Evaluation of static three-dimensional (3-D) laser scanning point cloud accuracy has become a topical research issue. Point cloud accuracy is typically estimated by comparing terrestrial laser scanning data related to a finite number of check point coordinates against those obtained by an independent source of higher accuracy. These methods can only estimate the point accuracy but not the point cloud accuracy, which is influenced by the positional error and sampling interval. It is proposed that the point cloud error ellipsoid is favorable for inspecting the point cloud accuracy, which is determined by the individual point error ellipsoid volume. The kernel of this method is the computation of the point cloud error ellipsoid volume and the determination of the functional relationship between the error ellipsoid and accuracy. The proposed point cloud accuracy evaluation method is particularly suited for small sampling intervals when there exists an intersection of two error ellipsoids, and is suited not only for planar but also for nonplanar target surfaces. The performance of the proposed method (PM) is verified using both planar and nonplanar board point clouds. The results demonstrate that the proposed evaluation method significantly outperforms the existing methods when the target surface is nonplanar or there exists an intersection of two error ellipsoids. The PM therefore has the potential for improving the reliability of point cloud digital elevation models and static 3-D laser scanning-based deformation monitoring.
Urban land use change is composed of a series of distinctive mutual transformation process, which has a dominant trend
to transfer from non-urban construction land to urban construction land. However, the land use transition is not a direct
change process from a certain land-use type to another one in a narrow area, but has a gradual process range between any
two land use types in a wide region. In this paper, a hybrid model for analyzing urban land use change based on fuzzy
reasoning and cellular automata is proposed to simulate the change process of land use type in the transition areas of
urban and rural area. Then, four transition rules are discussed in detail based on the feature of land conversion behaviour
in the contiguity areas of urban and rural area. An example of application research is experimented in Hankou Town
through remote sensing imagines in 1993, 1998 and 2003. The results suggest that the first transition rule is more
accurate than other three rules in the whole, by which the transition probability depends on by the edge pixels from 1993
to 1998. But different types of land use have own most compatible transition rule among those four rules.
The spatial resolution is an important measure about spatial scales, which affects the accuracy of interpretation for
remote sensing imagery and furtherer leads to some serious uncertain problems on fractal model of urban land use. In
this paper, the average local variance model based on spatial sampling method is used to select the appropriate spatial
resolution in order to improve fractal model of urban land use. The information entropy dimension is proposed to
quantitatively express spatial balance for a certain urban land use type. An example of application research is
experimented in Wuchang district through QuickBird remote sensing imagery in 2002. By scaling up with the initial
spatial resolution, the appropriate spatial resolution is 10m in round numbers. The information entropy dimension of
built-up area and water are 1.921 and 1.907, which are larger and imply more homogeneously spatial distribution. But
the information entropy dimension of farmland and unused land are 1.291 and 1.218, which are lower and imply more
concentrated spatial distribution. The results suggest that the average local variance is very advantageous to provide the
appropriate resolution for remote sensing imagery, which can greatly improve the accuracy of interpretation in extracting
feature information of urban land use.
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