Showing papers on "Digital soil mapping published in 2006"
TL;DR: In this paper, the authors developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and organic matter (OM), and combined these equations with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses.
Abstract: Hydrologic analyses often involve the evaluation of soil water infiltration, conductivity, storage, and plant-water relationships. To define the hydrologic soil water effects requires estimating soil water characteristics for water potential and hydraulic conductivity using soil variables such as texture, organic matter (OM), and structure. Field or laboratory measurements are difficult, costly, and often impractical for many hydrologic analyses. Statistical correlations between soil texture, soil water potential, and hydraulic conductivity can provide estimates sufficiently accurate for many analyses and decisions. This study developed new soil water characteristic equations from the currently available USDA soil database using only the readily available variables of soil texture and OM. These equations are similar to those previously reported by Saxton et al. but include more variables and application range. They were combined with previously reported relationships for tensions and conductivities and the effects of density, gravel, and salinity to form a comprehensive predictive system of soil water characteristics for agricultural water management and hydrologic analyses. Verification was performed using independent data sets for a wide range of soil textures. The predictive system was programmed for a graphical computerized model to provide easy application and rapid solutions and is available at http://hydrolab.arsusda. gov/soilwater/Index.htm.
1,986 citations
TL;DR: This chapter presents a generic framework that recognises the procedures required for digital soil mapping and suggests that SSINFOS must be populated and spatial soil inference systems (SSINFERS) must be developed to allow users to derive the data they require.
Abstract: Given the relative dearth of, and the huge demand for, quantitative spatial soil information, it is timely to develop and implement methodologies for its provision. We suggest that digital soil mapping, which can be defined as the creation, and population of spatial soil information systems (SSINFOS) by the use of field and laboratory observational methods, coupled with spatial and non-spatial soil inference systems, is the appropriate response. Problems of large extents and soil-cover complexity and coarse resolutions and short-range variability representation carry over from conventional soil survey to digital soil mapping. Meeting users’ requests and demands and the ability to deal with spatially variable and temporally evolving datasets must be the key features of any new approach. In this chapter, we present a generic framework that recognises the procedures required. Within quantitatively defined physiographic regions, SSINFOS must be populated and spatial soil inference systems (SSINFERS) must be developed. When combined this will allow users to derive the data they require. Further work is required on the development of these systems, and on the data requirements, the optimal forms of inference and the appropriate representation of the products of digital soil mapping.
202 citations
TL;DR: In this article, different geostatistical techniques for mapping organic carbon stock in the top 0.3 m of soil with or without secondary information were assessed in three large no-till fields (49 to 65 ha) in Nebraska.
182 citations
TL;DR: In this article, a negative exponential profile depth function was used to describe the soil carbon data at different depths, and its integral to represent the carbon storage was then proposed for mapping soil carbon storage in the Lower Namoi Valley, NSW.
Abstract: Estimation and mapping carbon storage in the soil is currently an important topic; thus, the knowledge of the distribution of carbon content with depth is essential. This paper examines the use of a negative exponential profile depth function to describe the soil carbon data at different depths, and its integral to represent the carbon storage. A novel method is then proposed for mapping the soil carbon storage in the Lower Namoi Valley, NSW. This involves deriving pedotransfer functions to predict soil organic carbon and bulk density, fitting the exponential depth function to the carbon profile data, deriving a neural network model to predict parameters of the exponential function from environmental data, and mapping the organic carbon storage. The exponential depth function is shown to fit the soil carbon data adequately, and the parameters also reflect the influence of soil order. The parameters of the exponential depth function were predicted from land use, radiometric K, and terrain attributes. Using the estimated parameters we map the carbon storage of the area from surface to a depth of 1 m. The organic carbon storage map shows the high influence of land use on the predicted storage. Values of 15-22 kg/m 2 were predicted for the forested area and 2-6 kg/m 2 in the cultivated area in the plains.
181 citations
TL;DR: In this article, an example of a pedotransfer function relating soil structure and soil hydrologic parameters was discussed, where the authors used the subset of 2149 samples from the US National Soil Characterization database that had values of water contents at - 33 kPa and bulk densities on clods, structure characterized with grade, size and shape, textural class determined in the field and from lab textural analysis.
