Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

References

• Applied Imagery. 2017. Quick Terrain Modeler (QTM) Software (Version 8.0.7.1). Chevy Chase, Maryland, USA.
   Google Scholar
• Bergen, K. M., S. J. Goetz, R. O. Dubayah, G. M. Henebry, C. T. Hunsaker, M. L. Imhoff, R. F. Nelson, G. G. Parker, and V. C. Radeloff. 2009. “Remote Sensing of Vegetation 3‐D Structure for Biodiversity and Habitat: Review and Implications for LiDaR and Radar Spaceborne Missions.” Journal of Geophysical Research 114 (G2): 1–13. doi:10.1029/2008JG000883.
   Google Scholar
• Boelman, N. T., J. D. Holbrook, H. E. Greaves, J. S. Krause, H. E. Chmura, T. S. Magney, J. H. Perez, J. U. H. Eitel, L. Gough, K. T. Vierling, J. C. Wingfield, and L. A. Vierling. 2016. “Airborne Laser Scanning and Spectral Remote Sensing Give a Bird’s Eye Perspective on Arctic Tundra Breeding Habitat at Multiple Spatial Scales.” Remote Sensing of Environment 184: 337–349. doi:10.1016/j.rse.2016.07.012.
   Web of Science ®Google Scholar
• Bouchhima, R. A., M. Sarti, M. Ciolfi, M. Lauteri, and M. Ksibi. 2018. “Decision Tree for Mapping of Halophyte Cover around Ghannouch, Tunisia.” Environmental Monitoring and Assessment 192 (12): 742. doi:10.1016/j.rse.2016.07.012.
   Google Scholar
• Campbell, J. B., and R. H. Wynne. 2011. Introduction to Remote Sensing. 5th ed. New York: Guilford Press.
   Google Scholar
• Chust, G., I. Galparsoro, Á. Borja, J. Franco, and A. Uriarte. 2008. “Coastal and Estuarine Habitat Mapping, Using LIDAR Height and Intensity and Multi-spectral Imagery.” Estuarine, Coastal and Shelf Science 78 (4): 633–643. doi:10.1016/j.ecss.2008.02.003.
   Web of Science ®Google Scholar
• Congalton, R. G., and K. Green. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton: CRC Press.
   Google Scholar
• Coulston, J. W., G. G. Moisen, B. T. Wilson, M. V. Finco, W. B. Cohen, and C. K. Brewer. 2012. “Modeling Percent Tree Canopy Cover: A Pilot Study.” Photogrammetric Engineering & Remote Sensing 78 (7): 715–727. doi:10.14358/PERS.78.7.715.
   Web of Science ®Google Scholar
• Crookston, N. L., and A. R. Stage. 1999. Percent Canopy Cover and Stand Structure Statistics from the Forest Vegetation Simulator. General Technical Report RMRS-GTR-24, 11. Ogden, UT:United States Department of Agriculture, Forest Service, Rocky Mountain Research Station. doi:10.2737/RMRS-GTR-24.
   Google Scholar
• Díaz, B. M., and G. A. Blackburn. 2003. “Remote Sensing of Mangrove Biophysical Properties: Evidence from a Laboratory Simulation of the Possible Effects of Background Variation on Spectral Vegetation Indices.” International Journal of Remote Sensing 24 (1): 53–73. doi:10.1080/01431160305012.
   Web of Science ®Google Scholar
• Environmental Systems Research Institute Inc. 2017. ArcMap™ Desktop Software (Version 10.6.1.9270). Redlands, California, USA.
   Google Scholar
• Erfanifard, Y., Z. Khodaei, and R. F. Shamsi. 2014. “A Robust Approach to Generate Canopy Cover Maps Using UltraCam-D Derived Orthoimagery Classified by Support Vector Machines in Zagros Woodlands, West Iran.” European Journal of Remote Sensing 47 (1): 773–792. doi:10.5721/EuJRS20144744.
