Mapping eucalypt forest susceptible to dieback associated with bell miners (Manorina melanophys) using laser scanning, SPOT 5 and ancillary topographical data

Ecological Modelling

Volumn 222, Issue 5, 2011, Pages 1174-1184

  Haywood, Andrew ^a   Stone, Christine ^b  

^a DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^b NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

Author keywords

Bell miners; Eucalypt forest dieback; Laser scanning; Predictive mapping; Spatial modelling; SPOT 5

Indexed keywords

EUCALYPT FOREST DIEBACK; LASER SCANNING; PREDICTIVE MAPPING; SPATIAL MODELLING; SPOT 5;

ADAPTIVE BOOSTING; ALGORITHMS; BELLS; CATCHMENTS; COMPUTER GRAPHICS; DECISION TREES; GEOGRAPHIC INFORMATION SYSTEMS; INFRARED RADIATION; LASER APPLICATIONS; MAPPING; MATHEMATICAL OPERATORS; MINERS; MOISTURE DETERMINATION; NEURAL NETWORKS; RUNOFF; SCANNING; SOIL MOISTURE; STATISTICAL METHODS; VEGETATION;

FORESTRY;

ABUNDANCE; ALGORITHM; CATCHMENT; COLONIZATION; DATA SET; DIEBACK; ECOLOGICAL MODELING; EVERGREEN FOREST; FOLIAGE; FOREST CANOPY; FOREST COVER; FOREST ECOSYSTEM; GIS; IMAGE ANALYSIS; MODEL TEST; MOISTURE CONTENT; PASSERINE; POPULATION DENSITY; POPULATION DISTRIBUTION; SOFTWARE; SOIL MOISTURE; SPATIAL ANALYSIS; SPOT; TOPOGRAPHIC EFFECT; UNDERSTORY; VEGETATION MAPPING;

ALGORITHMS; COMPUTER GRAPHICS; FORESTRY; GIS; MAPPING; PLANTS; RUNOFF; SCANNING; STATISTICAL METHODS;

AUSTRALIA;

AVES; MANORINA; MANORINA MELANOPHRYS; PSYLLIDAE;

EID: 79551566639     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2010.12.012     Document Type: Article

References (99)

