Application of LiDAR data to maximise the efficiency of inventory plots in softwood plantations

New Zealand Journal of Forestry Science

Volumn 45, Issue 1, 2015, Pages

  Melville, Gavin ^a   Stone, Christine ^b   Turner, Russell ^c  

^a NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^b NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^c Remote Census Pty Ltd   (Australia)

Author keywords

Design based sampling; Inventory surveys; LiDAR; Model based sampling; Sampling efficiency

Indexed keywords

EFFICIENCY; FORESTRY; INFORMATION USE; MEAN SQUARE ERROR; POPULATION STATISTICS; SAMPLING; SOFTWOODS; SURVEYS; TIMBER;

AUXILIARY INFORMATION; LIGHT DETECTION AND RANGING; MODEL-BASED OPC; NEW SOUTH WALES , AUSTRALIA; POTENTIAL EFFICIENCY; REGRESSION MODELLING; ROOT MEAN SQUARED ERRORS; SAMPLING EFFICIENCY;

OPTICAL RADAR;

DESIGN METHOD; ESTIMATION METHOD; FOREST INVENTORY; FOREST MANAGEMENT; LIDAR; PLANTATION FORESTRY; SAMPLING;

INVENTORIES; PLANTATIONS; REGRESSION ANALYSIS; SOFTWOODS; STRATIFICATION;

AUSTRALIA; NEW SOUTH WALES;

PINUS RADIATA;

EID: 84930617712     PISSN: 00480134     EISSN: 11795395     Source Type: Journal    
DOI: 10.1186/s40490-015-0038-7     Document Type: Article

References (50)