   Web of Science ®Google Scholar
• Faulhaber, C. A. 2003. “Updated distribution and reintroduction of the Lower Keys marsh rabbit.” PhD diss., Texas A&M University, College Station, TX, USA. http://hdl.handle.net/1969.1/1348
   Google Scholar
• Feagin, R. A., M. L. Martinez, G. Mendoza-Gonzalez, and R. Costanza. 2010. “Salt Marsh Zonal Migration and Ecosystem Service Change in Response to Global Sea Level Rise: A Case Study from an Urban Region.” Ecology and Society 15 (4): 14. doi:10.5751/ES-03724-150414.
   Web of Science ®Google Scholar
• Florida International University. 2020. “Lidar (Light Detection and Ranging) Data.” Accessed 17 January 2020. http://digir.fiu.edu/Lidar/lidarNew.php
   Google Scholar
• García, M., D. Riaño, E. Chuvieco, J. Salas, and F. M. Danson. 2011. “Multispectral and LiDAR Data Fusion for Fuel Type Mapping Using Support Vector Machine and Decision Rules.” Remote Sensing of Environment 115 (6): 1369–1379. doi:10.1016/j.rse.2011.01.017.
   Web of Science ®Google Scholar
• Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. doi:10.1016/j.patrec.2005.08.011.
   Web of Science ®Google Scholar
• Google Earth Pro software/application. 2019. Google LLC, Mountain View,California, USA.
   Google Scholar
• Griffin, A. M. R. 2006. “Using Lidar and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Cover at Varying Scales.” Thesis, Texas A&M University, College Station. http://hdl.handle.net/1969.1/ETD-TAMU-1117
   Google Scholar
• Griffin, A. M. R., S. C. Popescu, and K. Zhao. 2008. “Using LIDAR and Normalized Difference Vegetation Index to Remotely Determine LAI and Percent Canopy Cover.” In Proceedings of SilviLaser 2008: 8th international conference on LiDAR applications in forest assessment and inventory, Edinburgh, UK. September 17–19, pp 446–455. https://geography.swan.ac.uk/silvilaser/papers/poster_papers/Griffin.pdf
   Google Scholar
• Grue, C. E., R. R. Reid, and N. J. Silvy. 1976. “A Technique for Evaluating the Breeding Habitat of Mourning Doves Using Call-count Transects.” Proceeding of the Annual Conference of the Southeastern Association of Game and Fish Commissioners 30: 667–673.
   Google Scholar
• Haest, B., J. V. Borre, T. Spanhove, G. Thoonen, S. Delalieux, L. Kooistra, C. A. Mücher, D. Paelinckx, P. Scheunders, and P. Kempeneers. 2017. “Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy.” Remote Sensing 9 (3): 266. doi:10.3390/rs9030266.
   Web of Science ®Google Scholar
• Harris Geospatial Solutions, Inc. 2018. Environment for Visualizing Images (ENVI) Software (Version 5.5.1). Boulder, Colorado, USA.
   Google Scholar
• Harveson, P. M., W. E. Grant, R. R. Lopez, N. J. Silvy, and P. H. Frank. 2006. “The Role of Dispersal in Florida Key Deer Metapopulation Dynamics.” Ecological Modelling 195 (3–4): 393–491. doi:10.1016/j.ecolmodel.2005.11.021.
   Web of Science ®Google Scholar
• Hoagland, S., P. Beier, and D. Lee. 2018. “Using MODIS NDVI Phenoclasses and Phenoclusters to Characterize Wildlife Habitat: Mexican Spotted Owl as a Case Study.” Forest Ecology and Management 412: 80–93. doi:10.1016/j.foreco.2017.12.017.
   Web of Science ®Google Scholar
• Hogrefe, K., V. Patil, D. Ruthrauff, B. Meixell, M. Budde, J. Hupp, and D. Ward. 2017. “Normalized Difference Vegetation Index as an Estimator for Abundance and Quality of Avian Herbivore Forage in Artic Alaska.” Remote Sensing 9 (12): 1234. doi:10.3390/rs9121234.