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. Austin M. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 2007, 200:1-19.
3. Bell, S., Driscoll, C., 2006. Vegetation mapping of Watagans National Park and Jilliby State Conservation Area. Summary report to Parks and Wildlife Division, Department of Environment and Conservation. Eastcoast Flora Survey, Kotara Fair, NSW.
4. Blackburn G.A. Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery. Remote Sensing of Environment 2003, 82:311-321.
5. Binns D. Flora Survey Morisset Forestry District Central Region, New South Wales. Morisset Forestry District EIS Supporting Document No. 3 1996, State Forests of NSW Research Division, Sydney.
6. Bivand R.S. Using the R statistical data analysis language on GRASS 5.0 GIS database files. Computers and Geoscience 2000, 26:1043-1052.
7. Bivand R.S., Neteler M. Open source geocomputations: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems. Proceedings of the Fifth Conference on Geocomputation 2000.
8. Bradbury R.B., Hill R.A., Mason D.C., Hinsley S.A., Wilson J.D., Balzter H., Anderson G.Q.A., Whittingham M.J., Davenport I.J., Bellamy P.E. Modelling 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.
9. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees 1984, Wadsworth Int. Group.
10. Brieman L. Bagging predictor. Machine Learning 1996, 24:123-140.
11. Brieman L. Random forest. Machine Learning 2001, 45:5-32.
12. Bureau of Meteorology, 2008. Date accessed 10 July 2008. http://www.bom.gov.au/.
13. Cappo M., De'ath G., Boyle S., Aumend J., Olbrich R., Hoedt F., Perna C., Brunskill G. Development of a robust classifier of freshwater residence in barramundi (Lates calcarifer) life histories using elemental ratios in scales and boosted regression trees. Marine Freshwater Research 2005, 56:713-723.
14. Caudill M. Neural network training tips and techniques. Al Expert 1991, 6:56-61.
15. Chan J.C.-W., Paelinckx D. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airbourne hyperspectral imagery. Remote Sensing of Environment 2008, 112:2999-3011.
16. Chen J.M., Pavlic G., Brown L., Cihlar J., Leblanc S.G., White H.P., Hall R.J., Peddle D.R., King D.J., Trofymow J.A., Swift E., Van der Sanden J., Pellikka P.K.E. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment 2002, 80:165-184.
17. Chambers J.M., Hastie T.J. Statistical Models in S 1992, Wadsworth and Brooks/Cole, California.
18. Clarke M.F. The reproductive behaviour of the bell miner Manorina melanophrys. Emu 1988, 88:88-100.
19. Clarke M.F., Fitz-Gerald G.F. Spatial organisation of cooperatively breeding Bell Miner Manorina melanophrys. Emu 1994, 94:96-105.
20. Clarke M.F., Schedvin N. Removal of bell miners Manorina melanophrys from Eucalyptus radiata forest and its effect on avian diversity, psyllids and tree health. Biological Conservation 1999, 88:111-120.
21. Clawges R., Vierling K.T., Vierling L.A., Rowell E.M. The use of lidar remote sensing to estimate avian species diversity, density, and occurrence in a pine/aspen forest. Remote Sensing of Environment 2008, 112:2064-2073.
22. Coops N.C., Stone C., Culvenor D.S., Chisholm L. Assessment of crown condition in eucalypt vegetation by remotely sensed optical indices. Journal of Environmental Quality 2004, 33:956-964.
23. Coops N.C., Wulder M.A., White J.C. Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation. Remote Sensing of Environment 2006, 105:83-97.
24. Coops N.C., Hilker T., Wulder M.A., St-Onge B., Newnham G., Siggins A., Trofymow J.A. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees 2007, 21:295-310.
25. Crawford, M.M., Ham, J., Chen, Y., Ghosh, J., 2003. Random forests of binary hierarchical classifiers for analysis of hyperspectral data. IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. 27-28 Oct., pp. 337-345.
26. 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 2007, 88:2783-2792.
27. Dare A.J., McDonald P.G., Clarke M.F. The ecological context and consequences of colonisation of a site by bell miners (Manorina melanophrys). Wildlife Research 2007, 34:616-623.
28. De'ath G., Fabricius K. Classification and regression trees A powerful yet simple technique for ecological data analysis. Ecology 2000, 81:3178-3192.
29. De'ath G. Boosted trees for ecological modeling and prediction. Ecology 2007, 88:243-251.
30. Donoghue D.N.M., Watt P.J., Cox N.J., Wilson J. Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sensing of Environment 2007, 110:509-522.
31. Duggin J.A., Gentle C.B. Experimental evidence on the importance of disturbance intensity for invasion of Lantana camara L. in dry rainforest-open forest ecotones in north-eastern NSW Australia. Forest Ecology and Management 1998, 109:279-292.
32. Ewen J.G., Crozier R.H., Cassey P., Ward-Smith T., Painter J.N., Robertson R.J., Jones D.A., Clarke M.F. Faculative control of offspring sex in the cooperatively breeding bell miner Manorina melanophrys. Behavioral Ecology 2003, 14:57-164.
33. Ferrier S., Watson G., Pearce J., Drielsma M. Extended statistical approaches to modelling spatial pattern in biodiversity in north-east New South Wales. I. Species-level modelling. Biodiversity and Conservation 2002, 11:2275-2307.
