Shrub detection using disparate airborne laser scanning acquisitions over varied forest cover types

References

• Andreu, A., and L. A. Hermansen-Baez. 2008. “Fire in the South 2: The Southern Wildfire Risk Assessment.” Southern Group of State Foresters 32. https://www.srs.fs.usda.gov/pubs/34087
   Google Scholar
• Asner, G. P., J. Mascaro, H. C. Muller-Landau, G. Vieilledent, R. Vaudry, M. Rasamoelina, J. S. Hall, and M. van Breugel. 2012. “A Universal Airborne LiDAR Approach for Tropical Forest Carbon Mapping.” Oecologia 168 (4): 1147–1160. doi:10.1007/s00442-011-2165-z.
   PubMed Web of Science ®Google Scholar
• Bechtold, W. A., and P. L. Patterson. 2005. The Enhanced Forest Inventory and Analysis Program: National Sampling Design and Estimation Procedures. US Department of Agriculture Forest Service, Southern Research Station 85. doi:10.2737/SRS-GTR-80
   Google Scholar
• Beers, T. W., P. E. Dress, and L. C. Wensel. 1966. “Notes and Observations: Aspect Transformation in Site Productivity Research.” Journal of Forestry 64 (10): 691–692.
   Web of Science ®Google Scholar
• Blinn, C. E., T. J. Albaugh, T. R. Fox, R. H. Wynne, J. L. Stape, R. A. Rubilar, and H. L. Allen. 2012. “A Method for Estimating Deciduous Competition in Pine Stands Using Landsat.” Southern Journal of Applied Forestry 36 (2): 71–78. doi:10.5849/sjaf.10-034.
   Google Scholar
• Camprodon, J., and L. Brotons. 2006. “Effects of Undergrowth Clearing on the Bird Communities of the Northwestern Mediterranean Coppice Holm Oak Forests.” Forest Ecology and Management 221 (1–3): 72–82. doi:10.1016/j.foreco.2005.10.044.
   Web of Science ®Google Scholar
• Di Gregorio, A., and L. J. M. Jansen. 1998. Land Cover Classification System (LCCS): Classification Concepts and User Manual. Rome: FAO.
   Google Scholar
• Ellenberg, D., and D. Mueller-Dombois. 1974. Aims and Methods of Vegetation Ecology. New York: Wiley.
   Google Scholar
• Erdody, T. L., and L. M. Moskal. 2010. “Fusion of LiDAR and Imagery for Estimating Forest Canopy Fuels.” Remote Sensing of Environment 114 (4): 725–737. doi:10.1016/j.rse.2009.11.002.
   Web of Science ®Google Scholar
• Finley, A. O., S. Banerjee, B. D. Cook, and J. B. Bradford. 2013. “Hierarchical Bayesian Spatial Models for Predicting Multiple Forest Variables Using Waveform LiDAR, Hyperspectral Imagery, and Large Inventory Datasets.” International Journal of Applied Earth Observation and Geoinformation 22: 147–160. doi:10.1016/j.jag.2012.04.007.
   Web of Science ®Google Scholar
• García, M., D. Riaño, E. Chuvieco, and F. Mark Danson. 2010. “Estimating Biomass Carbon Stocks for a Mediterranean Forest in Central Spain Using LiDAR Height and Intensity Data.” Remote Sensing of Environment 114 (4): 816–830. doi:10.1016/j.rse.2009.11.021.
   Web of Science ®Google Scholar
• Gobakken, T., and E. Næsset. 2008. “Assessing Effects of Laser Point Density, Ground Sampling Intensity, and Field Sample Plot Size on Biophysical Stand Properties Derived from Airborne Laser Scanner Data.” Canadian Journal of Forest Research 38 (5): 1095–1109. doi:10.1139/X07-219.
   Web of Science ®Google Scholar
• Gopalakrishnan, R., V. A. Thomas, J. W. Coulston, and R. H. Wynne. 2015. “Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges.” Remote Sensing 7 (9): 11036–11060. doi:10.3390/rs70911036.
   Web of Science ®Google Scholar
• Hastie, T., G. James, R. Tibshirani, and D. Witten. 2013. An Introduction to Statistical Learning with Applications in R. New York: Springer.
