Assessing field-derived spectra in EO1 Hyperion hyperspectral linear unmixing models to map sub-pixel abundances of geologic and vegetation land cover types in northwestern New Mexico
Author: Jeremy C. Tensen West Virginia University
Project Committee: Timothy Warner, Ph.D (Chair) Jennifer Miller, Ph.D Rick Landenberger, Ph.D
Final Project for attainment of a Masters of Arts from the Department of Geography At West Virginia University
Table of Contents
I. Aims
II. Background a. Linear Spectral Unmixing b. Linear Spectral Unmixing in the Southwestern United States
III. Study Area
IV. Data and Methods a. Data b. Hyperion Preprocessing c. Linear Unmixing d. Maximum Likelihood Classification e. Unmixing Accuracy Assessment i. QuickBird Classification Sampling ii. Hyperion Unmixing Sampling iii. Comparison of Hyperion Unmixing and QuickBird Classification f. Class Error
V. Results and Discussion a. Linear Unmixing Model Performance b. Class Error
VI. Conclusion
ii Table of Figures
Figure 1. Project study area
Figure 2. The typical mesa-dominated landscape of the study area.
Figure 3. Examples of the major land cover materials for which spectra were collected
Figure 4. Spectra sample sites
Figure 5. Spectral endmembers
Figure 6. Classification training data seperability as shown by the ENVI n-dimensional visualization tool
Figure 7. Maximum likelihood classification of the QuickBird Image
Figure 8. Predicted overall abundances
Figure 9. Comparison of class error associated with the unconstrained and constrained linear unmixing models
iii Acknowledgements
This project was partially supported by Department of Energy (through grant 41817M2111 through Research
Development Solutions LLC), as part of the Southwestern Partnership which is a collaborative research initiative between the Federal government, industry and universities to asses the feasibility and scale of carbon storage in the greater San Juan Basin. I personally would like to thank the professors Timothy Warner Ph.D (chair), Tom
Wilson Ph.D, Jennifer Miller Ph.D, and Rick Landenberger Ph.D for their leadership, guidance and support in developing this research.
iv Abstract Linear spectral unmixing has proven to be an effective tool for mapping sub pixel abundances of vegetation & geologic land cover types within semi arid regions of the Southwestern United states. Linear spectral unmixing techniques are based on the assumption that the spectra combine linearly in proportion to the relative abundance or area occupied by spectral endmembers in the instrument’s instantaneous field of view (IFOV). Therefore high quality and representative endmembers are essential to the success of sub pixel abundance mapping. This study assesses the scalability of field derived spectra for use as endmembers in both a constrained and unconstrained hyperspectral linear unmixing model. Five endmembers (sandstone/soils, shale, pinyon/juniper, sagebrush and dead vegetation) were derived by averaging spectra obtained with a portable spectrometer from 83 sample sites within a 1km2 subsection of the study area. Accuracy of both of the linear unmixing models was assessed using classification of a
0.60 m Quickbird image of a subsection of the study site. The unconstrained linear unmixing model had the best overall accuracy with an average RMSE of +/- 17.5%. Shale, sandstone/soil and dead vegetation land cover types were more accurately mapped then the vegetative land cover types. It is suggested that vegetation’s spectral mixing at single and multi plant scales, due to the presence of branches, dead vegetation, background soils, and rocks was the reason for the error. Deriving endmember spectra at the plant and multi-plant scale, instead of the leaf and branch scale, may improve the vegetation abundance mapping of linear unmixing models in this region.
I. Aims
The purpose of this study is to evaluate the scalability of field collected spectra for use as endmembers in linear unmixing models with an EO1 Hyperion hyperspectral image to derive sub pixel abundances of geologic and vegetation land cover types in northwestern New Mexico. The study also evaluates how well the field collected endmembers perform in constrained linear unmixing compared to unconstrained linear unmixing.
a. Linear Spectral Unmixing
Land cover abundance mapping at the landscape scale using conventional multi- and hyperspectral image classification approaches are generally considered to be inaccurate due to the typical assumption
1 that each pixel comprises only one cover class. In reality pixels are rarely pure. Pixels are usually comprised of various ground constituents, including varying proportions of soils, rocks and vegetation
(Foody, 1999). Spectral mixture analysis (SMA) acknowledges this scaling problem, and unlike traditional classification approaches, seeks to estimate the proportion of each spectral class within each pixel (Harris
& Asner, 2003).
