Post-wildfire assessment of vegetation regeneration in Bastrop, Texas, using Landsat imagery

References

• Ahlgren, I. F., and C. E. Ahlgren. 1960. “Ecological Effects of Forest Fires.” The Botanical Review 26 (4): 483–533. doi:10.1007/BF02940573.
   Web of Science ®Google Scholar
• Al-Rabab’ah, M. A., and C. G. Williams. 2004. “An Ancient Bottleneck in the Lost Pines of Central Texas.” Molecular Ecology 13 (5): 1075–1084. doi:10.1111/j.1365-294X.2004.02142.x.
   PubMed Web of Science ®Google Scholar
• Brown, D. J., W. H. Nowlin, E. Ozel, I. Mali, D. Episcopo, M. C. Jones, and M. R. J. Forstner. 2014. “Comparison of Short Term Low, Moderate, and High Severity Fire Impacts to Aquatic and Terrestrial Ecosystem Components of a Southern USA Mixed Pine/Hardwood Forest.” Forest Ecology and Management 312: 179–192. doi:10.1016/j.foreco.2013.10.006.
   Web of Science ®Google Scholar
• Carey, J. 1992. Pinus Taeda. Missoula, MT: Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory.
   Google Scholar
• Chander, G., B. L. Markham, and D. L. Helder. 2009. “Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors.” Remote Sensing of Environment 113 (5): 893–903. doi:10.1016/j.rse.2009.01.007.
   Web of Science ®Google Scholar
• Clemente, R. H., R. M. N. Cerrillo, and I. Z. Gitas. 2009. “Monitoring Post-Fire Regeneration in Mediterranean Ecosystems by Employing Multitemporal Satellite Imagery.” International Journal of Wildland Fire 18 (6): 648–658.
   Web of Science ®Google Scholar
• Cuevas-González, M., F. Gerard, H. Balzter, and D. Riaño. 2009. “Analysing Forest Recovery after Wildfire Disturbance in Boreal Siberia Using Remotely Sensed Vegetation Indices.” Global Change Biology 15 (3): 561–577. doi:10.1111/j.1365-2486.2008.01784.x.
   Web of Science ®Google Scholar
• Díaz-Delgado, R., F. Lloret, and X. Pons. 2003. “Influence of Fire Severity on Plant Regeneration by Means of Remote Sensing Imagery.” International Journal of Remote Sensing 24 (8): 1751–1763. doi:10.1080/01431160210144732.
   Web of Science ®Google Scholar
• Epting, J., and D. Verbyla. 2005. “Landscape-Level Interactions of Prefire Vegetation, Burn Severity, and Postfire Vegetation over a 16-Year Period in Interior Alaska.” Canadian Journal of Forest Research 35 (6): 1367–1377. doi:10.1139/X05-060.
   Web of Science ®Google Scholar
• Epting, J., D. Verbyla, and B. Sorbel. 2005. “Evaluation of Remotely Sensed Indices for Assessing Burn Severity in Interior Alaska Using Landsat TM and ETM+.” Remote Sensing of Environment 96 (3–4): 328–339. doi:10.1016/j.rse.2005.03.002.
   Web of Science ®Google Scholar
• GIS Ag Maps. 2013. “Landsat 8 ESUN (for Atmospheric Correction), Radiance, and TOA Reflectance.” http://www.gisagmaps.com/landsat-8-atco/.
   Google Scholar
• Goetz, S. J., G. J. Fiske, and A. G. Bunn. 2006. “Using Satellite Time-Series Data Sets to Analyze Fire Disturbance and Forest Recovery across Canada.” Remote Sensing of Environment 101 (3): 352–365. doi:10.1016/j.rse.2006.01.011.
   Web of Science ®Google Scholar
• Hicke, J. A., G. P. Asner, E. S. Kasischke, N. H. F. French, J. T. Randerson, G. J. Collatz, B. J. Stocks, C. J. Tucker, S. O. Los, and C. B. Field. 2003. “Postfire Response of North American Boreal Forest Net Primary Productivity Analyzed with Satellite Observations.” Global Change Biology 9 (8): 1145–1157. http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2486.2003.00658.x/full
   Web of Science ®Google Scholar
• Hope, A., C. Tague, and R. Clark. 2007. “Characterizing Post-Fire Vegetation Recovery of California Chaparral Using TM/ETM+ Time-Series Data.” International Journal of Remote Sensing 28 (6): 1339–1354. doi:10.1080/01431160600908924.
