Environmental Science and Policy, George Mason University, 4400 University Drive, MS5F2, Fairfax, VA 22030-444, United States of America.
Environmental Science and Policy, George Mason University, 4400 University Drive, MS5F2, Fairfax, VA 22030-444, United States of America.
Sci Total Environ. 2021 Aug 20;783:147005. doi: 10.1016/j.scitotenv.2021.147005. Epub 2021 Apr 10.
While the Munsell Soil Color Chart (MSCC) is the most frequently used, well-established field method for reading soil color, the Nix Color Sensor (NCS) is an inexpensive, app-based alternative that can complement or potentially substitute for the MSCC. Soils were collected and their colors were measured from four forested sites across Northern Virginia within the Chesapeake Bay Watershed using both the MSCC and NCS. Three MSCC variables and 15 NCS variables were collected in the field; a methodology was established to use these "measured" (M) variables to derive 9 NCS calculated (C) variables. A stepwise correlation identified NCS variables most suitable for relating the NCS to each of the MSCC attributes: hue (H), value (V), and chroma (C). Ultimately, H, V, and C were deemed to be best represented by H 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 NCS variables (i.e., H, L, and ẑ) for H, V, and C 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 NCS 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 NCS 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 studies or environmental education programs desiring to use the NCS as a complement to the MSCC.
尽管 Munsell 土壤比色卡(MSCC)是最常用的、成熟的野外土壤颜色读取方法,但 Nix 颜色传感器(NCS)是一种廉价的、基于应用的替代方法,可以补充或潜在替代 MSCC。本研究在切萨皮克湾流域的弗吉尼亚州北部四个森林地区收集土壤样本,并使用 MSCC 和 NCS 测量其颜色。在野外收集了三个 MSCC 变量和 15 个 NCS 变量;建立了一种方法,利用这些“测量”(M)变量推导出 9 个 NCS 计算(C)变量。逐步相关分析确定了最适合将 NCS 与 MSCC 属性(Hue(H)、Value(V)和 Chroma(C))相关联的 NCS 变量。最终,H、V 和 C 分别被认为是由 RGB 颜色空间的 H calculated from the RGB color space(ρ = 0.56)、CIE-Lab 颜色空间的 L(ρ = 0.73)和 XYZ 颜色空间的 ẑ = Z/(X + Y + Z)(ρ = -0.80)最佳表示(p < 0.001)。最终 NCS 变量(即 H、L 和 ẑ)对 H、V 和 C 的解释能力分别为 26%、54%和 62%(p <0.01)。在被识别为湿地和非湿地的土壤之间,ẑ 存在显著差异,但缺乏重叠范围,表明 NCS 有可能以可访问和可重复的方式补充 MSCC 来评估湿地土壤颜色,包括湿地划界实践中的湿地土壤识别。需要进行更多数据的进一步研究,以建立 NCS 和 MSCC 之间更强的关系。尽管如此,本研究中提出的两种野外方法对土壤颜色变量的特征描述方法可以作为未来研究或希望将 NCS 用作 MSCC 补充的环境教育计划的模板。