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QName | Type | Fixed | Default | Use | Inheritable | Annotation | |
---|---|---|---|---|---|---|---|
arrange | restriction of xsd:NMTOKEN | optional | |||||
id | xsd:ID | optional | |||||
specific-use | xsd:string | optional | |||||
xml:base | xs:anyURI | optional |
|
Element Group | subsup.class |
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Semivariograms have been widely used in research to obtain optimal resolutions for ground features. To obtain the semivariogram curve and its attributes ( |
range |
and |
sill |
), parameters including |
sample size |
( |
SS |
), |
maximum distance |
( |
MD |
), and |
group number |
( |
GN |
) have to be defined, as well as a mathematic model for fitting the curve. However, a clear guide on parameter setting and model selection is currently not available. In this study, a Monte Carlo simulation-based approach (MCS) is proposed to enhance the performance of semivariograms by optimizing the parameters, and case studies in three regions are conducted to determine the optimal resolution for natural resource surveys. Those parameters are optimized one by one through several rounds of MCS. The result shows that exponential model is better than sphere model; |
sample size |
has a positive relationship with R |
2 |
, while the |
group number |
has a negative one; increasing the simulation number could improve the accuracy of estimation; and eventually the optimized parameters improved the performance of semivariogram. In case study, the average sizes for three general ground features (grassland, farmland, and forest) of three counties (Ansai, Changdu, and Taihe) in different geophysical locations of China were acquired and compared, and imagery with an appropriate resolution is recommended. The results show that the ground feature sizes acquired by means of MCS and optimized parameters in this study match well with real land cover patterns. |