34. Fettig C.J., Klepzig K.D., Billings R.F., Munson A.S., Nebeker T.E., Negron J.F., Nowak J.T. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States. Forest Ecology and Management 2007, 238:24-53.
35. Franklin J. Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 1995, 19:474-499.
36. Freeman, E., 2008. The PresenceAbsence: Package. http://cran.r-project.org/web/packages/PresenceAbsence/PresenceAbsence.pdf.
37. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System and Science 1997, 55:119-139.
38. Gislason P.O., Benediktsson J.A., Sveinsson J.R. Random forests for land cover classification. Pattern Recognition Letters 2006, 27:294-300.
39. 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 Environment 2007, 108:254-263.
40. Goodwin N.R., Coops N.C., Culvenor D.S. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sensing of Environment 2006, 103:140-152.
41. Guisan A., Zimmermann N. Predictive habitat distribution models in ecology. Ecological Modelling 2000, 135:147-186.
42. Guisan A., Edwards T.C., Hastie T. Generalised linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 2002, 157:89-100.
43. Hall R.J., Fernandes R.A., Hogg E.H., Brandt J.P., Butson C., Case B.S., Leblanc S.G. Relating aspen defoliation to changes in leaf area derived from field and satellite remote sensing data. Canadian Journal of Remote Sensing 2003, 29:299-313.
44. Ham J., Chen Y., Crawford M.M., Ghosh J. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 2005, 43:492-501.
45. Haralick R.M. Statistical and structural approaches to texture. Proceedings of the IEEE 1979, 67:786-804.
46. Harrell F.E. Regression Modeling Strategies, with Applications to Linear Models Survival Analysis and Logistic Regression 2001, Springer, New York.
47. Handbook of Australian New Zealand and Antarctic Birds, vol. 5. Tyrant-Flycatchers to Chats 2001, Oxford University Press, Melbourne.
48. Hill R.A., Thomson A.G. mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing 2005, 26:3763-3779.
49. Hornik, K., Buchta, C., Zeileis, A., 2007. Open-sourced machine learning - R meets Weka. Department of Statistics and Mathematics, Wirtschaftsuniversitat Wien, Research Report Series 50. Vienna, Austria.
50. Hudak A.T., Crookston N.L., Evans J.S., Hall D.E., Falkowski M.J. Nearest neighbour imputation of species-level, plot-scale forest structure attributes from IDAR data. Remote Sensing Environment 2008, 112:2232-2245.
51. Hyde P., Dubayah R., Peterson B.J.B., Hofton M., Hunsaker C., Knox R., Walker W. Mapping forest structure for wildlife habitat analysis using waveform lidar: validation of montane ecosystems. Remote Sensing of Environment 2005, 96:427-437.
52. Hyde P., Dubayah R., Walker W., Blair J.B., Hofton M., Hunsaker C. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment 2006, 102:63-73.
53. 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.
54. Jensen J.R. Introductory Digital Image Processing 2005, Upper Saddle River, Prentice Hall, NJ. 3rd ed.
55. Jurskis V. Eucalypt decline in Australia, and a general concept of tree decline and dieback. A review. Forest Ecology and Management 2005, 215:1-20.
56. Kemmerer E.P., Shields J.M., Tidemann C.R. High densities of bell miners Manorina melanophrys associated with reduced diversity of other birds in wet eucalypt forest: potential for adaptive management. Forest Ecology and Management 2008, 255:2094-2102.
57. Lawrence R.L., Wood S.D., Sheley R.L. Mapping invasive plants using hyperspectral imagery and Breiman and Cutler classifications (RandomForest). Remote Sensing of Environment 2006, 100:356-362.
58. Leathwick J.R., Elith J., Francis M.P., Hastie T., Taylor P. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series 2006, 321:267-281.
59. Lees B.G., Ritman K. Decision tree and rule induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environmental Management 1991, 15:823-831.
60. Lefsky M.A., Cohen W.B., Parker G.G., Harding D.J. LIDAR remote sensing for ecosystem studies. Bioscience 2002, 52:19-30.
61. Lek S., Guégan J.F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 1999, 120:65-73.
62. Longmore N.W. The Honeyeaters and their Allies of Australia 1991, Angus and Robertson and The National Photographic Index of Australian Wildlife, Sydney.
63. Lyon R.H., Runnalls R.G., Forwards G.Y. Territorial Bell Miners and other birds affecting population on insect prey. Science 1983, 221:1411-1413.
64. McCullagh P., Nelder J.A. Generalized Linear Models 1989, Chapman and Hall, New York, New York, USA. 2nd ed.
65. Maltamo M., Packalén P., Yu X., Eerikäinen K., Hyyppä J., Pitkänen J. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management 2005, 216:41-50.
66. Manel S., Williams H.C., Omerod S.J. Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology 2001, 38:921-931.
67. Monserud R.A., Leemans R. Comparing global vegetation maps with the Kappa statistics. Ecologiocal Modelling 1992, 62:275-293.
