An Object-Based Classification Approach in Mapping Tree Mortality Using High Spatial Resolution Imagery

References

• Arbia, G., Benedetti, R., and G. Espa, 1999, "Contextual Classification in Image Analysis: An Assessment of Accuracy of ICM," Computational Statistics & Data Analysis 30:443-455.
   Google Scholar
• Asner, G. P., Keller, M., Rodrigo Pereira, J., Zweede, J. C., and J. N. M. Silva, 2004, "Canopy Damage and Recovery after Selective Logging in Amazonia: Field and Satellite," Ecological Applications 14:S280-S298.
   Google Scholar
• Baatz, M. and A. Schape, 2000, "Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation," Angewandte Geographische Informationsverarbeitung 12:12-23.
   Google Scholar
• Baraldi, A. and F. Parmiggiani, 1996, "Single Linkage Region Growing Algorithms Based on the Vector Degree of Match," IEEE Transactions on Geoscience and Remote Sensing 34:137-148.
   Google Scholar
• Bardossy, A. and L. Samaniego, 2002, "Fuzzy Rule-Based Classification of Remotely Sensed Imagery," IEEE Transactions on Geoscience and Remote Sensing 40:362-374.
   Google Scholar
• Benediktsson, J. and I. Kanellopoulos, 1999, "Classification of Multisource and Hyperspectral Data Based on Decision Fusion," IEEE Transactions on Geoscience and Remote Sensing 37:1367-1377.
   Google Scholar
• Biederman, I., 1987, "Recognition-by-Components: A Theory of Human Image Understanding," Psychological Review 94:115-147.
   Google Scholar
• Binaghi, E., Madella, P., Montesano, M. G., and A. Rampini, 1997, "Fuzzy Contextual Classification of Multisource Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing 35:326-340.
   Google Scholar
• Boyer, M., Miller, J., Belanger, M., and E. Hare, 1988, "Senescence and Spectral Reflectance in Leaves of Northern Pin Oak ( Quercus palustris muenchh .)," Remote Sensing of Environment 25:71-87.
   Google Scholar
• Brandtberg, T. and F. Walter, 1998, "Automated Delineation of Individual Tree Crowns in High Spatial Resolution Aerial Images by Multiple-Scale Analysis," Machine Vision and Applications 11:64-73.
   Google Scholar
• Carleer, A. P., O. Debeir, and E. Wolff, 2005, "Assessment of Very High Spatial Resolution Satellite Image Segmentations," Photogrammetric Engineering & Remote Sensing 11:1285-1294.
   Google Scholar
• Clark, D. B., Castro, C. S., Alvarado, L. D. A., and J. M. Read, 2004, "Quantifying Mortality of Tropical Rain Forest Trees Using High-Spatial-Resolution Satellite Data," Ecological Letters 7:52-59.
   Google Scholar
• Congalton, R. G. and R. A. Mead, 1983, "A Quantitative Method to Test for Consistency and Correctness in Photointerpretation," Photogrammetric Engineering and Remote Sensing 49:69-74.
   Google Scholar
• Cumani, A., 1991, "Edge-Detection in Multispectral Images," Cvgip—Graphical Models and Image Processing 53:40-51.
   Google Scholar
• Debeir, O., Van den Steen, I., Latinne, P., Van Ham, P., and E. Wolff, 2002, "Textural and Contextual Land-Cover Classification Using Single and Multiple Classifier Systems," Photogrammetric Engineering and Remote Sensing 68:597-605.
   Google Scholar
• ERDAS, 1999, ERDAS Field Guide , Atlanta, GA: ERDAS, Inc.
   Google Scholar
• Everitt, J., Escobar, D., Appel, D., Riggs, W., and M. Davis, 1999, "Using Airborne Digital Imagery for Detecting Oak Wilt Disease," Plant Disease 83:502-505.
   Google Scholar
• Garbelotto, M., Svihra, P., and D. Rizzo, 2001, "Sudden Oak Death Syndrome Fells Three Oak Species," California Agriculture 55:9-19.
   Google Scholar
• Gong, P. and P. J. Howarth, 1990a, "Land Cover to Land Use Conversion: A Knowledge-Based Approach, in: Annual Conference of American Society of Photo-grammetry and Remote Sensing , Denver, CO, pp. 447-456.
   Google Scholar
• Gong, P. and P. J. Howarth, 1990b, "The Use of Structural Information for Improving Land-Cover Classification Accuracies at the Rural-Urban Fringe," Photogrammetric Engineering and Remote Sensing 56:67-73.