Linear spectral unmixing models (LSUM) are one SMA technique. Linear spectral unmixing models are based on the assumption that the spectra combine linearly in proportion to the relative abundance or area occupied by spectral endmembers in the instrument’s instantaneous field of view (IFOV) (Boardman,
1992). Thus a combined spectrum can be decomposed into a linear mixture or proportions of all spectral endmembers (Okin et al. 2001). This issue is important because spectral unmixing, in which multiple classes and their proportions are identified for each pixel, provides more information than conventional land cover classifications. The one disadvantage to this technique is that class proportion data cannot be mapped to specific locations within the unmixed pixel.
Many variations of linear unmixing models exist varying from unconstrained to fully constrained. The unconstrained method allows abundances to assume negative values and is not constrained to sum-to- unity (one). Conversely a variable-weight, unit-sum constraint allows a user defined weight of a sum-to- unity constraint to be applied to the endmember abundance fractions. Larger weights in relation to the variance of the data cause the unmixing to honor the unit-sum constraint more closely (Research
Systems Inc. 2004). A very larger weight therefore should constrain the unit-sum to achieve endmember proportions to sum to one.
Hyperspectral imagery is especially attractive for spectral unmixing analysis because of the range and number of spectral bands, typically as many as 200 or more bands ranging from 400-2500 nm. This broad wavelength range and large number of bands potentially provides more information than multispectral data, and thus increases the likelihood of successful spectral unmixing (Boardman, 1992;
Okin et al. 2001). Hyperspectral data tend to be noisy because in order to capture the 200 or more bands
2 simultaneously, the incoming radiance is split over many detectors. As a result, noise reduction algorithms are typically applied to hyperspectral data in an effort to reduce error in the linear unmixing process (Boardman & Kruse, 1994).
The linear spectral unmixing model requires input from all the major constituent spectra found in a pixel.
As a result, the quality of a model is only as good as the quality of the endmember spectra available.
Endmembers are derived from one of three sources:
1. Spectral libraries, which comprise spectra measured in a controlled laboratory setting (Clark, et
al. 1993).
2. Field spectra captured with a portable spectrometer (Curtiss & Goetz, 1994).
3. Spectra sampled from within the image itself, if it can be assumed that at least a small number of
image pixels comprise only one cover type (Boardman et al. 1995).
Usually the accuracy of linear spectral unmixing models is assessed using ground reference techniques, based either on field data, or high resolution imagery (Okin et al. 2001).
b. Linear Spectral Unmixing in the Southwestern United States
In the Southwestern United States, SMA applied to mapping the type and extent of vegetation abundances has been demonstrated to be superior to index based models such as Normalized
Difference Vegetation Index (NDVI) at distinguishing green vegetation and non photosynthetic vegetation
(Roberts et al. 1993). As a result, SMA has become an attractive abundance mapping method in the southwestern United States and has been utilized to improve land cover classifications, understand land cover changes, and improve surface modeling. SMA has also been applied in studies of fuel management, rangeland management, erosion prevention, geologic exploration, and invasive species management in the region (Miao et al. 2006, Okin et al. 2001, Drake et al. 1999)
Despite SMA’s effectiveness for mapping vegetation abundances in the southwest, many studies cite challenges to using SMA’s in semi-arid regions. These challenges include:
3 1. Strong soil albedo overwhelming vegetation spectra leading to underestimation of vegetation
(Elvidge C.D. 1990).
2. Multiple scattering of light rays can lead to non linear spectral mixing in semi arid regions
(Ray & Murry, 1996).
3. Drought resistant adaptations in leaf structure, which minimize leaf absorption in the visible
bands and reduces the size of the red edge in the near infrared (NIR) (Ehleringer, 1981).
4. Spectral variability of shrubs, which is due to in part to spatial variation in precipitation
(Duncan et al. 1993).
5. Open canopy structures of shrubs and trees, which typically results in mixing vegetation and
soil reflected radiance, and leads to spectral variability in the NIR. (Hurcom & Harrison,
1998).
