PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING Hanson, C.L., D. Long-term precipitation database, Reynolds Creek Experimental Watershed, Idaho, United States, Water Resources Research, 37:2831–2834. An Overview of the Landsat Data Continuity Mission (LDCM) in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI (Proceedings Volume) (S.S. Fusion of LiDAR and imagery for estimating forest canopy fuels, Remote Sensing of Environ-ment, 114:725–737. Using LIDAR to compare forest height estimates from IKONOS and Landsat ETM ϩ data in Sitka spruce plantation forest, International Journal of Remote Sensing, 27:2161–2175. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM ϩ vegetation cover estimates in a ponderosa pine forest, Remote Sensing of Environment, 91:14–26. Integrating LiDAR data and multispec-tral imagery for enhanced classification of rangeland vegetation: A meta analysis, Remote Sensing of Environment, 111:11–24. Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: Mixture tuned matched filtering, Proceedings of AVIRIS 1998, JPL: Pasadena, California, 6 p. Pinyon and juniper invasion in black sagebrush communities in east-central Nevada, Ecology, 51:841–848. Net changes in regional woody vegetation cover and carbon storage in Texas drylands, 1937-1999, Global Change Biology, 9:316–335. NA05OAR4601137 from the NOAA Earth System Research Laboratory Physical Sciences Division and the BLM Owyhee Uplands Pilot Project ( ISU-BLM Agreement No. Acknowledgments Research was funded by Grant No. However, spectral data are necessary for locating juvenile junipers dispersed among tall shrubs. Our results also indicated that lidar-derived estimates can be effectively used alone for sub-pixel tree cover classification. The recommended image source, fusion approach, and spectral unmixing technique can be easily applied to mapping other woody vegetation encroach-ment, which is currently attracting increasing concerns throughout the western US. We recommend both lidar and Landsat-5 TM data classified using the MTMF spectral unmixing technique and fused in a regression-based approach. Conclusions This study compared different image sources, fusion approaches, and spectral unmixing techniques for estimating sub-pixel tree cover in a semi-arid rangeland environment. In such cases, both bands resulting from MTMF cannot be effectively leveraged. If the regression model was to be further fine-tuned, the statisti-cally insignificant band would have to be excluded from the model. For example, the Landsat-5 TM matched filtering scores in this study resulted in no statistically significant correlation with the field measurements, while the infeasibility values did. Furthermore, the two bands are cumbersome to use even in a regression-based approach. Cover type (Mitchell and Glenn, 2009a and 2009b Sankey, 2009).
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