrared) brightness as a function of illumination intensity (cos(i) x l 00), R2 = 0.86, b1= 0.356, p < 0.000).
Figure 5
are visible on north-versus south-facing slopes. The second frame (5(b)) illustrates the result of applying the empirical model. In comparison to frame 5(a), the normalized data appear “flat” since the effect of differential illumination has been largely removed (R-squared value 0.86 for GNP TM band 4). Visual difference in GMV were difficult to observe, primarily as the weakness of relationships altered the data only slightly. LGW normalization, notwithstanding significance of the regression, was partially effective at removing topographic bias along ridgetops. However, visual
Figure 4
Regression relationship for Green Mountains, Vermont TM band 4 as a function of illumination intensity (cos(i) x 100), R2= 0.04, β1 = 31.781, p < 0.001).
Topographic normalization of GNP study site showing TM bands 4,3,2 (ROB) original (a) and normalized (b).
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Table 1 Model parameters for 1990 Landsat TM normalization in Glacier National Park (n=223.)
Parameter
Bands
Mean r
TM1 14.97 .71
TM2 16.4 .88
TM3 10.6 .77
TM4 33.6 .93
TM5 25.9 .87
TM7 10.4 .85
(r2) β0 β1 p
(.50) 10.356 .073 .001
(.78) 11.086 .084 .001
(.59) 6.288 .068
