Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data

References

• Adams, M. L., W. D. Philpot, and W. A. Norvell. 1999. “Yellowness Index: An Application of Spectral Second Derivatives to Estimate Chlorosis of Leaves in Stressed Vegetation.” International Journal of Remote Sensing 20: 3663–3675. doi:10.1080/014311699211264.
   Web of Science ®Google Scholar
• Anderson, D. R., J. L. Laake, B. R. Crain, and K. P. Burnham. 1979. “Guidelines for Line Transect Sampling of Biological Populations.” The Journal of Wildlife Management 43: 70–78. doi:10.2307/3800636.
   Web of Science ®Google Scholar
• Asner, G. P., M. Keller, R. Pereira Jr., J. C. Zweede, and J. N. M. Silva. 2004. “Canopy Damage and Recovery after Selective Logging in Amazonia: Field and Satellite Studies.” Ecological Applications 14: 280–298. doi:10.1890/01-6019.
   Web of Science ®Google Scholar
• Asner, G. P., J. R. Kellner, T. Y. Kennedy-Bowdoin, D. E. Knapp, C. Anderson, R. E. Martin, and H. Y. Chen. 2013. “Forest Canopy Gap Distributions in the Southern Peruvian Amazon.” PLoS ONE 8: e60875. doi:10.1371/journal.pone.0060875.
   Google Scholar
• Asner, G. P., M. Palace, M. Keller, R. Pereira Jr., J. N. M. Silva, and J. C. Zweede. 2002. “Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations.” Biotropica 34: 483–492. doi:10.1111/j.1744-7429.2002.tb00568.x.
   Web of Science ®Google Scholar
• Baatz, M., U. Bentz, S. Dehghani, and M. Heynen. 2004. eCognition User Guide 4. Műnchen: Definiens Imaging.
   Google Scholar
• Battles, J. J., J. G. Dushoff, and T. J. Fahey. 1996. “Line Intersects Sampling of Forest Canopy Gaps.” Journal of Forest Science 42: 131–138.
   Web of Science ®Google Scholar
• Berk, A., L. Bernstein, G. Anderson, P. Acharya, D. Robertson, J. Chetwynd, and S. Adler-Golden. 1998. “MODTRAN Cloud and Multiple Scattering Upgrade with Application to AVIRIS.” Remote Sensing of Environment 65: 367–375.
   Web of Science ®Google Scholar
• Betts, H. D., L. J. Brown, and G. H. Stewart. 2005. “Forest Canopy Gap Detection and Characterization by the Use of High-Resolution Digital Elevation Models.” The New Zealand Journal of Ecology 29: 95–103.
   Web of Science ®Google Scholar
• Blaschke, T., and J. Strobl. 2001. “What’s Wrong with Pixels? Some Recent Developments Interfacing Remote Sensing and GIS.” GeoBIT/GIS 14: 12–17.
   Google Scholar
• Brokaw, N., and J. Grear. 1991. “Forest Structure Before and After Hurricane Hugo at Three Elevations in the Luquillo Mountains, Puerto Rico.” Biotropica 23: 386–392. doi:10.2307/2388256.
   Web of Science ®Google Scholar
• Brokaw, N. V. L. 1982. “The Definition of Tree Fall Gap and Its Effect on Measure of Forest Dynamics.” Biotropica 11: 158–160.
   Web of Science ®Google Scholar
• Brokaw, N. V. L. 1985. “Gap-Phase Regeneration in a Tropical Forest.” Ecology 66: 682–687.
   Web of Science ®Google Scholar
• Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to Distance Sampling. Oxford: Oxford University Press.
   Google Scholar
• Chen, Q. 2009. “Improvement of the Edge‐Based Morphological (EM) Method for Lidar Data Filtering.” International Journal of Remote Sensing 30: 1069–1074. doi:10.1080/01431160802158310.
   Web of Science ®Google Scholar
• Cho, M. A., R. Mathieu, G. P. Asner, L. Naidoo, J. Van Aardt, A. Ramoelo, P. Debba, K. Wessels, R. Main, I. P. Smit, and B. Erasmus. 2012. “Mapping Tree Species Composition in South African Savannas Using an Integrated Airborne Spectral and Lidar System.” Remote Sensing of Environment 125: 214–226. doi:10.1016/j.rse.2012.07.010.
   Web of Science ®Google Scholar
• Cho, M. A., A. Ramoelo, P. Debbi, O. Mutanga, R. Mathieu, H. Van Deventer, and N. Ndlovu. 2013. “Assessing the Effects of Subtropical Forest fragmentation on Leaf Nitrogen Distribution Using Remote Sensing Data.” Landscape Ecology 28: 1479–1491. doi:10.1007/s10980-013-9908-7.