157 citations
TL;DR: In this paper, the authors evaluated the spatio-temporal changes that had occurred as a result of irrigation with drainage water over that time period, using geospatial electromagnetic induction (EMI) measurements of ECa and a spatial response surface sampling design, 40 sites were selected that reflected the spatial variability of the ECa measurements.
139 citations
TL;DR: In this paper, a distributed root zone soil moisture assessment tool (SMAT) is proposed based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a GIS platform.
Abstract: Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and environmental predictions. Soil moisture varies both in space and time because of spatio-temporal variations in precipitation, soil properties, topographic features, and vegetation characteristics. In recent years, air- and space-borne remote sensing campaigns have successfully demonstrated the use of passive microwave remote sensing to map soil moisture status near the soil surface (»0–0.05 m below the ground) at various spatial scales. In this study root zone (e.g., »0–0.6 m below the ground) soil moisture distributions were estimated across the Little Washita watershed (Oklahoma) by assimilating near-surface soil moisture data from remote sensing measurements using the Electronically Scanned Thinned Array Radiometer (ESTAR) with an ensemble Kalman filter (EnKF) technique coupled with a numerical one-dimensional vadose zone flow model (HYDRUS-ET). The resulting distributed root zone soil moisture assessment tool (SMAT) is based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a geographic information system (GIS) platform. The simulated soil moisture distribution at various depths and locations within the watershed were compared with measured profile soil moisture data using time domain reflectometry (TDR). A reasonable agreement was found under favorable conditions between footprint-scale model estimations and point-scale field soil moisture measurements in the root zone. However, uncertainties introduced by precipitation and soil hydraulic properties caused suboptimal performance of the integrated model. The SMAT holds great promise and offers flexibility to incorporate various data assimilation techniques, scaling, and other hydrological complexities across large landscapes. The integrated model can be useful for simulating profile soil moisture estimation and for predicting transient soil moisture behavior for a range of hydrological and environmental applications.
121 citations
TL;DR: In this paper, a prototype-based approach was developed to acquire and represent knowledge on soil-landscape relationships and apply the knowledge in digital soil mapping under fuzzy logic. But this approach was applied in a case study to map soils in central Wisconsin, USA.
103 citations
TL;DR: In this paper, the use of terrain attributes as co-variables for the spatial prediction of the soil properties was investigated, including relative elevation, slope of the catchment area, radiation angle and morphometric units such as slope elements.
91 citations
TL;DR: In this article, the authors summarized the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives.
Abstract: Digital soil mapping as a tool to generate spatial soil information provides solutions for the growing demand for high-resolution soil maps worldwide. Even in highly developed countries like Germany, digital soil mapping becomes essential due to the decreasing, time-consuming, and expensive field surveys which are no longer affordable by the soil surveys of the individual federal states.
This article summarizes the present state of soil survey in Germany in terms of digitally available soil data, applied digital soil mapping, and research in the broader field of pedometrics and discusses future perspectives.
Based on the geomorphologic conditions in Germany, relief is a major driving force in soil genesis. This is expressed by the digital–soil mapping research which highlights the great importance of digital terrain attributes in combination with information on parent material in soil prediction.
An example of digital soil mapping using classification trees in Thuringia is given as an introduction in digital soil-class mapping based on correlations to environmental covariates within the scope of the German classification system.
90 citations
TL;DR: In this article, the authors explored the relationship between soil electrical conductivity (ECa) and soil properties and evaluated the usefulness of ECa mapping to infer soil texture as soil water content changed from one mapping date to the next.
TL;DR: In this article, three techniques of geostatistics were used for the creation of several maps of soil properties in an experimental plot cultivated with lettuce, and a final map was created with the objective to determine which areas in the plot had optimal conditions for lettuce development.
TL;DR: In this paper, a method for optimizing sampling for digital soil mapping in cases where no directly measured prior information of the primary variable of interest is available is proposed, and three optimization approaches are evaluated: minimization of the shortest distance (MMSD), a uniform distribution of point pairs for variogram estimation (WM), and a combination of MMSD (2/3 of samples) and WM (1/3).
TL;DR: In this article, the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas was proposed for land use planning, but they are not always available.
