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.
Author: Bradley Miller
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
Classification of elevation rasters with this digital model of hillslope position represent base maps that can be used to (1) improve research on toposequences by providing explicit definitions of each hillslope element’s location, (2) facilitate the disaggregation of soil map unit complexes, and (3) identify map unit inclusions that occur due to subtle topographic variation.
Soil scientists like to remind everyone that “soil is not dirt.” They are of course, right, but what is the difference? I would argue that an important distinction is a question of where. Is it somewhere that it is useful, fulfilling its role as supporting life and improving environmental quality? Or has it been moved … Continue reading The Value of Soil: A Spatial Perspective
Simulation of late glacial atmospheric conditions with atmospheric general circulation models suggest a strong anticyclone over the Laurentide Ice Sheet and associated easterly winds along the glacial margin. In the upper Midwest of North America, evidence supporting this modeled air flow exists in the orientation of paleospits in northeastern Lower Michigan that formed ∼13 ka in association with glacial Lake Algonquin. Conversely, parabolic dunes that developed between 15 and 10 ka in central Wisconsin, northwestern Indiana, and northwestern Ohio resulted from westerly winds, suggesting that the wind gradient was indeed tight.
Despite the widespread availability of relatively detailed soil maps in the USA, few areas have a surficial geology map published with as much spatial detail. This apparent gap between disciplines calls to question the accuracy of soil maps to represent the spatial distribution of surficial geologic materials. Therefore, the purpose of this research was to test the agreement between maps from these two sources.
UPDATE: The tremendous response to this blog post led to an international and interdisciplinary survey on how these terms are/should be defined. The results of that survey were combined with an in-depth review of the literature to produce an article that has now been published in Earth-Science Reviews. The article updates and refines the patterns … Continue reading Colluvium vs Alluvium
Quantifying uncertainty can be a very useful and often important aspect of evaluating results of calculations, particularly in modelling. The same applies for spatial layer mashups where the grids provide the input variables for equations that are calculated spatially (i.e. raster calculator). This toolbox for ArcGIS uses standard error propagation equations to simultaneously calculate the … Continue reading Error Propagation Toolbox
A zip file containing a suite of tools for analyzing continuous particle size curves from laser diffractometry. Includes: export templates for Malvern software, analysis template for recommended quality control procedure, reporting templates for organized presentation of results with additional metrics, and a data filter for removing the larger particle size peak from bimodal curves.