ADAPTIVE LAYERED CARTESIAN CUT CELL METHOD FOR THE UNSTRUCTURED HEXAHEDRAL GRIDS GENERATION

Adaptive layered Cartesian cut cell method is presented to solve the difficulty of the tmstructured hexahedral anisotropic Cartesian grids generation from the complex CAD model. ＂Vertex merging algorithm based on relaxed AVL tree is investigated to construct topological structure for stereo lithography （STL） files, and a topology-based self-adaptive layered slicing algorithm with special features control strategy is brought forward. With the help of convex hull, a new points-in-polygon method is employed to improve the Cartesian cut cell method. By integrating the self-adaptive layered slicing algorithm and the improved Cartesian cut cell method, the adaptive layered Cartesian cut cell method gains the volume data of the complex CAD model in STL file and generates the unstructured hexahedral anisotropic Cartesian grids.

Scale effect and methods for accuracy evaluation of attribute information loss in rasterization

Rasterization is a conversion process accompanied with information loss, which includes the loss of features＇ shape, structure, position, attribute and so on. Two chief factors that affect estimating attribute accuracy loss in rasterization are grid cell size and evaluating method. That is, attribute accuracy loss in rasterization has a close relationship with grid cell size; besides, it is also influenced by evaluating methods. Therefore, it is significant to analyze these two influencing factors comprehensively. Taking land cover data of Sichuan at the scale of 1：250,000 in 2005 as a case, in view of data volume and its processing time of the study region, this study selects 16 spatial scales from 600 m to 30 km, uses rasterizing method based on the Rule of Maximum Area （RMA） in ArcGIS and two evaluating methods of attribute accuracy loss, which are Normal Analysis Method （NAM） and a new Method Based on Grid Cell （MBGC）, respectively, and analyzes the scale effect of attribute （it is area here） accuracy loss at 16 different scales by these two evaluating methods comparatively. The results show that： （1） At the same scale, average area accuracy loss of the entire study region evaluated by MBGC is significantly larger than the one estimated using NAM. Moreover, this discrepancy between the two is obvious in the range of 1 km to 10 km. When the grid cell is larger than 10 km, average area accuracy losses calculated by the two evaluating methods are stable, even tended to parallel. （2） MBGC can not only estimate RMA rasterization attribute accuracy loss accurately, but can express the spatial distribution of the loss objectively. （3） The suitable scale domain for RMA rasterization of land cover data of Sichuan at the scale of 1：250,000 in 2005 is better equal to or less than 800 m, in which the data volume is favorable and the processina time is not too Iona. as well as the area accuracv loss is less than 2.5%.