   Web of Science ®Google Scholar
• Cho, M. A., and A. K. Skidmore. 2009. “Hyperspectral Predictors for Monitoring Biomass Production in Mediterranean Mountain Grasslands: Majella National Park, Italy.” International Journal of Remote Sensing 30: 499–515. doi:10.1080/01431160802392596.
   Web of Science ®Google Scholar
• Cho, M. A., A. K. Skidmore, and C. Atzberger. 2008. “Towards Red‐Edge Positions Less Sensitive to Canopy Biophysical Parameters for Leaf Chlorophyll Estimation Using Properties Optique Spectrales Des Feuilles (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAILH) Simulated Data.” International Journal of Remote Sensing 29: 2241–2255. doi:10.1080/01431160701395328.
   Web of Science ®Google Scholar
• Clark, M. L., D. B. Clark, and D. A. Roberts. 2004. “Small-Footprint Lidar Estimation of Sub-Canopy Elevation and Tree Height in a Tropical Rain Forest Landscape.” Remote Sensing of Environment 91: 68–89. doi:10.1016/j.rse.2004.02.008.
   Web of Science ®Google Scholar
• Codilla, L. T., and E. B. Metillo. 2011. “Distribution and Abundance of the Invasive Plant Species Chromolaena odorata L. in the Zamboanga Peninsula, Philippines.” International Journal of Environmental Science and Development 2: 406–410. doi:10.7763/IJESD.2011.V2.160.
   Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37: 35–46.
   Web of Science ®Google Scholar
• Dawson, T. P., and P. J. Curran. 1998. “Technical Note A New Technique for Interpolating the Reflectance Red Edge Position.” International Journal of Remote Sensing 19: 2133–2139. doi:10.1080/014311698214910.
   Web of Science ®Google Scholar
• Dubayah, R. O., and J. B. Drake. 2000. “Lidar Remote Sensing for Forestry Applications.” Journal of Forestry 98: 44–46.
   Web of Science ®Google Scholar
• Eberhardt, L. L. 1968. “A Preliminary Appraisal of Line Transects.” The Journal of Wildlife Management 32: 82–88. doi:10.2307/3798239.
   Web of Science ®Google Scholar
• Emborg, J. 1998. “Understorey Light Conditions and Regeneration with Respect to the Structural Dynamics of a Near-Natural Temperate Deciduous Forest in Denmark.” Forest Ecology and Management 106: 83–95.
   Web of Science ®Google Scholar
• Exelis Visual Information Solutions. 2012. Environment for Visualizing Images. Boulder, CO: Exelis Visual Information Solutions.
   Google Scholar
• Ferrazoli, P., and L. Guerriero. 1994. “Interpretation and Model Analysis of Maestro 1 Flevoland Data.” International Journal of Remote Sensing 15: 2901–2915. doi:10.1080/01431169408954291.
   Google Scholar
• Foody, G. M. 2004. “Spatial Nonstationarity and Scale-Dependency in the Relationship between Species Richness and Environmental Determinants for the Sub-Saharan Endemic Avifauna.” Global Ecology and Biogeography 13: 315–320. doi:10.1111/j.1466-822x.2004.00097.x.
   Web of Science ®Google Scholar
• Forbes, S. A., and A. O. Gross. 1921. “The Orchard Birds of an Illinois Summer.” III. Nature, History and Survey Bulletin 14: 1–8.
   Google Scholar
• Fourcade, H. G. 1889. Report on the Natal Forests, 197. Pietermaritzburg: W. Watson.
   Google Scholar
• Fox, T. J., M. G. Knutson, and R. K. Hines. 2000. “Mapping Forest Canopy Gaps Using Air-Photo Interpretation and Ground Surveys.” Wildlife Society Bulletin 28: 882–889.
   Web of Science ®Google Scholar
• Geldenhuys, C. J. 1989. Biogeography of the Mixed Evergreen Forests of Southern Africa. Ecosystems Programmes Occasional Report no. 45. Pretoria: FRD.
   Google Scholar
• Gitelson, A. A., and M. N. Merzlyak. 1994. “Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus hippocastanum L. and Acer platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation.” Journal of Plant Physiology 143: 286–292. doi:10.1016/S0176-1617(11)81633-0.
   Web of Science ®Google Scholar
• Gong, P., G. S. Biging, and R. Standiford. 2000. “Use of Digital Surface Model for Hardwood Rangeland Monitoring.” Journal of Range Management 53: 622–626.
