A Study of Forested Wetland Soil Color and Biogeochemistry in the Region of Northern Virginia: Implications for Wetland Ecology and Management

Abstract

Soil color patterns are essential to understand hydrologic regime and biogeochemical processes in wetland ecosystems. The Munsell Soil Color Chart (MSCC) has been traditionally and predominantly used to identify and quantify hydric soil field indicators that are based in soil color and redoximorphic features (RMFs) using Munsell hue (H), value (V), and chroma (CM). In the first part of this study, hydro-physicochemical (HP) settings and soil color attributes including redoximorphic features (RMFs) were assessed at four forested wetlands in Northern Virginia, USA, to identify whether four simply measurable HP attributes—inundation/saturation frequency, bulk density, soil moisture, and percent sand—can provide an explanatory framework for characterizing and classifying soil color attributes related to hydric soil field indicators. Study plots (n = 16) were grouped by site for initial characterizations and comparisons of HP (n = 4) and color attributes (n = 11); each attribute was additionally characterized and compared between three HP-based clusters formulated through k-means clustering analysis. Whereas only one HP attribute (inundation/saturation frequency) significantly differed between sites, all HP attributes but percent sand differed between HP-based clusters (p < 0.05), with PCA Dimensions 1 and 2 explaining over 80% of variability in plot HP attributes. Moreover, more sets of color attributes were significantly different when plots were grouped by HP-based cluster (n = 5: frequency of concentrations, non-matrix color count, hue, chroma, and depth to concentrations) compared to by site (n = 3: value, frequency of depleted matrices, depth to depletions) (p < 0.10). Simply measurable HP attributes are thus closely associated with certain soil RMF and color characteristics beyond site identity, potentially serving as a suite of measurements that can be adopted to assess and monitor RMFs indicative of wetland soils. As an alternative to the MSCC, several simple, low-cost alternatives have become recently available and may be able to complement the MSCC in soil color assessment. An intensive literature review on studies utilizing different methods was conducted to identify and quantify hydric soil colors and associated patterns; these include 1) the MSCC, 2) the Nix Color Sensor (Nix), 3) mobile phone camera (MPC) and medium-end digital camera photography, and 4) colorimetry and spectrometry. A review of these methods elucidates their respective strengths and weaknesses and highlights the importance of considering study-specific attributes in determining which method to choose for field studies of hydric soil colors. RMFs require methods capable of capturing small and heterogeneous soil surfaces and features such that the MSCC and digital photography are the most appropriate methods; on the other hand, the Nix provides rapid assessment of soil color that does not necessitate rigorous training to overcome biases that might come about in more subjective methods such as the MSCC. Overall, all alternative methods reviewed have their own merits and capacity to complement measurements made by the MSCC. While the MSCC is the most frequently used, well-established field method for reading soil color, the Nix is an inexpensive, app-based alternative that can complement or potentially substitute for the MSCC. Using the Nix and the MSCC, soil colors were measured from each forested site in Northern Virginia (n = 4). For each observed color, 3 MSCC variables and 15 Nix variables were collected in the field; a methodology was established to use these measured (M) variables to derive 9 Nix calculated (C) variables. A stepwise correlation identified Nix variables most suitable for relating the Nix to each of the MSCC attributes; ultimately, Munsell H, V, and CM were deemed to be best represented by HRGB calculated from the RGB color space (ρ = 0.56), L from the CIE–Lab color space (ρ = 0.73), and ẑ = Z/(X+Y+Z) from the XYZ color space (ρ = ˗0.80), respectively (p < 0.001). The corresponding explanatory powers of final Nix variables (i.e., HRGB, L, and ẑ) for H, V, and CM were 26%, 54%, and 62%, respectively (p < 0.01). Significant differences in ẑ between soils identified as hydric and nonhydric, but lack of nonoverlapping ranges, indicate a potential for the Nix to complement the MSCC in assessing wetland soil color in an accessible and reproducible manner, including hydric soil identifications for wetland delineation practices. Further study with more data over various types of soils is necessary to establish stronger relationships between the Nix and MSCC. Nonetheless, the method of characterizing soil color variables from the two field methods presented in the study can serve as a template for future applications including use as indicators of soil biogeochemistry or environmental education programs. Using the same collected color measurements from the Nix in four forested wetland soils within the Piedmont and Coastal Plain physiographic provinces of Northern Virginia (NOVA), the utility of the Nix for predicting carbon contents (TC) and stocks (TC stocks) from on-site color measurements was investigated. Both the Nix color variables (n = 15) and carbon contents significantly differed between sites, with redder soils (higher a and h) at Piedmont sites, and higher TC at sites with darker soils (lower values of L, or lightness; p < 0.05). Nix–carbon correlation analysis revealed strong relationships between L (lightness), X (a virtual spectral variable), R (additive red), and KK (black) and log-transformed TC (Ln[TC]; |r| = 0.70; p < 0.01 for all). Simple linear regressions were conducted to identify how well these four final Nix variables could predict soil carbon. Using all color measurements, about 50% of Ln(TC) variability could be explained by L, X, R, or KK (p < 0.01), yet with higher predictive power obtained for Coastal Plain soils (0.55 < R2 < 0.65; p < 0.01). Regression model strength was maximized between Ln(TC) and the four final Nix variables using simple linear regressions when color measurements observed at a specific depth were first averaged (0.66 < R2 < 0.70; p < 0.01). While further study is warranted to investigate Nix applicability within various soil settings, these results demonstrate potential for the Nix and its soil color measurements to assist with rapid field-based assessments of soil carbon in forested wetlands. Finally, a case study is presented and discussed that highlights the applications of the Nix in monitoring and assessing soil colors for wetland ecology and land management. Finally, within an Ecological Sustainability undergraduate class at George Mason University, a class project was designed and executed in which students investigated soil colors across campus green sites using the Nix. Students were given direction on measurement steps and techniques, including at which depths to collect colors, through a Standard Operating Procedure (SOP) made to be adaptable to various locations and/or soil types. Not only were students able to collect, store, and share soil color data for various locations across campus more rapidly than possible using the MSCC, but they also gained an understanding and appreciation for soil ecology and the importance of color as an indicator. With continued refinement and adaptation to intended use, the SOP herein presented has the potential to aid land/watershed planning by providing data on soil colors that can be tracked over time and may identify wetland areas, while also encouraging citizen science endeavors in soil ecology that can engage and connect communities to their belowground soilscape.