Optimising nearest neighbour information—a simple, efficient sampling strategy for forestry plot imputation using remotely sensed data

Australian Forestry

Volumn 79, Issue 3, 2016, Pages 217-228

  Melville, G ^a   Stone, C ^b  

^a NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^b NSW DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

Author keywords

balanced sampling; LiDAR; Nearest centroid; nearest neighbour; plantation inventory; plot imputation; plot sampling design

Indexed keywords

OPTICAL RADAR; SURVEYS;

BALANCED SAMPLINGS; NEAREST CENTROID; NEAREST NEIGHBOUR; PLANTATION INVENTORY; PLOT IMPUTATION; SAMPLING DESIGN;

FORESTRY;

EID: 84983425589     PISSN: 00049158     EISSN: None     Source Type: Journal    
DOI: 10.1080/00049158.2016.1218265     Document Type: Article

References (36)

1. F.Baffetta, P.Corona, and L.Fattorini, (2011) Design-based diagnostics for k-NN estimators of forest resources. Canadian Journal of Forest Research 41, 59–72. doi:10.1139/X10-157
2. J.Breidenbach, A.Nothdurft, and G.Kändler, (2010) Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Research 129, 833–846. doi:10.1007/s10342-010-0384-1
3. D.J.Brus, (2015) Balanced sampling:a versatile sampling approach for statistical soil surveys. Geoderma 253–254, 111–121. doi:10.1016/j.geoderma.2015.04.009
4. W.Cochran, (1977) Sampling Techniques. John Wiley and Sons, New York, 428 pp.
5. J.P.Dash, H.M.Marshall, and B.Rawley, (2015) Methods for estimating multivariate stand yields and errors using k-NN and aerial laser scanning. Forestry 88, 237–247. doi:10.1093/forestry/cpu054
6. J.C.Deville, and Y.Tillé, (2004) Efficient balanced sampling:the cube method. Biometrika 91, 893–912. doi:10.1093/biomet/91.4.893
7. M.J.Falkowski, A.T.Hudak, N.L.Crookston, P.E.Gessler, E.H.Uebler, and A.M.S.Smith, (2010) Landscape-scale parameterization of a tree-level forest growth model:a k-nearest neighbour imputation approach incorporating LiDAR data. Canadian Journal of Forest Research 40, 184–199. doi:10.1139/X09-183
8. T.Gobakken, L.Korhonen, and E.Næsset, (2013) Laser-assisted selection of field plots for an area-based forest inventory. Silva Fennica 47, article id 943. doi:10.14214/sf.943
9. A.D.Gordon, and D.Pont, (2015) Inventory estimates of stem volume using nine sampling methods in thinned Pinus radiata stands, New Zealand. New Zealand Journal of Forestry Science 45, Article 8. doi:10.1186/s40490-015-0037-8
10. A.Grafström, (2012) Spatially correlated Poisson sampling. Journal of Statistical Planning and Inference 142, 139–147. doi:10.1016/j.jspi.2011.07.003
11. A.Grafström, and N.L.P.Lundström, (2013) Why well spread probability samples are balanced. Open Journal of Statistics 3, 36–41. doi:10.4236/ojs.2013.31005
12. A.Grafström, and H.Ringvall, (2013) Improving forest field inventories by using remote sensing data in novel sampling designs. Canadian Journal of Forest Research 43, 1015–1022. doi:10.1139/cjfr-2013-0123
13. A.Grafström, S.Saarela, and L.T.Ene, (2014) Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Canadian Journal of Forest Research 44, 1156–1164. doi:10.1139/cjfr-2014-0202
14. A.Grafström, and Y.Tillé, (2013) Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics 24, 120–131. doi:10.1002/env.v24.2
15. T.G.Gregoire, (1998) Design-based and model-based inference in survey sampling:appreciating the difference. Canadian Journal of Forest Research 28, 1429–1447. doi:10.1139/x98-166
16. J.A.Hartigan, and M.A.Wong, (1979) Algorithm AS 136:a K-means clustering algorithm. Applied Statistics 28, 100–108. doi:10.2307/2346830
17. T.J.Hawbaker, N.S.Keuler, A.A.Lesak, T.Gobakken, K.Contrucci, and V.C.Radeloff, (2009) Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. Journal of Geophysical Research:Biogeosciences 114, GE00E04. doi:10.1029/2008JG000870