Each of the challenges above is a result of ether the dispersed spatial composition of vegetation in semi- arid regions, or the internal and external structure of the branches and leaves of vegetation found in semi- arid regions. Studies that have attempted to overcome these inherent challenges vary greatly in their design and approach. The three major differences include the type of SMA models utilized, the derivation of endmembers to be unmixed, and the resolution and number of bands of hyperspectral data utilized.
Drake et al. (1999) conducted a study in Central Nevada that found linear mixture models to perform more effectively at vegetation abundance mapping than spectral matching techniques. Drake et al (1999) utilized library spectra as endmembers and the short wave infrared (SWIR) wavelengths of Airborne
Visible Infrared Imaging Spectrometer (AVIRIS) data.
In central California, Miao et al. (2006) used four different linear mixture models, ranging from unconstrained to fully constrained, to unmix star thistle abundances. In their study, endmembers were derived from within a Compact Airborne Spectrographic Imager (CASI) image with 3 m pixels. They found that unconstrained unmixing was the least effective of the approaches tested. Overall, the unmixed proportions averaged within 4-11% of the estimated true proportions in their study, these relatively high accuracies may in part be attributed to the high spatial resolution and the high quality of the data acquired
4 from an aerial platform. Therefore, in this study, which uses satellite imagery with a lower spatial resolution and a lower signal to noise ratio than typical aerial imagery, errors are likely to be even greater than 11%.
Okin et al (2001) designed a study to asses the practical limits of linear spectral unmixing in the Mojave
Desert of California using SWIR data and multiple modeled endmembers of varying vegetation/soil ratios.
The study found that the linear unmixing model was unreliable for estimating vegetation type, and also slightly overestimated vegetation cover.
This project attempts to further improve the understanding of using linear unmixing models for sub pixel abundance mapping in semi arid regions. To do so this project asses the scalability of field derived endmembers for use in an unconstrained and fully constrained linear unmixing model for an EO1
Hyperion image of Northwestern New Mexico.
5
Figure 1. Project study area
II. Study Area
The study area (figure 1) is located completely within San Juan County, New Mexico, and is part of the greater San Juan basin of the southern Rocky Mountains.
6
The greater San Juan basin is an asymmetrical syncline that extends from northwestern New Mexico into southwestern Colorado (US Bureau of Land Management (Farmington Field Office), 2003). The basin is approximately 200 miles long in the north-south direction, and 130 miles wide. The surface geology of the basin consists primarily of Quaternary to Cretaceous-aged alluvium (unconsolidated silts, sands, clays, and gravels), sandstones, siltstones, shales, limestones, conglomerates, and coal. Throughout the San
Juan basin, stream erosion has formed deep, steep-sided canyons around more resistant sandstone and shale outcrops, resulting in the flat top mesas characteristic of the region today (figure 2).
Figure 2. The typical mesa-dominated landscape of the study area.
The climate of this region is considered arid-continental, and characterized by cool, dry winters and warm dry summers (US Bureau of Land Management (Farmington Field Office), 2003). This climate has abundant sunshine and large daily variations in temperature. The average annual precipitation is 8.8 inches. The driest and wettest months are June and August, with averages of 0.3 and 1.2 inches of precipitation respectively (US Bureau of Land Management (Farmington Field Office), 2003).
7 This climate lends itself to semi-arid drought resistant vegetation types. The vegetation of this region is considered mixed coniferous, consisting primarily of pinyon pine (Pinus edulus), juniper (Juniperus osteosperma) and sagebrush (Artemisia tridentata) (figure 3), with riparian species dominant along the dry river beds.
Figure 3. Examples of the major land cover materials for which spectra were collected
The study is primarily designated as Bureau of Land Management (BLM) land, and is managed by the
Department of the Interior. In the early 1950’s, methane was discovered within a coal formation 1,000 m below the surface. Subsequent methane extraction operations in the region have resulted in significant disturbance from well pads, roads and pumping stations (U.S. DOE, 2004).
III. Data and Methods
a. Data
8 Data from two satellites form the primary image sources for this study: EO-1 and QuickBird. The EO-1 satellite was launched by NASA on November 21, 2000. Hyperion is one of the EO-1 instruments, and it provides a high spatial resolution hyperspectral image, with 30 meter pixels, and 220 spectral bands covering the spectral region from 400 to 2500 nm. The instrument field of view is relatively narrow, only
7.5 km wide, although each scene is 100 km long (USGS, 2006). The E01 Hyperion hyperspectral image used in the study was acquired on June 25, 2006. The data were obtained in terrain-corrected, geocoded format from the United States Geological Survey (USGS) Earth Resources Observation and Science
(EROS) Center.
