A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m‾²), covering an area of 122 km², with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.
From as early as 500 BCE, humans have recognized that some things vary together in space. This is essentially correlation, but the spatial aspect sometimes adds a special twist. Also, correlation requires evaluation of quantitative data, while this concept is not limited to quantitative characteristics. For example, Diophanes of Bithynia observed that “you can judge …Continue reading “CLORPT: Spatial Association in Soil Geography”
In the process of creating a map, geographers often have to engage in the activity of spatial prediction. Although there are many tools we use to accomplish this task, they generally boil down to the use of one or two fundamental concepts. Waldo Tobler is credited for identifying the ‘first law of geography’, stating “Everything …Continue reading “Fundamentals of Spatial Prediction”
This paper reviews the historical development of base maps used for soil mapping, and evaluates the dependence of soil mapping on base maps. Formerly, as a reference for spatial position, paper base maps controlled the cartographic scale of soil maps. However, this relationship is no longer true in geographic information systems. Today, as parameters for digital soil maps, base maps constitute the library of predictive variables and constrain the supported resolution of the soil map.