1. Álvarez J, Allen HL, Albaugh TJ, Stape JL, Bullock BP, & Song C. (2013). Factors influencing the growth of radiata pine plantations in Chile. Forestry, 86, 13–26. doi:10.1093/forestry/cps072.
2. Chen Y, & Zhu X. (2012). Site quality assessment of a Pinus radiata plantation in Victoria, Australia, using LiDAR technology. Southern Forests, 74, 217–227. doi:10.2989/20702620.2012.741767.
3. Chen Y, & Zhu X. (2013). An integrated GIS tool for automatic forest inventory estimates of Pinus radiata from LiDAR. GIScience & Remote Sensing, 50, 667–689. doi:10.1080/15481603.2013.866783.
4. Cochran W. (1977). Sampling Techniques. New York: John Wiley and Sons.
5. Corona P, & Fattorini L. (2008). Area-based lidar-assisted estimation of forest standing volume. Canadian Journal of Forest Research, 38, 2911–2916. doi:10.1139/X08-122.
6. Dalponte M, Martinez C, Rodeghiero M, & Gianelle D. (2011). The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 787–797. doi:10.1016/j.isprsjprs.2011.09.003.
7. Deville JC, & Tillé Y. (2004). Efficient balanced sampling: the cube method. Biometrika, 91, 893–912.
8. Ene LT, Næsset E, Gobakken T, Gregoire TG, Ståhl G, & Holm S. (2013). A simulation approach for accuracy assessment of two-phase post-stratified estimation in large-area LiDAR biomass surveys. Remote Sensing of Environment, 133, 210–224. Available from http://dx.doi.org/10.1016/j.rse.2013.02.002 (Accessed 28 April 2015).
9. Foreman EK. (1991). Survey Sampling Principles. New York: Marcel Dekker, Inc.
10. Gilabert H, & McDill ME. (2010). Optimizing inventory and yield data collection for forest management planning. Forest Science, 56, 578–591.
11. Gilmour AR, Thompson R, & Cullis BR. (1995). Average Information REML, an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics, 51, 1440–1450.
12. Gobakken T., Korhonen L. & Næsset E. (2013) Laser-assisted selection of field plots for an area-based forest inventory. Silva Fennica, 47(5), article id 943. http://dx.doi.org/10.14214/sf.943 (Accessed 28 April 2015)
13. González-Ferreiro E, Diéguez-Aranda U, & Miranda D. (2012). Estimation of stand variables in Pinus radiata D.Don plantations using different LiDAR pulse densities. Forestry, 85, 281–292. doi:10.1093/forestry/cps002.
14. Goulding CJ, & Lawrence ME. (1992). Inventory practice for managed forests (Forest Research Institute Bulletin 171, p. 52). New Zealand: Rotorua.
15. Grafström A. & Ringvall A.H. (2013) Improving forest field inventories by using remote sensing data in novel sampling designs. Canadian Journal Forest Research, 43, 1015–1022. dx.doi.org/10.1139/cjfr-2013-0123.
16. Grafström A., Saarela S. & Ene L.T. (2014) Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Canadian Journal of Forest Research, 44, 1156–1164. dx.doi.org/10.1139/cjfr-2014-0202.
17. Gregoire TG. (1998). Design-based and model-based inference in survey sampling: appreciating the difference. Canadian Journal of Forest Research, 28, 1429–1447.
18. Gregoire TG, Ståhl G, Næsset E, Gobakken T, Nelson R, & Holm S. (2011). Model-assisted estimation of biomass in a lidar sample survey in Hedmark count, Norway. Canadian Journal of Forest Research, 41, 83–95. doi:10.1139/X10-195.
19. Hansen MH, Madow WG, & Tepping BJ. (1983). An evaluation of model-dependent and probability-sampling inferences in sample surveys. Journal of the American Statistical Association, 78, 776–793. Available from http://www.jstor.org/stable/2288182 (Accessed 28 April 2015).
20. Hawbaker TJ, Keuler NS, Lesak AA, Gobakken T, & Contrucci, K. (2009). Improved estimates of forest vegetation structure and biomass with a lidar-optimized sampling design. Journal of Geophysical Research, 114: G00E04. doi:10.1029/2008jG000870.
21. Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, & Maltamo M. (2008). Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forest. International Journal of Remote Sensing, 29, 1339–1366. doi:10.1080/01431160701736489.
22. Hyyppä J, Yu X, Hyyppä H, Vastaranta M, Holopainen M, Kukko A, Kaartinen H, Jaakkola A, Vaaja M, Koskinen J, & Alho P. (2012). Advances in Forest InventoryUsing Airborne Laser Scanning. Remote Sensing, 4, 1190–1207. doi:10.3390/rs4051190.
23. Jakubowski M.K., Guo Q. & Kelly M. (2013) Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sensing of Environment, 130, 245–253. http://dx.doi.org/10.1016/j.rse.2012.11.024 (Accessed 28 April 2015).
24. Junttila V, Maltamo M, & Kauranne T. (2008). Sparse Bayesian estimation of forest stand characteristics from airborne laser scanning. Forest Science, 54, 543–552.
25. Junttila V, Kauranne T, & Leppänen V. (2010). Estimation of forest stand parameters from airborne laser scanning using calibrated plot databases. Forest Science, 56, 257–270.
26. Junttila V, Finley AO, Bradford JB, & Kauranne T. (2013). Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory. Forest Ecology and Management, 292, 75–85. Available from doi:10.1016/j.foreco.2012.12.019 (Accessed 28 April 2015).
27. Köhl M, Magnussen SS, & Marchetti M. (2006). Sampling methods, remote sensing and GIS multisource forestry inventory (p. 373). Heidelberg: Springer-Verlag.