   Web of Science ®Google Scholar
• Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Jensen, J. R. 2015. Introductory Digital Image Processing: A Remote Sensing Perspective. 4th ed. Prentice Hall, Upper Saddle River, New Jersey, USA.
   Google Scholar
• Korhonen, L., K. T. Korhonen, M. Rautiainen, and P. Stenberg. 2006. “Estimation of Forest Canopy Cover: A Comparison of Field Measurement Techniques.” Silva Fennica 40 (4): 577–588. doi:10.14214/sf.315.
   Web of Science ®Google Scholar
• LaFever, D. H., R. R. Lopez, R. A. Feagin, and N. J. Silvy. 2007. “Predicting the Impacts of Future Sea-level Rise on an Endangered Lagomorph.” Environmental Management 40 (3): 430–437. doi:10.1007/s00267-006-0204-z.
   PubMed Web of Science ®Google Scholar
• Lim, K., P. Treitz, M. Wulder, B. St-Onge, and M. Flood. 2003. “LiDAR Remote Sensing of Forest Structure.” Progress in Physical Geography: Earth and Environment 27 (1): 88–106. doi:10.1191/0309133303pp360ra.
   Google Scholar
• Lopez, R. R. 2001. “Population Ecology of Florida Key Deer.” Dissertation, Texas A&M University, College Station, Texas, USA. http://proxy.library.tamu.edu/login?url=https://search.proquest.com/docview/276225755?accountid=7082
   Google Scholar
• Lopez, R. R., N. J. Silvy, R. N. Wilkins, P. A. Frank, M. J. Peterson, and M. N. Peterson. 2004. “Habitat-use Patterns of Florida Key Deer: Implications of Urban Development.” Journal of Wildlife Management 68 (4): 900–908. doi:10.2193/0022-541X(2004)068[0900:HPOFKD]2.0.CO;2.
   Web of Science ®Google Scholar
• Lu, D., Q. Chen, G. Wang, L. Liu, G. Li, and E. Moran. 2016. “A Survey of Remote Sensing-based Aboveground Biomass Estimation Methods in Forest Ecosystems.” International Journal of Digital Earth 9 (1): 63–105. doi:10.1080/17538947.2014.990526.
   Web of Science ®Google Scholar
• Martínez, M. L., A. Intralawan, G. Vázquez, O. Pérez-Maqueo, P. Sutton, and R. Landgrave. 2007. “The Coasts of Our World: Ecological, Economic and Social Importance.” Ecological Economics 63 (2–3): 254–272. doi:10.1016/j.ecolecon.2006.10.022.
   Web of Science ®Google Scholar
• McIntosh, A. C. S., A. N. Gray, and S. L. Garman. 2012. “Estimating Canopy Cover from Standard Forest Inventory Measurements in Western Oregon.” Forest Science 58 (2): 154–167. doi:10.5849/forsci.09-127.
   Web of Science ®Google Scholar
• Monroe County Environmental Education Advisory Council, Inc. 1997. “The Florida Keys Environmental Story.” D. Gallagher, editor. Monroe County Environmental Education Advisory Council, Inc., Big Pine Key, Florida, USA.
   Google Scholar
• Mountrakis, G., J. Im, and C. Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” International Society for Photogrammetry and Remote Sensing Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. doi:10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Mutlu, M., S. C. Popescu, and K. Zhao. 2008. “Sensitivity Analysis of Fire Behavior Modeling with LIDAR-derived Surface Fuel Maps.” Forest Ecology and Management 256 (3): 289–294. doi:10.1016/j.foreco.2008.04.014.
   Web of Science ®Google Scholar
• Na, X., S. Zhang, X. Li, H. Yu, and C. Liu. 2010. “Improved Land Cover Mapping Using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data.” Photogrammetric Engineering & Remote Sensing 76 (7): 833–840. doi:10.14358/PERS.76.7.833.
