Estimating Stem Diameter Distributions in a Management Context for a Tolerant Hardwood Forest Using ALS Height and Intensity Data

Canadian Journal of Remote Sensing

Volumn 43, Issue 1, 2017, Pages 79-94

  Shang, Chen ^a   Treitz, Paul ^a   Caspersen, John ^b   Jones, Trevor ^c  

^a QUEEN S UNIVERSITY   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c ONTARIO MINISTRY OF NATURAL RESOURCES   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; HARDWOODS; NEAREST NEIGHBOR SEARCH;

AIRBORNE LASER SCANNING; DIAMETER DISTRIBUTIONS; FREQUENCY DISTRIBUTIONS; K-NEAREST NEIGHBORS; NON-PARAMETRIC MODEL; PREDICTIVE ACCURACY; PREDICTOR VARIABLES; SIMULTANEOUS ESTIMATION;

FORECASTING;

EID: 85008217116     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.1080/07038992.2017.1263152     Document Type: Article

References (70)

1. Ahmed, O.S., Franklin, S.E., Wulder, M.A., and White, J.C., 2015. “Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne LiDAR, and the random forest algorithm.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 101:89–101.
2. Andersen, H.E., Reutebuch, S.E., and McGaughey, R.J., 2006. “A rigorous assessment of tree height measurements obtained using airborne LiDAR and conventional field methods.” Canadian Journal of Remote Sensing, Vol. 32(No. 5):pp. 355–366.
3. Antonarakis, A.S., Richards, K.S., and Brasington, J., 2008. “Object-based land cover classification using airborne LiDAR.” Remote Sensing of Environment, Vol. 112(No. 6):pp. 2988–2998.
4. Arii, K., Caspersen, J.P., Jones, T.A., and Thomas, S.C., 2008. “A selection harvesting algorithm for use in spatially explicit individual-based forest simulation models.” Ecological Modeling, Vol. 211(No. 3):pp. 251–266.
5. Bailey, R.L., and Dell, T.R., 1973. “Quantifying diameter distributions with the Weibull function.” Forest Science, Vol. 19(No. 2):pp. 97–104.
6. Breiman, L., (1996). Bagging predictors. Machine Learning, Vol. 24(No. 2):pp. 123–140.
7. Breiman, L., (2001). Random forests. Machine Learning, Vol. 45(No. 1):pp. 5–32.
8. Crookston, N.L., and Finley, A.O., 2008. “yaimpute:An r package for knn imputation.” Journal of Statistical Software, Vol. 23(No. 10):pp. 1–16.
9. Donoghue, D.N., Watt, P.J., Cox, N.J., and Wilson, J., 2007. “Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data.” Remote Sensing of Environment, Vol. 110(No. 4):pp. 509–522.
10. Eskelson, B.N., Temesgen, H., Lemay, V., Barrett, T.M., Crookston, N.L., and Hudak, A.T., 2009. “The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases.” Scandinavian Journal of Forest Research, Vol. 24(No. 3):pp. 235–246.
11. García, M., Riaño, D., Chuvieco, E., and Danson, F.M., 2010. “Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data.” Remote Sensing of Environment, Vol. 114(No. 4):pp. 816–830.
12. Gaveau, D.L., and Hill, R.A., 2003. “Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data.” Canadian Journal of Remote Sensing, Vol. 29(No. 5):pp. 650–657.
13. Genuer, R., Poggi, J.M., and Tuleau-Malot, C., 2010. “Variable selection using random forests.” Pattern Recognition Letters, Vol. 31(No. 14):pp. 2225–2236.
14. Gobakken, T., and Næsset, E., 2004. “Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data.” Scandinavian Journal of Forest Research, Vol. 19(No. 6):pp. 529–542.
15. Goetz, S., Steinberg, D., Dubayah, R., and Blair, B., 2007. “Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA.” Remote Sensing of Environment, Vol. 108(No. 3):pp. 254–263.
16. Guo, L., Chehata, N., Mallet, C., and Boukir, S., (2011). Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 56–66.
