Aboveground biomass estimation of individual trees in a coastal planted forest using full-waveform airborne laser scanning data

Remote Sensing

Volumn 8, Issue 9, 2016, Pages

  Cao, Lin ^a   Gao, Sha ^a   Li, Pinghao ^a   Yun, Ting ^a   Shen, Xin ^a   Ruan, Honghua ^a  

^a NANJING FORESTRY UNIVERSITY   (China)

Author keywords

Aboveground biomass; ALS; Full waveform; Planted forest; Remote sensing

Indexed keywords

ALUMINUM; BIOMASS; CLIMATE CHANGE; ECOLOGY; ECOSYSTEMS; LASER APPLICATIONS; REMOTE SENSING; SURFACE ANALYSIS;

ABOVE GROUND BIOMASS; AIRBORNE LASER SCANNING; COEFFICIENT OF VARIATION; FULL-WAVEFORMS; HEIGHT OF MEDIAN ENERGIES; PLANTED FOREST; SUSTAINABLE FOREST MANAGEMENT; VOXEL-BASED APPROACHES;

FORESTRY;

EID: 85019707489     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8090729     Document Type: Article

References (76)

1. Brown, S., Sathaye, J., Cannell, M., Kauppi, P. Mitigation of carbon emission to the atmosphere by forest management. Commonw. For. Rev. 1996, 75, 80-91.
2. Dixon, R.K., Brown, S., Houghton, R.A., Solomon, A.M., Trexler, M.C., Wisniewski, J. Carbon pools and flux of global forest ecosystems. Science 1994, 263, 185-190.
3. Food and Agriculture Organization of the United Nations (FAO). Global Forest Resources Assessment 2010; Forestry Department at FAO: Rome, Italy, 2010.
4. Fang, J.Y., Guo, Z.D., Piao, S.L., Chen, A.P. Terrestrial vegetation carbon sinks in China, 1981-2000. Sci. China Ser. D Earth Sci. 2007, 50, 1341-1350.
5. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G., et al. A Large and Persistent Carbon Sink in the World's Forests. Science 2011, 333, 988-993.
6. Xu, X., Li, K. Biomass carbon sequestration by planted forests in China. Chin. Geogr. Sci. 2010, 20, 289-297.
7. Li, J., Hua, B., Nolandb, T.L. Classification of Tree Species Based on Structural Features Derived from High Density LiDAR Data. Agric. For. Meteorol. 2013, 171, 104-114.
8. Lefsky, M.A., Cohen,W.B., Harding, D.J. LiDAR remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr. 2002, 11, 393-399.
9. Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A., Harding, D. LiDAR Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sens. Environ. 1999, 70, 339-361.
10. Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Chazdon, R.L., Weishampel, J.F., Prince, S. Estimation of Tropical Forest Structural Characteristics Using Large-Footprint LiDAR. Remote Sens. Environ. 2002, 79, 305-319.
11. Lu, D., Chen, Q.,Wang, G., Moran, E., Batistella, M., Zhang, M., Vaglio Laurin, G., Saah, D. Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates. Int. J. For. Res. 2012, 2012, 436537.
12. Hollaus, M., Mücke, W., Roncat, A., Pfeifer, N., Briese, C. Full-Waveform Airborne Laser Scanning Systems and Their Possibilities in Forest Applications. In Forestry Applications of Airborne Laser Scanning Concepts and Case Studies; Maltamo, M., Naesset, E., Vauhkonen, J., Eds., Springer: New York, NY, USA, 2014; pp. 43-61.
13. Cao, L., Coops, N.C., Innes, J.L., Dai, J., She, G. Mapping above-and below-Ground biomass components in subtropical forests using small-footprint LiDAR. Forests 2014, 5, 1356-1373.
14. Li, Y., Andersen, H.E., McGaughey, R. A comparison of statistical methods for estimating forest biomass from light detection and ranging data. West. J. Appl. For. 2008, 23, 223-231.
15. Naesset, E., Gobakken, T. Estimation of above-and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens. Environ. 2008, 112, 3079-3090.
16. Popescu, S.C. Estimating biomass of individual pine trees using airborne LiDAR. Biomass Bioenergy 2007, 31, 646-655.
