Bradley A. Miller

SPATIAL DISTRIBUTION OF HISTORICAL WETLAND CLASSES ON THE DES MOINES LOBE, IOWA

We estimated the pre-settlement density and area of different classes of palustrine wetlands on the Des Moines Lobe based on soil characteristics. Six wetland classes, ranging from temporarily flooded to permanently flooded, were identified based on soil properties that persisted after artificial drainage. Prior to drainage, wetlands covered nearly half of the Des Moines Lobe and there were differences in both the types and relative abundance of wetlands among the four geologic subdivisions of the Lobe (Bemis, Altamont, and Algona till plains and Altamont Lake). In the flat Altamont Lake zone, the most common wetlands were equally split between temporarily flooded and saturated water regimes. Among the three till plain zones, saturated wetlands were the dominant wetland type. Differences in wetland distributions among the zones probably derive from differences in initial topography and post-glacial processes such as erosion-deposition processes and stream-network formation.

A Taxonomically Based Ordinal Estimate of Soil Productivity for Landscape-scale Analyses

Abstract In this article, we introduce, evaluate, and apply a new ordinally based soil Productivity Index (PI). The PI uses family-level Soil Taxonomy information, that is, interpretations of features or properties, recognized in Soil Taxonomy, that tend to be associated with low or high soil productivity, to rank soils from 0 (least productive) to 19 (most productive). The index has a wide application generally at landscape scales. Unlike competing indexes, it does not require copious amounts of soil data, for example, pH, organic matter, or cation exchange capacity, in its derivation. Geographic information system applications of the PI, in particular, have great potential. Results confirmed that for 1,000 sites in southern Michigan, the mean PI of cultivated sites is significantly higher (10.94) than that of forested sites (7.77). We also compared the PI with published productivity values for Illinois soils. The positive statistical correlations that resulted confirmed that the PI is an effective measure of productivity for areas that do not have robust productivity data or a wealth of local soil knowledge, as does Illinois. Last, 2009 crop yield data for 11 Midwestern states were used to further evaluate the PI. In a geographic information system, we determined the soils and crops in particular fields and thus were able to ascertain the mean PI value per soil, per crop, per county. Statewide summaries of these data produced statistical correlations among yields of specific crops and PI values that were all positive; many exceeded 0.60. For regionally extensive applications, the PI may be as useful and robust as other indexes that have much more exacting data requirements.

Wetland hydrologic class change from prior to European settlement to present on the Des Moines Lobe, Iowa

It has been hypothesized that wetland restoration policies have favored the restoration of the wettest classes of wetlands on the Des Moines Lobe of the prairie pothole region. To test this hypothesis we compared pre-drainage wetland distributions based on soils data and National Wetland Inventory (NWI) estimates of contemporary wetland distributions on the Des Moines Lobe. Based on the NWI data, the Des Moines Lobe today has only 3–4% of the wetland area that it had prior to the onset of drainage. On the basis of their soils, pre-drainage wetlands were predominantly temporarily flooded to saturated wetlands (84%), with only about 6% of the wetlands with water regimes classified as semi-permanently to permanently flooded. Depending on the interpretation of wetland modifiers on NWI maps, wetlands classified by the NWI as semi-permanent to permanently flooded make up more than 41% of the wetland area while wetlands with temporarily flooded to saturated water regimes account for 45–58% of the Lobe’s wetland area. The water regimes of contemporary wetlands when compared to their historic regimes suggest that many of today’s wetlands have different water regimes than they did prior to the onset of drainage. Because of the regional lowering of the groundwater table, many of today’s wetlands have drier water regimes, but some have wetter water regimes because they receive drainage tile inputs. Our results indicate that restoration has favored the wettest classes of wetlands and that temporarily to saturated wetland classes have not been restored in proportion to their relative abundance in the pre-drainage landscape.