   Google Scholar
• Gong, P. and P. J. Howarth, 1992, "Frequency-Based Contextual Classification and Gray-Level Vector Reduction for Land-Use Identification," Photogrammetric Engineering and Remote Sensing 58:423-437.
   Google Scholar
• Harris, P. M. and S. J. Ventura, 1995, "The Integration of Geographic Data with Remotely-Sensed Imagery to Improve Classification in an Urban Area," Photogrammetric Engineering and Remote Sensing 61:993-998.
   Google Scholar
• Hay, G., Blaschke, T., Marceau, D., and A. Bouchard, 2003, "A Comparison of Three Image Object Methods for the Multiscale Analysis of Landscape Structure," Journal of Photogrammetry & Remote Sensing 57:327-345.
   Google Scholar
• Hay, G., Castilla, G., Wulder M., and J. Ruiz, 2005, "An Automated Object-Based Approach for the Multiscale Image Segmentation of Forest Scenes," International Journal of Applied Earth Observation and Geoinformation 7:339-359.
   Google Scholar
• Hay, G., Marceau, D., Dube, P., and A. Bouchard, 2001, "A Multiscale Framework for Landscape Analysis: Object-Specific Analysis and Upscaling," Landscape Ecology 16:471-490.
   Google Scholar
• Holdenrieder, O., Pautasso, M., Weisberg, P., and D. Lonsdale, 2004, "Tree Diseases and Landscape Processes: The Challenge of Landscape Pathology," Trends in Ecology and Evolution 19:446-452.
   Google Scholar
• Karathanassi, V., Iossifidis, C., and D. Rokos, 2000, "A Texture-Based Classification Method for Classifying Built Areas According to their Density," International Journal of Remote Sensing 21:1807-1823.
   Google Scholar
• Kelly, M. and R. K. Meentemeyer, 2002, "Landscape Dynamics of the Spread of Sudden Oak Death," Photogrammetric Engineering & Remote Sensing 68:1001-1009.
   Google Scholar
• Kelly, M., Shaari, D., Guo, Q. H., and D. S. Liu, 2004, "A Comparison of Standard and Hybrid Classifier Methods for Mapping Hardwood Mortality in Areas Affected by ‘Sudden Oak Death’," Photogrammetric Engineering & Remote Sensing 70:1229-1239.
   Google Scholar
• Kelly, N. M. and B. A. McPherson, 2001, "Multi-scale Approaches Taken to Sudden Oak Death Monitoring," California Agriculture 55:15-16.
   Google Scholar
• Kermad, C. D. and K. Chehdi, 2002, "Automatic Image Segmentation System through Iterative Edge-Region Co-operation," Image and Vision Computing 20:541-555.
   Google Scholar
• Laliberte, A. S., Rango, A., Havstad, K. M., Paris, J. F., Beck, R. F., McNeely, R., and A. L. Gonzalez, 2004, "Object-Oriented Image Analysis for Mapping Shrub Encroachment from 1937 to 2003 in Southern New Mexico," Remote Sensing of Environment 93:198-210.
   Google Scholar
• Li, J. and R. M. Narayanan, 2003, "A Shape-Based Approach to Change Detection of Lakes Using Time Series Remote Sensing Images," IEEE Transactions on Geoscience and Remote Sensing 41:2466-2477.
   Google Scholar
• Liu, D., Kelly, M., and P. Gong, 2006, "A Spatial-Temporal Approach for Monitoring Forest Disease Dynamics Using Multi-temporal High Spatial Resolution Imagery," Remote Sensing of Environment 101(2):167-180.
   Google Scholar
• Martin, L. R. G. and P. J. Howarth, 1989, "Change-Detection Accuracy Assessment Using SPOT Multispectral Imagery of the Rural-Urban Fringe," Remote Sensing of Environment 30:55-66.
   Google Scholar
• Moghaddamzadeh, A. and N. Bourbakis, 1997, "A Fuzzy Region Growing Approach for Segmentation of Color Images," Pattern Recognition 30:867-881.
   Google Scholar
• Muchoney, D. M. and B. N. Haack, 1994, "Change Detection for Monitoring Forest Defoliation," Photogrammetric Engineering & Remote Sensing 60:1243-1251.
