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Journal of Integrative Agriculture  2018, Vol. 17 Issue (09): 2096-2106    DOI: 10.1016/S2095-3119(17)61882-3
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Design of a spatial sampling scheme considering the spatial autocorrelation of crop acreage included in the sampling units
WANG Di, ZHOU Qing-bo, YANG Peng, CHEN Zhong-xin
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
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Abstract  Information on crop acreage is important for formulating national food polices and economic planning.  Spatial sampling, a combination of traditional sampling methods and remote sensing and geographic information system (GIS) technology, provides an efficient way to estimate crop acreage at the regional scale.  Traditional sampling methods require that the sampling units should be independent of each other, but in practice there is often spatial autocorrelation among crop acreage contained in the sampling units.  In this study, using Dehui County in Jilin Province, China, as the study area, we used a thematic crop map derived from Systeme Probatoire d’Observation de la Terre (SPOT-5) imagery, cultivated land plots and digital elevation model data to explore the spatial autocorrelation characteristics among maize and rice acreage included in sampling units of different sizes, and analyzed the effects of different stratification criteria on the level of spatial autocorrelation of the two crop acreages within the sampling units.  Moran’s I, a global spatial autocorrelation index, was used to evaluate the spatial autocorrelation among the two crop acreages in this study.  The results showed that although the spatial autocorrelation level among maize and rice acreages within the sampling units generally decreased with increasing sampling unit size, there was still a significant spatial autocorrelation among the two crop acreages included in the sampling units (Moran’s I varied from 0.49 to 0.89), irrespective of the sampling unit size.  When the sampling unit size was less than 3 000 m, the stratification design that used crop planting intensity (CPI) as the stratification criterion, with a stratum number of 5 and a stratum interval of 20% decreased the spatial autocorrelation level to almost zero for the maize and rice area included in sampling units within each stratum.  Therefore, the traditional sampling methods can be used to estimate the two crop acreages.  Compared with CPI, there was still a strong spatial correlation among the two crop acreages included in the sampling units belonging to each stratum when cultivated land fragmentation and ground slope were used as stratification criterion.  As far as the selection of stratification criteria and sampling unit size is concerned, this study provides a basis for formulating a reasonable spatial sampling scheme to estimate crop acreage.
Keywords:  crop acreage        spatial autocorrelation       sampling unit        planting intensity        cultivated land fragmentation        ground slope  
Received: 08 September 2017   Accepted:
Fund: This research was financially supported by the National Natural Science Foundation of China (41471365, 41531179).
Corresponding Authors:  Correspondence ZHOU Qing-bo, Tel: +86-10-82106237, E-mail: zhouqingbo@caas.cn   
About author:  WANG Di, Tel: +86-10-82105067, E-mail: wangdicaas@126.com;

Cite this article: 

WANG Di, ZHOU Qing-bo, YANG Peng, CHEN Zhong-xin. 2018. Design of a spatial sampling scheme considering the spatial autocorrelation of crop acreage included in the sampling units. Journal of Integrative Agriculture, 17(09): 2096-2106.

