Traceable Calibration, Performance Metrics, and Uncertainty Estimates of Minirhizotron Digital Imagery for Fine-Root Measurements

<div><p>Even though fine-root turnover is a highly studied topic, it is often poorly understood as a result of uncertainties inherent in its sampling, <i>e.g.</i>, quantifying spatial and temporal variability. While many methods exist to quantify fine-root turnover, use of minirhizotrons has increased over the last two decades, making sensor errors another source of uncertainty. Currently, no standardized methodology exists to test and compare minirhizotron camera capability, imagery, and performance. This paper presents a reproducible, laboratory-based method by which minirhizotron cameras can be tested and validated in a traceable manner. The performance of camera characteristics was identified and test criteria were developed: we quantified the precision of camera location for successive images, estimated the trueness and precision of each camera's ability to quantify root diameter and root color, and also assessed the influence of heat dissipation introduced by the minirhizotron cameras and electrical components. We report detailed and defensible metrology analyses that examine the performance of two commercially available minirhizotron cameras. These cameras performed differently with regard to the various test criteria and uncertainty analyses. We recommend a defensible metrology approach to quantify the performance of minirhizotron camera characteristics and determine sensor-related measurement uncertainties prior to field use. This approach is also extensible to other digital imagery technologies. In turn, these approaches facilitate a greater understanding of measurement uncertainties (signal-to-noise ratio) inherent in the camera performance and allow such uncertainties to be quantified and mitigated so that estimates of fine-root turnover can be more confidently quantified.</p></div

Efficacy of different sampling strategies at measuring T_s and SWC to within 10% of the spatial mean with 90% confidence at scales of ∼1 ha.

<p>Values represent the percent of sites where the corresponding sampling strategy would achieve the accuracy requirement. The x-axis is based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#pone.0083216.e012" target="_blank">Equation 6c</a> and the y-axis is based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#pone.0083216.e015" target="_blank">Equation 7c</a>. Legend in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#pone-0083216-g014" target="_blank">Figure 14</a>.</p

Spatial Variation in Soil Properties among North American Ecosystems and Guidelines for Sampling Designs

<div><p>Soils are highly variable at many spatial scales, which makes designing studies to accurately estimate the mean value of soil properties across space challenging. The spatial correlation structure is critical to develop robust sampling strategies (<i>e.g.</i>, sample size and sample spacing). Current guidelines for designing studies recommend conducting preliminary investigation(s) to characterize this structure, but are rarely followed and sampling designs are often defined by logistics rather than quantitative considerations. The spatial variability of soils was assessed across ∼1 ha at 60 sites. Sites were chosen to represent key US ecosystems as part of a scaling strategy deployed by the National Ecological Observatory Network. We measured soil temperature (T_s) and water content (SWC) because these properties mediate biological/biogeochemical processes below- and above-ground, and quantified spatial variability using semivariograms to estimate spatial correlation. We developed quantitative guidelines to inform sample size and sample spacing for future soil studies, <i>e.g.</i>, 20 samples were sufficient to measure T_s to within 10% of the mean with 90% confidence at every temperate and sub-tropical site during the growing season, whereas an order of magnitude more samples were needed to meet this accuracy at some high-latitude sites. SWC was significantly more variable than T_s at most sites, resulting in at least 10× more SWC samples needed to meet the same accuracy requirement. Previous studies investigated the relationship between the mean and variability (<i>i.e.</i>, sill) of SWC across space at individual sites across time and have often (but not always) observed the variance or standard deviation peaking at intermediate values of SWC and decreasing at low and high SWC. Finally, we quantified how far apart samples must be spaced to be statistically independent. Semivariance structures from 10 of the 12-dominant soil orders across the US were estimated, advancing our continental-scale understanding of soil behavior.</p></div

Coefficient of variation calculated by dividing the standard deviation by the mean (CV_Traditional) versus coefficient of variation calculated using the semivariogram sill (CV_Sill) for T_s, SWC, and CV_Sill for T_s versus SWC. Dotted line is 1∶1.

<p>Coefficient of variation calculated by dividing the standard deviation by the mean (CV_Traditional) versus coefficient of variation calculated using the semivariogram sill (CV_Sill) for T_s, SWC, and CV_Sill for T_s versus SWC. Dotted line is 1∶1.</p

The sites were broadly representative of the US.

<p>Percent occurrence of land in latitudinal (a), longitudinal (b), elevational (c), and soil order (d) categories in the US (black bars), and our study sites (white bars). The study sites also occupied a wide range of climates (e) and T_s and SWC conditions at the time of samplings (f). Error bars in (f) represent ±1 SE.</p

Semivariogram range for T_s, SWC, and the largest range for T_s or SWC at each site versus the cumulative proportion of sites.

<p>A logistic curve was fitted to the data with R² of 1.00, 0.99, and 0.99 for T_s, SWC, and T_s and SWC, respectively. Equations for the curves can be found in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#s3" target="_blank">results</a> section (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#pone.0083216.e010" target="_blank">Eq. 6a</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083216#pone.0083216.e012" target="_blank">c</a>).</p

Semivariogram range for T_s (left column) and SWC (right column) at each site in relation to ecosystem type (a, b), canopy structure (c, d), and soil type (e, f).

<p>Error bars represent ±1 SE. Bars with different letter were significantly different (p<0.05) based on Tukey HSD tests. The letter “A” corresponds to the largest least square mean(s) and subsequent letters (B, C, …) correspond to progressively smaller least square means.</p

Mean sRGB values (and expanded uncertainties) as measured by the CI-600.

<p>Expanded uncertainties represent the repeatability of the camera for the specific color patch. Uncertainties for values at the limits of the sRGB spectrum are not provided. per color patch.</p><p>Mean sRGB values (and expanded uncertainties) as measured by the CI-600.</p

Correlation coefficients and statistical significance for T_s and SWC semivariogram properties and derived values.

<p>Correlation coefficients and statistical significance for T_s and SWC semivariogram properties and derived values.</p

Mean sRGB values (and expanded uncertainties) as measured by the AMR-A.

<p>Expanded uncertainties represent the repeatability of the camera for the specific color patch. Uncertainties for values at the limits of the sRGB spectrum are not provided. per color patch.</p><p>Mean sRGB values (and expanded uncertainties) as measured by the AMR-A.</p