17. Gleason, C.J., Im, J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens. Environ. 2012, 125, 80-91.
18. Hauglin, M., Dibdiakova, J., Gobakken, T., Naesset, E. Estimating single-tree branch biomass of Norway spruce by airborne laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 79, 147-156.
19. Kankare, V., Räty, M., Yu, X., Holopainen, M., Vastaranta, M., Kantola, T., Hyyppä, J., Hyyppä, H., Alho, P., Viitala, R. Single tree biomass modelling using airborne laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 85, 66-73.
20. Popescu, S.C., Hauglin, M. Estimation of Biomass Components by Airborne Laser Scanning. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies; Springer Science: New York, NY, USA, 2014; p. 294.
21. Lindberg, E., Olofsson, K., Holmgren, J., Olsson, H. Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data. Remote Sens. Environ. 2012, 118, 151-161.
22. Wulder, M.A., Bater, C., Coops, N.C., Hilker, T., White, J.C. The role of LiDAR in sustainable forest management. For. Chron. 2008, 84, 807-826.
23. Naesset, E., Økland, T. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sens. Environ. 2002, 79, 105-115.
24. Richardson, J.J., Moskal, L.M., Kim, S. Modeling approaches to estimate effective leaf area index from aerial discrete-return LiDAR. Agric. For. Meteorol. 2009, 149, 1152-1160.
25. Coops, N.C., Hilker, T.,Wulder, M.A., St-Onge, B., Newnham, G.J., Siggins, A., Trofymow, J.A. Estimating Canopy Structure of Douglas-Fir Forest Stands from Discrete-Return LiDAR. Trees Struct. Funct. 2007, 21, 295-310.
26. Parrish, C.E., Jeong, I., Nowak, R.D., Smith, R.B. Empirical Comparison of Full-Waveform LiDAR Algorithms: Range Extraction and Discrimination Performance. Photogramm. Eng. Remote Sens. 2011, 77, 825-838.
27. Wagner, W., Ullrich, A., Ducic, V., Melzer, T., Studnicka, N. Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J. Photogramm. Remote Sens. 2006, 60, 100-112.
28. Mallet, C., Bretar, F. Full-waveform topographic LiDAR: State-of-the-art. ISPRS J. Photogramm. Remote Sens. 2009, 64, 1-16.
29. Hancock, S., Armston, J., Li, Z., Gaulton, R., Lewis, P., Disney, M., Mark Danson, F., Strahler, A., Schaaf, C., Anderson, K., et al. Waveform LiDAR over vegetation: An evaluation of inversion methods for estimating return energy. Remote Sens. Environ. 2015, 164, 208-224.
30. Reitberger, J., Peter, K., Stilla, U. Analysis of full waveform LiDAR data for tree species classification. In International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (Part 3); International Society of Photogrammetry and Remote Sensing (ISPRS): Bonn, Germany, 2006; pp. 228-233.
31. Mallet, C., Bretar, F., Roux, M., Soergel, U., Heipke, C. Relevance assessment of full-waveform LiDAR data for urban area classification. ISPRS J. Photogramm. Remote Sens. 2011, 66, S71-S84.
32. Yao, W., Krzystek, P., Heurich, M. Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data1. Remote Sens. Environ. 2012, 123, 368-380.
33. Duong, V.H. Processing and Application of ICESat Large Footprint Full Waveform Laser Range Data; Delft University of Technology: Delft, The Netherlands, 2010.
34. Sumnall, M.J., Hill, R.A., Hinsley, S.A. Comparison of small-footprint discrete return and full waveform airborne LiDAR data for estimating multiple forest variables. Remote Sens. Environ. 2016, 173, 214-223.
35. Cao, L., Coops, N.C., Hermosilla, T., Innes, J.L., Dai, J., She, G. Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests. Remote Sens. 2014, 6, 7110-7135.
