Quantitative relationships between soil landscape index and classification grain

Abstract

Purpose

Understanding space grain impact on soil landscape patterns is helpful to discover specific spatial heterogeneity of soil landscape. The objectives of this study were to reveal the effect of soil classification grain on soil landscape indices and establish a quantitative characterization function between soil landscape index and soil classification grain then we can reveal the sensitivity of soil landscape indices to soil classification grain and the significance of the function with respect to practical applications.

Materials and methods

A soil and land use vector map of Dongxiang County was made at a scale of 1:50,000. The vector map units were classified into five hierarchies of soil classification grain, such as soil great group (G1), subgroup (G2), family (G3), species (G4), and species + land use (G5). Using Fragstats 4.2 software, 40 soil landscape indices of the five soil classification grains were calculated from the vector map and its derived raster maps, at different grain sizes ranging from 10 m × 10 m to 6 km × 6 km. A numerically investigated method was developed to quantify and analyze values of the different soil classification grains, quantitative relationships, and sensitivities of soil landscape indices to soil classification grains.

Results and discussion

The effects of soil classification grain varied according to different soil landscape indices. The quantitative values of G1-G5 soil classification grains were quantified to be 0.7 km × 0.7 km, 0.5 km × 0.5 km, 0.2 km × 0.2 km, 0.15 km × 0.15 km, and 0.06 km × 0.06 km, respectively. The quantitative relationships of the abovementioned 28 landscape indices to soil classification grain were described by linear, logarithmic, reciprocal, quadratic, and power function with different sensitivities. The 13 landscape indices and their functions of the quantitative relationships to soil classification grain, which exhibited high sensitivity coefficients (PEAR > 0.90), should be adopted as a new approach in the future to quantitatively assess and forecast the spatial heterogeneity of soil landscape.

Conclusions

The soil classification grain strongly affects the spatial heterogeneity of the soil landscape. The quantification method of soil classification grains and the quantitative expression functions between soil landscape indices and the classification grain developed in this study could potentially provide a basic tool to quantitatively assess and forecast the spatial heterogeneity of soil landscapes. Among them, the landscape indices with high sensitivity to soil classification grains should be preferentially adopted within the new approach.

from Energy Ecology Environment Ambio via Terpsi Hori on Inoreader http://bit.ly/2DILGa6