Predicting stem total and assortment volumes in an industrial Pinus taeda L. forest plantation using airborne laser scanning data and random forest

Forests

Volumn 8, Issue 7, 2017, Pages

  Silva, Carlos Alberto ^a,b,c   Klauberg, Carine ^b   Hudak, Andrew Thomas ^b   Vierling, Lee Alexander ^a   Jaafar, Wan Shafrina Wan Mohd ^d   Mohan, Midhun ^e   Garcia, Mariano ^f   Ferraz, António ^c   Cardil, Adrián ^g   Saatchi, Sassan ^c  

^a UNIVERSITY OF IDAHO   (United States)

^b ROCKY MOUNTAIN RESEARCH STATION   (United States)

^c CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^d UNIVERSITY OF EDINBURGH   (United Kingdom)

^e NORTH CAROLINA STATE UNIVERSITY   (United States)

^f UNIVERSITY OF LEICESTER   (United Kingdom)

^g Engineering Department   (Spain)

Author keywords

Forest inventory; Lidar; Remote sensing; Supply chain

Indexed keywords

DECISION TREES; EFFICIENCY; FORECASTING; FORESTRY; LEARNING ALGORITHMS; MEAN SQUARE ERROR; REMOTE SENSING; SUPPLY CHAINS;

AIRBORNE LASER SCANNING; ENVIRONMENTAL BENEFITS; FOREST INVENTORY; PLANTATION MANAGEMENTS; REMOTE SENSING TECHNIQUES; ROOT MEAN SQUARE ERRORS; SUPERVISED MACHINE LEARNING; VERTICAL DISTRIBUTIONS;

OPTICAL RADAR;

ALGORITHM; CONIFEROUS FOREST; FOREST INVENTORY; LIDAR; PLANTATION FORESTRY; REMOTE SENSING; SUPERVISED LEARNING; SUPPLY CHAIN MANAGEMENT; WOOD;

FOREST MANAGEMENT; PINUS; REMOTE SENSING; SAMPLING; SUPPLY CHAIN MANAGEMENT;

BRAZIL;

PINUS TAEDA;

EID: 85026365254     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f8070254     Document Type: Article

References (61)