36. Allouis, T., Durrieu, S., Vega, C., Couteron, P. Stem volume and above-ground biomass estimation of individual pine trees from LiDAR data: Contribution of full-waveform signals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, 6, 924-934.
37. Wu, J., Van Aardt, J., Asner, G.P., Knapp, D., Erasmus, B.F.N. LiDAR Waveform-based woody and foliar biomass estimation in savanna environments. In Proceedings of the 9th International Conference on LiDAR Applications for Assessing Forest Ecosystems (Silvilaser), College Station, TX, USA, 14-16 October 2009; pp. 20-29.
38. Zhou, D., Pu, D., Ge, Z., Yan, J., Ran, X., Ruan, H., Cao, G. Effects of nitrogen addition on soil fauna in poplar plantation with different ages in a coastal area of eastern China. Chin. J. Ecol. 2015, 34, 2553-2560.
39. Song, Y.B., Ding, Y.P., Shen, F. The development and latest progress of JSCORS. Bull. Surv. Mapp. 2009, 2, 73-74.
40. Ji, Y., Zhang, J., Kang, L. A study on biomass equations for Metasequoia glyptostroboides shelterbelt in the coastal agroforestry. J. Jiangsu For. Sci. Technol. 2015, 24, 1-4.
41. Pang, Y., Li, Z.Y., Ju, H.B., Liu, Q.W., Si, L., Li, S.M. LiCHy:CAF's LiDAR,CCD and Hyperspectral Airborne Observation System. In Proceedings of the 13th International Conference on LiDAR Applications for Assessing Forest Ecosystems, Beijing, China, 9-11 October 2013; pp. 45-54.
42. Reitberger, J., Schnörr, C., Krzystek, P., Stilla, U. 3D segmentation of single trees exploiting full waveform LiDAR data. ISPRS J. Photogramm. Remote Sens. 2009, 64, 561-574.
43. Levenberg, K. A Method for the Solution of Certain Problems in Least Squares. Q. Appl. Math. 1944, 2, 164-168.
44. Marquardt, D. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math. 1963, 11, 431-441.
45. Rapidlasso, G. LAStools Rapid LiDAR Processing. Available online: http://rapidlasso.com/category/ lastools/ (accessed on 12 August 2016).
46. Vaughn, N.R., Moskal, L.M., Turnblom, E.C. Tree Species Detection Accuracies Using Discrete Point LiDAR and Airborne Waveform LiDAR. Remote Sens. 2012, 4, 377-403.
47. Reitberger, J., Krzystek, P., Stilla, U. Analysis of full waveform LiDAR data for the classification of deciduous and coniferous trees. Int. J. Remote Sens. 2008, 29, 1407-1431.
48. Wagner, W. Radiometric calibration of small-footprint full-waveform airborne laser scanner measurements: Basic physical concepts. ISPRS J. Photogramm. Remote Sens. 2010, 65, 505-513.
49. Höfle, B., Pfeifer, N. Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS J. Photogramm. Remote Sens. 2007, 62, 415-433.
50. Pang, Y., Lefsky, M., Sun, G., Ranson, J. Impact of Footprint Diameter and Off-Nadir Pointing on the Precision of Canopy Height Estimates from Spaceborne LiDAR. Remote Sens. Environ. 2011, 115, 2798-2809.
51. Hermosilla, T., Coops, N.C., Ruiz, L.A., Moskal, L.M. Deriving pseudo-vertical waveforms from small-footprint full-waveform LiDAR data. Remote Sens. Lett. 2014, 5, 332-341.
52. Bretar, F., Pierrot-Deseilligny, M., Roux, M. Solving the strip adjustment problem of 3D airborne LiDAR data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '04), Anchorage, AK, USA, 20-24 September 2004; Volume 7, pp. 4734-4737.
53. Popescu, S.C., Wynne, R.H., Nelson, R.H. Estimating plot-level tree heights with LiDAR: Local filtering with a canopy-height based variable window size. Comput. Electron. Agric. 2002, 37, 71-95.
54. Popescu, S.C.,Wynne, R.H. Seeing the trees in the forest: using LiDAR and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm. Eng. Remote 2004, 70, 589-604.
55. Popescu, S.C., Wynne, R.H., Nelson, R.H. Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens. 2003, 29, 564-577.
