David Kao and Marc Kramer and Alison Luo and Jennifer Dungan and Alex Pang.
Visualizing Distributions from Multi-Return Lidar Data to Understand Forest Structure.
In The Cartographic Journal, vol. 42, no. 1, pp. 35--47, 2005.


Links:

Abstract:

Spatially distributed probability density functions (pdfs) are becoming more relevant to Earth scientists and ecologists because of stochastic models and new sensors that provide numerous realizations or data points per unit area. One source of these data is from multi-return airborne lidar, a type of laser that records multiple returns for each pulse of light sent towards the ground. Data from multi-return lidar is a vital tool in helping us understand the structure of forest canopies over large extents. This paper presents visualization tools to allow scientists to rapidly explore, interpret and discover characteristic distributions within the entire spatial field. The major contribution of this work is a paradigm shift which allows ecologists to think of and analyse their data in terms of full distributions, not just summary statistics. The tools allow scientists to depart from traditional parametric statistical analyses and to associate multimodal distribution characteristics to forest structures. Information on the modality and shape of distributions, previously ignored, can now be visualized as well. Examples are given using data from High Island, southeast Alaska.

Bibtex:

@Article{        kao:2005:VDML,
  author = 	 {David Kao and Marc Kramer and Alison Luo and
                  Jennifer Dungan and Alex Pang},
  title = 	 {Visualizing Distributions from Multi-Return Lidar
                  Data to Understand Forest Structure},
  journal = 	 {The Cartographic Journal},
  year = 	 {2005},
  volume = 	 {42},
  number = 	 {1},
  pages = 	 {35--47},
}

Images:

References:


T. Cover and J. Thomas. Elements of Information Theory. John Wiley & Sons, 1991.
N. T. Eggleston, M. Watson, D. L. Evans, R. J. Moorhead, and J. W. McCombs, II. Visualization of Airborne Multiple-Return LIDAR Imagery from a Forested Landscape. In Second International Conference on Geospatial Information in Agriculture and Forestry, 10-12 January 2000, Lake Buena Vista, Florida, 2000.
J. Hyyppa, O. Kelle, M.Lehikoinen, and M. Inkinen. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience and Remote Sensing, 39:969 -975, 2001.
D. Kao, J. Dungan, and A. Pang. Visualizing 2D probability distributions from EOS satellite image-derived data sets: A case study. In Proceedings of Visualization '01, pages 457-460, 2001.
D. Kao, A. Luo, J. Dungan, and A. Pang. Visualizing spatially varying distribution data. In Proceedings of the 6th International Conference on Information Visualization '02, pages 219-225. IEEE Computer Society, 2002.
M. G. Kramer, A.J. Hansen, M. Taper, and E. Kissinger. Abiotic controls on windthrow and forest dynamics in a coastal temperate rainforest, Kuiu Island, southeast Alaska. Ecology, 82:2749-2768, 2001.
S. Kullback and R. A. Leibler. On information and sufficiency. Ann. Math. Stat., 1951.
A. Luo, D. Kao, and A. Pang. Visualizing spatial distribution data sets. In Eurographics/IEEE TCVG Visualization Symposium Proceedings, pages 29-38, 238, May 2003. www.cse.ucsc.edu/research/avis/operator.html.
Y. Raja and S. Gong. Gaussian mixture models. www.dai.ed.ac.uk/CVonline/LOCAL COPIES/RAJA/CV.html, 2003.
B. W. Silverman. Density Estimation for Statistics and Data Analysis. London: Chapman and Hall, 1986.
K. C. Slatton, M. M. Crawford, and B. L. Evans. Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights. IEEE Transactions on Geoscience and Remote Sensing, 39:2470 -2482, 2001.
M. Watson, N. Eggleston, D. Irby, R. Moorhead, and D. Evans. A virtual reality interface for analyzing remotely sensed forestry data. In Siggraph 2000 Conference Abstracts and Applications Catalog and CD-ROM, Sketches & Applications, 2000.
P. Yin and L. Chen. A new method for multilevel thresholding using symmetry and duality of the histogram. In 1994 International Symposium on Speech, Image Processing and Neural Networks, volume 1, pages 45-8, April 1994.