Semantic calibration of digital terrain analysis scale

Digital terrain analysis (DTA) provides efficient, repeatable, and quantified metrics of landscape characteristics that are important to the Earth sciences, particularly for detailed soil mapping applications. However, DTA has not been field tested to the extent that traditional field metrics of topography have been. Human assessment of topography synthesizes multiple parameters at multiple scales to characterize a landscape, based on field experience. In order to capture the analysis scale used by field scientists, this study introduces a method for calibrating the analysis scale of DTA to field assessments. This method is used to calibrate land-surface derivatives of relative elevation, profile curvature, and slope gradient in the context of the commonly used field description of hillslope position. For a topographically diverse landscape in MI, USA, a peak in agreement between field assessment and DTA was found at field equivalent distances of 135 m for relative elevation, 63 m for profile curvature, and 9 m for slope gradient. Given the field experience of soil scientists, these calibrations of DTA metrics are likely to have stronger correlations with hillslope properties and could be used together to classify hillslope position consistently across large extents.

Soil Mapping and Processes Modeling for Sustainable Land Management

Soil maps and models are indispensable tools in sustainable land management. The sustainable land use of our territory is fundamental to providing long-term socio-economic and environmental benefits. The risk of land degradation and corresponding declines in ecosystem services depends on the type of land use. Soil restoration can be extremely expensive, more than the implementation of sustainable land use practices. This is especially important in the context of climate change and the increasing pressure that a growing population places on soil resources, which is a global phenomenon. The objective of this chapter is to show the advantages of using soil mapping and modeling in sustainable land use planning and management. Soil mapping is fundamental to understand the distribution of soil properties, allowing us to implement sustainable practices in vulnerable areas and prevent land degradation. Soil indicators and models provide indispensable information for an accurate evaluation of land degradation status. Alone, or integrated with other disciplines, soil information is extremely important for understanding the causes of land degradation and implementation of sustainable land management. Accurate information and models are key tools for managers and decision makers to implement sustainable land use management policies.

Historical Perspectives on Soil Mapping and Process Modeling for Sustainable Land Use Management

Basic soil management goes back to the earliest days of agricultural practices, approximately 9000 BCE. Through time humans developed soil management techniques of ever increasing complexity, including plows, contour tillage, terracing, and irrigation. Spatial soil patterns were being recognized as early as 3000 BCE, but the first soil maps did not appear until the 1700s and the first soil models finally arrived in the 1880s. The beginning of the 20th century saw an increase in standardization in many soil science methods and wide-spread soil mapping in many parts of the world, particularly in developed countries. However, the classification systems used, mapping scale, and national coverage varied considerably from country to country. Major advances were made in pedologic modeling starting in the 1940s, and in erosion modeling starting in the 1950s. In the 1970s and 1980s advances in computing power, remote and proximal sensing, geographic information systems, global positioning systems, statistical and spatial statistics among other numerical techniques significantly enhanced our ability to map and model soils. These types of advances positioned soil science to make meaningful contributions to sustainable land use management as we moved into the 21st century.

Use of soil maps and surveys to interpret soil-landform assemblages and soil-landscape evolution

Soils form in unconsolidated parent materials, which make them a key link to the geologic system that originally deposited the parent material. In young soils, i.e. those that post-date the last glaciation, parent materials can often be easily identified as to type and depositional system. In a GIS, soil map units can then be geospatially tied to parent materials, enabling the user to create maps of surficial geology. We suggest that maps of this kind have a wide variety of applications in the Earth Sciences, and to that end provide five examples from temperate climate soil-landscapes.

Geographic Information Systems and Spatial Statistics Applied for Soil Mapping: A Contribution to Land Use Management

Abstract Soil maps support sustainable land management because they provide important information about where different management practices are most appropriate. However, mapping soil is not a trivial task as limitations in directly observing the soil make spatial prediction a key component in the quality of the map. Geographic information systems and spatial statistics offer powerful tools for producing soil maps, but to maximize their utility it is important for the mapper to understand the principles and assumptions behind the methods. This chapter provides a broad overview of digital soil mapping methods, categorizing methods by geographic principle and describing sampling methods that support the strengths of the respective prediction approaches. For each category of spatial prediction approaches, examples are provided from sampling to the production of the final map, including the quantified mapping of uncertainty.