IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v29y2024i4d10.1007_s13253-024-00611-3.html
   My bibliography  Save this article

Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska

Author

Listed:
  • Andrew O. Finley

    (Michigan State University
    Michigan State University)

  • Hans-Erik Andersen

    (Pacific Northwest Research Station)

  • Chad Babcock

    (University of Minnesota)

  • Bruce D. Cook

    (Goddard Space Flight Center)

  • Douglas C. Morton

    (Goddard Space Flight Center)

  • Sudipto Banerjee

    (University of California)

Abstract

A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory plot measurements. The model is motivated by the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) objective to provide biomass estimates for the remote Tanana Inventory Unit (TIU) in interior Alaska. The proposed model yields stratum-level biomass estimates for arbitrarily sized areas. Model-based estimates are compared with the TIU FIA design-based post-stratified estimates. Model-based small area estimates (SAEs) for two experimental forests within the TIU are compared with each forest’s design-based estimates generated using a dense network of independent inventory plots. Model parameter estimates and biomass predictions are informed using FIA plot measurements, LiDAR data that are spatially aligned with a subset of the FIA plots, and complete coverage remotely detected data used to define landuse/landcover stratum and percent forest canopy cover. Results support a model-based approach to estimating forest parameters when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods. Supplementary materials accompanying this paper appear on-line

Suggested Citation

  • Andrew O. Finley & Hans-Erik Andersen & Chad Babcock & Bruce D. Cook & Douglas C. Morton & Sudipto Banerjee, 2024. "Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 695-722, December.
    • Handle: RePEc:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-024-00611-3
    DOI: 10.1007/s13253-024-00611-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-024-00611-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-024-00611-3?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Finley, Andrew O. & Banerjee, Sudipto & MacFarlane, David W., 2011. "A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes With Uncertain Forest Areas," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 31-48.
    2. Qian Ren & Sudipto Banerjee, 2013. "Hierarchical Factor Models for Large Spatially Misaligned Data: A Low-Rank Predictive Process Approach," Biometrics, The International Biometric Society, vol. 69(1), pages 19-30, March.
    3. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    4. Alec M. Chan‐Golston & Sudipto Banerjee & Mark S. Handcock, 2020. "Bayesian inference for finite populations under spatial process settings," Environmetrics, John Wiley & Sons, Ltd., vol. 31(3), May.
    5. 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.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    8. 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.
    9. Paul B. May & Andrew O. Finley & Ralph O. Dubayah, 2024. "A Spatial Mixture Model for Spaceborne Lidar Observations Over Mixed Forest and Non-forest Land Types," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(4), pages 671-694, December.
    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. 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.
    2. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    3. Marco Gramatica & Peter Congdon & Silvia Liverani, 2021. "Bayesian modelling for spatially misaligned health areal data: A multiple membership approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 645-666, June.
    4. Lu Zhang & Sudipto Banerjee & Andrew O. Finley, 2021. "High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    5. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    6. Sudipto Banerjee, 2024. "Finite Population Survey Sampling: An Unapologetic Bayesian Perspective," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 95-124, November.
    7. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    8. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    9. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    10. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    11. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    12. Cristian David Correa-Álvarez & Juan Carlos Salazar-Uribe & Luis Raúl Pericchi-Guerra, 2023. "Bayesian multilevel logistic regression models: a case study applied to the results of two questionnaires administered to university students," Computational Statistics, Springer, vol. 38(4), pages 1791-1810, December.
    13. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    14. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    15. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    16. Marco Minozzo & Luca Bagnato, 2021. "A unified skew‐normal geostatistical factor model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    17. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    18. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    19. Fourrier-Nicolaï Edwin & Lubrano Michel, 2024. "Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 319-336, April.
    20. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.

    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:spr:jagbes:v:29:y:2024:i:4:d:10.1007_s13253-024-00611-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.