Munsell colors for some common soil minerals.
data("soil_minerals")
A data frame with 20 observations on the following 5 variables.
mineralcolorhuevaluechromaSoil color and other properties including texture, structure, and consistence are used to distinguish and identify soil horizons (layers) and to group soils according to the soil classification system called Soil Taxonomy. Color development and distribution of color within a soil profile are part of weathering. As rocks containing iron or manganese weather, the elements oxidize. Iron forms small crystals with a yellow or red color, organic matter decomposes into black humus, and manganese forms black mineral deposits. These pigments paint the soil (Michigan State Soil). Color is also affected by the environment: aerobic environments produce sweeping vistas of uniform or subtly changing color, and anaerobic (lacking oxygen), wet environments disrupt color flow with complex, often intriguing patterns and points of accent. With depth below the soil surface, colors usually become lighter, yellower, or redder.
http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/edu/?cid=nrcs142p2_054286
1. Lynn, W.C. and Pearson, M.J., The Color of Soil, The Science Teacher, May 2000. 2. Schwertmann, U. 1993. Relations Between Iron Oxides, Soil Color, and Soil Formation. "Soil Color". SSSA Special Publication no. 31, pages 51--69.
## Not run: ------------------------------------ # library(aqp) # library(ape) # library(cluster) # library(colorspace) # # # load common soil mineral colors # data(soil_minerals) # # convert Munsell to R colors # soil_minerals$col <- munsell2rgb(soil_minerals$hue, soil_minerals$value, # soil_minerals$chroma) # # # make a grid for plotting # n <- ceiling(sqrt(nrow(soil_minerals))) # # read from top-left to bottom-right # g <- expand.grid(x=1:n, y=n:1)[1:nrow(soil_minerals),] # # # convert Munsell -> sRGB -> LAB # col.rgb <- munsell2rgb(soil_minerals$hue, soil_minerals$value, # soil_minerals$chroma, return_triplets = TRUE) # col.lab <- as(sRGB(as.matrix(col.rgb)), 'LAB')@coords # row.names(col.lab) <- soil_minerals$mineral # # # divisive hierarchical clustering of LAB coordinates # d <- daisy(col.lab) # h <- as.hclust(diana(d)) # p <- as.phylo(h) # # # plot grid of mineral names / colors # layout(matrix(c(1,2), nrow=1), widths = c(1.25,1)) # par(mar=c(1,0,0,1)) # plot(g$x, g$y, pch=15, cex=12, axes=FALSE, xlab='', ylab='', # col=rev(soil_minerals$col[h$order]), xlim=c(0.5,5.5), ylim=c(1.5,5.5)) # text(g$x, g$y, rev(soil_minerals$mineral[h$order]), adj=c(0.45,5), cex=1, font=2) # text(g$x, g$y, rev(soil_minerals$color[h$order]), col='white', pos=1, cex=0.85, font=2) # title(main='Common Soil Minerals', line=-2, cex.main=2) # mtext('http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/edu/?cid=nrcs142p2_054286', # side=1, cex=0.75, line=-1.5) # mtext('U. Schwertmann, 1993. SSSA Special Publication no. 31, pages 51--69', side=1, # cex=0.75, line=-0.5) # # # dendrogram + tip labels with mineral colors # plot(p, cex=0.85, label.offset=1, font=1) # tiplabels(pch=15, cex=4, col=soil_minerals$col) # ## ---------------------------------------------