   Google Scholar
• Goodall, J. M., and D. J. Erasmus. 1996. “Review of the Status and Integrated Control of the Invasive Alien Weed, Chromolaena odorata, in South Africa.” Agriculture, Ecosystems & Environment 56: 151–164. doi:10.1016/0167-8809(95)00647-8.
   Web of Science ®Google Scholar
• Green, K. 2000. “Selecting and Interpreting High Resolution Images.” Journal of Forestry 98: 37–39.
   Web of Science ®Google Scholar
• Harding, D. J., M. A. Lefsky, G. G. Parker, and J. B. Blair. 2001. “Laser Altimeter Canopy Height Profiles: Methods and Validation for Closed-Canopy, Broadleaf Forests.” Remote Sensing of Environment 76: 283–297. doi:10.1016/S0034-4257(00)00210-8.
   Web of Science ®Google Scholar
• Hess, L. L., J. M. Melack, and D. S. Simonett. 1990. “Radar Detection of Flooding Beneath the Forest Canopy: A Review.” International Journal of Remote Sensing 11: 1313–1325. doi:10.1080/01431169008955095.
   Web of Science ®Google Scholar
• Hodgson, M. E., and P. Bresnahan. 2004. “Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget.” Photogrammetric Engineering and Remote Sensing 70: 331–339. doi:10.14358/PERS.70.3.331.
   Web of Science ®Google Scholar
• Huete, A. R., H. Q. Liu, K. Batchily, and W. J. Van Leeuwen. 1997. “A Comparison of Vegetation Indices Over a Global Set of TM Images for EOS-MODIS.” Remote Sensing of Environment 59: 440–451. doi:10.1016/S0034-4257(96)00112-5.
   Web of Science ®Google Scholar
• Imhoff, M. L. 1995. “A Theoretical Analysis of the Effect of Forest Structure on Synthetic Aperture Radar Backscatter and the Remote Sensing of Biomass.” IEEE Transactions on Geoscience and Remote Sensing 33: 341–352. doi:10.1109/36.377934.
   Web of Science ®Google Scholar
• Jackson, R. D., P. N. Slater, and P. J. Pinter. 1983. “Discrimination of Growth and Water Stress in Wheat by Various Vegetation Indices Through Clear and Turbid Atmospheres.” Remote Sensing of Environment 13: 187–208. doi:10.1016/0034-4257(83)90039-1.
   Web of Science ®Google Scholar
• Jeon, B., and D. A. Landgrebe. 1999. “Partially Supervised Classification Using Weighted Unsupervised Clustering.” IEEE Transactions on Geoscience and Remote Sensing 37: 1073–1079. doi:10.1109/36.752225.
   Web of Science ®Google Scholar
• Kaufman, Y. J., and D. Tanre. 1992. “Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS.” IEEE Transactions on Geoscience and Remote Sensing 30: 261–270. doi:10.1109/36.134076.
   Web of Science ®Google Scholar
• Kellner, J. R., D. B. Clark, and S. P. Hubbell. 2009. “Pervasive Canopy Dynamics Produce Short-Term Stability in a Tropical Rain Forest Landscape.” Ecology Letters 12: 155–164. doi:10.1111/j.1461-0248.2008.01274.x.
   PubMed Web of Science ®Google Scholar
• Kim, S., G. F. Schreuder, D. Briggs, and T. Hinckley. 2009. “Individual Tree Species Identification Using Lidar-Derived Crown Structures and Intensity Data.” Doctoral thesis., 1–137. University of Washington, College of Forest Resources.
   Google Scholar
• Knipling, E. B. 1970. “Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from Vegetation.” Remote Sensing of Environment 1: 155–159. doi:10.1016/S0034-4257(70)80021-9.
   Google Scholar
• Kupfer, J. A., and J. R. Runkle. 1996. “Early Gap Successional Pathways in a Fagus-Acer Forest Preserve: Patterns and Determinants.” Journal of Vegetation Science 7: 247–256. doi:10.2307/3236325.
   Web of Science ®Google Scholar
• Lillesand, T. M., R. W. Kiefer, and J. W. Chipman. 2004. Remote Sensing and Image Interpretation. 5th ed., 418–423. New York: Wiley Press.
   Google Scholar
• Lim, K., P. Treitz, M. Wulder, B. Stonge, and M. Flood. 2003. “Lidar Remote Sensing of Forest Structure.” Progress in Physical Geography 27: 88–106. doi:10.1191/0309133303pp360ra.
