Airborne laser scanning for modelling understory shrub abundance and productivity

Forest Ecology and Management

Volumn 377, Issue , 2016, Pages 46-54

  Barber, Quinn E ^a   Bater, Christopher W ^b   Braid, Andrew C R ^a   Coops, Nicholas C ^c   Tompalski, Piotr ^c   Nielsen, Scott E ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b Forest Management Branch   (Canada)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Airborne laser scanning (ALS); Grizzly bear (Ursus arctos); Light detection and ranging (LiDAR); Species habitat modelling; Understory vegetation

Indexed keywords

DECISION TREES; ECOSYSTEMS; FORESTRY; FRUITS; LASER APPLICATIONS; OPTICAL RADAR; PRODUCTIVITY; SCANNING; SURFACE ANALYSIS; VEGETATION;

AIRBORNE LASER SCANNING; GRIZZLY BEAR; HABITAT MODELLING; LIGHT DETECTION AND RANGING; UNDERSTORY VEGETATION;

CLIMATE MODELS;

ABUNDANCE; AIRBORNE SENSING; BEAR; DATA PROCESSING; ECOSYSTEM MODELING; FRUIT PRODUCTION; HABITAT QUALITY; HABITAT TYPE; LANDSCAPE; LASER; LIDAR; SHRUB; STAND STRUCTURE; UNDERSTORY; VEGETATION STRUCTURE;

ALBERTA; CANADA;

AMELANCHIER ALNIFOLIA; SHEPHERDIA; SHEPHERDIA CANADENSIS; URSUS ARCTOS; VACCINIUM MEMBRANACEUM;

EID: 84976525079     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2016.06.037     Document Type: Article

References (50)