The QuickBird image utilized in the study was acquired on July 22, 2006, and has four visible and a near- infrared (VNIR) multispectral bands with 2.44 m pixels, and a single panchromatic band, with higher spatial resolution, 0.6 m, pixels. The QuickBird scene is 8 by 8 km (figure 1). The Gram-Schmidt spectral sharpening technique was used to combine the panchromatic and four multispectral bands to produce a combined data set of four VNIR bands with 0.6 m pixels (Laben & Brower, 2000).
Approximately coincident with the Hyperion image acquisition, field spectra were collected June 23-26,
2006 within a subsection of the study area (figure 1). The spectrometer used was an Analytical Spectral
Devices (ASD) Full Range (400-2500nm) instrument, which is mounted on a backpack for field deployment. The spectrometer utilizes a fiber optic cable to collect radiation. A reflectance standard was used to normalize the radiance measurements to reflectance factor format.
Spectra of soils, rocks, dead vegetation and dominant vegetation were obtained from 83 different sites within a 1 km² area (figure 4). The field spectra were averaged to produce a single spectrum for each major class: sagebrush, pinyon pine, juniper, sandstone, soil, dead vegetation, and shale. The soils and sandstone classes were found to be very similar, and were therefore combined, as were the pinyon and juniper classes. The final averaged spectra therefore were: sandstone/soil, shale, pinyon/juniper, sagebrush, and dead vegetation. These spectra were imported into ENVI as a spectral library for incorporation into the linear unmixing model as spectral endmembers (figure 5).
9
Figure 4. Spectra sample sites.
10 Field Spectra Averaged Over all Sample Sites
0.6
SAGEBRUSH 0.5 PINYON JUNIPER SHALE SANDSTONE 0.4 SOIL DEAD
0.3
Reflectance Factor Reflectance 0.2
0.1
0.0 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 Wavelength (Nanometers)
Figure 5. Spectral endmembers produced by averaging the field-collected spectra.
All image processing for this project was carried out using The Environment for Visualizing Images (ENVI) software (Research Systems Inc. 2004). ENVI was chosen because of its strength in the analysis of hyperspectral data.
b. Hyperion Preprocessing
The EO-1 Hyperion image was imported into ENVI and a spatial subset was created to include the portions of the image that fell within San Juan County (figure 1). The Hyperion radiance data were then converted to estimated reflectance values. This was necessary because the endmembers for the spectral unmixing were derived from field spectra, which were in reflectance format. The Fast Line-of- sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction tool was used to convert radiance values to reflectance values (Felde et al., 2003). This program uses a radiative transfer model to estimate the illumination and atmospheric conditions at the time the imagery was acquired
11 based on user defined parameters (Felde et al., 2003). For this analysis, a mid summer atmospheric and rural aerosol model were chosen. The FLAASH model is an optional add-on to ENVI.
Once the imagery was converted to estimated reflectance, noisy bands associated with atmospheric water absorption were removed, leaving 157 bands including bands 10 (447nm) - 52 (874nm), 81
(952nm) -120 (1346nm), 133 (1477nm) - 165 (1800nm), 180 (1951nm) - 220 (2355nm)
To further reduce the noise, the minimum noise fraction (MNF) rotation was performed on the 157 remaining good bands. The MNF transform, as modified from Green et al. (1988) and implemented in
ENVI, is a linear transformation that consists of two separate principal components analysis rotations.
The first rotation uses the principal components of the noise covariance matrix to decorrelate and rescale the noise in the data, in a process known as noise whitening. The first rotation results in transformed data in which the noise has unit variance and zero band-to-band correlation. The second rotation is applied to the noise-whitened data (Boardman & Kruse, 1994). The MNF process resulted in 157 transformed MNF bands of data, which are ordered in terms of decreasing proportion of the original variance explained.
These bands were then viewed in an ENVI image viewer to asses which bands contained useful information, and which appeared to be dominated by random noise. In this way, the first 20 MNF bands were selected for further analysis.