   Google Scholar
• Jakubowski, M. K., Q. Guo, and M. Kelly. 2013. “Tradeoffs between Lidar Pulse Density and Forest Measurement Accuracy.” Remote Sensing of Environment 130: 245–253. doi:10.1016/j.rse.2012.11.024.
   Web of Science ®Google Scholar
• Kimmins, J. P. 2004. Forest Ecology; A Foundation for Sustainable Forest Management and Environmental Ethics in Forestry. 3rd ed. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Korpela, I., A. Hovi, and F. Morsdorf. 2012. “Understory Trees in Airborne LiDAR Data—Selective Mapping Due to Transmission Losses and Echo-Triggering Mechanisms.” Remote Sensing of Environment 119: 92–104. doi:10.1016/j.rse.2011.12.011.
   Web of Science ®Google Scholar
• Kuhn, M., and K. Johnson. 2013. Applied Predictive Modeling. New York, NY: Springer.
   Google Scholar
• Martinuzzi, S., L. A. Vierling, W. A. Gould, M. J. Falkowski, J. S. Evans, A. T. Hudak, and K. T. Vierling. 2009. “Mapping Snags and Understory Shrubs for a LiDAR-Based Assessment of Wildlife Habitat Suitability.” Remote Sensing of Environment 113 (12): 2533–2546. doi:10.1016/j.rse.2009.07.002.
   Web of Science ®Google Scholar
• McRoberts, R. E. 2010. “The Effects of Rectification and Global Positioning System Errors on Satellite Image-Based Estimates of Forest Area.” Remote Sensing of Environment 114 (8): 1710–1717. doi:10.1016/j.rse.2010.03.001.
   Web of Science ®Google Scholar
• Morsdorf, F., A. Mårell, B. Koetz, N. Cassagne, F. Pimont, E. Rigolot, and B. Allgöwer. 2010. “Discrimination of Vegetation Strata in a Multi-Layered Mediterranean Forest Ecosystem Using Height and Intensity Information Derived from Airborne Laser Scanning.” Remote Sensing of Environment 114 (7): 1403–1415. doi:10.1016/j.rse.2010.01.023.
   Web of Science ®Google Scholar
• Oswalt, S. N., M. T. Thompson, and W. B. Smith. 2009. US Forest Resource Facts and Historical Trends. Washington DC: US Department of Agriculture, Forest Service.
   Google Scholar
• Ottmar, R. D., D. V. Sandberg, C. L. Riccardi, and S. J. Prichard. 2007. “An Overview of the Fuel Characteristic Classification System-Quantifying, Classifying, and Creating Fuelbeds for Resource Planning This Article is One of a Selection of Papers Published in the Special Forum on the Fuel Characteristic Classification System.” Canadian Journal of Forest Research 37 (12): 2383–2393. doi:10.1139/X07-077.
   Web of Science ®Google Scholar
• Peduzzi, A., H. L. Allen, and R. H. Wynne. 2010. “Leaf Area of Overstory and Understory in Pine Plantations in the Flatwoods.” Southern Journal of Applied Forestry 34 (4): 154–160.
   Google Scholar
• Peduzzi, A., R. H. Wynne, T. R. Fox, R. F. Nelson, and V. A. Thomas. 2012. “Estimating Leaf Area Index in Intensively Managed Pine Plantations Using Airborne Laser Scanner Data.” Forest Ecology and Management 270: 54–65. doi:10.1016/j.foreco.2011.12.048.
   Web of Science ®Google Scholar
• Provost, F., and T. Fawcett. 2001. “Robust Classification for Imprecise Environments.” Machine Learning 42 (3): 203–231. doi:10.1023/A:1007601015854.
   Web of Science ®Google Scholar
• Rollins, M. G., R. E. Keane, and R. A. Parsons. 2004. “Mapping Fuels and Fire Regimes Using Remote Sensing, Ecosystem Simulation, and Gradient Modeling.” Ecological Applications 14 (1): 75–95. doi:10.1890/02-5145.
