Estimating Above Ground Biomass of Eucalyptus Using Surface Reconstruction Techniques from Terrestrial Laser Scanning
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Abstract
Above-ground biomass (AGB) is an important ecological variable that should be correctly measured for tree carbon storage estimation. The traditional way of measuring the AGB was to use the destructive tree-cutting method that is not cost-effective for large study areas and may cause high standard variations. Many reports demonstrated that modern terrestrial laser scanning (TLS) techniques do not require tedious fieldwork and are more accurate than the traditional methods. This research aimed to estimate AGB from a point cloud of TLS for Eucalyptus kamaldulensis 24 plants using the Qualitative surface model (QSM) and the Poisson surface reconstruction (PSR) methods. The AGB of the two methods is compared with the actual AGB using the water displacement method. The accuracy of the results was calculated with the square root of mean square error (RMSE). The RMSE value from the QSM method was 3.08 kg, and the RMSE value of the PSR method was 1.78 kg. The outcome of this study confirmed the effectiveness of the TLS techniques for AGB estimation. It is anticipated the use of the proposed method in fast-growing timber industries in Thailand.
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Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä, J., Maltamo, M., Nilsson, M., Olsson, H., Persson, Å. and Söderman, U., 2004, Laser scanning of forest resources: The nordic experience, Scand. J. Forest Res. 19: 482-499.
Küßner, R. and Mosandl, R., 2000, Comparison of direct and indirect estimation of leaf area index in mature Norway spruce stands of eastern Germany, Can. J. Forest Res. 30: 440-447.
Henning, J.G. and Radtke, P.J., 2006, Ground-based laser imaging for assessing three-dimensional forest canopy structure, Photogramm. Eng. Remote Sens. 72: 1349-1358.
Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.E. and Hudak, A.T., 2009, Charac terizing forest succession with lidar data: An evaluation for the Inland Northwest, USA, Remote Sens. Environ. 113: 946-956.
Popescu, S.C., 2007, Estimating biomass of individual pine trees using airborne lidar, Biomass Bioenergy 31: 646-655.
Hudak, A.T., Evans, J.S. and Smith, A.M.S., 2009, LiDAR utility for natural resource managers, Remote Sens. 1: 934-951.
Lim, K., Treitz, P., Wulder, M., St-Onge, B. and Flood, M., 2003, LiDAR remote sensing of forest structure, Prog. Phys. Geogr. 27: 88-106.
Dassot, M., Constant, T. and Fournier, M., 2011, The use of terrestrial LiDAR techno logy in forest science: Application fields, benefits and challenges, Ann. Forest Sci. 68: 959-974.
Loudermilk, E.L., Hiers, J.K., O'Brien, J.J., Mitchell, R.J., Singhania, A., Fernandez, J.C., Cropper, W.P.Jr. and Slatton, K.C., 2009, Ground-based LIDAR: A novel approach to quantify fine-scale fuelbed characteristics, Int. J. Wildland Fire 18: 676-685.
Holopainen, M., Vastaranta, M., Kankare, V., Räty, M., Vaaja, M., Liang, X., Yu, X., Hyyppä, J., Hyyppä, H., Viitala R. and Kaasalainen, S., 2011, Biomass estimation of individual trees using stem and crown diameter TLS measurements, Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci. 38-5W12: 91-95.
Mengesha, T., Hawkins, M. and Nieuwen huis, M., 2015, Validation of terrestrial laser scanning data using conventional forest inventory methods, Eur. J. For. Res. 134: 211-222.
Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakkola, A., Guan, F., Holopainen, M. and Vastaranta, M., 2016, Terrestrial laser scanning in forest inventories, J. Photogramm. Remote Sens. 115: 63-77.
Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H. and Danson, F.M., 2015, Terrestrial laser scanning for plot-scale forest measurement, Curr. Forestry Rep. 1: 239-251.
Olsoy, P.J., Glenn, N.F., Clark, P.E. and Derryberry, D.R., 2014, Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning, ISPRS J. Photogram. Remote Sens. 88: 166-173.
Raumonen, P., Kaasalainen, M., Markku, A., Kaasalainen, S., Kaartinen, H., Vastaranta, M., Holopainen, M., Disney M. and Lewis, P., 2013, Fast automatic precision tree models from terrestrial laser scanner data, Remote Sens. 5: 491-520.
Kazhdan, M., Bolitho, M. and Hoppe, H., 2006, Poisson surface reconstruction, in Proceedings of the 4th Eurographics Symposium on Geometry Processing 7: 21-30.
