Multi-sensor modelling of a forest productivity index for radiata pine plantations

New Zealand Journal of Forestry Science

Volumn 46, Issue 1, 2016, Pages

  Watt, Michael S ^a   Dash, Jonathan P ^a   Watt, Pete ^b   Bhandari, Santosh ^b  

^a SCION   (New Zealand)

^b Indufor Asia Pacific Ltd   (New Zealand)

Author keywords

300 Index; ALS; Climate; Geospatial layers; LiDAR; Plantation forestry; Productivity layers; RapidEye; Satellite imagery

Indexed keywords

ALUMINUM; FORESTRY; MEAN SQUARE ERROR; NEAREST NEIGHBOR SEARCH; PRODUCTIVITY; SATELLITE IMAGERY;

300 INDEX; CLIMATE; GEO-SPATIAL; PLANTATION FORESTRY; RAPIDEYE;

OPTICAL RADAR;

CONIFEROUS FOREST; FOREST MANAGEMENT; FOREST RESOURCE; LIDAR; PLANTATION FORESTRY; PRODUCTIVITY; REMOTE SENSING; SATELLITE IMAGERY;

NEW ZEALAND; NORTH ISLAND;

PINUS RADIATA; RADIATA;

EID: 84965121604     PISSN: 00480134     EISSN: 11795395     Source Type: Journal    
DOI: 10.1186/s40490-016-0065-z     Document Type: Article

References (78)