28. Maltamo M, Bollandsås OM, Næsset E, Gobakken T, & Packalén P. (2009a) Different sampling strategies for field training plots in ALS inventory. In: Proceedings of the SilviLaser 2009: The 9th International Conference on Lidar Applications for Assessing Forest Ecosystems. Editors: S. Popescu and K. Zhao. Independent Publisher, USA. ISBN 9781616239978.
29. Maltamo M, Peuhkurinen J, Malinen J, Vauhkonen J, Packalén P, & Tokola T. (2009b). Predicting tree attributes and quality characteristics. Silva Fennica, 43, 507–521. Available from http://www.metla.fi/silvafennica/full/sf43/sf433507.pdf (Accessed 28 April 2015).
30. Maltamo M, Bollandsås OM, Næsset E, Gobakken T, & Packalén P. (2011). Different plot selection strategies for field training data in ALS-assisted forest inventory. Forestry, 84, 23–31. doi:10.1093/forestry/cpq039.
31. McRoberts RE. (2006). A model-based approach to estimating forest area. Remote Sensing of Environment, 103, 56–66. doi:10.1016/j.rse.2006.03.005.
32. McRoberts RE. (2010). Probability- and model-based approaches to inference for proportion forest using satellite imagery and ancillary data. Remote Sensing of Environment, 114, 1017–1025. doi:10.1016/j.rse.2009.12.013.
33. McRoberts RE, Gobakken T, & Næsset E. (2012). Post-stratification of forest area and growing stock volume using lidar-based stratifications. Remote Sensing of Environment, 125, 157–166. doi:10.1016/j.rse.2012.07.002.
34. McRoberts RE, Næsset E, & Gobakken T. (2013). Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment, 128, 268–275. Available from http://dx.doi.org/10.1016/j.rse.2012.10.007 (Accessed 28 April 2015).
35. Melville GJ, Welsh AH and Stone C. (2015) Improving the efficiency and precision of tree counts in pine plantations using airborne LiDAR data and flexible-radius plots: model-based and design-based approaches. Journal of Agricultural, Biological and Environmental Statistics 20, 29pp.
36. Næsset E, Gobakken T, Solberg S, Gregoire TG, Nelson R, Ståhl G, et al. (2011). Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area. Remote Sensing of Environment, 115, 3599–3614. doi:10.1016/j.rse.2011.08.021.
37. Næsset E, Bollandsås OM, Gobakken T, Gregoire TG, & Ståhl G. (2013). Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide ``activity data". Remote Sensing of Environment, 128, 299–314. Available from http://dx.doi.org/10.1016/j.rse.2012.10.008 (Accessed 28 April 2015).
38. Rombouts J, Ferguson I, Leech J, & Culvenor D. (2010). An evaluation of the field sampling design of the first operational LiDAR based site quality survey of radiata pine plantations in South Australia. Freiburg, Germany: Proceedings of the 2011 Silvilaser conference, September 14–17, 2010.
39. Saremi H, Kumar L, Turner R, & Stone C. (2014). Airborne LiDAR derived canopy height model reveals a significant difference in radiata pine (Pinus radiata D.Don) heights based on slope and aspect of sites. Trees, 28, 733–744. doi:10.1007/s00468-014-0985-2.
40. Särndal C, Swensson B, & Wretman J. (1992). Model Assisted Survey Sampling. New York: Springer-Verlag.
41. Ståhl G, Holm S, Gregoire TG, Gobakken T, Næsset E, & Nelson R. (2011). Model-based inference for biomass estimation in a lidar sample survey in Hedmark County, Norway. Canadian Journal of Forest Research, 41, 96–107. doi:10.1139/X10-161.
42. Stone C, Penman T, & Turner R. (2011). Determining an optimal model for processing lidar data at the plot level: results for a Pinus radiata plantation in New South Wales, Australia. New Zealand Journal of Forestry Science, 41, 191–205.
43. Turner R, Kathuria A, & Stone C. (2011). Building a case for lidar-derived structure stratification for Australian softwood plantations. Hobart, Tasmania, Australia: Proceedings of the SilviLaser 2011 conference, Oct. 16–19, 2011.
44. Valliant R, Dorfman A, & Royall R. (2000). Finite population sampling and inference: a prediction approach. New York: John Wiley.
45. van Aardt JAN, Wynne RH, & Oderwald RG. (2006). Forest volume and biomass estimation using small-footprint lidar-distributional parameters on a per-segment basis. Forest Science, 52, 636–648.
46. Watt P, & Watt MS. (2013). Development of a national model of Pinus radiata stand volume from lidar metrics for New Zealand. International Journal of Remote Sensing, 34, 5892–5904. Available at http://dx.doi.org/10.1080/01431161.2013.798053 (Accessed 28 April 2015).
47. Watt MS, Adams T, Marshall H, Pont D, Lee J, Crawley D, & Watt P. (2013). Modelling variation in Pinus radiata stem volume and outerwood stress-velocity from LiDAR metrics. New Zealand Journal of Forestry Science, 43, 1–7. Available from http://www.nzjforestryscience.com/content/43/1/1 (Accessed 28 April 2015).
48. White JC, Wulder MA, Varhola A, Vastaranta M, Coops NC, Cook BD, Pitt D, & Woods M. (2013). A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Canadian Forest Service Canadian Wood Fibre Centre Information Report FI-X-010. The Forestry Chronicle, 89(6), 722–723.
49. Wulder MA, White JC, Nelson RF, Næsset E, Ørka HO, Coops NC, et al. (2012). Lidar sampling for large-area forest characterization: A review. Remote Sensing of Environment, 121, 196–209. doi:10.1016/j.rse.2012.02.001.
50. Yu, X, Hyyppä J, Holopainen M, & Vastaranta M. (2010). Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sensing, 2, 1481–1495. doi:10.3390/rs2061481.