   Web of Science ®Google Scholar
• Mallinis, G., N. Koutsias, M. Tsakiri-Strati, and M. Karteris. 2008. “Object-Based Classification Using Quickbird Imagery for Delineating Forest Vegetation Polygons in a Mediterranean Test Site.” ISPRS Journal of Photogrammetry & Remote Sensing 63: 237–250. doi:10.1016/j.isprsjprs.2007.08.007.
   Web of Science ®Google Scholar
• Merzlyak, M. N., A. A. Gitelson, O. B. Chivkunova, and V. Y. Rakitin. 1999. “Non-destructive Optical Detection of Pigment Changes During Leaf Senescence and Fruit Ripening.” Physiologia Plantarum 106: 135–141. doi:10.1034/j.1399-3054.1999.106119.x.
   Web of Science ®Google Scholar
• Moore, A. B. 2004. Alien Invasive species: Impacts on Forests and Forestry. Forest Health and Biosecurity Working Papers FAO, 66. Rome: FAO.
   Google Scholar
• Mutanga, O., E. Adam, and M. A. Cho. 2012. “High Density Biomass Estimation for Wetland Vegetation Using Worldview-2 Imagery and Random Forest Regression Algorithm.” International Journal of Applied Earth Observation and Geoinformation 18: 399–406. doi:10.1016/j.jag.2012.03.012.
   Web of Science ®Google Scholar
• Mutanga, O., and A. K. Skidmore. 2004. “Narrow Band Vegetation Indices Overcome the Saturation Problem in Biomass Estimation.” International Journal of Remote Sensing 25: 3999–4014. doi:10.1080/01431160310001654923.
   Web of Science ®Google Scholar
• Negrón-Juárez, R. I., J. Q. Chambers, D. M. Marra, G. H. Ribeiro, S. W. Rifai, N. Higuchi, and D. Roberts. 2011. “Detection of Subpixel Treefall Gaps with Landsat Imagery in Central Amazon Forests.” Remote Sensing of Environment 115: 3322–3328. doi:10.1016/j.rse.2011.07.015.
   Web of Science ®Google Scholar
• Nelson, R., R. Oderwald, and T. G. Gregoire. 1997. “Separating the Ground and Airborne Laser Sampling Phases to Estimate Tropical Forest Basal Area, Volume, and Biomass.” Remote Sensing of Environment 60: 311–326. doi:10.1016/S0034-4257(96)00213-1.
   Web of Science ®Google Scholar
• Orians, G. H. 1982. “The Influence of Tree-Falls in Tropical Forests in Tree Species Richness.” Tropical Ecology 23: 255–279.
   Google Scholar
• Ozdemir, I., and A. Karnieli. 2011. “Predicting Forest Structural Parameters Using the Image Texture Derived from Worldview-2 Multispectral Imagery in a Dryland Forest, Israel.” International Journal of Applied Earth Observation and Geoinformation 13: 701–710. doi:10.1016/j.jag.2011.05.006.
   Web of Science ®Google Scholar
• Peñuelas, J., F. Baret, and I. Filella. 1995. “Semi-Empirical Indices to Assess Carotenoids/Chlorophyll a Ratio from Leaf Spectral Reflectance.” Photosynthetica 31: 221–230.
   Web of Science ®Google Scholar
• Perumal, K., and R. Bhaskaran. 2010. “Supervised Classification Performance of Multispectral Images.” Journal of Computing 2. ISSN 2151-9617.
   Google Scholar
• Ramoelo, A., A. K. Skidmore, M. A. Cho, M. Schlerf, R. Mathieu, and I. M. A. Heitkönig. 2012. “Regional Estimation of Savanna Grass Nitrogen Using the Red-Edge Band of the Spaceborne Rapideye Sensor.” International Journal of Applied Earth Observation and Geoinformation 19: 151–162. doi:10.1016/j.jag.2012.05.009.
   Web of Science ®Google Scholar
• Runkle, J. R. 1982. “Patterns of Disturbance in Some Old-Growth Mesic Forests of Eastern North-America.” Ecology 63: 1533–1546. doi:10.2307/1938878.
   Web of Science ®Google Scholar
• San, B. T., and M. L. Suzen. 2010. “Evaluation of Different Atmospheric Correction Algorithms for EO-1 Hyperion Imagery.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science 38: 392–397.