56. McGaughey, R.J. FUSION/LDV: Software for LiDAR Data Analysis and Visualization; United States Department of Agriculture Forest Service: Seattle, WA, USA, 2015.
57. Xu, M., Harrington, T.B. Foliage biomass distribution of loblolly pine as affected by tree dominance crown size and stand characteristics. Can. J. For. Res. 1998, 28, 887-892.
58. Bailey, R.L., Dell, T.R. Quantifying diameter distributions with theWeibull distribution. For. Sci. 1973, 19, 97-104.
59. Neuenschwander, A. Mapping vegetation structure in a wooded savanna at Freeman Ranch, TX using airborne waveform LiDAR. In Proceedings of the 12th International Conference on LiDAR Applications for Assessing Forest Ecosystems (SilviLaser), Vancouver, BC, Canada, 16-19 September 2012; pp. 42-53.
60. Ranson, K.J., Sun, G., Kovacs, K., Kharuk, V.I. Landcover attributes from ICESat GLAS data in central Siberia. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS '04), Anchorage, AK, USA, 20-24 September 2004; pp. 753-756.
61. Sun, G., Ranson, K.J., Kimes, D.S., Blair, J.B., Kovacs, K. Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sens. Environ. 2008, 112, 107-117.
62. Harding, D.J. ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys. Res. Lett. 2005, 32, L21S10.
63. Wulder, M.A., White, J.C., Bater, C.W., Coops, N.C., Hopkinson, C., Chen, G. LiDAR plots-A new large-area data collection option: Context, concepts, and case study. Can. J. Remote Sens. 2014, 38, 600-618.
64. Lu, Y., Coops, N.C., Bolton, D.K., Wang, T., Wang, G. Comparing Stem Volume Predictions of Coastal Douglas-Fir Stands in British Columbia Using a Simple Physiological Model and LiDAR Remote Sensing. For. Sci. 2015, 61, 1-11.
65. Sprugel, D.G. Correcting for Bias in Log-Transformed Allometric Equations. Ecology 1983, 64, 209-210.
66. Lumley, T. Regression Subset Selection. Available online: http://cran.r-project.org/web/packages/leaps/ leaps.pdf (accessed on 3 May 2016).
67. LeBreton, J.M., Tonidandel, S. Multivariate relative importance: Extending relative weight analysis to multivariate criterion spaces. J. Appl. Psychol. 2008, 93, 329-345.
68. Johnson, J. A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression. Multivar. Behav. Res. 2000, 35, 21-50.
69. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19, 716-723.
70. Vauhkonen, J., Maltamo, M., McRoberts, R.E., Naesset, E. Introduction to Forestry Applications of Airborne Laser Scanning. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies; Springer: New York, NY, USA, 2014; p. 294.
71. Nelson, R., Valenti, M.A., Short, A., Keller, C. A multiple resource inventory of Delaware using airborne laser data. Bioscience 2003, 53, 981-992.
72. Cao, L., Coops, N.C., Innes, J.L., Dai, J., Ruan, H., She, G. Tree species classification in subtropical forests using small-footprint full-waveform LiDAR data. Int. J. Appl. Earth Obs. Geoinf. 2016, 49, 39-51.
73. Zavitkovski, J. Ground vegetation biomass, production, and efficiency of energy utilization in some Northern Wisconsin forest ecosystems. Ecology 1976, 57, 694-706.
74. Breidenbach, J., Astrup, R. The Semi-Individual Tree Crown Approach. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies; Springer: Dordrecht, Netherlands, 2014; pp. 113-133.
75. Breidt, F.J. Simulation estimation of quantiles from a distribution with known mean. J. Comput. Graph. Stat. 2004, 13, 487-498.
76. Muss, J.D., Mladenoff, D.J., Townsend, P.A. Remote Sensing of Environment A pseudo-waveform technique to assess forest structure using discrete LiDAR data. Remote Sens. Environ. 2011, 115, 824-835.