How much can natural resource inventory benefit from finer resolution auxiliary data?

Zhengyang Hou, Ronald E. McRoberts, Göran Ståhl, Petteri Packalen, Jonathan A. Greenberg, Qing Xu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

For remote sensing-assisted natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the response variable of interest was firewood volume (m3/ha). A sample consisting of 160 field plots was selected from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, μ, and the variance of the estimator of the population mean, Vμ̂, were estimated using two inferential frameworks, model-based and model-assisted, and compared. For each framework, Vμ̂ was estimated both analytically and empirically. Empirical variances were estimated using bootstrapping that accounted for the two-stage sampling. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributed to greater precision for estimators of population parameter, but despite the finer spatial resolution of RapidEye, the increase was only marginal, on the order of 10% for model-based variance estimators and 36% for model-assisted variance estimators; (2) subpixel information on texture was marginally beneficial for inference of large area population parameters; (3) RapidEye did not offer enough of an improvement to justify its cost relative to the free Landsat 8 imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) sampling distribution for the model-based V̂μ̂ was more concentrated and smaller on the order of 42% to 59% than that for the model-assisted V̂μ̂, suggesting superior consistency and efficiency of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

Original languageEnglish (US)
Pages (from-to)31-40
Number of pages10
JournalRemote Sensing of Environment
Volume209
DOIs
StatePublished - May 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Inc.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Bootstrapping
  • Model-assisted estimators
  • Model-based inference
  • Modeling
  • Natural resource inventory
  • Remote sensing
  • Two-stage sampling
  • Uncertainty

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