1. Spectral Discrimination of Plant Functional Types and Species across diverse North American Ecosystems Dar A. Roberts 1, Keely L. Roth 2, Philip E. Dennison 3, and Abigail Guess 4 1 Dept of Geography, Univ. Calif. Santa Barbara, 3 Dept of Geography, Univ. Utah, Salt Lake City, UT, 84112 1 dar@geog.ucsb.edu, 2 klroth@geog.ucsb.edu, 3 dennison@geog.utah.edu, 4 abigail.guess@geog.utah.edu Abstract Imaging spectrometry has considerable potential for providing improved capabilities to discriminate plant species and plant functional types globally. In this poster, we report upon a generalized approach designed to build statistically robust spectral libraries for species discrimination and for map validation. We use multiple endmember spectral mixture analysis (MESMA) with two endmember models to map plant species. We introduce an iterative endmember selection approach to MESMA, in which endmembers for each class of interest are selected from a spectral library in such a way to produce maximum accuracies as expressed by a kappa statistic. We apply this generalized protocol to four AVIRIS data sets representing distinct ecosystems including a mid-latitude broadleaf forest (SERC, MD), a continental Great Basin shrubland to mixed forest (Wasatch Range, UT), a semi-arid coastal shrubland (Santa Barbara, CA) and a Pacific Northwest, coniferous forest (Wind River, WA). We also apply this approach to evaluate spectral separability at species and plant functional type levels. We conclude with an analysis of the impact of coarsening spatial resolution on the ability to discriminate PFT at Wasatch at 20, 40 and 60 m spatial resolutions. Using iterative endmember selection, map accuracy varied between a low of 59% at SERC, mapping eight tree genera to a high of 73% at Wind River for 11 species dominants. An intermediate accuracy was observed at Santa Barbara, of 65% for 15 dominant species. Map accuracy tended to improve at the life form or PFT level, improving to 73% for Santa Barbara (10 PFTs plus soil) and 96.8% for Wind River. PFT map accuracies at Wasatch were also high at 85.6%. Spatial degradation of 20 m Wasatch data to 60 m, resulted in only a minor decline in accuracy if finer spatial resolution endmembers were used for modeling (83.7%) but a larger drop in accuracy if the original endmembers were derived from 60 m data (75.9%). Study Areas Summary Results A systematic, standardized approach was developed to create statistically robust training and test spectral libraries from image data. This approach was applied to AVIRIS data acquired at four locations in North America, covering a diversity of ecosystems. A new approach for identifying optimal subsets of EMs was also developed and applied to these libraries to evaluate the ability to discriminate PFTs and species. This approach provides a robust, repeatable and efficient means for building spectral libraries and selecting optimal EMs at different organizational levels, spatial, spectral and temporal resolutions. Specific findings included: Map accuracy was significantly higher at the PFT than species levels in all cases, with accuracies exceeding 95% at Wind River. Map accuracies varied between regions. Lowest accuracies were observed at SERC, a mid-latitude broadleaf forest, producing an accuracy of only 59%. However, the most common genus, Liriodendron was mapped at accuracies exceeding 75%. Highest species level accuracies were achieved at Wind River at 72.7% with several species mapped at accuracies exceeding 85%. Analysis at Wasatch at 20, 40 and 60 m spatial resolutions demonstrated that EMs derived at 20 m produced high accuracies at all spatial scales. Methods 1. Spectral Sample Size3. Wasatch and Spatial Scale Acknowledgements: This research was funded in part by “Spatial, Spectral and Temporal Requirements for Improved Hyperspectral Mapping of Plant Functional Types, Plant Species, Canopy Biophysics and Canopy Biochemistry ”, NASA Terrestrial Ecology grant NNX08AM89G. Institutional support was also provided, in part by CSIRO through the CSIRO McMasters Research Fellowship, which supported Roberts while on sabbatical in Australia. Figure 1: Showing the location of the four study sites. Four study areas are included in this poster (Fig. 1), including western hemlock/Douglas-fir (Wind River), mixed broadleaf deciduous forest (SERC), continental Great Basin shrubland to mixed forest (Wasatch) and semi-arid shrubland (Santa Barbara). AVIRIS data include 4 m AVIRIS at Wind River (2003) and SERC (2006) and 16-20 m AVIRIS at Santa Barbara (2004) and Wasatch (1998). Field polygons were established at Wind River, Santa Barbara and Wasatch to construct species-level spectral libraries. Polygons at SERC were developed from a stem map. 2. Regional Analysis SERC  Random sampling was used to develop statistically robust spectral libraries. This process is complicated by classes that are poorly represented across the landscape result in under- representation of rare classes and over representation of dominant classes in the library. To provide a balance between rare and common classes, small polygons were sampled up to 50% where as larger polygons (from common classes) were restricted to an upper limit from any one polygon. In this study, two maximum sample sizes were tested, 10 and 20. We hypothesized that a maximum sample of 20 would result in higher overall accuracy, but also result in rarer classes not being selected. By contrast a sample of 10 would allow rarer classes to be selected, but also result in lower accuracy. At Wind River, a sample of 10 (Table 1) did result in