   Google Scholar
• Satellite Imaging Corporation. 2014. “Atmospheric/Topographic Correction (ATCOR).” Accessed August 21. http://www.satimagingcorp.com/services/atcor/
   Google Scholar
• Sellers, P. J. 1985. “Canopy Reflectance, Photosynthesis and Transpiration.” International Journal of Remote Sensing 6: 1335–1372.
   Web of Science ®Google Scholar
• Smith, K. L., M. D. Steven, and J. J. Colls. 2004. “Use of Hyperspectral Derivative Ratios in the Red-Edge Region to Identify Plant Stress Responses to Gas Leaks.” Remote Sensing of Environment 92: 207–217. doi:10.1016/j.rse.2004.06.002.
   Web of Science ®Google Scholar
• Story, M., and R. Congalton. 1986. “Accuracy Assessment: A User’s Perspective.” Photogrammetry, Engineering and Remote Sensing 52: 397–399.
   Web of Science ®Google Scholar
• Suarez, A. V., K. S. Pfennig, and S. K. Robinson. 1997. “Nesting Success of a Disturbance-Dependent Songbird on Different Kinds of Edges.” Conservation Biology 11: 928–935.
   Web of Science ®Google Scholar
• Swellengrebel, E. J. G. 1959. “On the Value of Large-Scale Aerial Photographs in British Guiana Forestry.” Empire Forestry Review 38: 54–64.
   Google Scholar
• Touw, W. G., J. R. Bayjanov, L. Overmars, L. Backus, J. Boekhorst, M. Wels, and S. A. F. T. Van Hijum. 2013. “Data Mining in the Life Sciences with Random Forest: A Walk in the Park or Lost in the Jungle?” Briefing in Bioinformatics 14: 315–326. doi:10.1093/bib/bbs034.
   Google Scholar
• Updike, T., and C. Comp. 2010. Radiometric Use of WorldView-2 Imagery, DigitalGlobe. Technical Note. Longmont, CO: DigitalGlobe.
   Google Scholar
• Ustin, S. L., A. A. Gitelson, S. Jacquemoud, M. Schaepman, G. P. Asner, J. A. Gamon, and P. Zarco-Tejada. 2009. “Retrieval of Foliar Information about Plant Pigment Systems from High Resolution Spectroscopy.” Remote Sensing of Environment 113 (Suppl. 1): S67–S77. doi:10.1016/j.rse.2008.10.019.
   Google Scholar
• Ustin, S. L., and A. Trabucco. 2000. “Using Hyperspectral Data to Assess Forest Structure.” Journal of Forestry 98: 47–49.
   Web of Science ®Google Scholar
• Van Gils, H., M. Mwanangi, and D. Rugege. 2006. “Invasion of an Alien Shrub across Four Land Management Regimes, West of St Lucia, South Africa.” South African Journal of Science 102: 9–12.
   Web of Science ®Google Scholar
• Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York: Springer.
   Google Scholar
• Vepakomma, U., B. S. Onge, and D. Kneeshaw. 2008. “Spatially Explicit Characterization of Boreal Forest Gap Dynamics Using Multi-Temporal Lidar Data.” Remote Sensing of Environment 112: 2326–2340.
   Web of Science ®Google Scholar
• Weiss, M., and F. Baret. 1999. “Evaluation of Canopy Biophysical Variable Retrieval Performances from the Accumulation of Large Swath Satellite Data.” Remote Sensing of Environment 70: 293–306.
   Web of Science ®Google Scholar
• Weiss, P. W., and I. R. Noble. 1984. “Status of Coastal Dune Communities Invaded by Chrysanthemoides monilifera.” Australian Journal of Ecology 9: 93–98. doi:10.1111/j.1442-9993.1984.tb01347.x.
   Web of Science ®Google Scholar
• Whitmore, T. C. 1989. “Canopy Gaps and the Two Major Groups of Forest Trees. Special Feature – Treefall Gaps and Forest Dynamics.” Ecology 70: 536–538. doi:10.2307/1940195.
   Web of Science ®Google Scholar
• Woodcock, C. E., S. A. Macomber, M. Pax-Lenney, and W. C. Cohen. 2001. “Monitoring Large Areas for Forest Change Using Landsat: Generalization across Space, Time and Landsat Sensors.” Remote Sensing of Environment 78: 194−203. doi:10.1016/S0034-4257(01)00259-0.
   Web of Science ®Google Scholar
• Zhou, Q., and M. Robson. 2001. “Automated Rangeland Vegetation Cover and Density Estimation Using Ground Digital Images and a Spectral-Contextual Classifier.” International Journal of Remote Sensing 22: 3457–3470. doi:10.1080/01431160010004504.
   Web of Science ®Google Scholar