1. Payn, T., Carnus, J.-M., Freer-Smith, P., Kimberley, M., Kollert, W., Liu, S., Orazio, C., Rodriguez, L., Silva, L.N., Wingfield, M.J. Changes in planted forests and future global implications. For. Ecol. Manag. 2015, 352, 57-67.
2. Indústria Brasileira de árvores (IBá). Brazilian Tree Industry. 2015. Available online: http://www.iba.org/ images/shared/iba_2015.pdf (accessed on 10 November 2016).
3. Kohler, S.V., Wolff, N.I., Figueiredo Filho, A., Arce, J.E. Dynamic of assortment of Pinus taeda L. plantation in different site classes in Southern Brazil. Sci. For. 2014, 42, 403-410.
4. Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Liesenberg, V., Bernett, L.G., Scheraiber, C.F., Schoeninger, E.R. Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Ismputation. Ann. Braz. Acad. Sci. 2017, 90, 1-15, in press.
5. Silva, C.A., Klauberg, C., Hudak, A.T., Vierling, L.A., Liesenberg, V., Carvalho, S.P., Rodriguez, L.C. A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne Lidar data. Forestry 2016, 89, 422-433.
6. Næsset, E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. 1997, 52, 49-56.
7. Næsset, E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 2002, 80, 88-99.
8. Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä, J., Maltamo, M., Nilsson, M., Olsson, H., Persson, Å., Söderman, U. Laser scanning of forest resources: The Nordic experience. Scand. J. For. Res. 2004, 19, 482-499.
9. Næsset, E. Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 2007, 22, 433-442.
10. Hudak, A.T., Evans, J.S., Stuart, A.M. Lidar utility for natural resource managers. Remote Sens. 2009, 1, 934-951.
11. Andersen, H.E., McGaughey, R.J., Reutebuch, S.E. Estimating forest canopy fuel parameters using lidar data. Remote Sens. Environ. 2005, 94, 441-449.
12. Hudak, A.T., Crookston, N.L., Evans, J.S., Falkowski, M.J., Smith, A.M., Gessler, P.E., Morgan, P. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Can. J. Remote Sens. 2006, 32, 126-138.
13. White, J.C., Wulder, M.A., Buckmaster, G. Validating estimates of merchantable volume from airborne laser scanning (ALS) data using weight scaled data. For. Chron. 2014, 90, 378-385.
14. Silva, C.A., Klauberg, C., Carvalho, S.D.P.C., Hudak, A.T. Mapping aboveground carbon stocks using Liar data in Eucalyptus spp. plantations in the state of São Paulo, Brazil. Sci. For. 2014, 42, 591-604.
15. Korhonen, L., Peuhkurinen, J., Jukka, M., Suvanto, A., Maltamo, M., Packalen, P., Kangas, J. The use of airborne laser scanning to estimate sawlog volumes. Forestry 2008, 81, 499-510.
16. Peuhkurinen, J., Maltamo, M., Malinen, J. 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 Fenn. 2008, 42, 600-625.
17. Sherrill, J.R., Bullock, B.P., Mullin, T.J., McKeand, S.E., Purnell, R.C. Total and merchantable stem volume equations for mid rotation loblolly pine (Pinus taeda L.). South. J. Appl. For. 2011, 35, 105-108.
18. Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., Falkowski, M.J. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from lidar data. Remote Sens. Environ. 2008, 112, 2232-2245.
19. Ahmed, O.S., Franklin, S.E.,Wulder, M.A., White, J.C. Characterizing stand-level forest canopy cover and height using landsat time series, samples of airborne lidar, and the random forest algorithm. ISPRS J. Photogramm. Remote Sens. 2015, 101, 89-101.
20. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
21. Grossmann, E., Ohmann, J., Kagan, J., May, H., Gregory, M. Mapping ecological systems with a random forest model: Tradeoffs between errors and bias. GAP Anal. Bull. 2010, 17, 16-22.
22. Naidoo, L., Cho, M.A., Mathieu, R., Asner, G. Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and Lidar data in a Random Forest data mining environment. ISPRS J. Photogramm. Remote Sens. 2012, 69, 167-179.
23. Yu, X., Hyyppä, J., Vastaranta, M., Holopainen, M., Viitala, R. Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J. Photogramm. Remote Sens. 2011, 661, 28-37.
24. Ko, C., Sohn, G., Remmel, T.K., Miller, J.R. Maximizing the Diversity of Ensemble Random Forests for Tree Genera Classification Using High Density Lidar Data. Remote Sens. 2016, 8, 646.
25. Olden, J.D., Lawler, J.J., Poff, N.L. Machine learning methods without tears: A primer for ecologists. Q. Rev. Biol. 2008, 83, 171-193.
26. Stumpf, A., Kerle, N. Object-oriented mapping of landslides using Random Forests. Remote Sens. Environ. 2011, 115, 2564-2577.
27. Lawrence, R.L., Wood, S.D., Sheley, R.L. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 2006, 100, 356-362.
28. Köppen, W., Geiger, R. Klimakarte der Erde. Wall-Map 150 cm × 200 cm; Verlag Justus Perthes: Gotha, Germany, 1928.
29. Curtis, R.O. Height-diameter and height-diameter-age equations for second-growth Douglas-fir. For. Sci. 1967, 13, 365-375.
30. Schöpfer,W. Automatisierung Des Massem, Sorten Und Wertberechnung Stenender Waldbestande Schriftenreihe Bad;Wurtt-Forstl: Koblenz, Germany, 1966.
31. McGauchey, R.J. FUSION/LDV: Software for LiDAR Data Analysis and Visualization; Forest Service Pacific Northwest Research Station USDA: Seattle, WA, USA, 2015. Available online: http://forsys.cfr.washington. edu/fusion/FUSIONmanual.pdf (accessed on 15 October 2015).
32. Kraus, K., Pfeifer, N. Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 1998, 53, 193-203.
33. Hudak, A.T., Strand, E.K., Vierling, L.A., Byrne, J.C., Eitel, J.U.H., Martinuzzi, S., Falkowski, M.J. Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sens. Environ. 2012, 123, 25-40.
34. Silva, C.A., Hudak, A.T., Klauberg, C., Vierling, L.A., Gonzalez-Benecke, C., de Padua Chaves Carvalho, S., Rodriguez, L.C.E., Cardil, A. Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data. Carbon Balance Manag. 2017, 12, 13.