1. Aertsen, W, Kint, V, van Orshoven, J, Ozkan, K, & Muys, B (2010). Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecological Modelling, 221(8), 1119–1130. doi:10.1016/j.ecolmodel.2010.01.007.
2. Baraldi, A, & Parmiggiani, F (1995). An investigation of the textural characteristics associated with gray-level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293–304. doi:10.1109/36.377929.
3. Bollandsås, OM, Maltamo, M, Gobakken, T, & Næsset, E. (2013). Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest. Forestry, 86(4), 493–501.
4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
5. Brosofske, KD, Froese, RE, Falkowski, MJ, & Banskota, A (2014). A review of methods for mapping and prediction of inventory attributes for operational forest management. Forest Science, 60(4), 733–756. doi:10.5849/forsci.12-134.
6. Coops, NC, Hilker, T, Wulder, MA, St-Onge, B, Newnham, G, Siggins, A, et al. (2007). Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR. Trees-Structure and Function, 21, 295–310.
7. Crookston, NL, & Finley, AO. (2008). yaImpute: an R package for κNN imputation. Journal of Statistical Software, 23(10), 1–16.
8. Dash, JP, Marshall, HM, & Rawley, B (2015). Methods for estimating multivariate stand yields and errors using k-NN and aerial laser scanning. Forestry, 88(2), 237–247. doi:10.1093/forestry/cpu054.
9. Dash, JP, Watt, MS, Bhandari, S, & Watt, P. (2016) Characterising forest structure using combinations of airborne laser scanning data, RapidEye satellite imagery, and environmental variables. Forestry, 89(2), 159–169. doi:10.1093/forestry/cpv048.
10. Donoghue, DNM, & Watt, PJ. (2006). Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests. International Journal of Remote Sensing, 27(11), 2161–2175.
11. Ghosh, AK. (2006). On optimum choice of k in nearest neighbor classification. Computational Statistics and Data Analysis, 50(11), 3113–3123.
12. Haralick, RM, Shanugam, K, Dinstein, I. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics (Volume:SMC-3, Issue 6), pages 610–621 doi:10.1109/TSMC.1973.4309314.
13. Hewitt, AE. (1993). New Zealand Soil Classification (Landcare Research Science Series 1): Manaaki Whenua-Landcare Research.
14. Holmgren, J, Nilsson, M, & Olsson, H. (2003). Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Science, 49, 419–428.
15. Hunter, IR, & Gibson, AR. (1984). Predicting Pinus radiata site index from environmental variables. New Zealand Journal of Forestry Science, 14(1), 53–64.
16. Hyyppä, J, Kelle, O, Lehikoinen, M, & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39, 969–975.
17. Jackson, DS, & Gifford, HH. (1974). Environmental variables influencing the increment of radiata pine. (1) Periodic volume increment. New Zealand Journal of Forestry Science, 4(1), 3–26.
18. Kayitakire, F, Hamel, C, & Defourny, P (2006). Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102(3–4), 390–401. doi:10.1016/j.rse.2006.02.022.
19. Kimberley, MO, West, G, Dean, M, & Knowles, L. (2005). The 300 Index—a volume productivity index for radiata pine. New Zealand Journal of Forestry, 50, 13–18.
20. Kirschbaum, MUF, & Watt, MS (2011). Use of a process-based model to describe spatial variation in Pinus radiata productivity in New Zealand. Forest Ecology and Management, 262(6), 1008–1019. doi:10.1016/j.foreco.2011.05.036.
21. Leathwick, JR, & Stephens, RTT. (1998). Climate surfaces for New Zealand (Landcare Res. Contract Report LC9798/126, p. 19). Lincoln: Landcare Research.
22. Leathwick, J, Morgan, F, Wilson, G, Rutledge, D, McLeod, M, & Johnston, K. (2002). Land environments of New Zealand: a technical guide (p. 184). Hamilton: Ministry for the Environment, Wellington, and Manaaki Whenua Landcare Research.
23. Leathwick, J, Wilson, G, Rutledge, D, Wardle, P, Morgan, F, Johnston, K, et al. (2003). Land environments of New Zealand. Hamilton: Ministry for the Environment, Wellington, and Manaaki Whenua Landcare Research.
24. Liaw, A, & Wiener, M. (2012). Breiman and Cutler’s random forests for classification and regression (46-7th ed.).
25. Lim, KS, & Treitz, PM. (2004). Estimation of aboveground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research, 19, 558–570.
26. Lu, D (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12), 2509–2525. doi:10.1080/01431160500142145.
27. Magnussen, S, & Tomppo, E. (2014). The k-nearest neighbor technique with local linear regression. Scandinavian Journal of Forest Research, 29(2), 120–131.
28. Maltamo, M, Peuhkurinen, J, Malinen, J, Vauhkonen, J, Packalén, P, & Tokola, T. (2009). Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica, 43, 507–521.
29. McRoberts, RE. (2012). Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management, 272, 3–12.