   Web of Science ®Google Scholar
• Hudak, A. T., P. Morgan, M. J. Bobbitt, A. M. S. Smith, S. A. Lewis, L. B. Lentile, P. R. Robichaud, J. T. Clark, and R. A. McKinley. 2007. “The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects.” Fire Ecology 3 (1): 64–90. doi:10.4996/fireecology.0301064.
   Google Scholar
• Jin, Y., J. T. Randerson, S. J. Goetz, P. S. A. Beck, M. M. Loranty, and M. L. Goulden. 2012. “The Influence of Burn Severity on Postfire Vegetation Recovery and Albedo Change During Early Succession in North American Boreal Forests.” Journal of Geophysical Research: Biogeosciences 117 (G1): 1–15. doi:10.1029/2011JG001886.
   Web of Science ®Google Scholar
• Johnstone, J. F., and F. S. Chapin. 2006. “Effects of Soil Burn Severity on Post-Fire Tree Recruitment in Boreal Forest.” Ecosystems 9 (1): 14–31. doi:10.1007/s10021-004-0042-x.
   Web of Science ®Google Scholar
• Johnstone, J. F., and E. S. Kasischke. 2005. “Stand-Level Effects of Soil Burn Severity on Postfire Regeneration in a Recently Burned Black Spruce Forest.” Canadian Journal of Forest Research 35 (9): 2151–2163. doi:10.1139/X05-087.
   Web of Science ®Google Scholar
• Kokaly, R. F., B. W. Rockwell, S. L. Haire, and T. V. V. King. 2007. “Characterization of Post-Fire Surface Cover, Soils, and Burn Severity at the Cerro Grande Fire, New Mexico, Using Hyperspectral and Multispectral Remote Sensing.” Remote Sensing of Environment 106 (3): 305–325. doi:10.1016/j.rse.2006.08.006.
   Web of Science ®Google Scholar
• Lambert, J., C. Drenou, J.-P. Denux, G. Balent, and V. Cheret. 2013. “Monitoring Forest Decline through Remote Sensing Time Series Analysis.” GIScience & Remote Sensing 50 (4): 437–457. doi:10.1080/15481603.2013.820070.
   Web of Science ®Google Scholar
• Lentile, L. B., Z. A. Holden, A. M. S. Smith, M. J. Falkowski, A. T. Hudak, P. Morgan, S. A. Lewis, P. E. Gessler, and N. C. Benson. 2006. “Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects.” International Journal of Wildland Fire 15 (3): 319–345. doi:10.1071/WF05097.
   Web of Science ®Google Scholar
• Lentile, L. B., P. Morgan, A. T. Hudak, M. J. Bobbitt, S. A. Lewis, A. M. S. Smith, and P. R. Robichaud. 2007. “Post-Fire Burn Severity and Vegetation Response Following Eight Large Wildfires Across the Western United States.” Fire Ecology 3 (1): 91–108. doi:10.4996/fireecology.0301091.
   Google Scholar
• Lentile, L. B., F. W. Smith, and W. D. Shepperd. 2005. “Patch Structure, Fire-Scar Formation, and Tree Regeneration in a Large Mixed-Severity Fire in the South Dakota Black Hills, USA.” Canadian Journal of Forest Research 35 (12): 2875–2885. doi:10.1139/X05-205.
   Web of Science ®Google Scholar
• Li, P., L. Jiang, and Z. Feng. 2014. “Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors.” Remote Sensing 6 (1): 310–329. doi:10.3390/rs6010310.
   Web of Science ®Google Scholar
• Li, S., and C. Potter. 2012. “Patterns of Aboveground Biomass Regeneration in Post-Fire Coastal Scrub Communities.” GIScience & Remote Sensing 49 (2): 182–201. doi:10.2747/1548-1603.49.2.182.
   Web of Science ®Google Scholar
• Miller, J. D., and A. E. Thode. 2007. “Quantifying Burn Severity in a Heterogeneous Landscape with a Relative Version of the Delta Normalized Burn Ratio (dNBR).” Remote Sensing of Environment 109 (1): 66–80. doi:10.1016/j.rse.2006.12.006.
   Web of Science ®Google Scholar
• Mitchell, M., and F. Yuan. 2010. “Assessing Forest Fire and Vegetation Recovery in the Black Hills, South Dakota.” GIScience & Remote Sensing 47 (2): 276–299. doi:10.2747/1548-1603.47.2.276.
   Web of Science ®Google Scholar
• NASA (National Aeronautics and Space Administration). 2010. “Landsat Data Continuity Mission [Pdf File].” http://landsat.gsfc.nasa.gov/pdf_archive/20101119_LDCMbrochure.pdf
   Google Scholar
• NOAA (NOAA National Climatic Data Center). 2014. “Monthly Summaries of the Global Historical Climatology Network - Daily (GHCN-D).” Asheville, NC.
