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Flitcroft, R.L., Brignon, W.R., Staab, B., Bellmore, J.R., Burnett, J., Burns, P., Cluer, B., Giannico, G., Helstab, J.M., Jennings, J., Mayes, C., Mazzacano, C., Mork, L., Meyer, K., Munyon, J., Penaluna, B.E., Powers, P., Scott, D.N. and Wondzell, S.M. (2022) Rehabilitating Valley Floors to a Stage 0 Condition: A Synthesis of Opening Outcomes. Frontiers in Environmental Science, 10, Article 892268.
https://doi.org/10.3389/fenvs.2022.892268

has been cited by the following article:

• TITLE: Sampling-Based Approaches to Estimating Two-Dimensional Large Wood Area from UAS Imagery

  AUTHORS: Matthew I. Barker, Jonathan D. Burnett, Francisco Mauro Gutiérrez, Steven M. Wondzell, Michael G. Wing

  KEYWORDS: UAS, Large in-Stream Wood, Sampling, Image Classification, River Restoration

  JOURNAL NAME: Journal of Geographic Information System, Vol.14 No.6, December 9, 2022

  ABSTRACT: Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high qualityhabitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m2 hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m2, 95% CI 10,613 - 23,952 m2) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m2, 95% CI 13,054 - 20,133 m2). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.