Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition

Remote Sensing of Environment

Volumn 145, Issue , 2014, Pages 68-80

  Garabedian, James E ^a   McGaughey, Robert J ^b   Reutebuch, Stephen E ^b   Parresol, Bernard R ^c   Kilgo, John C ^c   Moorman, Christopher E ^a   Peterson, M Nils ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c USDA FOREST SERVICE   (United States)

Author keywords

Forest structure; Habitat conservation; Landscape; Prediction confidence interval; Red cockaded woodpecker; Remote sensing; Savannah River Site; Spatially explicit; Wildlife

Indexed keywords

EID: 84894318423     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.01.022     Document Type: Article

References (77)

1. Andersen H.E., McGaughey R.J., Reutebuch S.E. Estimating forest canopy fuel parameters using LiDAR data. Remote Sensing of Environment 2005, 94:441-449.
2. Bässler C., Stadler J., Müller J., Förster B., Göttlein A., Brandl R. LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks. Biodiversity and Conservation 2011, 20:465-481.
3. Beier P., Drennan J.E. Forest structure and prey abundance in foraging areas of northern goshawks. Ecological Applications 1997, 7:564-571.
4. Beier P., Rogan E.C., Ingraldi M.F., Rosenstock S.S. Does forest structure affect reproduction of northern goshawks in ponderosa pine forests?. Journal of Applied Ecology 2007, 45:342-350.
5. Bradbury R.B., Hill R.A., Mason D.C., Hinsley S.A., Wilson J.D., Balzter H., et al. Modeling relationships between birds and vegetation structure using airborne LiDAR data: A review with case studies from agricultural and woodland environments. Ibis 2005, 147:443-452.
6. Breidenbach J., McGaughey R.J., Andersen H.E., Kändler G., Reutebuch S.E. A mixed effects model to estimate stand volume by means of small footprint airborne lidar data for an American and German study site. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2007, 36:77-83.
7. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
8. Brock J.C., Wright C.W., Clayton T.D., Nayegandhi A. LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs 2004, 23:48-59.
9. Brock J.C., Wright C.W., Kuffner I.B., Hernandez R., Thompson P. Airborne lidar sensing of massive stony coral colonies on patch reefs in the northern Florida reef tract. Remote Sensing of Environment 2006, 104:31-42.
10. Broughton R.K., Hill R.A., Freeman S.N., Bellamy P.E., Hinsley S.A. Describing habitat occupation by woodland birds with territory mapping and remotely sensed data: an example using the marsh tit (Poecile palustris). The Condor 2012, 114:812-822.
11. Carroll C., Johnson D.S. The importance of being spatial (and reserved): Assessing northern spotted owl habitat relationships with hierarchical Bayesian models. Conservation Biology 2008, 22:1026-1036.
12. Clarkin T. Modeling global navigation satellite system positional error under forest canopy based on LIDAR-derived canopy densities. Thesis, College of Forest Resources, University of Washington, Seattle, WA 2007.
13. Clawges R., Vierling K., Vierling L., Rowell E. The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sensing of Environment 2008, 112:2064-2073.
14. Crookston N.L., Finley A.O. yaImpute: An R package for kNN imputation. Journal of Statistical Software 2008, 23:1-16.
15. ESRI ArcGIS (version 9.3) 2011, Environmental Systems Research Institute, Inc., Redlands.
16. Fox J., Monette G. Generalized collinearity diagnostics. Journal of the American Statistical Association 1992, 87:178-183.
17. García-Feced C., Tempel D.J., Kelly M. LiDAR as a tool to characterize wildlife habitat: California spotted owl nesting habitat as an example. Journal of Forestry 2011, 109:436-443.
18. Gobakken T., Næsset E., Nelson R., Bollandsås O.M., Gregoire T.G., Ståhl G., et al. Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sensing of Environment 2012, 123:443-456.
19. Goetz S.J., Steinberg D., Betts M.G., Holmes R.T., Doran P.J., Dubayah R., et al. Lidar remote sensing variables predict breeding habitat of a Neotropical migrant bird. Ecology 2010, 91:1569-1576.
20. Goetz S., Steinberg D., Dubayah R., Blair B. Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sensing of Environment 2007, 108:254-263.
21. Graf R.F., Mathys L., Bollman K. Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. Forest Ecology and Management 2009, 257:160-167.
22. Greenwald D.N., Crocker-Bedford D.C., Broberg L., Suckling K.F., Tibbitts T. A review of northern goshawk habitat selection in the home range and implications for forest management in the western United States. Wildlife Society Bulletin 2005, 33:120-128.