The MNF bands cannot be used directly in the linear unmixing process. Therefore, the 20 selected MNF bands were rotated back to the original estimated reflectance space, a procedure termed inverse MNF. In this process, 157 bands of noise reduced data were produced. These 157 noise reduced bands where then utilized in both linear unmixing models.
c. Linear Unmixing
The linear unmixing model implemented in ENVI has two major options: unconstrained unmixing and partially constrained unmixing. In the unconstrained linear unmixing model (ULUM), abundances may assume negative values and are not constrained to sum to unity (i.e., one). The partially constrained
12 linear unmixing model (CLUM) provides a variable-weight, unit-sum constraint in the linear-mixing algorithm, which allows the user to specify the weight of a sum-to-unity constraint on the abundance fractions (Research Systems Inc. 2004). Both linear unmixing models were run so that their performances, at deriving realistic endmember proportions, could be compared. In the constrained model, a weight of 100,000 (i.e., a very large weight) was assigned to achieve a sum of 1 of endmember values for each pixel. For each of these models, the inputs comprised the inverse MNF of the Hyperion image, along with the five spectral endmembers.
Each unmixing model results in a six band image in which each of the first five bands represents the proportion of each endmember, and the sixth band is the root mean square error (RMSE). The estimates of endmember proportions can be used to determine the percent cover of each endmember within each
30 x 30 m (900 m2) pixel.
d. Maximum Likelihood Classification
The Gram-Schmidt-enhanced multispectral QuickBird image, with 0.6 m pixels, was then classified into six separate cover classes using a maximum likelihood supervised classification. Training regions representing each of six cover classes were identified through visual interpretation. The first five training classes were the same as those used in the spectral unmixing, namely sagebrush, pinyon or juniper, sandstone or soil, shale, and dead vegetation. A sixth class, shadow, was added to account for the shadows cast by vegetation and the steep topography.
13
Figure 6. Classification training data separability as shown by the ENVI n-dimensional visualization tool. The white lines represent the projections of the axis of each original image band.
Once sufficient training areas for each class were identified, the ENVI n-dimensional visualizer was utilized to plot training areas in a projection of the spectral space to help understand class seperability and refine the training class selection process (figure 6). Maximum likelihood classification assumes that the digital numbers (DNs) for each class are multivariate normal in their distribution. The probability that a given pixel belongs to each class is calculated, and the pixel is assigned to the class with the highest probability (that is, the maximum likelihood). Maximum likelihood allows the specification of prior probabilities, which establishes likelihood for each class occurring in each pixel of the study area. In the absence of information regarding the relative proportions of each class in the study area, equal prior probability values were assigned to each class. The result of the classification was a thematic land cover
14 classification of sagebrush, pinyon or juniper, sandstone or soil, shale, dead vegetation and shadow for the entire QuickBird Image (figure 7).
Figure 7. Maximum likelihood classification of the QuickBird Image.
e. Unmixing Accuracy Assessment
i. QuickBird Classification Sampling
In order to assess the accuracy of the linear unmixing models, a pixel by pixel comparison was conducted comparing the results of the unmixing to the maximum likelihood classification. To facilitate these
15 comparisons both the QuickBird classification and Hyperion image were exported from ENVI into ArcInfo.
In ArcInfo the images were registered to one another as accurately as possible. The Hyperion image was clipped to the size of the QuickBird classification. A shapefile was created based on the Hyperion 30 m raster grid, in which each Hyperion pixel was delineated by a vector polygon. Hawth’s Analysis Tools, an extension to ArcMap, was utilized to randomly select 200 of the polygons (Beyer, 2004). The Hawth’s
Analysis Tools thematic raster summary procedure (Beyer, 2004) was used to summarize the number of pixels of each QuickBird class that were found in each of the 200 polygons selected. The result was a table with 200 rows, each representing a sample Hyperion pixel polygon, and eight columns that specified the sample cell’s X and Y coordinates, and the number of pixels for each of the six cover types from the classified QuickBird image for that polygon.
ii. Hyperion Unmixing Sampling
The shapefile containing the 200 sample sites created in ArcInfo was imported into ENVI as a vector file.
