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ISPRS Int. J. Geo-Inf. 2018, 7(1), 13; https://doi.org/10.3390/ijgi7010013

Using Monte Carlo Simulation to Improve the Performance of Semivariograms for Choosing the Remote Sensing Imagery Resolution for Natural Resource Surveys: Case Study on Three Counties in East, Central, and West China

1,2,* , 3,* and 1,4
1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
Australasian Joint Research Centre for Building Information Modelling, School of Built Environment, Curtin University, Bentley, WA 6102, Australia
4
University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 23 December 2017 / Accepted: 28 December 2017 / Published: 4 January 2018
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Abstract

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 R2, 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. View Full-Text
Keywords: optimal resolution; Monte Carlo simulation; semivariogram; natural resource survey; remotely sensed image interpretation optimal resolution; Monte Carlo simulation; semivariogram; natural resource survey; remotely sensed image interpretation
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Wang, J.; Zhu, J.; Han, X. Using Monte Carlo Simulation to Improve the Performance of Semivariograms for Choosing the Remote Sensing Imagery Resolution for Natural Resource Surveys: Case Study on Three Counties in East, Central, and West China. ISPRS Int. J. Geo-Inf. 2018, 7, 13.

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