23. Hardesty J.L., Gault K.E., Percival F.P. Ecological correlates of red-cockaded woodpecker (Picoides borealis) foraging preference, habitat use, and home range size in northwest Florida (Eglin Air Force Base). Final Report Research Work Order 99 1997, Florida Cooperative Fish and Wildlife Research Unit, University of Florida, Gainesville, Florida, (137 pp.).
24. Hamel P.B. Cerulean warbler status assessment 2000, U.S. Fish and Wildlife Service, Ft. Snelling, Minnesota, USA.
25. Helmer E.H., Ruzycki T.S., Wunderle J.M., Vogesser S., Ruefenacht B., Kwit C., Brandeis T.J., Ewert D.N. Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat. Remote Sensing of Environment 2010, 114:2457-2473.
26. Henningsen A., Hamann J.D. systemfit: A package for estimating systems of simultaneous equations in R. Journal of Statistical Software 2007, 24:1-40.
27. Hinsley S.A., Hill R.A., Bellamy P.E., Balzter H. The application of lidar in woodland bird ecology: Climate, canopy structure, and habitat quality. Photogrammetric Engineering and Remote Sensing 2006, 72:1399-1406.
28. Hinsley S.A., Hill R.A., Bellamy P.E., Harrison N.M., Speakman J.R., Wilson A.K., et al. Effects of structural and functional habitat gaps on breeding woodland birds: Working harder for less. Landscape Ecology 2008, 23:615-626.
29. Holmgren J., Persson Å., Söderman U. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing 2008, 29:1537-1552.
30. Hyde P., Dubayah R., Peterson B., Blair J.B., Hofton M., Hunsaker C., et al. Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment 2005, 96:427-437.
31. Hyyppä J., Hyyppä H., Leckie D., Gougeon F., Yu X., Maltamo M. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing 2008, 29:1339-1366.
32. Imm D.W., McLeod K.W. Plant communities. Ecology and management of a forested landscape: fifty years of natural resource stewardship on the Savannah River Site 2005, 106-160. Island Press, Washington, D. C. J.C. Kilgo, J.I. Blake (Eds.).
33. James F.C., Hess C.A., Kufrin D. Species-centered environmental analysis: indirect effects of fire history on red-cockaded woodpeckers. Ecological Applications 1997, 7:118-129.
34. James F.C., Hess C.A., Kicklighter B.C., Thum R.A. Habitat management and the niche gestalt of the red-cockaded woodpecker in longleaf pine forests. Ecological Applications 2001, 11:854-870.
35. Johnston P.A. Red-cockaded woodpecker. Ecology and management of a forested landscape: Fifty years of natural resource stewardship on the Savannah River Site 2005, 301-312. Island Press, Washington, D. C. J.C. Kilgo, J.I. Blake (Eds.).
36. Jones J.L. Side channel mapping and fish habitat suitability analysis using lidar topography and orthophotography. Photogrammetric Engineering and Remote Sensing 2006, 72:1202-1206.
37. Jones J., Robertson R.J. Territory and nest-site selection of cerulean warblers in eastern Ontario. The Auk 2001, 118:727-735.
38. Leeuwen M., Nieuwenhuis M. Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research 2010, 129:749-770.
39. Lefsky M.A., Cohen W.B., Parker G.G., Harding D.J. Lidar remote sensing for ecosystem studies. Bioscience 2002, 52:19-30.
40. Lesak A.A., Radeloff V.C., Hawbaker T.J., Pidgeon A.M., Gobakken T., Contrucci K. Modeling forest songbird species richness using LiDAR-derived measures of forest structure. Remote Sensing of Environment 2011, 115:2823-2835.
41. Liaw A., Wiener M. Classification and regression by Random Forest. R News 2002, 2:18-22.
42. Lim K., Treitz P., Wulder M., St-Onge B., Flood M. LiDAR remote sensing of forest structure. Progress in Physical Geography 2003, 27:88-106.
43. Lipscomb D.J., Williams T.M. A technique for using COV-ARC/INFO GIS to determine red-cockaded woodpecker foraging areas on private lands. Proceedings of the southern forestry geographic information systems conference 1996, 255-264. University of Georgia, Athens, Georgia, USA.
44. Lumley T. Leaps: Regression subset selection (using Fortran code by Alan Miller) 2009, (http://CRAN.R-project.org/package=leaps. Accessed 20 December 2011).
45. Maltamo M., Packalén P., Yu X., Eerikäinen K., Hyyppä J., Pitkänen J. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanning data. Forest Ecology and Management 2005, 216:41-50.
46. Martinuzzi S., Vierling L.A., Gould W.A., Falkowski M.J., Evans J.S., Hudak A.T., et al. Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability. Remote Sensing of Environment 2009, 113:2533-2546.
47. McGaughey R.J. FUSION/LDV: software for LIDAR data analysis and visualization, version 2.9 2009, U.S. USDA Forest Service, Pacific Northwest Research Station.
48. Means J.E., Acker S.A., Brandon J.F., Renslow M., Emerson L., Hendrix C.J. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering and Remote Sensing 2000, 66:1367-1371.
49. Müller J., Brandl R. Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. Journal of Applied Ecology 2009, 46:897-905.