30. McRoberts, RE, & Tomppo, E. (2007). Remote sensing support for national forest inventories. Remote Sensing of Environment, 110, 412–419.
31. McRoberts, RE, Næsset, E, & Gobakken, T. (2015). Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. Remote Sensing of Environment, 163, 13–22.
32. Means, JE, Acker, SA, Harding, DJ, Blair, JB, Lefsky, MA, Cohen, WB, et al. (1999). Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment, 67(3), 298–308.
33. Means, JE, Acker, SA, Fitt, BJ, Renslow, M, Emerson, L, & Hendrix, CJ. (2000). Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric Engineering and Remote Sensing, 66(11), 1367–1371.
34. Melville, G, Stone, C, & Turner, R. (2015). Application of LiDAR data to maximise the efficiency of inventory plots in softwood plantations. New Zealand Journal of Forestry Science, 45, 9.
35. Mitchell, ND. (1991). The derivation of climate surfaces for New Zealand, and their application to the bioclimatic analysis of the distribution of kauri (Agathis australis). Journal of the Royal Society of New Zealand, 21, 13–24.
36. Moeur, M, & Stage, A. (1995). Most similar neighbour: an improved sampling inference procedure for natural resource planning. Forest Science, 41, 337–359.
37. Mohamed, A, Reich, RM, Khosla, R, Aguirre-Bravo, C, & Briseno, MM (2014). Influence of climatic conditions, topography and soil attributes on the spatial distribution of site productivity index of the species rich forests of Jalisco, Mexico. Journal of Forestry Research, 25(1), 87–95. doi:10.1007/s11676-014-0434-5.
38. Mora, B, Wulder, MA, White, JC, & Hobart, G (2013). Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LiDAR. Remote Sensing, 5(5), 2308–2326. doi:10.3390/Rs5052308.
39. Næsset, E (2002). Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment, 80(1), 88–99. doi:10.1016/S0034-4257(01)00290-5.
40. Næsset, E (2004). Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scandinavian Journal of Forest Research, 19(2), 164–179. doi:10.1080/02827580310019257.
41. Næsset, E (2005). Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sensing of Environment, 98(2–3), 356–370. doi:10.1016/j.rse.2005.07.012.
42. Næsset, E, & Økland, T. (2002). Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment, 79(1), 105–115.
43. Nilsson, M. (1996). Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sensing of Environment, 56(1), 1–7.
44. Nord-Larsen, T, & Schumacher, J. (2012). Estimation of forest resources from a country wide laser scanning survey and national forest inventory data. Remote Sensing of Environment, 119, 148–157. doi:10.1016/j.rse.2011.12.022.
45. Ozdemir, I (2008). Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 29(19), 5643–5655. doi:10.1080/01431160802082155.
46. Packalén, P, & Maltamo, M. (2006). Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. Forest Science, 52(6), 611–622.
47. Packalén, P, Mehtätalo, L, & Maltamo, M. (2011). ALS-based estimation of plot volume and site index in a eucalyptus plantation with a nonlinear mixed-effect model that accounts for the clone effect. Annals of Forest Science, 68(6), 1085–1092.
48. Palmer, DJ. (2008). Development of national extent terrain attributes (TANZ), soil water balance surfaces (SWatBal), and environmental surfaces, and their application for spatial modelling of Pinus radiata productivity across New Zealand (PhD thesis, p. 410). Hamilton: University of Waikato.
49. Palmer, D, Höck, B, Kimberley, M, Watt, M, Lowe, D, & Payn, T (2009a). Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand. Forest Ecology and Management, 258(9), 2046–2055. doi:10.1016/j.foreco.2009.07.057.
50. Palmer, DJ, Höck, B, Kimberley, MO, Watt, MS, Lowe, DJ, & Payn, TW. (2009b). Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand. Forest Ecology and Management, 258(9), 2046–2055.
51. Palmer, DJ, Watt, MS, Höck, BK, & Lowe, DJ. (2009). A dynamic framework for spatial modelling Pinus radiata soil water balance (SWatBal) across New Zealand. Scion Bulletin, 234, 93.
52. Penner, M, Pitt, D, & Woods, M. (2013). Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario. Canadian Journal of Remote Sensing, 39(5), 426–443.
53. Pierce, KB, Ohmann, JL, Wimberly, MC, Gregory, MJ, & Fried, JS. (2009). Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods. Canadian Journal of Forest Research, 39, 1901–1916.
54. Popescu, SC, Wynne, RH, & Scrivani, JA. (2004). Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA. Forest Science, 50, 551–565.
55. R Development Core Team. (2014). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org/.
