A Regularized Raking Estimator for Small-Area Mapping from Forest Inventory Surveys

Abstract

In this paper; we propose a new estimator for creating expansion factors for survey plots in the US Forest Service (USFS) Forest Inventory and Analysis program. This estimator was previously used in the GIS literature; where it was called Penalized Maximum Entropy Dasymetric Modeling. We show here that the method is a regularized version of the raking estimator widely used in sample surveys. The regularized raking method differs from other predictive modeling methods for integrating survey and ancillary data; in that it produces a single set of expansion factors that can have a general purpose which can be used to produce small-area estimates and wall-to-wall maps of any plot characteristic. This method also differs from other more widely used survey techniques; such as GREG estimation; in that it is guaranteed to produce positive expansion factors. Here, we extend the previous method to include cross-validation; and provide a comparison to expansion factors between the regularized raking and ridge GREG survey calibration.

  • Citation: Nagle, Nicholas N.; Schroeder, Todd A.; Rose, Brooke. 2019. A Regularized Raking Estimator for Small-Area Mapping from Forest Inventory Surveys. Forests. 10(11): 1045. https://doi.org/10.3390/f10111045.
  • Keywords: FIA, Forest inventory, Small-area estimation, Survey weight
  • Posted Date: August 26, 2021
  • Modified Date: August 31, 2021
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