   Google Scholar
• Mumby, P. J. and A. J. Edwards, 2002, "Mapping Marine Environments with IKONOS Imagery: Enhanced Spatial Resolution Can Deliver Greater Thematic Accuracy," Remote Sensing of Environment 82:248-257.
   Google Scholar
• Murai, H. and S. Omatu, 1997, "Remote Sensing Image Analysis Using a Neural Nnetwork and Knowledge-Based Processing," International Journal of Remote Sensing 18:811-828.
   Google Scholar
• Pal, N. R. and S. K. Pal, 1993, "A Review on Image Segmentation Techniques," Pattern Recognition 26: 1277-1294.
   Google Scholar
• Read, J. M., 2003, "Spatial Analyses of Logging Impacts in Amazonia Using Remotely Sensed Data," Photogrammetric Engineering and Remote Sensing 69: 275-282.
   Google Scholar
• Richards, J. and X. Jia, 1999, Remote Sensing Digital Image Analysis: An Introduction , New York, NY: Springer-Verlag.
   Google Scholar
• Rizzo, D. M. and M. Garbelotto, 2003, "Sudden Oak Death: Endangering California and Oregon Forest Ecosystems," Frontiers in Ecology and the Environment 1:197-204.
   Google Scholar
• Rizzo, D., Garbelotto, M., Davidson, J. M., Slaughter, G. W. and S. T. Koike, 2002, "Phytophthora ramorum as the Cause of Extensive Mortality of Quercus spp. and Lithocarpus densiflorus in California," Plant Disease 86:205-213.
   Google Scholar
• Sali, E. and H. Wolfson, 1992, "Texture Classification in Aerial Photographs and Satellite Data," International Journal of Remote Sensing 13:3395-3408.
   Google Scholar
• Sarabi, A. and J. K. Aggarwal, 1981, "Segmentation of Chromatic Images," Pattern Recognition 13: 417-427.
   Google Scholar
• Solberg, A. H. S., 1999, "Contextual Data Fusion Applied to Forest Map Revision," IEEE Transactions on Geoscience and Remote Sensing 37:1234-1243.
   Google Scholar
• Steele, B. M. and R. L. Redmond, 2001, "A Method of Exploiting Spatial Information for Improving Classification Rules: Application to the Construction of Polygon-Based Land Cover Maps," International Journal of Remote Sensing 22:3143-3166.
   Google Scholar
• Stefanov, W. L., Ramsey, M. S. and P. R. Christensen, 2001, "Monitoring Urban Land Cover Change: An Expert System Approach to Land Cover Classification of Semiarid to Arid Urban Centers," Remote Sensing of Environment 77:173-185.
   Google Scholar
• Svihra, P., 1999, "Tanoak and Coast Live Oak under Attack," Oaks ‘n’ Folks 14:1.
   Google Scholar
• Toll, D. L., 1984, "An Evaluation of Simulated Thematic Mapper Data and Landsat MSS Data for Discriminating Suburban and Regional Land Use and Land Cover," Photogrammetric Engineering & Remote Sensing 50:1713-1724.
   Google Scholar
• Tonjes, R., Growe, S., Buckner, J. and C. E. Liedtke, 1999, "Knowledge-Based Interpretation of Remote Sensing Images Using Semantic Nets," Photogrammetric Engineering and Remote Sensing 65:811-821.
   Google Scholar
• Treitz, P. M., Howarth, P. J., Suffling, R. C. and P. Smith, 1992, "Application of Detailed Ground Information to Vegetation Mapping with High Spatial-Resolution Digital Imagery," Remote Sensing of Environment 42:65-82.
   Google Scholar
• Tremeau, A. and N. Borel, 1997, "A Region Growing and Merging Algorithm to Color Segmentation," Pattern Recognition 30:1191-1203.
   Google Scholar
• Xia, L., 1996, "A Method to Improve Classification with Shape Information," International Journal of Remote Sensing 17:1473-1481.
   Google Scholar
• Zhang, Y. J., "Evaluation and Comparison of Different Segmentation Algorithms," Pattern Recognition Letters 18:963-974.
   Google Scholar
• Zhang, Y. J. and J. J. Gerbrands, 1994, "Objective and Quantitative Segmentation Evaluation and Comparison," Signal Processing 39:43-54.
   Google Scholar
• Zhang, Y. J. and H. T. Luo, 2000, "Optimal Selection of Segmentation Algorithms Based on Performance Evaluation," Optical Engineering 39:1450-1456.
   Google Scholar