Brian K. Slater

Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin

Abstract Numerical classification methods may provide an alternative to manual landform delineation using aerial photographs, a subjective process that requires much knowledge of the landscape in question. Continuous classification (fuzzy set) methods and unsupervised (ISODATA) classification techniques were used to classify the landscape of a study area in southwestern Wisconsin, USA. Each pixel of a 10-m resolution digital elevation model (DEM) was grouped according to its membership in a continuous landform class. These classes were determined by the natural clustering of the data in attribute space. Attributes used for the classification were elevation, slope, profile and tangent (related to plan) curvature, compound topographic (wetness) index, and incident solar radiation. The ISODATA classification assigned pixels to one, and only one, landform class while the continuous classification allocated relative class memberships to each pixel. The resulting classifications roughly follow subjective manual delineation lines but give more detailed results. These classification methods may prove useful for statistical analyses and determination of sample schemes.

Soil-landscape resource assessment for plantations - a conceptual framework towards an explicit multi-scale approach

Abstract Soil survey is a major component of forest land resource assessment. Conceptual and operational problems arise from employing the conventional methods of survey in forest lands, namely: implicit methods of landscape interpretation (lack of explicit procedures), transfer of data by analogy to unsampled landscapes by inferences which are scale-, and interpreter-dependent, variability of intuitive surveyor judgement, and poor expression of soil variation within map units. These issues are being addressed through the forestland resource assessment and modeling study (FRAMS). This study redefines the conceptual process of resource assessment, and applies soil–landscape modeling (developed here as regolith–terrain modeling) by developing explicit relationships between soil–landscape attributes within a digital, spatial geographic information system (GIS) framework. Soil survey (advanced here as regolith–terrain modeling) is the science and art of predicting soil attribute patterns in the 3D landscape. The FRAMS attempts to overcome some scale and procedural issues related to soil mapping in forest site assessment by adopting a multi-scale and explicit landscape modeling approach. The conceptual aspects of the method presented here aim to predict the ranges in variation of soil–geomorphic attributes that are relevant to forest plantation management. Soil–landscape analysis is adapted in this study to encompass regolith–terrain analysis (i.e. the complete regolith within an understanding of geomorphic systems) employed at three environmental scales: ‘hillslope’, ‘catenary’, and ‘landscape’. There is no linear relationship of data resolution and expression of regolith–terrain attributes between these scales. Each scale is a scale-dependent system linked by an explicit multi-scale method. When combined with geological and climatic data analysis the resultant model provides an advanced, stratified sampling scheme for subsequent field survey procedures in forestland resource assessment. The field analysis, remote-sensing and digital terrain model (DTM) analyses are managed in a raster GIS and can then be effectively classified, a posteriori, according to ‘fuzzy logic’ rules. In the FRAMS, we investigate the scale effect on both the regolith–terrain parameters and their notional relationships to forestland management by investigation at finer scales: hillslope and catenary scales (in southeast Queensland for planted native hoop pine (Araucaria cunninghamii)), and at a broader scale: the landscape scale (in north Queensland for native species reforestation). The study is still in the preliminary stages so the model is not yet fully functional nor have the components been validated so far.

Soils, water quality, and watershed size: interactions in the Maumee and Sandusky river basins of northwestern Ohio.

Soil variability in watersheds accounts for the problem of partitioning downstream water quality data and evaluating sources of non-point pollution. This review of previous water quality studies was conducted to examine more closely the influence of soil properties on pollutant export. The approach used in this paper was to start with data from the two largest watersheds (Maumee and Sandusky) and then compare them on a unit area export basis with data from intermediate-size and smaller watersheds. General relationships between pollutant levels at the river mouth and upstream soil conditions are vague and seemingly contradictory at the large-watershed scale. With smaller watersheds, it can be determined that soil texture, slope, and internal drainage are controlling factors for pollutant export. Although Paulding (very-fine, illitic, nonacid, mesic Typic Epiaquept) and Roselms (very-fine, illitic, mesic Aeric Epiaqualf) soils occupy only 5% of the Maumee basin, they generate more than 10 times as much sediment per unit area as the tile-drained Hoytville (fine, illitic, mesic Mollic Epiaqualf) soils that occupy 16% of the Maumee basin. Tile drainage of very poorly drained soils that are formed from either glacial till or silty to sandy lake deposits reduces runoff and increases downward movement of soluble nutrients into tile drains. The assumption that sloping moraine areas are the primary source of pollutants should be reexamined based on this review.

Alfalfa Responses to Gypsum Application Measured Using Undisturbed Soil Columns

Gypsum is an excellent source of Ca and S, both of which are required for crop growth. Large amounts of by-product gypsum [Flue gas desulfurization gypsum-(FGDG)] are produced from coal combustion in the United States, but only 4% is used for agricultural purposes. The objective of this study was to evaluate the effects of (1) untreated, (2) short-term (4-year annual applications of gypsum totaling 6720 kg ha−1), and (3) long-term (12-year annual applications of gypsum totaling 20,200 kg ha−1) on alfalfa (Medicago sativa L.) growth and nutrient uptake, and gypsum movement through soil. The study was conducted in a greenhouse using undisturbed soil columns of two non-sodic soils (Celina silt loam and Brookston loam). Aboveground growth of alfalfa was not affected by gypsum treatments when compared with untreated (p > 0.05). Total root biomass (0–75 cm) for both soils series was significantly increased by gypsum application (p = 0.04), however, increased root growth was restricted to 0–10 cm depth. Soil and plant analyses indicated no unfavorable environmental impact from of the 4-year and 12-year annual application of FGDG. We concluded that under sufficient water supply, by-product gypsum is a viable source of Ca and S for land application that might benefit alfalfa root growth, but has less effect on aboveground alfalfa biomass production. Undisturbed soil columns were a useful adaptation of the lysimeter method that allowed detailed measurements of alfalfa nutrient uptake, root biomass, and yield and nutrient movement in soil.

Updating Legacy Soil Maps for Climate Resilient Agriculture: A Case of Kilombero Valley, Tanzania

Since the first documented soil survey in Tanzania by Milne (J Ecol 35:192–265, 1936), a number of other soil inventory exercises at different scales have been made. The main challenge has been the fragmented nature of the often outdated detailed soil maps and small-scale less-informative country-wide soil maps. Recent advances in information and computational technology have created vast potential to collect, map, harness, communicate and update soil information. These advances present favorable conditions to support the already popular shift from qualitative (conventional) to quantitative (digital) soil mapping (DSM). In this study, two decision tree machine learning algorithms, J48 and Random Forest (RF), were applied to digitally predict k-means numerically classified soil clusters to update a soil map produced in 1959. Predictors were derived from 1 arc SRTM digital elevation data and a 5 m RapidEye satellite image. J48 and RF predicted the soil units of the legacy maps with greater detail. However, RF showed superiority for predicting clusters J48 could not predict and for showing higher pixel contiguity. No significant difference (P = 0.05) was observed between the soil properties of the predicted soil clusters and the actual field validation points. Young soils (Entisols and Inceptisols) were found to occupy about 56 % of the study site’s 30,000 ha followed by Alfisols, Mollisols and Vertisols at 31, 9 and 4 %, respectively. This study demonstrates the usefulness of DSM techniques to update conventionally prepared legacy maps to offer soil information at improved detail to agricultural land use planners and decision makers of Tanzania to make evidence-based decisions for climate-resilient agriculture and other land uses.