   Google Scholar
• Potter, C. 2014. “Regional Analysis of MODIS Satellite Greenness Trends for Ecosystems of Interior Alaska.” GIScience & Remote Sensing 51 (4): 390–402. doi:10.1080/15481603.2014.933606.
   Web of Science ®Google Scholar
• Potter, C., S. Li, S. Huang, and R. L. Crabtree. 2012. “Analysis of Sapling Density Regeneration in Yellowstone National Park with Hyperspectral Remote Sensing Data.” Remote Sensing of Environment 121 (June): 61–68. doi: 10.1016/j.rse.2012.01.019.
   Web of Science ®Google Scholar
• Ridenour, K., S. Rissel, W. Powell, R. Gray, M. Fisher, and J. Sommerfield. 2012. Bastrop Complex Wildfire Case Study. College Station, TX: Texas Forest Service.
   Google Scholar
• Song, C., C. E. Woodcock, K. C. Seto, M. P. Lenney, and S. A. Macomber. 2001. “Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?” Remote Sensing of Environment 75 (2): 230–244. doi:10.1016/S0034-4257(00)00169-3.
   Web of Science ®Google Scholar
• Soverel, N. O., D. D. B. Perrakis, and N. C. Coops. 2010. “Estimating Burn Severity from Landsat dNBR and RdNBR Indices across Western Canada.” Remote Sensing of Environment 114 (9): 1896–1909. doi:10.1016/j.rse.2010.03.013.
   Web of Science ®Google Scholar
• Texas Forest Service. 2011. “2010-2011 Fire Season Statistics [Pdf File].” http://ticc.tamu.edu/Documents/Home/tx_sitrep.pdf.
   Google Scholar
• van Leeuwen, W. J. D. 2008. “Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data.” Sensors 8 (3): 2017–2042. doi:10.3390/s8032017.
   PubMed Web of Science ®Google Scholar
• van Leeuwen, W. J. D., G. M. Casady, D. G. Neary, S. Bautista, J. A. Alloza, Y. Carmel, L. Wittenberg, D. Malkinson, and B. J. Orr. 2010. “Monitoring Post-Wildfire Vegetation Response with Remotely Sensed Time-Series Data in Spain, USA and Israel.” International Journal of Wildland Fire 19 (1): 75–93. doi:10.1071/WF08078.
   Web of Science ®Google Scholar
• van Wagtendonk, J. W., R. R. Root, and C. H. Key. 2004. “Comparison of AVIRIS and Landsat ETM+ Detection Capabilities for Burn Severity.” Remote Sensing of Environment 92 (3): 397–408. doi:10.1016/j.rse.2003.12.015.
   Web of Science ®Google Scholar
• Vila, J., and P. Barbosa. 2010. “Post-Fire Vegetation Regrowth Detection in the Deiva Marina Region (Liguria-Italy) Using Landsat TM and ETM+ Data.” Ecological Modelling 221 (1): 75–84. doi:10.1016/j.ecolmodel.2009.03.011.
   Web of Science ®Google Scholar
• Wang, Y. Q., Y. Zhou, J. Yang, H. S. He, Z. Zhu, and D. Ohlen. 2009. “Simulation of Short-Term Post-Fire Vegetation Recovery by Integration of LANDFIRE Data Products, DNBR Data and LANDIS Modeling.” Annals of GIS 15 (1): 47–59. doi:10.1080/19475680903271083.
   Google Scholar
• Whelan, R. J. 1995. The Ecology of Fire. Cambridge: Cambridge University Press.
   Google Scholar
• White, J. D., K. C. Ryan, C. C. Key, and S. W. Running. 1996. “Remote Sensing of Forest Fire Severity and Vegetation Recovery.” International Journal of Wildland Fire 6 (3): 125–136. doi:10.1071/WF9960125.
   Web of Science ®Google Scholar
• Wilder, C. M., F. W. Holtzclaw Jr., and E. E. C. Clebsch. 1999. “Succession, Sapling Density and Growth in Canopy Gaps Along a Topographic Gradient in a Second Growth East Tennessee Forest.” The American Midland Naturalist 142 (2): 201–212. doi:10.1674/0003-0031(1999)142[0201:SSDAGI]2.0.CO;2.
   Web of Science ®Google Scholar
• Wimberly, M., and M. Reilly. 2007. “Assessment of Fire Severity and Species Diversity in the Southern Appalachians Using Landsat TM and ETM+ Imagery.” Remote Sensing of Environment 108 (2): 189–197. doi:10.1016/j.rse.2006.03.019.
   Web of Science ®Google Scholar