IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v33y2022i7ne2750.html
   My bibliography  Save this article

From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources

Author

Listed:
  • Rosa Maria Di Biase
  • Lorenzo Fattorini
  • Sara Franceschi
  • Mirko Grotti
  • Nicola Puletti
  • Piermaria Corona

Abstract

A completely data‐driven, design‐based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least‐squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave‐one‐out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:envmet:v:33:y:2022:i:7:n:e2750
    DOI: 10.1002/env.2750
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2750
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.2750?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
    3. Raphaël Jauslin & Yves Tillé, 2020. "Spatial Spread Sampling Using Weakly Associated Vectors," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 431-451, September.
    4. L. Fattorini & M. Marcheselli & L. Pratelli, 2018. "Design-Based Maps for Finite Populations of Spatial Units," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 686-697, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alice Bartolini & Rosa Maria Di Biase & Lorenzo Fattorini & Sara Franceschi & Agnese Marcelli, 2021. "Design-based mapping of plant species presence, association and richness by nearest-neighbor interpolation," Department of Economics University of Siena 854, Department of Economics, University of Siena.
    2. 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.
    3. Kunihama, T. & Herring, A.H. & Halpern, C.T. & Dunson, D.B., 2016. "Nonparametric Bayes modeling with sample survey weights," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 41-48.
    4. Marivoet, Wim & De Herdt, Tom, 2017. "From figures to facts: making sense of socio-economic surveys in the Democratic Republic of the Congo (DRC)," IOB Analyses & Policy Briefs 23, Universiteit Antwerpen, Institute of Development Policy (IOB).
    5. J. Andrew Royle, 2009. "Analysis of Capture–Recapture Models with Individual Covariates Using Data Augmentation," Biometrics, The International Biometric Society, vol. 65(1), pages 267-274, March.
    6. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    7. Bijak Jakub & Bryant Johan & Gołata Elżbieta & Smallwood Steve, 2021. "Preface," Journal of Official Statistics, Sciendo, vol. 37(3), pages 533-541, September.
    8. Tenan, Simone & Rotger Vallespir, Andreu & Igual, José Manuel & Moya, Óscar & Royle, J. Andrew & Tavecchia, Giacomo, 2013. "Population abundance, size structure and sex-ratio in an insular lizard," Ecological Modelling, Elsevier, vol. 267(C), pages 39-47.
    9. Lorenzo Fattorini & Marzia Marcheselli & Caterina Pisani & Luca Pratelli, 2022. "Design‐based properties of the nearest neighbor spatial interpolator and its bootstrap mean squared error estimator," Biometrics, The International Biometric Society, vol. 78(4), pages 1454-1463, December.
    10. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
    11. Hwanhee Hong & Kara E. Rudolph & Elizabeth A. Stuart, 2017. "Bayesian Approach for Addressing Differential Covariate Measurement Error in Propensity Score Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1078-1096, December.
    12. Geoffrey Jones & Wesley O. Johnson, 2014. "Prior Elicitation: Interactive Spreadsheet Graphics With Sliders Can Be Fun, and Informative," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 42-51, February.
    13. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    14. 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.
    15. Lukachko, Alicia & Hatzenbuehler, Mark L. & Keyes, Katherine M., 2014. "Structural racism and myocardial infarction in the United States," Social Science & Medicine, Elsevier, vol. 103(C), pages 42-50.
    16. Yves Tillé, 2022. "Some Solutions Inspired by Survey Sampling Theory to Build Effective Clinical Trials," International Statistical Review, International Statistical Institute, vol. 90(3), pages 481-498, December.
    17. 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.
    18. Ivan Faiella, 2010. "The use of survey weights in regression analysis," Temi di discussione (Economic working papers) 739, Bank of Italy, Economic Research and International Relations Area.
    19. Ciro Velasco-Cruz & Luis Fernando Contreras-Cruz & Eric P. Smith & José E. Rodríguez, 2016. "A Varying Coefficients Model For Estimating Finite Population Totals: A Hierarchical Bayesian Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 548-568, September.
    20. Malay Ghosh, 2012. "Finite population sampling: a model-design synthesis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 13(2), pages 235-242, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:33:y:2022:i:7:n:e2750. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.