improved representation of rare classes, but did not reduce accuracy compared to 20 (Table 2). Figure 3) a) Showing genus-level spectra selected as optimal for SERC; b) Showing a classified map for the six genera modeled. A spectral library was developed consisting of 15 plant species and sampled into 1369 training and 4831 test spectra. From this library 69 EMs (Fig. 6) were selected producing an overall accuracy 65% (Table 5). Analysis of the map showed most of the major plant dominants were mapped correctly and that lower accuracy was, in part, a product of mixed-pixels of senesced grass and soil in training data (Fig. 7). Higher accuracies were achieved at the PFT level (73.5%), but this model over-mapped evergreen broadleaf shrubs at the expense of evergreen broadleaf trees. A spectral library was constructed for eight plant genera consisting of 792 training spectra and 2762 test spectra. From this spectral library 10 EMs were selected using iterative EM selection (Fig. 3a), resulting in an overall accuracy of 59% at the generic level, and individual accuracies exceeding 70% for the most common genus, Liriodendron (Table 3). Two rare classes, Pinus and Platanus were not selected, even using a maximum 10 criterion. When applied to the image data, the resulting map matched the stem map well (Fig.3b). Table 1: Error matrix for 2 EM models selected with a maximum upper limit of 10 spectra from any polygon. Figure 4: Spectra of dominant land-cover classes and plant species from Wind River. a)b) A spectral library consisting of five PFTs was generated from the Wasatch Mountains along an elevational gradient. Spectral libraries were developed at 20, 40 and 60 m resolution from degraded imagery to determine the impact of spatial degradation on spectral discrimination using libraries generated at different spatial resolutions. These same libraries were applied to spatially degraded imagery. Classification accuracies at the PFT level were high (85.6%) at 20 m spatial resolution, decreasing slightly to 83.7% when a 20 m spatial-resolution library was applied to 60 m data (Table 6). The spectral library at 60 m produced a much lower accuracy at 75.9%. (Fig. 8). A general procedure is followed for developing statistically robust spectral libraries and selecting the optimal population of endmembers (EMs) for a specified level of complexity (Fig.2). Two levels were tested here, plant species and PFTs. Libraries can be built from spatially or spectrally degraded images. Specific steps include: 1) Image preprocessing (reflectance retrieval & georectification) 2) Development of training polygons. 3) Spectral sampling & metadata development. 4) Random spectral sampling. A smaller subset is set aside for training and larger one for testing. Training for small polygons is restricted to no more than 50% of a polygon, larger polygons are sampled with a maximum upper limit (10, 20, other) 5) Iterative EM selection is employed starting with the two spectra that produce the highest classification accuracies. EMs are added progressively through an exhaustive search adding the EM that increases Kappa most. The search stops once Kappa no longer improves. All EMs must meet RMS and fraction constraints. 6) The 2 EM library is applied to the test library. 7) The process repeats with a second random sample, or the library is applied to the image. The image can be at an original resolution, or spatially degraded. Figure 2: Flow chart illustrating the process of randomized spectral library development, followed by iterative 2 EM selection and testing. In this study this procedure was applied to fine spatial resolution data at all four sites using two hierarchical levels, species and PFT. At Wasatch 20 m resolution data were degraded to 40 and 60 m to evaluate the impact of spatial resolution on classification accuracy. Table 2: Error matrix for 2 EM models selected with a maximum upper limit of 20 spectra from any polygon. Table 3: Error matrix for 2 EM models selected with a maximum upper limit of 10 spectra at SERC. Wind River A spectral library was constructed for eleven dominant land-cover classes and species consisting of 1050 training & 23353 test spectra. 63 EMs were selected using iterative EM selection (Fig. 4), including as many as 13 psme, the most abundant species and 2 for acci, a rare species. Species-level accuracy was 72.7%, with senesced grass mapped at over 95% accuracy, and reference accuracies exceeding 85% for acma an alru (Table 1). Twenty EMs were selected at the life form level, producing an overall accuracy of 96.8%, with needleleaf accuracies exceeding 97%, but low accuracies for forbs (Table 4). Life form maps (Fig. 5) mapped lifeforms well, including forbs in clear cuts. Table 4: Error matrix of 2 EM models at Wind River at the lifeform level. Figure 5: Map of plant life forms at Wind River. All lifeforms are mapped accurately, including forbs which are clearly shown in clear cuts. Santa Barbara Front Range Figure 6) Spectra of dominant land-cover classes and plant species from the Santa Barbara Front Range Figure 7) Showing species-level and PFT level maps for the Santa Barbara Front Range Table 5: Error matrix of 2 EM models at the Santa Barbara Front Range at the species level. Figure 8. PFTs at 20m (left) and 60m (right) resolution using EMs selected at 20 m resolution. Red is broadleaf deciduous tree, bright green is broadleaf deciduous shrub, dark green is needleleaf evergreen tree, yellow is grass/herbaceous, and gray is rock/soil. Table 6: Error matrix of 2 EM models at Wasatch for 20, 40 and 60 m spatial resolutions