WEI Zhou, LI Guanglu, REN Lei. Ascertainment of Optimum Point Cloud Density for Microtopography DEM[J]. Bulletin of Soiland Water Conservation, 2015, 35(6): 155-158.
DOI:
WEI Zhou, LI Guanglu, REN Lei. Ascertainment of Optimum Point Cloud Density for Microtopography DEM[J]. Bulletin of Soiland Water Conservation, 2015, 35(6): 155-158. DOI: 10.13961/j.cnki.stbctb.2015.06.027.
Ascertainment of Optimum Point Cloud Density for Microtopography DEM
[Objective] Through the analysis and research for optimal point cloud density mico DEM
this paper aimed to select the best point cloud density from it to achieve efficient
rapid access to micro-surface topography observations purpose. Upon which the computational cost was expected to be reduced under the precondition of observation accuracy.[Methods] Previous research of point cloud data proved that the optimal level of resolution about the data was 4 mm. The raw data were compressed by 7 ratings:100%
75%
50%
25%
10%
5% and 1% to generate the corresponding DEM. Three most common DEM accuracy indicators:average error
root mean square error
standard deviation were used to evaluate the accuracy of the DEM above.[Results] (1) When the point cloud compression level was set above 15%
the average error unchanged and maintained at a low value with the decrease of point cloud density
when it was compressed <15%
the average error increased rapidly with the decrease of point cloud density.(2) When the compression of the point cloud was >10%
standard deviation was in a flat status with the decrease of the point cloud density. When it was <10%
the standard deviation of point cloud density decreases rapidly with the increasing of point cloud density.(3) When point cloud was compressed over 20%
with the decrease of the point cloud density
root mean square error was in the flat segmentation; when it was <20%
the root mean square error rapidly increased with the decrease of point cloud density.[Conclusion] The comparison of different compression ratings verified that the optimal compression level to generate micro-topography DEM should be 20%.
Hutchinson M F. A locally adaptive approach to the interpolation of digital elevation models[C]//Santa Fe, New Mexico:NCGIA National Center for Geographic Information and Analysis, Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling.1996:21-26.
Thompson J A, Bell J C, Butler C A. Digital elevation model resolution:Effects on terrain attribute calculation and quantitative soil-landscape modeling[J]. Geoderma, 2001,100(1):67-89.
Florinsky I V, Kuryakova G A. Determination of grid size for digital terrain modelling in landscape investigations-exemplified by soil moisture distribution at a micro-scale[J]. International Journal of Geographical Information Science, 2000,14(8):815-832.