Geology topics

Skye A. Wills

Publications and source records attributed to Skye A. Wills.

5 recordsLinked to original sources

USGS2018

Approaches for improving field soil identification

Use of soil survey information by non-soil-scientists is often limited by their inability to select the correct soil map unit component (COMP). Here, we developed two approaches that can be deployed to smartphones for non-soil-scientists to identify COMP from the location alone or location together with easily observed field data (i.e., slope, depth to the restrictive layer, and soil texture by depth). In addition, we also compared the two newly developed approaches with a traditional approach identifying COMP based on the dominant COMP (DC-based approach). All three approaches were tested with the Rapid Assessment of US Soil Carbon database and the combined USDA- NRCS Soil Survey Geographic database and the USDA-NRCS State Soil Geographic Database. The results indicated that the observation-based approach performed significantly better than the other two approaches, suggesting that a small set of easy-to-measure site-specific observations could significantly improve COMP identification. The location- and DC-based approaches had similar low performance overall. However, the location-based approach slightly improved identifications over the DC-based approach for cases where (i) there were multiple possible components within the soil map unit and (ii) the components were located in close proximity to a boundary of a different soil map unit polygon. The benefit of using the location-based approach may be greater in specific soil survey areas where topography was the major factor leading to the creation of the map unit legend.

Soil Science Society of America Journal↗

USGS2018

Soil property and class maps of the conterminous United States at 100-meter spatial resolution

With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 ( R 2 = 0.68), 17.8 wt% ( R 2 = 0.57), 12 wt% ( R 2 = 0.46), 3.63 wt% ( R 2 = 0.41), 0.2 g cm −3 ( R 2 = 0.42), and 0.27 wt% ( R 2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.

Soil Science Society of America Journal↗

USGS2016

Human land-use and soil change

Soil change refers to the alteration of soil and soil properties over time in one location, as opposed to soil variability across space. Although soils change with pedogensis, this chapter focuses on human caused soil change. Soil change can occur with human use and management over long or short time periods and small or large scales. While change can be negative or positive; often soil change is observed when short-term or narrow goals overshadow the other soil’s ecosystem services. Many soils have been changed in their chemical, physical or biological properties through agricultural activities, including cultivation, tillage, weeding, terracing, subsoiling, deep plowing, manure and fertilizer addition, liming, draining, and irrigation. Assessing soil change depends upon the ecosystem services and soil functions being evaluated. The interaction of soil properties with the type and intensity of management and disturbance determines the changes that will be observed. Tillage of cropland disrupts aggregates and decreases soil organic carbon content which can lead to decreased infiltration, increased erosion, and reduced biological function. Improved agricultural management systems can increase soil functions including crop productivity and sustainability. Forest management is most intensive during harvesting and seedling establishment. Most active management in forests causes disturbance of the soil surface which may include loss of forest floor organic materials, increases in bulk density, and increased risk of erosion. In grazing lands, pasture management often includes periods of biological, chemical and physical disturbance in addition to the grazing management imposed on rangelands. Grazing animals have both direct and indirect impacts on soil change. Hoof action can lead to the disturbance of biological crusts and other surface features impairing the soil’s physical, biological and hydrological function. There are clear feedbacks between vegetative systems and soil properties; when vegetation is altered because of grazing or other disturbances, soil property changes often follow. Some soils are very sensitive to management and disturbance and can undergo rapid change: cropping led to massive gully formation in the southeastern USA, exposure of acid-sulfate soils led to irreversible changes in soil minerology and thawing of cold soils has created thermokarst features. These soil changes alter soil properties and functions and may impact soil ecosystem services far into the future.

World Soils Book Series↗

USGS2015

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

New Mexico, Utah, Wyoming↗

USGS2012

A holistic strategy for adaptive land management

Adaptive management is widely applied to natural resources management (Holling 1973; Walters and Holling 1990). Adaptive management can be generally defined as an iterative decision-making process that incorporates formulation of management objectives, actions designed to address these objectives, monitoring of results, and repeated adaptation of management until desired results are achieved (Brown and MacLeod 1996; Savory and Butterfield 1999). However, adaptive management is often criticized because very few projects ever complete more than one cycle, resulting in little adaptation and little knowledge gain (Lee 1999; Walters 2007). One significant criticism is that adaptive management is often used as a justification for undertaking actions with uncertain outcomes or as a surrogate for the development of specific, measurable indicators and monitoring programs (Lee 1999; Ruhl 2007).

Journal of Soil and Water Conservation↗