Anselin L. 1988. Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. pp. 86–95.
Benedetti R, Bee M, Espa G, Piersimoni F. 2010. Agricultural Survey Methods. John Wiley and Sons, Chippenham. pp. 125–136.
Betts M G, Diamond A W, Forbes G J, Villard M A, Gunn J S. 2006. The importance of spatial autocorrelation, extent and resolution in predicting forest bird occurrence. Ecological Modelling, 191, 197–224.
Buarque D C, Clarke R T, Mendes C A B. 2010. Spatial correlation in precipitation trends in the Brazilian Amazon. Jouranl of Geophysical Research, 115, 1–14.
Carfagna E, Gallego F J. 2005. Using remote sensing for agricultural statistics. International Statistical Review, 73, 389–404.
Cliff A D, Ord J K. 1981. Spatial Processes: Models and Applications. Pion, London. pp. 2–10.
Fisher M, Scholten H J, Unwin D. 1996. Spatial Analytical Perspectives on GIS. Taylor & Francis, London. pp. 104–112.
Frutos A, Olea P P, Vera R. 2007. Analyzing and modelling spatial distribution of summering lesser kestrel: The role of spatial autocorrelation. Ecological Modelling, 200, 33–44.
Gallego F J. 2004. Remote sensing and land cover area estimation. International Journal of Remote Sensing, 25, 3019–3047.
Gertner G, Wang G X, Anderson A B, Howard H. 2007. Combining stratification and up-scaling method-block cokriging with remote sensing imagery for sampling and mapping an erosion cover factor. Ecological Informatics, 2, 373–386.
Gilbert B, Lowell K. 1997. Forest attributes and spatial autocorrelation and interpolation: Effects of alternative sampling schemata in the boreal forest. Landscape and Urban Planning, 37, 235–244.
Goodchild M F, Anselin L, Deichmann U. 1993. A framework for the areal interpolation of socio-economic data. Environmental and Planning, 25, 383–397.
Griffith D A. 1988. Spatial autocorrelation: A primer. Economic Geography, 64, 88–92.
Haining R P. 2003. Spatial Data Analysis: Theory and Practice. Cambridge University Press, Cambridge. pp. 54–62.
Hoeting J A. 2009. The importance of accounting for spatial and temporal correlation in analyses of ecological data. Ecological Applications, 19, 574–577.
Holmberg H, Lundevaller E H. 2015. A test for robust detection of residual spatial autocorrelation with application to mortality rates in Sweden. Spatial Statistics, 14, 365–381.
Jardim E, Ribeiro P J. 2007. Geostatistical assessment of sampling designs for Portuguese bottom trawl surveys. Fisheries Research, 85, 239–247.
Keitt T H, Bjornstad O N, Dixon P M, Citron-Pousty S. 2002. Accounting for spatial pattern when modeling organism-environment interactions. Ecography, 25, 616–625.
Kulkarni M A, Singh A, Mohanty U C. 2012. Effect of spatial correlation on regional trends in rain events over India. Theoretical and Applied Climatology, 109, 497–505.
Legendre P, Legendre L. 1998. Numerical Ecology. Elsevier, Amsterdam. pp. 44–52.
Lichstein J W, Simons T, Shriner S A, Franzreb K E. 2002. Spatial autocorrelation and autoregressive models in ecology. Ecological Monographs, 72, 445–463.
Macdonald R B, Hall F G. 1980. Global crop forecasting. Science, 208, 670–679.
Melecky L. 2015. Spatial autocorrelation method for local analysis of the EU. Procedia Economics and Finance, 23, 1102–1109.
Nagendra H, Pareeth S, Ghate R. 2006. People within parks-forest villages, landcover change and landscape fragmentation in the Tadoba Andhari Tiger Reserve, India. Applied Geography, 26, 96–112.
NBSC (National Bureau of Statistics of the People’s Republic of China). 2002. The Operation Manual on Rural Statistics and Survey. China Statistics Press, Beijing. pp. 40–46. (in Chinese)
Overmars K P, Koning G H J, Veldkamp A. 2003. Spatial autocorrelation in multi-scale land use models. Ecological Modelling, 164, 257–270.
Rest K L, Pinaud D, Bretagnolle V. 2013. Accounting for spatial autocorrelation from model selection to statistical inference: Application to a national survey of a diurnal raptor. Ecological Informatics, 14, 17–24.
Reynolds C A, Yitayew M, Slack D C. 2000. Estimating crop yields and production by integrating the FAO crop specific water balance model with real-time satellite data and ground-based ancillary data. International Journal of Remote Sensing, 21, 3487–3508.
Song X P, Peter V P, Alexander K, LeeAnn K, CarlosM D B, Amy H, Ahmad K, Bernard A, Stephen V S, Matthew C H. 2017. National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sensing of Environment, 190, 383–395.
Tao F L, Masayuki Y, Zhan Z. 2005. Remote sensing of crop production in China by production efficiency models: models comparisons estimates and uncertainties. Ecology Modeling, 183, 385–396.
Tobler W. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.
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