1. Arnason, T., Hebda, R.J., Johns, T., Use of plants for food and medicine by Native Peoples of eastern Canada. Can. J. Bot. 59 (1981), 2189–2325, 10.1139/b81-287.
2. Bater, C.W., Wulder, M.A., Coops, N.C., Nelson, R.F., Hilker, T., Nasset, E., Stability of sample-based scanning-LiDAR-derived vegetation metrics for forest monitoring. IEEE Trans. Geosci. Remote Sens. 49 (2011), 2385–2392, 10.1109/TGRS.2010.2099232.
3. Braid, A.C.R., Nielsen, S.E., Prioritizing sites for protection and restoration for grizzly bears (Ursus arctos) in southwestern Alberta, Canada. PLoS ONE 10 (2015), 1–15, 10.1371/journal.pone.0132501.
4. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32, 10.1023/A:1010933404324.
5. Brown, M.J., Parker, G.G., Canopy light transmittance in a chronosequence of mixed-species deciduous forests. Can. J. For. Res. 24 (1994), 1694–1703, 10.1139/x94-219.
6. Chen, H.Y.H., Klinka, K., Height growth models for high-elevation subalpine fir, Engelmann Spruce, and Lodgepole Pine in British Columbia. West. J. Appl. For. 15 (2000), 62–69.
7. 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 Sens. Environ. 112 (2008), 2064–2073, 10.1016/j.rse.2007.08.023.
8. Coops, N.C., Hilker, T., Wulder, M.A., St-Onge, B., Newnham, G., Siggins, A., (Tony) Trofymow, J.A., Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 21 (2007), 295–310, 10.1007/s00468-006-0119-6.
9. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J., Random forests for classification in ecology. Ecology 88 (2007), 2783–2792, 10.1890/07-0539.1.
10. Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J., Pasteris, P.P., Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28 (2008), 2031–2064, 10.1002/joc.1688.
11. Davies, A.B., Asner, G.P., Advances in animal ecology from 3D-LiDAR ecosystem mapping. Trends Ecol. Evol. 29 (2014), 681–691, 10.1016/j.tree.2014.10.005.
12. Díaz-Uriarte, R., de Andrés, S. Alvarez, Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, 2006, 3, 10.1186/1471-2105-7-3.
13. Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.E., Hudak, A.T., Characterizing forest succession with lidar data: an evaluation for the Inland Northwest, USA. Remote Sens. Environ. 113 (2009), 946–956, 10.1016/j.rse.2009.01.003.
14. Feldhamer, G.A., Thompson, B.C., Chapman, J.A., Wild Mammals of North America: Biology, Management, and Conservation. 2003, John Hopkins University Press, Baltimore, Maryland, USA.
15. Government of Alberta, 2005. Alberta Vegetation Inventory Interpretation Standards, Version 2.1.1. Resource Information Management Branch, Alberta Sustainable Resource Development, Government of Alberta, Edmonton, Alberta, Canada.
16. Haeussler, S., Bedford, L., Boateng, J.O., MacKinnon, A., Plant community responses to mechanical site preparation in northern interior British Columbia. Can. J. For. Res. 29 (1999), 1084–1100, 10.1139/x99-057.
17. Hamer, D., Herrero, S., Wildfire's Influence on grizzly bear feeding ecology in Banff National Park, Alberta. Bears Their Biol. Manage. 7 (1987), 179–186, 10.2307/3872624.
18. Hamer, D., Herrero, S., Brady, K., Food and habitat used by grizzly bears, Ursus arctos, along the continental divide in Waterton Lakes National Park, Alberta. Can. Field-Naturalist. Ottawa 105 (1991), 325–329, 10.1644/05-MAMM-A-410R3.1.
19. Hamilton, E.H., Yearsley, H.K., 1988. Vegetation development after clearcutting and site preparation in the SBS Zone. B.C. Min. For. FRDA Rep. 018, Victoria, B.C.
20. Korpela, I., Hovi, A., Morsdorf, F., Understory trees in airborne LiDAR data – selective mapping due to transmission losses and echo-triggering mechanisms. Remote Sens. Environ. 119 (2012), 92–104, 10.1016/j.rse.2011.12.011.
21. Korpela, I.S., Mapping of understory lichens with airborne discrete-return LiDAR data. Remote Sens. Environ. 112 (2008), 3891–3897, 10.1016/j.rse.2008.06.007.
22. Kuhn, M., Caret package. J. Stat. Softw., 2008, 28.
23. Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J., Lidar remote sensing for ecosystem studies. Bioscience, 52, 2002, 19, 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2.
24. Lefsky, M.A., Harding, D., Cohen, W.B., Parker, G., Shugart, H.H., Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens. Environ. 67 (1999), 83–98, 10.1016/S0034-4257(98)00071-6.
25. Liaw, A., Wiener, M., Classification and regression by randomForest. R News 2 (2002), 18–22.
26. Martin, P., Factors influencing globe huckleberry fruit production in northwestern Montana. Bears Their Biol. Manage. 5 (1983), 159–165, 10.2307/3872533.
27. Martinuzzi, S., Vierling, L.A., Gould, W.A., Falkowski, M.J., Evans, J.S., Hudak, A.T., Vierling, K.T., Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability. Remote Sens. Environ. 113 (2009), 2533–2546, 10.1016/j.rse.2009.07.002.