68. Murphy, C.L., 1993. Soil Landscapes of the Gosford-Lake Macquarie 1:100,000 Sheet Report. NSW Department of Conservation and Land Management, Sydney.
69. Næsset E., Økland T. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment 2002, 79:105-115.
70. Næsset E., Bjerknes K.-O. Estimating tree height and number of stems in young forests using airborne laser scanner data. Remote Sensing of Environment 2001, 78:328-340.
71. Næsset E., Gobakken T. Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment 2005, 96:453-465.
72. Neteler M., Mitasova H. Open Source GIS: A GRASS GIS Approach 2004, Kluer Academic Publishers/Springer, Boston. 2nd ed.
73. Pearce J., Menkhorst P., Burgman M.A. Niche overlap and competition for habitat between the helmeted honeyeater and the bell miner. Wildlife Research 1995, 22:633-646.
74. Pearce J., Minchin P.R. Vegetation of the Yellingbo Nature Conservation Reserve and its relationship to the distribution of the helmeted honeyeater, bell miner and white-eared honeyeater. Wildlife Research 2001, 28:41-52.
75. Pearce J., Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling 2000, 133:225-245.
76. Poiani A. Reproductive biology of bell miner (Manorina melanophrys Meliphagidae) at Healesville, south-eastern Victoria. Wildlife Research 1993, 20:579-5987.
77. R Development Core Team, 2005. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN3-900051-07-0.
78. Recknagel F. Applications of machine learning to ecological modelling. Ecological modelling 2001, 146:303-310.
79. Riaño D., Meier E., Allgöwer B., Chuvieco E., Ustin S.L. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behaviour modelling. Remote Sensing Environment 2003, 86:177-186.
80. Robertson M.P., Peter C.I., Villet M.H., Ripley B.S. Comparing models for predicting species potential distributions: a case study using correlative and mechanistic predictive modelling techniques. Ecological Modelling 2003, 164:153-167.
81. Rogan J., Franklin J., Stow D., Miller J., Woodcock C., Roberts D. Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sensing of Environment 2008, 112:2272-2283.
82. Rooker Jensen J.L., Humes K.S., Conner T., Williams C.J., DeGroot J. Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar. Canadian Journal of Forest Research 2006, 36:1129-1138.
83. Sampson P.H., Zarco-Tejada P.J., Mohammed G.H. Hyperspectral remote sensing of forest condition: estimating chlorophyll content in tolerant hardwoods. Forest Science 2003, 49:381-391.
84. Scrinzi G., Marzullo L., Galvagni D. Development of a neural network model to update forest distribution data for managed alpine stands. Ecological Modelling 2007, 206:331-346.
85. Sørensen R., Seibert J. Effects of DEM resolution on the calculation of topographical indices: TWI and its components. Journal of Hydrology 2007, 347:79-89.
86. Specht R.L. Foliage projective covers of overstorey and understorey strata of mature vegetation in Australia. Australian Journal of Ecology 1983, 8:433-439.
87. State Forests of NSW, 1995. Morisset Forestry District Environmental Impact Statement Main Report. State Forests of NSW, Sydney.
88. Stone C. The role of psyllids (Hemiptera: Psyllidae) and bell miners (Manorina melanophyrs) in canopy dieback of Sydney blue gum (Eucalypt saligna Sm.). Australian Journal of Ecology 1996, 21:450-458.
89. Stone C. Bell miner associated dieback at the tree crown scale: a multi-trophic process. Australian Forestry 2005, 68:237-241.
90. Stone C., Kathurial A., Carney C., Hunter J. Forest canopy health and stand structure associated with bell miners (Manorina melanophrys) on the central coast of New South Wales. Australian Forestry 2008, 71:294-302.
91. St-Onge B., Hu Y., Vega C. Mapping the height and above-ground biomass of a mixed forest using lidar and stereo Ikonos images. International Journal of Remote Sensing 2008, 29:1277-1294.
92. Suárez L., Zarco-Tejada P.J., Sepulcre-Cantó G., Pérez-Priego O., Miller J.R., Jiménez-Muñoz J.C., Sobrino J. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sensing of Environment 2008, 112:560-575.
93. Tickle P.K., Lee A., Lucas R.M., Austin J., Witte C. Quantifying Australian forest floristics and structure using smallfootprint LiDAR and large scale aerial photography. Forest Ecology and Management 2006, 223:379-394.
94. Vierling K.T., Vierling L.A., Gould W.A., Martinuzzi S., Clawges R.M. Lidar: Shedding new light on habitat characterization and modelling. Frontiers of Ecology and the Environment 2008, 6:90-98.
95. Wardell-Johnson G., Stone C., Recher H., Lynch A.J.J. A review of eucalypt dieback associated with bell miner habitat in south-eastern Australia. Australian Forestry 2005, 68:231-236.
96. Wintle B.A., Elith J., Potts J.M. Fauna habitat modelling and mapping: a review and case study in the Lower Hunter Central Coast region of NSW. Austral Ecology 2005, 30:719-728.
97. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Technology 2005, Elsevier, San Francisco. 2nd ed.
98. Wulder M.A., White J.C., Bentz B., Alvarez M.F., Coops N.C. Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment 2006, 101:150-166.
99. 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.