50. Müller J., Moning C., Bässler C., Heurich M., Brandl R. Using airborne laser scanning to model potential abundance and assemblages of forest passerines. Basic and Applied Ecology 2009, 10:671-681.
51. Müller J., Stadler J., Brandl R. Composition versus physiognomy of vegetation as predictors of bird assemblages: The role of lidar. Remote Sensing of Environment 2010, 114:490-495.
52. Næsset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 2002, 80:88-99.
53. Næsset E. Accuracy of forest inventory using airborne laser scanning: Evaluating the first Nordic full-scale operational project. Scandinavian Journal of Forest Research 2004, 19:554-557.
54. Nelson R., Keller C., Ratnaswamy M. Locating and estimating the extent of Delmarva fox squirrel habitat using an airborne LiDAR profiler. Remote Sensing of Environment 2005, 96:292-301.
55. Parresol B.R. Baldcypress height-diameter equations and their prediction confidence intervals. Canadian Journal of Forest Research 1992, 22:1429-1434.
56. Parresol B.R. Additivity of nonlinear biomass equations. Canadian Journal of Forest Research 2001, 31:865-878.
57. Patton D.R. A model to evaluate Abert's squirrel habitat in uneven-aged ponderosa pine. Wildlife Society Bulletin 1984, 12:408-414.
58. R Development Core Team R: A language and environment for statistical computing 2012, R Foundation for Statistical Computing, Vienna, Austria.
59. Reutebuch S.E., McGaughey R.J. LIDAR-assisted inventory: 2012 Final report to Savannah River Site. Report on file with USDA Forest Service-Savannah River, PO Box 700, New Ellenton, South Carolina 29809 2012, (55pp.).
60. Reynolds R.T., Graham R.T., Reiser M.H., Bassett R.L., Kennedy P.L., Boyce D.A., et al. Management recommendations for the northern goshawk in the southwestern United States 1992, USDA, Forest Service Southwest Region, Albuquerque, New Mexico, USA.
61. Seavy N.E., Viers J.H., Wood J.K. Riparian bird response to vegetation structure: A multiscale analysis using LiDAR measurements of canopy height. Ecological Applications 2009, 19:1848-1857.
62. Smart L.S., Swenson J.J., Christensen N.L., Sexton J.O. Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar. Forest Ecology and Management 2012, 281:100-110.
63. Storch I. Annual home ranges and spacing patterns of capercaillie in Central Europe. Journal of Wildlife Management 1995, 59:392-400.
64. Tattoni C., Rizzolli F., Pedrini P. Can LiDAR data improve bird habitat suitability models?. Ecological Modelling 2012, 45:103-110.
65. Tweddale S., Londo H.A., Carney J.M., Evans D.L., Roberts S.D., Parker R.C., et al. Proceedings of SilviLaser 2008: 8th International Conference on LiDAR applications in forest assessment and inventory 2008, 586-595. Heriot-Watt University, Edinburgh, UK. R.A. Hill, J. Rosette, J. Suarez (Eds.).
66. U.S.Fish and Wildlife Service United States list of endangered fish and wildlife. Federal Register 1970, 35:16047.
67. U.S.Fish and Wildlife Service Recovery plan for the red-cockaded woodpecker (Picoides borealis), second revision 2003, U.S. Fish and Wildlife Service, Southeast Region, Atlanta, Georgia, USA.
68. U.S.Fish and Wildlife Service Revised recovery plan for the northern spotted owl (Strix occidentalis caurina) 2011, U.S. Fish and Wildlife Service, Portland, Oregon, USA.
69. Valbuena R., Mauro F., Rodriguez-Solano R., Manzanera J.A. Accuracy and precision of GPS receivers under forest canopies in a mountainous environment. Spanish Journal of Agricultural Research 2010, 8:1047-1057.
70. Venables W.N., Ripley B.D. Modern Applied Statistics with S 2002, Springer, New York, New York, USA. 4th ed.
71. Vierling K.T., Bässler C., Brandl R., Vierling L.A., Weib I., Müller J. Spinning a laser web: Predicting spider distributions using LiDAR. Ecological Applications 2011, 21:577-588.
72. Vierling K.T., Vierling L.A., Gould W.A., Martinuzzi S., Clawges R.M. Lidar: Shedding new light on habitat characterization and modeling. Frontiers in Ecology and the Environment 2008, 6:90-98.
73. Walters J.R., Daniels S.J., Carter J.H., Doerr P.D. Defining quality of red-cockaded woodpecker foraging habitat based on habitat use and fitness. The Journal of Wildlife Management 2002, 66:1064-1082.
74. Weakland C.A., Wood P.B. Cerulean warbler (Dendroica cerulea) microhabitat and landscape-level habitat characteristics in southern West Virginia. The Auk 2005, 122:497-508.
75. Williams G.J. Rattle: A data mining GUI for R. The R Journal 2009, 1:45-55.
76. Wulder M.A., White J.C., Nelson R.F., Næsset E., Orka H.O., Coops N.C., et al. Lidar sampling for large-area forest characterization: A review. Remote Sensing of Environment 2012, 121:196-209.
77. Zimble D.A., Evans D.L., Carlson G.C., Parker R.C., Grado S.C., Gerard P.D. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment 2003, 87:171-182.