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A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

Kang M, Wang M, Du Q - PLoS ONE (2015)

Bottom Line: The results indicate that the QIN method is the most accurate method tested.The difference in accuracy rank seems to be caused by the locations of the data points sampled.Although the QIN method has drawbacks, it is an alternative method for DTM construction.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China; Key laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China.

ABSTRACT
A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

No MeSH data available.


Quadrangular Irregular Network.
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pone.0127592.g001: Quadrangular Irregular Network.

Mentions: Let In,m = Hn∩Vm. In,m indicates the intersection of one horizontal vector and one vertical vector; there is only, at most, one element belonging to two different vectors. There are two situations for In,m: (1) If In,m is nonempty, call the element it contains Pn,m; (2) If In,m is empty, a derivative point must be constructed that will satisfy conditions (1) and (2) for Hn and Vm; call this point “new point”. Fig 1 show a quadrangular irregular network constructed from a sample data case. Black dots indicate original data and black triangular points indicate new points.


A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

Kang M, Wang M, Du Q - PLoS ONE (2015)

Quadrangular Irregular Network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4440715&req=5

pone.0127592.g001: Quadrangular Irregular Network.
Mentions: Let In,m = Hn∩Vm. In,m indicates the intersection of one horizontal vector and one vertical vector; there is only, at most, one element belonging to two different vectors. There are two situations for In,m: (1) If In,m is nonempty, call the element it contains Pn,m; (2) If In,m is empty, a derivative point must be constructed that will satisfy conditions (1) and (2) for Hn and Vm; call this point “new point”. Fig 1 show a quadrangular irregular network constructed from a sample data case. Black dots indicate original data and black triangular points indicate new points.

Bottom Line: The results indicate that the QIN method is the most accurate method tested.The difference in accuracy rank seems to be caused by the locations of the data points sampled.Although the QIN method has drawbacks, it is an alternative method for DTM construction.

View Article: PubMed Central - PubMed

Affiliation: School of Resource and Environment Science, Wuhan University, Wuhan 430079, China; Key laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China.

ABSTRACT
A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

No MeSH data available.