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Design‐based strategies for sampling spatial units from regular grids with applications to forest surveys, land use, and land cover estimation

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  • Lorenzo Fattorini
  • Piermaria Corona
  • Gherardo Chirici
  • Maria Chiara Pagliarella

Abstract

The purpose of this paper is to compare some spatial strategies for sampling polygons onto a grid partitioning a study area. Most of the schemes considered in the paper are aimed at avoiding the selection of neighboring polygons. When one or more auxiliary variables are similar or well correlated with the values of the survey variable, the auxiliary information is adopted at estimation level by means of the difference or the regression estimators, or at design level, using the values of auxiliary variables to determine the inclusion probabilities. Applications to large‐scale forest inventories, land use estimation, and forest cover estimation are discussed. A simulation study is performed to compare the adopted strategies in terms of bias (if present), accuracy, and accuracy estimation. The simulation is designed to mimic forest inventories and forest cover estimation, starting from some real situations. An application to plan future surveys for land use estimation in Italy is reported. Copyright © 2015 John Wiley & Sons, Ltd.

Suggested Citation

  • Lorenzo Fattorini & Piermaria Corona & Gherardo Chirici & Maria Chiara Pagliarella, 2015. "Design‐based strategies for sampling spatial units from regular grids with applications to forest surveys, land use, and land cover estimation," Environmetrics, John Wiley & Sons, Ltd., vol. 26(3), pages 216-228, May.
    • Handle: RePEc:wly:envmet:v:26:y:2015:i:3:p:216-228
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    Citations

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    Cited by:

    1. Tomasz Bąk, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    2. Guillaume Chauvet & Ronan Le Gleut, 2021. "Inference under pivotal sampling: Properties, variance estimation, and application to tesselation for spatial sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 108-131, March.
    3. R. Benedetti & F. Piersimoni & P. Postiglione, 2017. "Alternative and complementary approaches to spatially balanced samples," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 249-264, December.
    4. Chauvet, Guillaume & Ruiz-Gazen, Anne, 2017. "A comparison of pivotal sampling and unequal probability sampling with replacement," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 1-5.
    5. Rosa Maria Di Biase & Lorenzo Fattorini & Sara Franceschi & Mirko Grotti & Nicola Puletti & Piermaria Corona, 2022. "From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    6. Matt Higham & Jay Ver Hoef & Lisa Madsen & Andy Aderman, 2021. "Adjusting a finite population block kriging estimator for imperfect detection," Environmetrics, John Wiley & Sons, Ltd., vol. 32(1), February.
    7. L. Fattorini & M. Marcheselli & C. Pisani & L. Pratelli, 2017. "Design-based asymptotics for two-phase sampling strategies in environmental surveys," Biometrika, Biometrika Trust, vol. 104(1), pages 195-205.
    8. ak Tomasz B, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    9. Sara Franceschi & Rosa Maria Di Biase & Agnese Marcelli & Lorenzo Fattorini, 2022. "Some Empirical Results on Nearest-Neighbour Pseudo-populations for Resampling from Spatial Populations," Stats, MDPI, vol. 5(2), pages 1-16, April.

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