8 September 2010 Science Briefs

SNAMP Publication #4: Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods

Qinghua Guo, Wenkai Li, Hong Yu, and Otto Alvarez

    Research Highlights:


    • We used lidar data to create a detailed digital elevation model for our two study sites.

    • We investigated five different interpolation methods to create the DEMs.

    • We examined how topography, sampling density, and spatial resolution affected accuracy of the DEMs.

    • We found that simple interpolation models are more efficient and faster in creating DEMs, but more complex interpolation models are more accurate, but slower.

    • We found that DEMs are less accurate in areas with more complex topography.

    • We found that DEM error also increases as lidar sampling density decreases.

    • We found that some of the interpolation methods do not work well with larger cell sizes.

    • These results might be helpful to guide the choice of appropriate lidar interpolation methods for DEM generation.

    Background:

    The SNAMP project uses “Lidar” (light detection and ranging) data as one of the inputs to the many science team models. Lidar data is often used to create very detailed (for example from 1m - 10m spatial resolution) digital elevation models (DEMs). There are many different methods that convert the raw lidar “point cloud” data into a grid-based elevation model, and the accuracy of the resulting DEM can be influenced by many factors, such as topography, or sampling density or spatial resolution. In this study we quantified the effects of these three variables (1. topographic variability, 2. lidar sampling density, and 3. spatial resolution) on DEM accuracy across six interpolation methods. The interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK).

    Results:

    Our results show that simple interpolation methods, such as IDW, NN, and TIN, are more efficient algorithms, and generate DEMs from lidar data faster than the more complex algorithms, but kriging-based methods, such as OK and UK, produce more accurate DEMs. We also show that topography matters: in areas with higher topographic variability, the DEM has higher uncertainties and errors no matter what interpolation method and resolution are used. DEM error increases as lidar sampling density decreases, especially at smaller cell sizes. Finally, spatial resolution also plays an important role when generating DEMs from lidar data: at larger cell sizes, the choice of interpolation methods becomes increasingly important, as some of the methods (for example: spline), produce high error at larger cell sizes.

    Conclusions:

    1. Simple interpolation models are more efficient and faster in creating DEMs from lidar data, but more complex interpolation models are more accurate, and slower.
    2. Lidar-based DEMs are less accurate in areas with more complex topography.
    3. Lidar-based DEM error also increases as lidar sampling density decreases.
    4. Some of the interpolation methods (e.g. Spline) do not work well at larger cell sizes (coarser scales).
    5. These results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability.


    Full Reference: Guo, Q., Li, W., Yu, H., Alvarez, O. 2010. Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods. Photogrammetric Engineering and Remote Sensing 76(6): 701–712.

    The full paper is available at: http://faculty.ucmerced.edu/qguo/Publications.html.

    To learn more about lidar data, check out our lidar FAQs sheet. For more information on lidar data in SNAMP, see the spatial team website: http://snamp.cnr.berkeley.edu/teams/spatial, our lidar FAQs handout, and our spatial team newsletters that focus on lidar: Vol. 2, No. 3, and Vol. 5, No. 1.

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