56. RapidEye AG. (2011). Satellite imagery product specifications. 3.2 edn. RapidEye AG. http://www.planet.com/assets/themes/planet/pdf/1601.RapidEye.Image.Product.Specs_Jan16_V6.1_ENG.pdf.
57. Rombouts, J, Ferguson, IS, & Leech, JW. (2010). Campaign and site effects in LiDAR prediction models for site-quality assessment of radiata pine plantations in South Australia. International Journal of Remote Sensing, 31(5), 1155–1173.
58. Sabatia, CO, & Burkhart, HE (2014). Predicting site index of plantation loblolly pine from biophysical variables. Forest Ecology and Management, 326, 142–156. doi:10.1016/j.foreco.2014.04.019.
59. Saremi, H, Kumar, L, Stone, C, Melville, G, & Turner, R. (2014). Sub-compartment variation in tree height, stem diameter and stocking in a Pinus radiata D.Don plantation examined using airborne LiDAR data. Remote Sensing, 6(8), 7592–7609.
60. SAS-Institute-Inc. (2008). SAS/STAT 9.2 User’s Guide. Cary: SAS Institute Inc.
61. Shamsoddini, A, Trinder, JC, & Turner, R (2013). Pine plantation structure mapping using WorldView-2 multispectral image. International Journal of Remote Sensing, 34(11), 3986–4007. doi:10.1080/01431161.2013.772308.
62. Sharma, RP, Brunner, A, & Eid, T (2012). Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway. Scandinavian Journal of Forest Research, 27(7), 619–636. doi:10.1080/02827581.2012.685749.
63. Skovsgaard, JP, & Vanclay, JP. (2008). Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands. Forestry, 81, 13–31.
64. Solberg, AHS (1999). Contextual data fusion applied to forest map revision. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1234–1243. doi:10.1109/36.763280.
65. Tait, A, Henderson, R, Turner, R, & Zheng, Z. (2006). Thin plate smoothing interpolation of daily rainfall for New Zealand using a climatological rainfall surface. International Journal of Climatology, 26, 2097–2115.
66. Tomppo, E, & Katila, M. (1991). Satellite image-based national forest inventory of finland for publication in the igarss' 91 digest. In Geoscience and Remote Sensing Symposium, 1991. IGARSS'91. Remote Sensing: Global Monitoring for Earth Management., International (Vol. 3, pp. 1141–1144). IEEE.
67. Tuominen, S, & Pekkarinen, A (2005). Performance of different spectral and textural aerial photograph features in multi-source forest inventory. Remote Sensing of Environment, 94(2), 256–268. doi:10.1016/j.rse.2004.10.001.
68. Véga, C, & St-Onge, B. (2009). Mapping site index and age by linking a time series of canopy height models with growth curves. Forest Ecology and Management, 257(3), 951–959.
69. Watt, MS, & Palmer, DJ (2012). Use of regression kriging to develop a Carbon:Nitrogen ratio surface for New Zealand. Geoderma, 183, 49–57. doi:10.1016/j.geoderma.2012.03.013.
70. Watt, PJ, & Watt, MS. (2013). Development of a national model of tree volume from LiDAR metrics for New Zealand. Remote Sensing o\f Environment, 34, 5892–5904.
71. Watt, M, Palmer, D, Kimberley, M, Hock, B, Payn, T, & Lowe, D (2010). Development of models to predict Pinus radiata productivity throughout New Zealand. Canadian Journal of Forest Research, 40(3), 488–499. doi:10.1139/X09-207.
72. Watt, MS, Meredith, A, Watt, P, & Gunn, A. (2013). Use of LiDAR to estimate stand characteristics for thinning operations in young Douglas-fir plantations. New Zealand Journal of Forestry Science, 43(1), 18.
73. Watt, P, Meredith, A, Yang, C, & Watt, MS. (2013). Development of regional models of Pinus radiata height from GIS spatial data supported with supplementary satellite imagery. New Zealand Journal of Forestry Science, 43(1), 11.
74. Watt, MS, Meredith, A, Watt, P, & Gunn, A. (2014). The influence of LiDAR pulse density on the precision of inventory metrics in young unthinned Douglas-fir stands during initial and subsequent LiDAR acquisitions. New Zealand Journal of Forestry Science, 44, 18.
75. Watt, MS, Dash, JP, Bhandari, S, & Watt, P (2015). Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning. Forest Ecology and Management, 357, 1–9. doi:10.1016/j.foreco.2015.08.001.
76. West, G, Moore, JR, Shula, RG, Harrington, JJ, Snook, J, Gordon, JA, et al. (2013). Forest management DSS development in New Zealand. Paper presented at the Implementation of DSS Tools into the Forestry Practice. Slovakia: Technical University of Zvolen.
77. Zhou, GM, Xu, XJ, Du, HQ, Ge, HL, Shi, YJ, & Zhou, YF. (2011). Estimating aboveground carbon of Moso bamboo forests using the k nearest neighbors technique and satellite imagery. Photogrammetric Engineering and Remote Sensing, 77(11), 1123–1131. Accessed 4 February 2015
78. Zvoleff, A. (2014). glcm: Calculate textures from grey-level co-occurrence matrices GLCMs in R. R package version 1.0. http://CRAN.R-project.org/package=glcm. Accessed 4 February 2015.