The vector layer was then converted into an ENVI region of interest (ROI) file. The ROI file was overlaid on both the constrained and unconstrained unmixed images to identify the associated endmember proportions for each of the 200 sample pixels. The overlay results were exported to ASCII as a table containing 200 rows, each representing a sample site, and eight columns, with the X,Y coordinates and the proportions of each endmember in the Hyperion unmixing.
iii. Comparison of Hyperion Unmixing and QuickBird Classification
The maximum likelihood classification, and constrained and unconstrained sample data tables were merged into one spreadsheet based on the common record of X, Y coordinates. Shadow was not utilized as an endmember in the linear unmixing processes, and therefore the small proportion of pixels with a high shadow proportion were regarded as poor test pixels. Six samples were found to contain 5% or more of the shadow class in the maximum likelihood classification, and were eliminated from further analysis.
As a result, 194 sample sites were available for the accuracy assessment.
16 Class percentages where then averaged over all 194 remaining sample sites to derive an overall predicted abundance for each class for each of the three processing techniques. To compute the accuracy of each linear unmixing model, the QuickBird classification averages were subtracted from the averages obtained by each of the linear unmixing models. Because the QuickBird classification was regarded as the true cover type proportion for the pixel, the difference between the QuickBird classification and Hyperion unmixing was assumed to represent the error for the sample pixel, and was used to derive the summary accuracy metrics described below.
1. Sample Root mean square error (RMSE) was calculated by taking the square root of the average
sum of the squares of the error for each class on a per sample basis.
2. Average sample RMSE was derived for each linear unmixing model by averaging the 194 sample
RMSE values.
3. Class RMSE was computed by calculating the RMSE across all samples for each individual
class.
4. Average absolute class error was calculated by averaging across all samples the absolute error
value for each class.
5. Finally, average class error was calculated by averaging the errors of each class, thus providing
estimates of overall over-prediction or under-prediction.
A total of five metrics were therefore derived for use in the model accuracy comparison.
IV. Results and Discussion
a. Linear Unmixing Model Performance
Each linear unmixing model’s performance accuracy was determined by comparing the results of the predicted abundances (figure 8), average absolute class error (figure 9), average sample RMSE (figure 9) and, class RMSE (figure 9). For each metric, the ULUM outperformed the CLUM for each class, with the exception of the geologic classes of sandstone and shale. In the case of sandstone, the CLUM model derived almost identical predicted abundances, and had smaller average absolute class error, and a smaller class RMSE error. The shale class for the CLUM also outperformed the ULUM in average absolute class error. Indicating the CLUM may be better at accurately predicting geologies then the
17 ULUM. However, the ULUM outperformed the CLUM in average sample RMSE, with accuracies of +/- 17.5% and +/- 21.3% respectively. The large difference in average sample RMSE between the two models is attributed to the very large errors in the CLUM’s unmixing of the vegetation classes. The CLUM’s erroneous unmixing could also have been attributed to the model’s class proportion output range. The constrained model was expected to fall within the range of 0-1.0, however, the output was found to range from approximately + 10,000 to -10,000. This suggests that perhaps there was a problem with the scaling of the input image reflectance data, which was scaled by the FLAASH program between 0 and 10,000.
For the purposed of this study, the ULUM therefore was the preferred linear unmixing model for use in abundance mapping of EO-1 Hyperion Imagery in the Southwest. However, the results suggest a combination of the models may provide the best overall accuracy whereby the CLUM is used to map spectrally homogenous land cover types such as geologic features, and ULUM used to map more spectrally heterogeneous land cover features such as vegetation. Although the ULUM’s performance was better then those of the CLUM at unmixing the vegetation classes, its results are still considered unsatisfactory for the purposes of this study.
Predicted Abundances
45.0
Maximum Likelihood 40.0 Classification 40.7
Unconstrained 35.0 Linear Unmixing Model 30.0 31.9 31.9 31.9 Constrained Linear 30.2 Unmixing Model
25.0 25.3
20.0 21.8 21.4 20.1
Percent Abundances 15.0
12.9 10.0 9.8 7.8 8.4 5.0 4.1 1.5 0.0 Dead Sand Pin/Jun Sage Shale Classes/Endmembers
Figure 8. Predicted overall abundances of each class for (a) unconstrained linear unmixing model (b) constrained linear unmixing model, and (c) the maximum likelihood classification.