18. A.T.Hudak, A.T.Haren, N.L.Crookston, R.J.Liebermann, and J.L.Ohmann, (2014) Imputing forest structure attributes from stand inventory and remotely sensed data in western Oregon, USA. Forest Science 60, 253–269. doi:10.5849/forsci.12-101
19. V.Junttila, A.O.Finley, J.B.Bradford, and T.Kauranne, (2013) Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory. Forest Ecology & Management 292, 75–85. doi:10.1016/j.foreco.2012.12.019
20. H.Latifi, A.Nothdurft, and B.Koch, (2010) Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest:application of multiple optical/LiDAR-derived predictors. Forestry 83, 395–407. doi:10.1093/forestry/cpq022
21. S.M.Lesch, (2005) Sensor-directed response surface sampling designs for characterizing spatial variation in soil properties. Computers and Electronics in Agriculture 46, 153–179. doi:10.1016/j.compag.2004.11.004
22. M.Maltamo, O.M.Bollandsas, E.Næsset, T.Gobakken, and P.Packalen, (2011) Different plot selection strategies for field training data in ALS-assisted forest inventory. Forestry 84, 23–31. doi:10.1093/forestry/cpq039
23. R.E.McRoberts, (2010) Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data. Remote Sensing of Environment 114, 1017–1025. doi:10.1016/j.rse.2009.12.013
24. R.E.McRoberts, (2012) Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management 272, 3–12. doi:10.1016/j.foreco.2011.06.039
25. R.E.McRoberts, M.D.Nelson, and D.G.Wendt, (2002) Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique. Remote Sensing of Environment 82, 457–468. doi:10.1016/S0034-4257(02)00064-0
26. R.E.McRoberts, E.O.Tomppo, A.O.Finley, and J.Heikkinen, (2007) Estimating areal means and variances of forest attributes using the k-nearest neighbors technique and satellite imagery. Remote Sensing of Environment 111, 466–480. doi:10.1016/j.rse.2007.04.002
27. G.J.Melville, C.Stone, and R.Turner, (2015a) Application of LiDAR data to maximise the efficiency of inventory plots in softwood plantations. New Zealand Journal of Forestry Science 45. doi:10.1186/s40490–015–0038–7
28. G.J.Melville, A.H.Welsh, and C.Stone, (2015b) 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, 229–257. doi:10.1007/s13253-015-0205-6
29. R Development Core Team. (2013) R:a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Available from http://www.R-project.org/
30. W.M.Rand, (1971) Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66, 846–850. doi:10.1080/01621459.1971.10482356
31. B.Rawley, and D.Herries, (2005) PlotSafe:forest inventory data collection software (Version 1.4.0.2). Available from http://www.interpine.co.nz/SitePages/PlotSafe%20-%20Forest%20Inventory%20Data%20Collection%20Software.aspx
32. J.Rombouts, G.Melville, A.Kathuria, B.Rawley, and C.Stone, (2014) Operational deployment of LiDAR derived information into softwood resource systems. Final Report for Project PNC305-1213, 143 pp. Available from http://www.fwpa.com.au/rd-and-e/resources/611-operational-deployment-of-lidar-derived-information-into-softwood-resource-systems.html
33. D.L.Stevens Jr, and A.R.Olsen, (2004) Spatially balanced sampling of natural resources. Journal of the American Statistical Association 99, 262–278. doi:10.1198/016214504000000250
34. C.Stone, T.Penman, and R.Turner, (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.
35. C.Straub, C.Stepper, R.Seitz, and L.T.Wasere, (2013) Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests. Canadian Journal of Forest Research 43, 731–741. doi:10.1139/cjfr-2013-0125
36. R.Turner, A.Kathuria, and C.Stone, (2011) Building a case for LiDAR-derived structure stratification for Australian softwood plantations. In:Proceedings of the SilviLaser 2011 Conference, Oct 16–19, 2011, Hobart, Tasmania.