   Web of Science ®Google Scholar
• Sonnentag, O., J. Talbot, J. M. Chen, and N. T. Roulet. 2007. “Using Direct and Indirect Measurements of Leaf Area Index to Characterize the Shrub Canopy in an Ombrotrophic Peatland.” Agricultural and Forest Meteorology 144 (3–4): 200–212. doi:10.1016/j.agrformet.2007.03.001.
   Web of Science ®Google Scholar
• Stage, A. R. 1976. “Notes: An Expression for the Effect of Aspect, Slope, and Habitat Type on Tree Growth.” Forest Science 22 (4): 457–460.
   Web of Science ®Google Scholar
• Sumnall, M. J., T. R. Fox, R. H. Wynne, C. Blinn, and V. A. Thomas. 2016. “Estimating Leaf Area Index at Multiple Heights within the Understorey Component of Loblolly Pine Forests from Airborne Discrete-Return Lidar.” International Journal of Remote Sensing 37 (1): 78–99. doi:10.1080/01431161.2015.1117683.
   Web of Science ®Google Scholar
• Sverdrup-Thygeson, A., H. O. Ørka, T. Gobakken, and E. Naesset. 2016. “Can Airborne Laser Scanning Assist in Mapping and Monitoring Natural Forests?” Forest Ecology and Management 369: 116–125. doi:10.1016/j.foreco.2016.03.035.
   Web of Science ®Google Scholar
• Tian, J., L. Wang, X. Li, C. Shi, and H. Gong. 2017. “Differentiating Tree and Shrub LAI in a Mixed Forest with ICESat/GLAS Spaceborne LiDAR.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (1): 87–94. doi:10.1109/JSTARS.2016.2557074.
   Web of Science ®Google Scholar
• Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 58 (1): 267–288.
   Web of Science ®Google Scholar
• Vauhkonen, J., L. Ene, S. Gupta, J. Heinzel, J. Holmgren, J. Pitkänen, S. Solberg, et al. 2011. “Comparative Testing of Single-Tree Detection Algorithms under Different Types of Forest.” Forestry 85 (1): 27–40.
   Google Scholar
• Vogelmann, J. E., S. M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and N. Van Driel. 2001. “Completion of the 1990s National Land Cover Data Set for the Conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources.” Photogrammetric Engineering and Remote Sensing 67 (6): 650–662.
   Web of Science ®Google Scholar
• Wear, D. N., and J. G. Greis. 2013. The Southern Forest Futures Project: Technical Report, 1–542. Vol. 178. Gen. Tech. Rep. SRS-GTR-178. Asheville: USDA-Forest Service, Southern Research Station.
   Google Scholar
• Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam. 2006. “Warming and Earlier Spring Increase Western US Forest Wildfire Activity.” Science 313 (5789): 940–943. doi:10.1126/science.1128834.
   PubMed Web of Science ®Google Scholar
• Westfall, J. A., and C. W. Woodall. 2007. “Measurement Repeatability of a Large-Scale Inventory of Forest Fuels.” Forest Ecology and Management 253 (1): 171–176. doi:10.1016/j.foreco.2007.07.014.
   Web of Science ®Google Scholar
• White, J. C., M. A. Wulder, A. Varhola, M. Vastaranta, N. C. Coops, B. D. Cook, D. Pitt, and M. Woods. 2013. “A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach.” The Forestry Chronicle 89 (6): 722–723. doi:10.5558/tfc2013-132.
   Web of Science ®Google Scholar
• Wing, B. M., M. W. Ritchie, K. Boston, W. B. Cohen, A. Gitelman, and M. J. Olsen. 2012. “Prediction of Understory Vegetation Cover with Airborne Lidar in an Interior Ponderosa Pine Forest.” Remote Sensing of Environment 124: 730–741. doi:10.1016/j.rse.2012.06.024.
   Web of Science ®Google Scholar
• Wing, B. M., M. W. Ritchie, K. Boston, W. B. Cohen, and M. J. Olsen. 2015. “Individual Snag Detection Using Neighborhood Attribute Filtered Airborne Lidar Data.” Remote Sensing of Environment 163: 165–179. doi:10.1016/j.rse.2015.03.013.
   Web of Science ®Google Scholar