17. Holmgren, J., and Persson, Å., (2004). Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90(4), 415–423.
18. Höfle, B., and Pfeifer, N., (2007). Correction of laser scanning intensity data:Data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 62(6), 415–433.
19. Hopkinson, C., and Chasmer, L. E., (2007). Modeling canopy gap fraction from lidar intensity. In ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007 (pp. 190–194). ISPRS Espoo, Finland.
20. Hopkinson, C., (2007). The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution. Canadian Journal of Remote Sensing, 33(4), 312–324.
21. Hossain, S. M. Y., and Caspersen, J. P., (2012). In-situ measurement of twig dieback and regrowth in mature Acer saccharum trees. Forest Ecology and Management, 270, 183–188.
22. Hudak, A. T., Crookston, N. L., Evans, J. S., Hall, D. E., and Falkowski, M. J., (2008). Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112(5), 2232–2245.
23. Kim, S., McGaughey, R.J., Andersen, H.E., and Schreuder, G., 2009. “Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data.” Remote Sensing of Environment, Vol. 113(No. 8):pp. 1575–1586.
24. Korpela, I., Ørka, H.O., Maltamo, M., Tokola, T., and Hyyppä, J., 2010. “Tree species classification using airborne LiDAR–effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type.” Silva Fennica, Vol. 44(No. 2):pp. 319–339.
25. Liaw, A., and Wiener, M., 2002. “Classification and regression by randomForest.” R news, Vol. 2(No. 3):pp. 18–22.
26. Lindberg, E., Holmgren, J., Olofsson, K., Wallerman, J., and Olsson, H., 2010. Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods. International Journal of Remote Sensing, Vol. 31(No. 5):pp. 1175–1192.
27. Liu, C., Zhang, S., Lei, Y., Newton, P.F., and Zhang, L., 2004. “Evaluation of three methods for predicting diameter distributions of black spruce (Picea mariana) plantations in central Canada.” Canadian Journal of Forest Research, Vol. 34(No. 12):pp. 2424–2432.
28. Lim, K., Treitz, P., Wulder, M., St-Onge, B., and Flood, M., 2003. “LiDAR remote sensing of forest structure.” Progress in Physical Geography, Vol. 27(No. 1):pp. 88–106.
29. Maltamo, M., 1997. “Comparing basal area diameter distributions estimated by tree species and for the entire growing stock in a mixed stand.” Silva Fennica, Vol. 31(No. 1):pp. 53–65.
30. Maltamo, M., and Kangas, A., 1998. “Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution.” Canadian Journal of Forest Research, Vol. 28(No. 8):pp. 1107–1115.
31. Maltamo, M., Suvanto, A., and Packalén, P., 2007. “Comparison of basal area and stem frequency diameter distribution modeling using airborne laser scanner data and calibration estimation.” Forest Ecology and Management, Vol. 247(No. 1):pp. 26–34.
32. Maltamo, M., Næsset, E., Bollandsås, O.M., Gobakken, T., and Packalén, P., 2009. “Non-parametric prediction of diameter distributions using airborne laser scanner data.” Scandinavian Journal of Forest Research, Vol. 24(No. 6):pp. 541–553.
33. Maltamo, M., and Gobakken, T., 2014. “Predicting tree diameter distributions.” In Forestry Applications of Airborne Laser Scanning (pp. 17–191). Netherlands:Springer.
34. Martin, A.R., Caspersen, J.P., Fuller, M.M., Jones, T.A., and Thomas, S.C., 2014. “Temporal dynamics and causes of postharvest mortality in a selection-managed tolerant hardwood forest.” Forest Ecology and Management, Vol. 314:pp. 183–192.
35. Martin Bollandsås, O., and Næsset, E., 2007. “Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data.” Scandinavian Journal of Forest Research, Vol. 22(No. 1):pp. 33–47.