   Web of Science ®Google Scholar
• National Oceanic and Atmospheric Administration [NOAA]. 2019. “Historical Hurricane Tracks Data.” https://coast.noaa.gov/hurricanes
   Google Scholar
• Natural Resources Canada. 2019. “The Canada Centre for Mapping and Earth Observation: Fundamentals of Remote Sensing.” https://www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf
   Google Scholar
• Ozdemir, I. 2014. “Linear Transformation to Minimize the Effects of Variability in Understory to Estimate Percent Tree Canopy Cover Using RapidEye Data.” GIScience & Remote Sensing 51 (3): 288–300. doi:10.1080/15481603.2014.912876.
   Web of Science ®Google Scholar
• Paletto, A., and V. Tosi. 2009. “Forest Canopy Cover and Canopy Closure: Comparison of Assessment Techniques.” European Journal of Forest Research 128 (3): 265–272. doi:10.1007/s10342-009-0262-x.
   Web of Science ®Google Scholar
• Popescu, S. C., T. Zhou, R. Nelson, A. Neuenschwander, R. Sheridan, L. Narine, and K. M. Walsh. 2018. “Photon Counting LiDAR: An Adaptive Ground and Canopy Height Retrieval Algorithm for ICESat-2 Data.” Remote Sensing of the Environment 208: 154–170. doi:10.1016/j.rse.2018.02.019.
   Web of Science ®Google Scholar
• Purevdorj, Ts., R. Tateishi, T. Ishiyama, and Y. Honda. 1998. “Relationships between Percent Vegetation Cover and Vegetation Indices.” International Journal of Remote Sensing 19 (18): 3519–3535. doi:10.1080/014311698213795.
   Web of Science ®Google Scholar
• Putman, E. B., and S. C. Popescu. 2018. “Automated Estimation of Standing Dead Tree Volume Using Voxelized Terrestrial Lidar Data.” IEEE Transactions on Geoscience and Remote Sensing 56 (11): 6484–6503. doi:10.1109/TGRS.2018.2839088.
   Web of Science ®Google Scholar
• R Core Team. 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
   Google Scholar
• rapidlasso GmbH. 2014. LAStools Software (Version 141017). Friedrichshafener Straße 1, 82205 Gilching, Germany. http://lastools.org/download/LAStools.zip
   Google Scholar
• Rodgers III, J., A. Murrah, and W. H. Cooke. 2009. “The Impact of Hurricane Katrina on the Coastal Vegetation of the Weeks Bay Reserve, Alabama from NDVI Data.” Estuaries and Coasts 32 (3): 496–507. doi:10.1007/s12237-009-9138-z.
   Web of Science ®Google Scholar
• Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• Sanchez-Hernandez, C., D. S. Boyd, and G. M. Foody. 2007. “Mapping Specific Habitats from Remotely Sensed Imagery: Support Vector Machine and Support Vector Data Description Based Classification of Coastal Saltmarsh Habitats.” Ecological Informatics 2 (2): 83–88. doi:10.1016/j.ecoinf.2007.04.003.
   Web of Science ®Google Scholar
• Santacruz, A. 2019. “Image Classification with RandomForests in R (And QGIS).” amsantac.co. http://amsantac.co/blog/en/2015/11/28/classification-r.html
   Google Scholar
• Schmidt, J. A., R. McCleery, J. R. Seavey, S. E. C. Devitt, and P. M. Schmidt. 2012. “Impacts of a Half Century Sea-level Rise and Development on an Endangered Mammal.” Global Change Biology 18 (12): 3536–3542. doi:10.1111/gcb.12024.
   Web of Science ®Google Scholar
• Schreuder, H. T., S. Bain, and R. C. Czaplewski. 2003. Accuracy Assessment of Percent Canopy Cover, Cover Type, and Size Class. General Technical Report. RMRS-GTR-108, 10. Fort Collins, Colorado:United States Department of Agriculture, Forest Service, Rocky Mountain Research Station. doi:10.2737/RMRS-GTR-108.
