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Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric

1 Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, NM 88003, USA
2 Geospatial Sciences Center of Excellence (GSCE), South Dakota State University, Brookings, SD 57007-3510, USA

Special Issue: Special Issue on Satellite Remote Sensing

There have been few approaches developed for the estimation of height using waveform LiDAR data. Unlike any existing methods, we illustrate how the new Moment Distance (MD) framework can characterize the canopy height based on the geometry and return power of the LiDAR waveform without having to go through curve modeling processes. Our approach offers the possibilities of using the raw waveform data to capture vital information from the variety of complex waveform shapes in LiDAR. We assess the relationship of the MD metrics to the key waveform landmarks—such as locations of peaks, power of returns, canopy heights, and height metrics—using synthetic data and real Laser Vegetation Imaging Sensor (LVIS) data. In order to verify the utility of the new approach, we use field measurements obtained through the DESDynI (Deformation, Ecosystem Structure and Dynamics of Ice) campaign. Our results reveal that the MDI can capture temporal dynamics of canopy and segregate generations of stands based on curve shapes. The method satisfactorily estimates the canopy height using the synthetic (r2 = 0.40) and the LVIS dataset (r2 = 0.74). The MDI is also comparable with existing RH75 (relative height at 75%) and RH50 (relative height at 50%) height metrics. Furthermore, the MDI shows better correlations with ground-based measurements than relative height metrics. The MDI performs well at any type of waveform shape. This opens the possibility of looking more closely at single-peaked waveforms that usually carries complex extremes.
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Keywords Moment Distance Index; MD framework; waveform LiDAR; MDI; canopy height; LVIS; shape metric

Citation: Eric Ariel L. Salas, Geoffrey M. Henebry. Canopy Height Estimation by Characterizing Waveform LiDAR Geometry Based on Shape-Distance Metric. AIMS Geosciences, 2016, 2(4): 366-390. doi: 10.3934/geosci.2016.4.366

