Digital Soil Mapping in the Absence of Field Training Data: A Case Study Using Terrain Attributes and Semiautomated Soil Signature Derivation to Distinguish Ecological Potential

Spatially explicit data for soil properties governing plant water availability are needed to understand mechanisms influencing plant species distributions and predict plant responses to changing climate. This is especially important for arid and semiarid regions. Spatial data representing surrogates for soil forming factors are becoming widely available (e.g., spectral and terrain layers). However, field-based training data remain a limiting factor, particularly across remote and extensive drylands. We present a method to map soils with Landsat ETM+ imagery and high-resolution (5 m) terrain (IFSAR) data based on statistical properties of the input data layers that do not rely on field training data. We then characterize soil classes mapped using this semiautomated technique. The method distinguished spectrally distinct soil classes that differed in subsurface rather than surface properties. Field evaluations of the soil classification in conjunction with analysis of long-term vegetation dynamics indicate the approach was successful in mapping areas with similar soil properties and ecological potential

Human Land-Use and Soil Change

Soil change is the central, if under-recognized, component of land and ecosystem changes (Yaalon 2007). Soils change naturally over a long timescale (decades to millennia) in response to soil-forming factors (biota, climate, parent material, time, and topography). However, human land-use pressures are currently the driving force in maintaining, aggrading, and degrading soil properties across nearly all ecosystems. Traditionally, in order to simplify and standardize the relationships between soils and soil-forming factors, pedology and soil survey have often focused on “natural” or “virgin” soil (e.g., Hilgard 1860; Jenny 1980), but many argue that humans should be thought of as a part of soil genesis and formation (Amundson and Jenny 1991; Yaalon and Yaron 1966; Bidwell and Hole 1965). Landscapes and soils have been altered by wide-scale conversion to agriculture, use of vegetative products, and development for direct human use. Land-use impacts can be gradual or abrupt, subtle, or catastrophic (Table 18.1). The interactions between environmental changes and geomorphic and biotic feedback loops vary across temporal and spatial scales depending on the setting (Monger and Bestelmeyer 2006). The effects of land use can linger for decades to centuries and beyond (Hall et al. 2013; Jangid et al. 2011; Sandor et al. 1986). While each land resource region has some specific soil–land use interactions, this chapter will focus on general uses and topical areas: croplands, wetlands, grazing lands (both pasture and rangelands), and forest lands with smaller sections devoted to special issues including acid sulfate soils, strip-mined lands, and cold soils

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 most accurate, or was among the most accurate, classifiers 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. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 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

Climate change reduces extent of temperate drylands and intensifies drought in deep soils

Drylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas

Synoptic analysis and WRF-Chem model simulation of dust events in the Southwestern United States

Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March-May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large-scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High-resolution Weather Research and Forecasting coupled with Chemistry model (WRF-Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo-based drag-partition was used to explore land surface-atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub-tropical jet stream interaction was a common upper-level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast-directed pressure gradient upstream and over the source areas resulted in strong near-surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid-tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF-Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust-on-snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo-based drag partition provides a basis for additional dust-on-snow simulations to assess the hydrologic impact in the Southwest US

Reducing sampling uncertainty in aeolian research to improve change detection

Measurements of aeolian sediment transport support our understanding of mineral dust impacts on Earth and human systems and assessments of aeolian process sensitivities to global environmental change. However, sample design principles are often overlooked in aeolian research. Here, we use high‐density field measurements of sediment mass flux across land use and land cover types to examine sample size and power effects on detecting change in aeolian transport. Temporal variances were 1.6 to 10.1 times the magnitude of spatial variances in aeolian transport for six study sites. Differences in transport were detectable for >67% of comparisons among sites using ~27 samples. Failure to detect change with smaller sample sizes suggests that aeolian transport measurements and monitoring are much more uncertain than recognized. We show how small and selective sampling, common in aeolian research, gives the false impression that differences in aeolian transport can be detected, potentially undermining inferences about process and impacting reproducibility of aeolian research

Enhancing wind erosion monitoring and assessment for U.S. rangelands

Wind erosion is a major resource concern for rangeland managers because it can impact soil health, ecosystem structure and function, hydrologic processes, agricultural production, and air quality. Despite its significance, little is known about which landscapes are eroding, by how much, and when. The National Wind Erosion Research Network was established in 2014 to develop tools for monitoring and assessing wind erosion and dust emissions across the United States. The Network, currently consisting of 13 sites, creates opportunities to enhance existing rangeland soil, vegetation, and air quality monitoring programs. Decision-support tools developed by the Network will improve the prediction and management of wind erosion across rangeland ecosystems. © 2017 The Author(s)The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

Assessing Impacts of Roads: Application of a Standard Assessment Protocol

Adaptive management of road networks depends on timely data that accurately reflect the impacts those systems are having on ecosystem processes and associated services. In the absence of reliable data, land managers are left with little more than observations and perceptions to support management decisions of road-associated disturbances. Roads can negatively impact the soil, hydrologic, plant, and animal processes on which virtually all ecosystem services depend. The Interpreting Indicators of Rangeland Health (IIRH) protocol is a qualitative method that has been demonstrated to be effective in characterizing impacts of roads. The goal of this study were to develop, describe, and test an approach for using IIRH to systematically evaluate road impacts across large, diverse arid and semiarid landscapes. We developed a stratified random sampling approach to plot selection based on ecological potential, road inventory data, and image interpretation of road impacts. The test application on a semiarid landscape in southern New Mexico, United States, demonstrates that the approach developed is sensitive to road impacts across a broad range of ecological sites but that not all the types of stratification were useful. Ecological site and road inventory strata accounted for significant variability in the functioning of ecological processes but stratification based on apparent impact did not. Analysis of the repeatability of IIRH applied to road plots indicates that the method is repeatable but consensus evaluations based on multiple observers should be used to minimize risk of bias. Landscape-scale analysis of impacts by roads of contrasting designs (maintained dirt or gravel roads vs. non- or infrequently maintained roads) suggests that future travel management plans for the study area should consider concentrating traffic on fewer roads that are well designed and maintained. Application of the approach by land managers will likely provide important insights into minimizing impacts of road networks on key ecosystem services.The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform August 202