18
The difficulty of both models in unmixing of the pinyon-juniper and sagebrush classes may be due to spectral mixing resulting from the structure of the vegetation and the scalability of endmembers collected at branch and leaf scales. The bushy and small leaf area characteristic of vegetation in semi-arid environments may result in spectral mixing of leaf and branch reflected energy. This spectral mixing only increases at plant and multi-plant scales, where the presence of background soils, branches, and dead vegetation material further dilute the leaf signal. Therefore, the Hyperion spectra with 30m pixels will not be represented well by endmember spectra collected at leaf and branch scales. To overcome this scaling and spectral mixing problem, improved results might be achieved if the endmember spectra were collected at the plant or multi-plant scales. Additionally, Non linear spectral mixing of vegetation, whereby reflected energy is being recorded multiple times due to ray scattering, could further contribute to the difficulty of discriminating the type and abundance of vegetation. Both plant structure and non linear mixing are frequently cited as being challenges in linear unmixing studies in semi arid environments (Okin et al. 2003, Miao et al. 2006, Ray & Murry, 1996).
b. Class Error
Individual class accuracy was determined through comparing the results of predicted abundances (figure
8), class RMSE, average absolute class error and average class error (figure 9). After comparing these metrics it appears that the geologic classes were in general more accurately predicted by the unmixing models then the vegetative classes. Sagebrush abundances had the lowest unmixing accuracy, showing abundances less then half those of the MLC. While the dead vegetation and pinyon/juniper classes had averages close to twice those predicted by the MLC.
This over prediction trend of the dead vegetation and pinyon/juniper classes and under prediction of the sagebrush class is also seen in the average class error. These trends may suggest that the reflectance of the sagebrush class is being integrated into the pinyon/juniper and dead vegetation classes. This would be logical since the leaves of sagebrush are somewhat spectrally similar to those of pinyon pine & juniper leaves, and sagebrush branch spectra have some similarities with the dead vegetation class.
Since sagebrush is a bushy plant, with small leaf area, exposed braches, some of which may be dead
19 vegetation, it is likely that erroneous spectral unmixing could be occurring at the plant or multi-plant scales. Another reason the dead vegetation class may be over-predicted is because of its spectral similarity to shadow. Shadow comprised less than 5% of the QuickBird pixels used for accuracy assessment, and was not used as an endmember in the unmixing models. Therefore, the dead vegetation class may be including some shadowed areas, leading to the observed over-prediction.
Erroneous vegetation discrimination has also been in observed in other similar studies in this region (Okin et al. 2001)
On a per-class basis, shale had the lowest average absolute class error, varying from 6.7- 7.5%. This was followed by dead vegetation +/-13.5 -15%, sandstone/soil +/-16.5 -16.9%, pinyon/juniper +/-17.2 -
22.7%, and sagebrush +/-18.5 -28.7%. It appears that although the ULUM predicted a sandstone class abundance that was almost perfectly correct, the shale and dead vegetation classes had less overall error. In summary, the shale, dead vegetation and sandstone classes were the most accurately unmixed land cover classes. This point is interesting because the shale and dead vegetation classes are least abundant, while sandstone is the most abundant. As a result there may be no correlation between abundance of a cover type (spectral dominance) and unmixing accuracy. The unmixing accuracy found in these classes may be attributed to unique and distinct characteristics within their spectral profiles. The sandstone/soil class for example have a very strong absorption feature in the in the 2,300 nm region due to the presence of quartz. These features may make spectral discrimination easier. Another reason the geologic and dead vegetation classes may be more accurately discriminated may again be due to their homogenous make up creating a stronger signal. Vegetation tends to be spectrally mixed and therefore less distinguishable. Again, deriving endmembers at plant and multi-plant scales rather then the leaf and branch scale might improve the ability of the unmixing models to accurately distinguish plant type and abundance.
20 a. Average Absolute Class Error
30.0
28.7
25.0
22.7 Unconstrained Linear Unmixing Model 20.0
18.5 Constrained Linear Unmixing Model 17.2 16.9 16.5 15.0 15.0 13.5 Absolute Error Absolute
10.0
7.5 6.7 5.0
0.0 Dead Sand/Soil Pin/Jun Sage Shale Classes/Endmember b. Class RMSE
40
35 Unconstrained 33.6 Linear Unmixing 30 Model Constrained Linear 25 25.7 Unmixing 25.4 Model
20 20.9 20.5
RMSE 20.1
15 16.4 15.3
10 10.3 9.4
5
0 Dead Sand Jun-Pin Sage Shale Classes/Endmembers
21 c. Average Class Error
30.0 28.7
20.0
17.3
10.0 Unconstrained Linear Unmixing 6.6 Model 0.0 4.2 0.0 -1.4 Constrained Linear Dead Sand Pin/Jun Sage Shale Unmixing Model Average Error -10.5 -10.0 -12.2 -14.0
-19.3 -20.0
-30.0 Classes/Endmembers
Figure 9. Comparison of error associated with unconstrained and constrained linear unmixing models by class. (a) Class RMSE error (b) average absolute class error (c) average class error.
