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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
Wang, Juanle1,2; Zhu, Junxiang3; Han, Xuehua1,4
2018
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume7Issue:1Pages:20
Corresponding AuthorWang, Juanle(wangjl@igsnrr.ac.cn) ; Zhu, Junxiang(junxiang.zhu@postgrad.curtin.edu.au)
AbstractSemivariograms 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.
Keywordoptimal resolution Monte Carlo simulation semivariogram natural resource survey remotely sensed image interpretation
DOI10.3390/ijgi7010013
WOS KeywordSPATIAL-RESOLUTION ; DIGITAL IMAGES ; FOREST ; CLASSIFICATION ; VARIOGRAMS ; SUPPORT ; CLIMATE ; CROWN ; SIZE
Indexed BySCI
Language英语
Funding ProjectNational Science Foundation of China[41421001] ; Science & Technology Basic Research Program of China[2013FY114600] ; Science & Technology Basic Research Program of China[2011FY110400] ; China Knowledge Center for Engineering Sciences and Technology[CKCEST-2017-3-1] ; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science[TSYJS03]
Funding OrganizationNational Science Foundation of China ; Science & Technology Basic Research Program of China ; China Knowledge Center for Engineering Sciences and Technology ; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000424123000013
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/56928
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Juanle; Zhu, Junxiang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
3.Curtin Univ, Sch Built Environm, Australasian Joint Res Ctr Bldg Informat Modellin, Bentley, WA 6102, Australia
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Wang, Juanle,Zhu, Junxiang,Han, Xuehua. 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[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2018,7(1):20.
APA Wang, Juanle,Zhu, Junxiang,&Han, Xuehua.(2018).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 INTERNATIONAL JOURNAL OF GEO-INFORMATION,7(1),20.
MLA Wang, Juanle,et al."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 INTERNATIONAL JOURNAL OF GEO-INFORMATION 7.1(2018):20.
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