35. Evans, J.S., Cushman, S.A. Gradient modeling of conifer species using Random Forests. Landsc. Ecol. 2009, 5, 673-683.
36. Evans, J.S., Murphy, M.A., Holden, Z.A., Cushman, S.A. Modeling species distribution and change using Random Forests. In Predictive Modeling in Landscape Ecology; Drew, C.A., Huettmann, F.,Wiersma, Y., Eds., Springer: New York, NY, USA, 2010; pp. 139-159.
37. Liaw, A., Wiener, M. RandomForest: Breiman and Cutler's Random Forests for Classification and Regression, Version 4.6-12. 2015. Available online: https://cran.rproject.org/web/packages/randomForest/ (accessed on 15 October 2016).
38. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2017. Available online: http://www.R-project.org (accessed on 20 October 2016).
39. Bright, B.C., Hudak, A.T., McGaughey, R., Andersen, H.E., Negron, J. Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar. Can. J. Remote Sens. 2013, 39, S99-S111.
40. Lopatin, J., Dolos, K., Hernández, H.J., Galleguillos, M., Fassnacht, F.E. Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sens. Environ. 2016, 173, 200-210.
41. Robinson, A.P., Duursma, R.A., Marshall, J.D. A regression-based equivalence test for model validation: Shifting the burden of proof. Tree Physiol. 2005, 25, 903-913.
42. Holopainen, M., Vastaranta, M., Rasinmäki, J., Kalliovirta, J., Mäkinen, A., Haapanen, R., Melkas, T., Yu, X., Hyyppä, J. Uncertainty in timber assortment estimates predicted from forest inventory data. Eur. J. For. Res. 2010, 129, 1131-1142.
43. Sibona, E., Vitali, A., Meloni, F., Caffo, L., Dotta, A., Lingua, E., Motta, R., Garbarino, M. Direct Measurement of Tree Height Provides Different Results on the Assessment of LiDAR Accuracy. Forests 2017, 8, 7.
44. Kankare, V., Vauhkonen, J., Tanhuanpaa, T., Holopainen, M., Vastaranta, M., Joensuu, M. Accuracy in estimation of timber assortments and stem distribution-A comparison of airborne and terrestrial laser scanning techniques. ISPRS J. Photogramm. Remote Sens. 2014, 97, 89-97.
45. Zhao, K., Popescu, S., Meng, X., Pang, Y., Agca, M. Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sens Environ. 2011, 115, 1978-1996.
46. Mascaro, J., Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Martin, R.E., Anderson, C., Higgins, M., Chadwick, K.D. A tale of two "forests": Random forest machine learning AIDS tropical forest carbon mapping. PLoS ONE 2014, 9, e85993.
47. García Gutiérrez, J., Martínez Álvarez, F., Troncoso Lora, A., Riquelme Santos, J.C. A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing 2015, 167, 24-31.
48. Hudak, A.T., Bright, B.C., Pokswinski, S.M., Loudermilk, E.L., O'Brien, J.J., Hornsby, B.S., Klauberg, C., Silva, C.A. Mapping forest structure and composition from low-density LiDAR for informed forest, fuel, and fire management at Eglin Air Force Base, Florida, USA. Can. J. Remote Sens. 2016, 42, 411-427.
49. Hayashi, R.,Weiskittel, A., Sader, S. Assessing the feasibility of low-density lidar for stand inventory attribute predictions in complex and managed forests of Northern Maine, USA. Forests 2014, 5, 363-383.
50. Kankare, V., Vastaranta, M., Holopainen, M., Raty, M., Yu, X., Hyyppa, J., Hyyppa, H., Alho, P., Viitala, R. Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sens. 2013, 5, 2257-2274.
51. Wu, J., Yao,W., Choi, S., Park, T., Myneni, R.B. A Comparative Study of Predicting DBH and Stem Volume of Individual Trees in a Temperate Forest Using Airborne Waveform LiDAR. IEEE Geosci. Remote Sens. Lett. 2015, 12, 2267-2271.
52. Shataeea, S.,Weinaker, H., Babanejad, M. Plot-level Forest Volume Estimation Using Airborne Laser Scanner and TM Data, Comparison of Boosting and Random Forest Tree Regression Algorithms. Procedia Environ. Sci. 2011, 7, 68-73.
53. Peuhkurinen, J., Maltamo, M., Malinen, J., Pitkänen, J., Packalén, P. Pre-harvest measurement of marked stands using airborne laser scanning. For. Sci. 2007, 53, 653-661.
54. Holmgren, J., Barth, A., Larsson, H., Olsson, H. Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters. Silva Fen. 2012, 46, 227-239.
55. Hawbaker, T.J., Gobakken, T., Lesak, A., Trømborg, E., Contrucci, K., Radeloff, V. Light detection and ranging-based measures of mixed hardwood forest structure. For. Sci. 2010, 56, 313-326.
56. Van Aardt, J.A.N., Wynne, R.H., Oderwald, R.G. Forest Volume and Biomass Estimation Using Small-Footprint Lidar Distributional Parameters on a Per-Segment Basis. For. Sci. 2006, 52, 636-649.
57. Ota, T., Ahmed, O.S., Franklin, S.E., Wulder, M.A., Kajisa, T., Mizoue, N. Estimation of Airborne Lidar Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia. Remote Sens. 2014, 6, 10750-10772.
58. Coops, N.C., Hilker, T., Wulder, M.A., St-Onge, B., Newnham, G., Siggins, A. Estimating canopy structure of Douglas-fir forest stands from discrete-return lidar. Trees 2007, 21, 295-310.
59. Tilley, B.K., Munn, I.A., Evans, D.L., Parker, R.C., Roberts, S.D. Cost Considerations of Using Lidar for Timber Inventory. 2004. Available online: http://sofew.cfr.msstate.edu/papers/0504tilley.pdf/ (accessed on 21 March 2016).
60. Hummel, S., Hudak, A.T., Uebler, E.H., Falkowski, M.J., Megown, K.A. A comparison of accuracy and cost of LiDAR versus stand exam data for landscape management on the Malheur national forest. J. For. 2011, 109, 267-273.
61. Silva, C.A., Hudak, A.T., Vierling, L.A., Loudermilk, E.L., O'Brien, J.J., Hiers, J.K., Jack, S.B., Gonzalez-Benecke, C.A., Lee, H., Falkowski, M.J., et al. Imputation of individual longleaf pine forest attributes from field and LiDAR data. Can. J. Remote Sens. 2016, 42, 554-573.