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).