Skip to Main Content
How much can natural resource inventory benefit from finer resolution auxiliary data?Author(s): Zhengyang Hou; Ronald E. McRoberts; Göran Ståhl; Petteri Packalen; Jonathan A. Greenberg; Qing Xu
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
View PDF (881.0 KB)
DescriptionFor 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 d0esign. Models were fit using weighted least squares; the population mean, μ, and the variance of the estimator of the population mean, V (μ̅), were estimated using two inferential frameworks, model-based and model-assisted, and compared. For each framework, V (μ̅) 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 V̅ (μ̅) was more concentrated and smaller on the order of 42% to 59% than that for the model-assisted V̅(μ̅), 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.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationHou, Zhengyang; McRoberts, Ronald E.; Ståhl, Göran; Packalen, Petteri; Greenberg, Jonathan A.; Xu, Qing. 2018. How much can natural resource inventory benefit from finer resolution auxiliary data? Remote Sensing of Environment. 209: 31-40. https://doi.org/10.1016/j.rse.2018.02.039.
KeywordsNatural resource inventory, Two-stage sampling, Modeling, Model-based inference, Model-assisted estimators, Bootstrapping, Uncertainty, Remote sensing
- Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.
- Effects of temporally external auxiliary data on model-based inference
- Hybrid estimators for mean aboveground carbon per unit area
XML: View XML