V. Summary/Conclusion
This study assessed the scalability of field derived endmembers for use in a constrained and unconstrained linear spectral unmixing model at mapping abundances of geologic and vegetative land cover types in an EO-1 Hyperion image of northwestern New Mexico. Spectra of six major land cover classes were collected in the field using a portable spectrometer. Averages of these spectra were utilized as spectral endmembers in both linear unmixing models. The models’ accuracies were assessed by comparing results to those of a land cover classification, using the same classes as the endmembers, of a high resolution 0.6m QuickBird Image.
Based on a random sample of 194 Hyperion pixels, it was found that the ULUM had the highest accuracy, with an average RMSE of +/- 17.5%. The CLUM, which had an average RMSE of +/- 21.3%, had slightly
22 higher accuracies in the geologic classes then that of the ULUM. Poor performance of the models at discriminating vegetation classes was the root of most of the error.
The accuracy of class discrimination was also assessed. It was found that the shale (+/- 6.7- 7.5%), dead vegetation (+/-13.5 -15%), and sandstone/soil (+/16.5 -16.9%) classes were the most accurately predicted land cover types. These accuracies might be attributed to the spectral homogeneity of each cover class. Abundance of a land cover class showed no correlation with the class discrimination accuracy. This was demonstrated by the most accurately discriminated classes being shale and sandstone, representing the least, and most abundant land cover types, respectively.
The pinyon/juniper and dead vegetation classes were over predicted while the sagebrush was under predicted. These trends have been noted in similar studies (Okin et al. 2001) and may demonstrate that sagebrush is being incorrectly labeled as pinyon/juniper and dead vegetation. This may be likely due to problems of scaling of the spectral endmembers used in the LUMs. The endmember were derived from field spectra taken at the leaf and branch scale, and appeared to be spectral dissimilar to the pinyon/juniper and dead vegetation classes, but at the plant and multi-plant scales these dissimilarities might be reduced due to spectral mixing of leaves, branches, background soils and dead vegetation material. Deriving spectral endmembers at the plant or multi plant scale might therefore improve discrimination of the sagebrush and pinyon/juniper classes
Based on these results, unconstrained linear unmixing models may perform more accurately then constrained linear unmixing models at abundance mapping of EO-1 Hyperion data in semi-arid regions.
However unconstrained linear unmixing may perform better at mapping spectrally homogeneous cover types such as geologic materials. Geologic and dead vegetation cover types are the most accurately discriminated cover types in this region. Vegetation cover types are the least accurately discriminated which is likely due to their spectral mixing at plant and multi plant scales. These results suggest improvements need to be made in the field methods used to collect vegetation spectra. For example, perhaps mounting the spectrometer on a boom elevated over plants would allow for collection of more spectrally realistic endmembers for use in the model. Nevertheless, overall, the results achieved in this
23 paper are similar to other abundance mapping studies conducted in semi-arid environments, suggesting the results were reasonable. These results seem to reinforce the challenges cited by other studies for accurately mapping vegetation type and abundance in semi arid regions. Specifically those challenges posed by non linear mixing, strong soil albedo, the open canopy structure of the vegetation.
The high accuracies derived in the shale, dead vegetation, and sandstone classes suggest the results from this study could be applied in further studies. Next steps in using this data could include making geologic and dead vegetation abundance maps for the entire study area. Although distribution of the land cover classes cannot be mapped to discrete boundaries at the subpixel level, bar graphs or pie charts might be a way to represent pictorially land cover per region at the 30m pixel level. Another use of this data could be modeling distribution patterns of sandstone, shale and dead vegetation. I think modeling dead vegetation could be the most interesting use of this data. By understanding vegetation mortality patterns, historic vegetation distributions might be hypothesized or reasons for mortality revealed.
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