Abstract: Soil surveys are necessary sources of information for land use planning, but they are not always available. This study proposes the use of multiple logistic regressions on the prediction of occurrence of soil types based on reference areas. From a digitalized soil map and terrain parameters derived from the digital elevation model in ArcView environment, several sets of multiple logistic regressions were defined using statistical software Minitab, establishing relationship between explanatory terrain variables and soil types, using either the original legend or a simplified legend, and using or not stratification of the study area by drainage classes. Terrain parameters, such as elevation, distance to stream, flow accumulation, and topographic wetness index, were the variables that best explained soil distribution. Stratification by drainage classes did not have significant effect. Simplification of the original legend increased the accuracy of the method on predicting soil distribution.
TL;DR: The soil classification system developed by a Purhepecha community in central Mexico was formalized, incorporating symbolic, cognitive and practical components as mentioned in this paper, and the local soil map units were compared to those provided by a technical soil classification of general scope (USDA soil taxonomy), using spatial analysis within a GIS environment to determine levels of cartographic correlation.
TL;DR: In this paper, an enhanced version of the SIBERIA that incorporates a soil evolution module is used to simulate evolving landforms and soils depths over geologic timescales.
Abstract: [1] The evolution of soil depths is investigated by modeling the interaction between soil production and surface erosion within a landform evolution model. An enhanced version of the landform evolution model SIBERIA that incorporates a soil evolution module is used to simulate evolving landforms and soils depths over geologic timescales. The spatial and temporal evolution of soil depths are examined at the hillslope scale. Though it is widely accepted among the geomorphology community that soil water enhances chemical, physical and biological weathering processes, its effect has not been explicitly included in published models of soil production. The main scientific questions that we address are (1) what are the implications of incorporating soil moisture dependency in the soil production function and (2) what type of soil production dynamics is needed to generate a bedrock topography that has a different spatial pattern from that of the ground surface. A range of physics for the soil production model is explored. The effect of soil moisture is included using the wetness index obtained from drainage analysis of either surface elevations or the bedrock topography. The results show that the various soil production functions that incorporate either a wetness index or subsurface flow depth based on the bedrock topography give rise to soils that self-organize with well-defined spatial patterns and bedrock elevations with spatial organization significantly different from that of the surface. The model that incorporates the influence of subsurface water on soil production is able to naturally generate a soil production rate with a maximum value for a nonzero soil depth and overcomes an inconsistency of previously published “humped” soil production models.
TL;DR: In this article, the authors present an applied example where such a narrative is developed -it emphasizes landscape history, provenance of soil parent materials and pedogenic process, which is then expressed as rules that are used to produce digital soil maps.
Abstract: Conventional and quantitative methods require synthesis for digital soil mapping to reach its full potential, particularly when survey extents are large (i.e. >10,000 km2), data are sparse and resources are limited. One of the challenges is to express narratives of pedogenesis in an explicit form that can be incorporated into digital soil mapping. We present an applied example where such a narrative is developed – it emphasizes landscape history, provenance of soil parent materials and pedogenic process. The narrative is then expressed as rules that are used to produce digital soil maps. The rules are parsimonious; they encourage testing and enable spatial prediction; and are easy to update. The rules rely on just a few terrain and geophysical variables. We demonstrate how a new terrain variable, the multiresolution valley-bottom flatness (MrVBF) index, can be used with the familiar topographic wetness index (TWI), to map geomorphic and soil features.
TL;DR: In this article, the authors used participatory methods to exploit local knowledge about soils and to document it in a "Local Soil Map" for agricultural land-use planning in N Thailand.
Abstract: For the development of sustainable land-management systems in the highlands of N Thailand, detailed knowledge about soil distribution and soil properties is a prerequisite. Yet to date, there are hardly any detailed soil maps available on a watershed scale.
In this study, soil maps on watershed level were evaluated with regard to their suitability for agricultural land-use planning. In addition to common scientific methods (as underlying the WRB classification), participatory methods were used to exploit local knowledge about soils and to document it in a “Local Soil Map”.
Where the WRB classification identified eight soil units, the farmers distinguished only five on the basis of soil color and “hardness”. The “Local Soil Map” shows little resemblance with the detailed, patchy pattern of the WRB-based soil map. On the contrary, the “Local Soil Map” is fairly similar to the petrographic map suggesting that soil color is directly related to parent material.
