id

10.3390/ijgi7010013

ijgi-07-00013

Article

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

https://orcid.org/0000-0002-5641-0813

Wang

Juanle

1

2

*

Zhu

Junxiang

3

*

Han

Xuehua

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;

hanxh@lreis.ac.cn

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

*

Correspondence:

wangjl@igsnrr.ac.cn

(J.W.);

junxiang.zhu@postgrad.curtin.edu.au

(J.Z.); Tel.: +86-010-6488-8016 (J.W.)

04

01

2018

01

2018

7

1

13

20

09

2017

28

12

2017

© 2018 by the authors.

2018

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (

http://creativecommons.org/licenses/by/4.0/

).

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.

optimal resolution

Monte Carlo simulation

semivariogram

natural resource survey

remotely sensed image interpretation