Еmpirical method for estimation of the optimum size of random point samples for assessment areas of land cover from space images


View or download the article (Rus)

About the Author

Pavel A. Ukrainskiy

Belgorod State National Research University, Federal and Regional Centre for aerospace and ground monitoring of objects and natural resources,
Pobedy str., 85, 308015, Belgorod, Russia;
E-mail: pa.ukrainski@gmail.com


A promising fast method for estimating land cover areas from satellite imagery is the use of random point sampling. This method allows you to obtain area values without spatially continuous mapping of land areas. The accuracy of the area estimate by this method depends on the sample size. The presented work describes a method for empirically finding the optimal sample size. To use this method, you must select a key site for which a reference land cover exists. For the key site, we perform multiple generation of samples of different sizes. Further, using these samples, we estimate the area of land cover. Comparison of the obtained areas with the reference areas allows you to calculate the measurement error. Analysis of the mean and the range of errors for different sample sizes allows us to identify the moment when the error ceases to decrease significantly with an increase in the sample size. This sample size is optimal. We tested the proposed method on the example of the Kalach Upland. The size range from 100 to 3000 sampling points per key site is analyzed (the size of the sampling in the row increases by 100 points). For each element of this row, we created 1000 samples of the corresponding size. We then analyzed the effect of sample size on the overall relative error in area estimates. The analysis showed that for the investigated key site the optimal sample size is 1000 points (1.1 points/km2). With this sample size, the overall relative error in determining areas was 4.0 % on average, and the maximum error was 9.9 %. Similar accuracy should be at the same sample size for other uplands in the foreststeppe and steppe zones of the East European plain.


land cover, area measurement, random sample, measurement error, elbow method.


  1. Barry S.C. How much impact does the choice of a random number generator really have? International Journal of Geographical Information Science. 2011. V. 25. No. 4. P. 523–530. DOI: 10.1080/13658810903093185.
  2. Bivand R.S., Pebesma E., Gomez Rubio V. Applied spatial data analysis with R. New York: Springer-Verlag, 2013. 405 p. DOI: 10.1007/978-1-4614-7618-4.
  3. Gallego F.J. Review of the main remote sensing methods for crop area estimates. ISPRS Archives, 2006. V. XXXVI. No. 8/W48. P. 65–70.
  4. Milkov F.N., Akhtyrtseva N.I., Akhtyrtsev B.P. Kalachskaya Upland Voronezh: Voronezh University Publishing House, 1972. 178 p.
  5. Pebesma E.J., Bivand R.S. Classes and methods for spatial data in R. R News, 2005. V. 5. No. 2. P. 9–13.
  6. Thyagharajan K.K., Vignesh T. Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: a review. Archives of Computational Methods in Engineering, 2019. V. 26. No. 2. P. 275–301. DOI: 10.1007/s11831-017-9239-y.
  7. Ukrainskiy P.A. The choice of the optimal order of the neighborhood for separation a spatial point pattern into a cluster and noise component (by the example of analysis of location of antique settlements in the Kerch peninsula). InterCarto. InterGIS. 2020. V. 26 (4). P. 257–265.
  8. Van Niel K.P., Laffan S.W. Gambling with randomness: the use of pseudorandom number generators in GIS. International Journal of Geographical Information Science. 2003. V. 17. No. 1. P. 49–68. DOI: 10.1080/713811743.

For citation: Ukrainskiy P.A. Еmpirical method for estimation of the optimum size of random point samples for assessment areas of land cover from space images InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2021. V. 27. Part 2. P. 368–378. DOI: 10.35595/2414-9179-2021-2-27-368-378 (In Russian)