   Google Scholar
• Silvy, N. J. 1975. “Population Density, Movements, and Habitat Utilization of Key Deer, Odocoileus virginianus clavium. ” Dissertation, Southern Illinois University, Carbondale, Illinois, USA.
   Google Scholar
• Srinivasan, S., S. C. Popescu, M. Eriksson, R. Sheridan, and N. W. Ku. 2014. “Multi-temporal Terrestrial Laser Scanning for Modelling Tree Biomass Change.” Forest Ecology and Management 319: 304–317. doi:10.1016/j.foreco.2014.01.038.
   Google Scholar
• St-Louis, V., A. Pidgeon, T. Kuemmerle, R. Sonnenschein, V. Radeloff, M. Clayton, B. Locke, D. Bash, and P. Hostert. 2014. “Modelling Avian Biodiversity Using Raw, Unclassified Satellite Imagery.” Philosophical Transactions of the Royal Society B 369: 20130197. doi:10.1098/rstb.2013.0197.
   PubMed Web of Science ®Google Scholar
• Svejkovsky, J., D. E. Ogurcak, M. S. Ross, and A. Arkowitz. 2020. “Satellite Image-Based Time Series Observations of Vegetation Response to Hurricane Irma in the Lower Florida Keys.” Estuaries and Coasts 43 (5): 1058–1069. doi:10.1007/s12237-020-00701-8.
   Web of Science ®Google Scholar
• Szantoi, Z., F. Escobedo, A. Abd-Elrahman, S. Smith, and L. Pearlstine. 2013. “Analyzing Fine-scale Wetland Composition Using High Resolution Imagery and Texture Features.” International Journal of Applied Earth Observation and Geoinformation 23: 204–212. doi:10.1016/j.jag.2013.01.003.
   Web of Science ®Google Scholar
• Texas A&M Natural Resources Institute [NRI]. 2017. Florida Key Deer Hurricane Irma Report. Natural Resources Institute, Texas A&M University, College Station, Texas, USA.
   Google Scholar
• Texas A&M Natural Resources Institute [NRI]. 2018. Lower Keys Marsh Rabbit Post-hurricane Irma Report. Natural Resources Institute, Texas A&M University, College Station, Texas, USA.
   Google Scholar
• United States Fish and Wildlife Service [USFWS]. 2019. “Multi-species Recovery Plan for South Florida.” https://www.fws.gov/verobeach/MSRPPDFs/LowerKeysRabbit.pdf
   Google Scholar
• United States Geological Survey [USGS]. 2019. “NAIP (National Agricultural Imagery Program) Data.” Accessed 5 July 2019. https://earthexplorer.usgs.gov
   Google Scholar
• Ustuner, M., F. B. Sanli, and B. Dixon. 2015. “Application of Support Vector Machines for Landuse Classification Using High-resolution RapidEye Images: A Sensitivity Analysis.” European Journal of Remote Sensing 48 (1): 403–422. doi:10.5721/EuJRS20154823.
   Web of Science ®Google Scholar
• Walton, J. T., D. J. Nowak, and E. J. Greenfield. 2008. “Assessing Urban Forest Canopy Cover Using Airborne or Satellite Imagery.” Arboriculture & Urban Forestry 34 (6): 334–340. https://www.nrs.fs.fed.us/pubs/9515
   Google Scholar
• Weishampel, J.F., K.J. Ranson, and D.J. Harding. 1996. “ Remote Sensing of Forest Canopies.„ Selbyana 17(1):6–14. https://www.jstor.org/stable/41759918
   Google Scholar
• Wood, E., A. Pidgeon, V. Radeloff, and N. Keuler. 2012. “Image Texture as a Remotely Sensed Measure of Vegetation Structure.” Remote Sensing of Environment 121: 516–526. doi:10.1016/j.rse.2012.01.003.
   Web of Science ®Google Scholar