36. McRoberts, R.E., Næsset, E., and Gobakken, T., 2015. “Optimizing the k-nearest neighbors technique for estimating forest aboveground biomass using airborne laser scanning data.” Remote Sensing of Environment, Vol. 163:pp. 13–22.
37. Morsdorf, F., Mårell, A., Koetz, B., Cassagne, N., Pimont, F., Rigolot, E., and Allgöwer, B., (2010). “Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning.” Remote Sensing of Environment, Vol. 114(No. 7):pp. 1403–1415.
38. Mrosek, T., Balsillie, D., and Schleifenbaum, P., 2006. “Field testing of a criteria and indicators system for sustainable forest management at the local level. Case study results concerning the sustainability of the private forest Haliburton Forest and Wild Life Reserve in Ontario, Canada.” Forest Policy and Economics, Vol. 8(No. 6):pp. 593–609.
39. OMNR. 2004. Ontario Tree Marking Guide, Version 1.1. Toronto, Ontario, Canada:Ontario Ministry of Natural Resources.
40. Packalén, P., and Maltamo, M., 2007. “The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs.” Remote Sensing of Environment, Vol. 109(No. 3):pp. 328–341.
41. Packalén, P., and Maltamo, M., 2008. “Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs.” Canadian Journal of Forest Research, Vol. 38(No. 7):pp. 1750–1760.
42. Packalén, P., Temesgen, H., and Maltamo, M., 2012. “Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory.” Canadian Journal of Remote Sensing, Vol. 38(No. 5):pp. 557–569.
43. Pal, M., 2005. “Random forest classifier for remote sensing classification.” International Journal of Remote Sensing, Vol. 26(No. 1):pp. 217–222.
44. Palahí, M., Pukkala, T., Blasco, E., and Trasobares, A., 2007. “Comparison of beta, Johnson's SB, Weibull and truncated Weibull functions for modeling the diameter distribution of forest stands in Catalonia (north-east of Spain).” European Journal of Forest Research, Vol. 126(No. 4):pp. 563–571.
45. Penner, M., Pitt, D.G., and Woods, M.E., 2013. “Parametric vs. nonparametric LiDAR models for operational forest inventory in boreal Ontario.” Canadian Journal of Remote Sensing, Vol. 39(No. 5):pp. 426–443.
46. Penner, M., Woods, M., and Pitt, D.G., 2015. “A comparison of airborne laser scanning and image point cloud derived tree size class distribution models in boreal Ontario.” Forests, Vol. 6(No. 11):pp. 4034–4054.
47. Peuhkurinen, J., Maltamo, M., and Malinen, J., 2008. “Estimating species-specific diameter distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs:a distribution-based approach.” Silva Fennica, Vol. 42(No. 4):pp. 625–641.
48. Pitt, D., and Pineau, J., 2009. “Forest inventory research at the Canadian Wood Fibre Centre:Notes from a research coordination workshop, June 3–4, 2009, Pointe Claire, QC.” The Forestry Chronicle, Vol. 85(No. 6):pp. 859–869.
49. Podlaski, R., and Zasada, M., 2008. “Comparison of selected statistical distributions for modeling the diameter distributions in near-natural Abies–Fagus forests in the Świętokrzyski National Park (Poland).” European Journal of Forest Research, Vol. 127(No. 6):pp. 455–463.
50. Reynolds, M.R., Burk, T.E., and Huang, W.C., 1988. “Goodness-of-fit tests and model selection procedures for diameter distribution models.” Forest Science, Vol. 34(No. 2):pp. 373–399.
51. Riaño, D., Valladares, F., Condés, S., and Chuvieco, E., 2004. “Estimation of leaf area index and covered ground from airborne laser scanner (LiDAR) in two contrasting forests.” Agricultural and Forest Meteorology, Vol. 124(No. 3):pp. 269–275.