28. Mattson, D.J., Blanchard, B.M., Knight, R.R., Yellowstone grizzly bear mortality, human habituation, and whitebark pine seed crops. J. Wildl. Manage., 56, 1992, 432, 10.2307/3808855.
29. McGaughey, R.J., FUSION/LDV: Software for LIDAR Data Analysis and Visualization. 2012, US Dep. Agric. For. Serv. Pacific Northwest Res. Stn, Seattle, WA, USA, 123.
30. McKenzie, D., Halpern, C.B., Nelson, C.R., Overstory influences on herb and shrub communities in mature forests of western Washington, U.S.A. Can. J. For. Res. 30 (2011), 1655–1666, 10.1139/x00-091.
31. Meentemeyer, R.K., Moody, A., Franklin, J., Landscape-scale patterns of shrub-species abundance in California chaparral – the role of topographically mediated resource gradients. Plant Ecol. 156 (2011), 19–41, 10.1023/A:1011944805738.
32. Minore, D., Smart, A.W., Dubrasich, M.E., 1979. Huckleberry ecology and management research in the Pacific Northwest, Gen Tech. Rep. PNW-GTR-093. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Tech. Rep. PNW-GTR-093. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station.
33. Moss, E.H., Flora of Alberta. 1983, University of Toronto Press.
34. Munro, R.H.M., Nielsen, S.E., Price, M.H., Stenhouse, G.B., Boyce, M.S., Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87 (2006), 1112–1121, 10.1644/05-MAMM-A-410R3.1.
35. Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä, J., Maltamo, M., Nilsson, M., Olsson, H., Persson, Å., Söderman, U., Laser scanning of forest resources: the Nordic experience. Scand. J. For. Res. 19 (2004), 482–499, 10.1080/02827580410019553.
36. Nelson, R., Short, A., Valenti, M., Measuring biomass and carbon in Delaware using an airborne profiling LIDAR. Scand. J. For. Res. 19 (2006), 500–511, 10.1080/02827580410019508.
37. Nielsen, S.E., McDermid, G., Stenhouse, G.B., Boyce, M.S., Dynamic wildlife habitat models: seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears. Biol. Conserv. 143 (2010), 1623–1634, 10.1016/j.biocon.2010.04.007.
38. Nielsen, S.E., Munro, R.H.M., Bainbridge, E.L., Stenhouse, G.B., Boyce, M.S., Grizzly bears and forestry: II. Distribution of grizzly bear foods in clearcuts of west-central Alberta. Canada. For. Ecol. Manage. 199 (2004), 67–82, 10.1016/j.foreco.2004.04.015.
39. Nijland, W., Coops, N.C., Ellen Macdonald, S., Nielsen, S.E., Bater, C.W., John Stadt, J., Comparing patterns in forest stand structure following variable harvests using airborne laser scanning data. For. Ecol. Manage. 354 (2015), 272–280, 10.1016/j.foreco.2015.06.005.
40. Nijland, W., Nielsen, S.E., Coops, N.C., Wulder, M.A., Stenhouse, G.B., Fine-spatial scale predictions of understory species using climate- and LiDAR-derived terrain and canopy metrics. J. Appl. Remote Sens., 8, 2014, 16, 10.1117/1.JRS.8.083572.
41. R Core Team, 2015. R: A Language and Environment for Statistical Computing.
42. Roberts, D.R., Nielsen, S.E., Stenhouse, G.B., Idiosyncratic responses of grizzly bear habitat to climate change based on projected food resource changes. Ecol. Appl. 24 (2014), 1144–1154, 10.1890/13-0829.1.
43. Singh, K.K., Davis, A.J., Meentemeyer, R.K., Detecting understory plant invasion in urban forests using LiDAR. Int. J. Appl. Earth Obs. Geoinf. 38 (2015), 267–279, 10.1016/j.jag.2015.01.012.
44. Solberg, S., Næsset, E., Hanssen, K.H., Christiansen, E., Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens. Environ. 102 (2006), 364–376, 10.1016/j.rse.2006.03.001.
45. Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., Steininger, M., Remote sensing for biodiversity science and conservation. Trends Ecol. Evol. 18 (2003), 306–314, 10.1016/S0169-5347(03)00070-3.
46. Wang, T., Hamann, A., Spittlehouse, D.L., Murdock, T.Q., ClimateWNA—high-resolution spatial climate data for western North America. J. Appl. Meteorol. Climatol. 51 (2012), 16–29, 10.1175/JAMC-D-11-043.1.
47. White, J.C., Wulder, M.A., Varhola, A., Vastaranta, M., Coops, N.C., Cook, B.D., Pitt, D., Woods, M., A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. For. Chron. 89 (2013), 722–723, 10.5558/tfc2013-132.
48. Wing, B.M., Ritchie, M.W., Boston, K., Cohen, W.B., Gitelman, A., Olsen, M.J., Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sens. Environ. 124 (2012), 730–741, 10.1016/j.rse.2012.06.024.
49. Wulder, M.A., Bater, C.W., Coops, N.C., Hilker, T., White, J.C., The role of LiDAR in sustainable forest management. For. Chron. 84 (2008), 807–826, 10.5558/tfc84807-6.
50. Wulder, M.A., Coops, N.C., Hudak, A.T., Morsdorf, F., Nelson, R., Newnham, G., Vastaranta, M., Status and prospects for LiDAR remote sensing of forested ecosystems. Can. J. Remote Sens. 39 (2013), S1–S5, 10.5589/m13-051.