References

  • 1. Lefsky MA, Harding D, Parker G, et al. (1999) Lidar remote sensing of forest canopy and stand attributes. Remote Sens Environ 67: 83-98.    
  • 2. Means JE, Acker SA, Harding DJ, et al. (1999) Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the western cascades of Oregon. Remote Sens Environ 67: 298-308.    
  • 3. Lefsky MA, Cohen WB, Harding DJ, et al. (2002) Lidar remote sensing of above-ground biomass in three biomes. Glob Ecol Biogeogr 11: 393-399.    
  • 4. Banskota A, Wynne RH, Johnson P, et al. (2011) Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests. Ann For Sci 68: 347-356.    
  • 5. Yao W, Krzystek P, Heurich M (2012) Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sens Environ 123: 368-380.    
  • 6. McGlinchy J, Aardt JAN van, Erasmus B, et al. (2014) Extracting structural vegetation components from small-footprint waveform Lidar for biomass estimation in Savanna ecosystems. IEEE J Sel Top Appl Earth Obs Remote Sens 7: 480-490.    
  • 7. Dubayah R, Blair JB, Clark DB, et al. (1997) The vegetation canopy Lidar mission. In: Proc Conf Land Satellite Information in the Next Decade II. p. 100-112.
  • 8. Hurtt GC, Dubayah R, Drake J, et al. (2004) Beyond potential vegetation: Combining Lidar data and a height-structured model for carbon studies. Ecol Appl 14: 873-883.    
  • 9. Fieber KD, Davenport IJ, Tanase MA, et al. (2015) Validation of canopy height profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment. ISPRS J Photogramm Remote Sens 104: 144-157.
  • 10. Lim K, Treitz P, Wulder M, et al. (2003) LiDAR remote sensing of forest structure. Prog Phys Geogr 27: 88-106.    
  • 11. Sun G, Ranson KJ, Kimes DS, et al. (2008) Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sens Environ 112: 107-117.    
  • 12. Rosette JAB, North PRJ, Suárez JC, et al. (2010) Uncertainty within satellite LiDAR estimations of vegetation and topography. Int J Remote Sens 31: 1325-1342.    
  • 13. Wagner W, Ullrich A, Ducic V, et al. (2006) Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J Photogramm Remote Sens 60: 100-112.    
  • 14. Mallet C, Bretar F (2009) Full-waveform topographic lidar: State-of-the-art. ISPRS J Photogramm Remote Sens 64: 1-16.    
  • 15. El-Baz A, Gimel’farb G (2007) EM-based approximation of empirical distributions with linear combinations of discrete Gaussians. In: 2007 IEEE International Conference on Image Processing. p. IV 373-376.
  • 16. Zwally HJ, Schutz B, Abdalati W, et al. (2002) ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J Geodyn 34: 405-445.    
  • 17. Chauve A, Vega C, Durrieu S, et al. (2009) Processing full-waveform lidar data in an alpine coniferous forest: Assessing terrain and tree height quality. Int J Remote Sens 30: 27.
  • 18. Hofton MA, Minster JB, Blair JB (2000) Decomposition of laser altimeter waveforms. IEEE Trans Geosci Remote Sens 38: 1989-1996.    
  • 19. Jutzi B, Stilla U (2006) Range determination with waveform recording laser systems using a Wiener Filter. ISPRS J Photogramm Remote Sens 61: 95-107.    
  • 20. Chauve A, Mallet C, Bretar F, et al. (2007) Processing full-waveform lidar data: modelling raw signals. In: International archives of photogrammetry, remote sensing and spatial information sciences [cited 2016 Jul 5]. p. 102-107. Available from: http://hal-lirmm.ccsd.cnrs.fr/lirmm-00293129/.
  • 21. Mallet C, Lafarge F, Bretar F, et al. (2009) A stochastic approach for modelling airborne lidar waveforms. Object Extr 3D City Models Road Databases Traffic Monit-Concepts Algorithms Eval CMRT 2009, 38(Part 3): W8.
  • 22. Hancock S, Lewis P, Foster M, et al. (2012) Measuring forests with dual wavelength lidar: A simulation study over topography. Agric For Meteorol 161: 123-133.    
  • 23. Wallace A, Nichol C, Woodhouse I (2012) Recovery of forest canopy parameters by inversion of multispectral LiDAR data. Remote Sens 4: 509-531.
  • 24. Dubayah RO, Sheldon SL, Clark DB, et al. (2010) Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J Geophys Res Biogeosciences 115(G2): G00E09.
  • 25. Levenberg KA (1944) Method for the solution of certain problems in least squares. Q Appl Math 2: 164-168.
  • 26. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11: 431-441.    
  • 27. Brenner AC, Zwally HJ, Bentley CR, et al. (2012) The algorithm theoretical basis document for the derivation of range and range distributions from laser pulse waveform analysis for surface elevations, roughness, slope, and vegetation heights [cited 2016 Jul 5]; Available from: http://ntrs.nasa.gov/search.jsp?R=20120016646.
  • 28. Duong H, Lindenbergh R, Pfeifer N, et al. (2009) ICESat full-waveform altimetry compared to airborne laser scanning altimetry over The Netherlands. IEEE Trans Geosci Remote Sens 47: 3365-3378.    
  • 29. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 7: 1-38.
  • 30. Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82: 711-732.
  • 31. Hernandez-Marin S, Wallace AM, Gibson GJ (2007) Bayesian analysis of Lidar signals with multiple returns. IEEE Trans Pattern Anal Mach Intell 29: 2170-2180.
  • 32. Jaskierniak D, Lane PNJ, Robinson A, et al. (2011) Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions. Remote Sens Environ 115: 573-585.
  • 33. Wu J, Van Aardt JAN, Asner GP, et al. (2009) Lidar waveform-based woody and foliar biomass estimation in savanna environments. Proc Silvilaser 1-10.
  • 34. Parrish CE, Rogers JN, Calder BR (2014) Assessment of waveform features for Lidar uncertainty modeling in a coastal salt marsh environment. IEEE Geosci Remote Sens Lett 11: 569-573.
  • 35. Muss JD, Aguilar-Amuchastegui N, Mladenoff DJ, et al. (2013) Analysis of waveform Lidar data using shape-based metrics. IEEE Geosci Remote Sens Lett 10: 106-110.    
  • 36. Hopkinson C, Chasmer L (2009) Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sens Environ 113: 275-288.
  • 37. Blair JB, Rabine DL, Hofton MA (1999) The Laser Vegetation Imaging Sensor (LVIS): A medium-altitude, digitization-only, airborne laser altimeter for mapping vegetation and topography. ISPRS J Photogramm Remote Sens 54: 115-122.    
  • 38. Cook B, Dubayah R, Griffith P, et al. (2011) NACP New England and Sierra National Forests biophysical measurements: 2008–2010 [cited 2016 Aug 16]; Available from: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1046.
  • 39. Sun G, Ranson KJ (2000) Modeling lidar returns from forest canopies. IEEE Trans Geosci Remote Sens 38: 2617-2626.
  • 40. Urban DL (1990) A versatile model to simulate forest pattern. In: A User’s Guide to ZELIG Version 1.0. Charlottesville, VA: The University of Virginia [cited 2016 Jul 5]. Available from: https://daac.ornl.gov/BOREAS/bhs/Models/Zelig.html.
  • 41. Lefsky MA, Harding D, Cohen WB, et al. (1999) Surface Lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens Environ 67: 83-98.    
  • 42. Harding DJ, Carabajal CC (2005) ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys Res Lett 32: L21S10.
  • 43. García M, Riaño D, Chuvieco E, et al. (2010) Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens Environ 114: 816-830.
  • 44. Salas EAL, Henebry GM (2012) Separability of maize and soybean in the spectral regions of chlorophyll and carotenoids using the Moment Distance Index. Isr J Plant Sci 60: 65-76.    
  • 45. Salas EAL, Henebry GM (2013) A new approach for the analysis of hyperspectral data: Theory and sensitivity analysis of the Moment Distance Method. Remote Sens 6: 20-41.    
  • 46. Lefsky MA, Harding DJ, Keller M, et al. (2005) Estimates of forest canopy height and aboveground biomass using ICESat: ICESAT estimates of canopy height. Geophys Res Lett 32: 441.
  • 47. Jenkins JC, Chojnacky DC, Heath LS, et al. (2004) Comprehensive database of diameter-based biomass regressions for North American tree species. U.S. For. Servo Gen. Tech. Rep. NE-319.
  • 48. Young HE, Ribe JH, Wainwright K (1980) Weight tables for tree and shrub species in Maine. Life Sciences & Agriculture Experiment Station Miscellaneous Report 230.
  • 49. Cook BD, Rosette J, North PR, et al. (2010) Effect of ground surface reflectance on LiDAR waveforms, height metrics and biomass estimation. AGU Fall Meet Abstract [cited 2016 Jul 5]. Available from: http://adsabs.harvard.edu/abs/2010AGUFM.B33A0386C.
  • 50. Andersen H-E, McGaughey RJ, Reutebuch SE (2005) Estimating forest canopy fuel parameters using LIDAR data. Remote Sens Environ 94: 441-449.    
  • 51. Muss JD, Mladenoff N, Townsend PA (2011) A pseudo-waveform technique to assess forest structure using discrete lidar data. Remote Sens Environ 115: 824-835.    

 

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Copyright Info: © 2016, Eric Ariel L. Salas, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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