52. Sackett, T.E., Smith, S.M., and Basiliko, N., 2012. “Exotic earthworm distribution in a mixed-use northern temperate forest region:influence of disturbance type, development age, and soils.” Canadian Journal of Forest Research, Vol. 42(No. 2):pp. 375–381.
53. Stage, A.R., and Crookston, N.L., 2007. “Partitioning error components for accuracy-assessment of near-neighbor methods of imputation.” Forest Science, Vol. 53(No. 1):pp. 62–72.
54. Thomas, V., Oliver, R., Lim, K., and Woods, M., 2008. “LiDAR and Weibull modeling of diameter and basal area.” The Forestry Chronicle, Vol. 84(No. 6):pp. 866–875.
55. Treitz, P., Lim, K., Woods, M., Pitt, D., Nesbitt, D., and Etheridge, D., 2012. “LiDAR sampling density for forest resource inventories in Ontario, Canada.” Remote Sensing, Vol. 4(No. 4):pp. 830–848.
56. Tuominen, S., Fish, S., and Poso, S., 2003. “Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multisource forest inventory.” Canadian Journal of Forest Research, Vol. 33(No. 4):pp. 624–634.
57. van Ewijk, K.Y., Treitz, P.M., and Scott, N.A., 2011. “Characterizing forest succession in central Ontario using LiDAR-derived indices.” Photogrammetric Engineering and Remote Sensing, Vol. 77(No. 3):pp. 261–269.
58. van Ewijk, K.Y., Randin, C.F., Treitz, P.M., and Scott, N.A., 2014. “Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery.” Remote Sensing of Environment, Vol. 150:pp. 120–131.
59. Vauhkonen, J., Korpela, I., Maltamo, M., and Tokola, T., 2010. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sensing of Environment, Vol. 114(No. 6):pp. 1263–1276.
60. Vauhkonen, J., Packalen, P., Malinen, J., Pitkänen, J., and Maltamo, M., 2014. “Airborne laser scanning-based decision support for wood procurement planning.” Scandinavian Journal of Forest Research, Vol. 29(Sup 1):pp. 132–143.
61. Wagner, W., Ullrich, A., Ducic, V., Melzer, T., and Studnicka, N., 2006. “Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 60(No. 2):pp. 100–112.
62. Wang, Q.J., 1996. “Direct sample estimators of L moments.” Water Resources Research, Vol. 32(No. 12):pp. 3617–3619.
63. Wehr, A., and Lohr, U., 1999. “Airborne laser scanning—an introduction and overview.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 54(No. 2):pp. 68–82.
64. Wing, B.M., Ritchie, M.W., Boston, K., Cohen, W.B., Gitelman, A., and Olsen, M.J., 2012. “Prediction of understory vegetation cover with airborne LiDAR in an interior ponderosa pine forest.” Remote Sensing of Environment, Vol. 124:pp. 730–741.
65. Woods, M., Lim, K., and Treitz, P., 2008. “Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence Forest of Ontario.” Forestry Chronicle, Vol. 84(No. 6):pp. 827–839.
66. Woods, M., Pitt, D., Penner, M., Lim, K., Nesbitt, D., Etheridge, D., and Treitz, P., 2011. “Operational implementation of a LiDAR inventory in boreal Ontario.” The Forestry Chronicle, Vol. 87(No. 4):pp. 512–528.
67. Xu, Q., Hou, Z., Maltamo, M., and Tokola, T., 2014. “Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 93:pp. 65–75.
68. Yan, W.Y., Shaker, A., Habib, A., and Kersting, A.P., 2012. “Improving classification accuracy of airborne LiDAR intensity data by geometric calibration and radiometric correction.” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 67:pp. 35–44.
69. Zhang, L., and Liu, C., 2006. “Fitting irregular diameter distributions of forest stands by Weibull, modified Weibull, and mixture Weibull models.” Journal of Forest Research, Vol. 11(No. 5):pp. 369–372.
70. Zhao, K., Popescu, S., and Nelson, R., 2009. “LiDAR remote sensing of forest biomass:a scale-invariant estimation approach using airborne lasers.” Remote Sensing of Environment, Vol. 113(No. 1):pp. 182–196.