Feliciano, E.A., Wdowinski, S. and Potts, M.D., 2014, Assessing mangrove above-ground biomass and structure using terrestrial laser scanning: A case study in the everglades national park, Wetlands 34: 955-968.
Ishak, N.I., Abu Bakar, M.A., Abdul Rahman, M.Z.A., Rasib, A.W., Kanniah, K.D., Meng Shin, A.L. and Razak, K.A., 2015, Estimating single tree stem and branch biomass using terrestrial laser scanning, J. Teknol. 77: 59-67.
Maas, H.G., Bienert, A., Scheller, S. and Keane, E., 2008, Automaticforest inventory parameter determination from terrestrial laser scanner data, Int. J. Remote Sens. 29: 1579-1593.
Hackenberg, J., Wassenberg, M., Spiecker, H. and Sun, D., 2015, Nondestructive method for biomass prediction combining TLS derived tree volume and wood density, Forests 6: 1274-1300.
Stovall, A.E.L., Vorster, A.G., Anderson, R.S., Evangelista, P.H. and Shugart, H.H., 2017, Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR, Remote Sens. Environ. 200: 31-42.
Berger, M., Tagliasacchi, A., Seversky, L.M., Alliez, P., Guennebaud, G., Levine, J.A., Sharf, A. and Silva, C.T., 2017, A survey of surface reconstruction from point clouds, Comput. Graph. Forum 36: 301-329.
Owers, C.J., Rogers, K. and Woodroffe, C.D., 2018, Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation, Estuarine, Coastal Shelf Sci. 204: 164-176.
Åkerblom, M., 2012, Quantitative Tree Modeling from Laser Scanning Data, Master Thesis, Tampere University of Technology, Tampere.
Ribeiro, S.C., Soares, C.P.B., Fehrmann, L., Jacovine, L.A.G. and von Gadow, K., 2015, Aboveground and belowground biomass and carbon estimates for clonal eucalyptus trees in Southeast Brazil, Revista Arvore 39: 353-363.
Wassenberg, M., Montwé, D., Kahle, H.P., and Spiecker, H., 2014, Exploring high frequency densitometry calibration functions for different tree species, Dendrochronologia 32: 273-281.
Wassenberg, M., Chiu, H.S., Guo, W. and Spiecker, H., 2015, Analysis of wood density profiles of tree stems: Incorporating vertical variations to optimize wood sampling strategies for density and biomass estimations, Trees Struct. Funct. 29: 551-561.
Komiyama, A., Ong, J.E. and Poungparn, S., 2008, Allometry, biomass, and productivity of mangrove forests: A review, Aquat. Bot. 89: 128-137.
Intharat, T. and Vaipasa, C., 2018, Modeling mangrove above-ground biomass using terrestrial laser scanning technique: A case study of the Avicennia marina species in the Bang Pu district, Thailand, Thai J. Sci. Technol. 7(3): 307-318. (in Thai)
Olagoke, A., Proisy, C., Féret, J.B., Blanchard, E., Fromard, F., Mehlig, U., de Menezes, M.M., Dos Santos, V.F. and Berger, U., 2016, Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data, Trees 30: 935-947.
Robert, U.W., Etuk, S.E. and Agbasi, O.E., 2019, Bulk volume determination by modified water displacement method, Iraqi J. Sci. 60: 1704-1710.
Kazhdan, M. and Hoppe, H., 2013, Screened poisson surface reconstruction, ACM Transact. Graph. 32(3): 29.
Wilkes, P., Lau, A., Disney, M., Calders, K., Burt, A., de Tanago, J.G., Bartholomeus, H., Brede, B. and Herold, M., 2017, Data acquisition considerations for Terrestrial Laser Scanning of forest plots, Remote Sens. Environ. 196: 140-153.
Clark, D.B. and Kellner, J.R., 2012, Tropical forest biomass estimation and the fallacy of misplaced concreteness, J. Veg. Sci. 23: 1191-1196.
Danson, F.M., Gaulton, R., Armitage, R.P., Disney, M., Gunawan, O., Lewis, P., Pearson, G. and Ramirez, A.F., 2014, Developing a dual-wavelength full-waveform terrestrial laser scanner to characterize forest canopy structure, Agric. Forest Meteorol. 198-199: 7-14.
Morel, J., Bac, A. and Véga, C., 2018, Surface reconstruction of incomplete datasets: A novel poisson surface approach based on CSRBF, Comput. Graph. 74: 44-55.