The farmers' perception about soil fertility and soil suitability for cropping could be confirmed by analytical data. We conclude that integrating local soil knowledge, petrographic information, and knowledge of local cropping practices allows for a rapid compilation of information for land-evaluation purposes at watershed level. It is the most efficient way to build a base for regional land-use planning.
TL;DR: In this paper, the authors explore how the existing methods may be extended to the case in which the auxiliary information is spatially exhaustive and where soil mapping is done using universal kriging.
Abstract: Digital soil mapping makes extensive use of auxiliary information, such as that contained in remote sensing images and digital elevation models. However, it cannot do without taking samples of the soil itself. Therefore, methods and guidelines need to be developed that assist users in designing spatial sample configurations for use in digital soil mapping. Existing geostatistical methods are insufficient because these typically have been developed for situations in which there is no auxiliary information. In this chapter, we explore how the existing methods may be extended to the case in which the auxiliary information is spatially exhaustive and where soil mapping is done using universal kriging. We develop and illustrate a methodology that optimizes the spatial configuration of observations by minimizing the spatially averaged universal kriging variance. The universal kriging variance incorporates trend estimation error as well as spatial interpolation error. Hence, the optimized sample configuration strikes a balance between an optimal distribution of observations in feature and geographic space. The results show that optimal distribution in feature space prevails over optimal distribution in geographic space when the stochastic component of the universal kriging model is weakly spatially autocorrelated. It also prevails when the total number of observations is small. In all other cases, the optimal configuration is close to that obtained with minimization of only the spatial interpolation error. Application to a variety of real-world cases with multiple predictors and different spatial dependence structures is needed to support and generalise these preliminary findings.
TL;DR: In this article, a wavelet analysis is used to decompose the variables into hierarchical spatial components of decreasing spatial resolution, which can then be used as separate layers in predicting soil classes or soil attributes.
Abstract: Prediction of soil attributes and soil classes in digital soil mapping relies on finding relationships between soil and the predictor variables of soil-forming factors and processes. The predictor variables can be remotely or proximally sensed images of soil, landscape, parent material or climatic factors. Till date, most prediction methods are based on performing regression on the predictor variables directly to predict soil attributes or classes. There are problems using data layers from different sources, particularly, multicollinearity, and the fact that the relationships between soil and environmental variables can change with spatial scale. To overcome the problem of correlation between variables, principal component analysis can be performed on the predictor variables. With respect to the spatial dependency, each of these variables can be decomposed into separate spatial components and mapped separately. One of the methods of achieving this is wavelet analysis, which decomposes the variables into separate hierarchical spatial components of decreasing spatial resolution. These components could all be derived and subsequently used as separate layers in predicting soil classes or soil attributes. In this chapter, data are decomposed using the wavelet method and examples of predictions of soil classes and surface-clay content are shown, in order to evaluate the effect of using the decomposed layers in comparison with the original data.
Journal Article•
TL;DR: In this article, the authors evaluate how the precision of soil organic carbon (SOC) maps is affected by the laboratory method used for measuring SOC and by the spatial prediction method for mapping.
Abstract: Digital mapping of soil organic carbon (SOC) is important for site-specific crop management and for environmental modeling and planning. Our objective was to evaluate how the precision of SOC maps is affected by the laboratory method used for measuring SOC and by the spatial prediction method used for mapping. In two irrigated maize fields in Nebraska, soil samples were collected, and SOC was either determined directly by automated CN analyzer (reference method, SOC c ) or estimated from weight loss-on-ignition (LOI) as a cheaper alternative. The latter involved conversion of LOI to soil organic matter (SOM) content using a standard laboratory calibration, followed by converting SOM to SOC values by assuming a constant C mass fraction in SOM (estimates denoted as SOC L ) or by regressing SOC on LOI (denoted as SOC R ). Interpolation methods evaluated were ordinary kriging (OK) and regression kriging (RK). Exhaustive ancillary variables used in RK included relative elevation, slope, soil electrical conductivity, and remotely sensed soil surface reflectance. Soil organic C was correlated with most of these ancillary variables, but the magnitudes of correlation varied among locations. Direct measurement of SOC C in combination with RK as spatial prediction method resulted in the most precise SOC maps. The relative improvement in map precision was 15% in Field 1 and 6% in Field 2 over OK of SOC C . Maps of SOC derived from LOI estimates were biased and less precise than maps that were based on direct measurement of SOC, but utilizing secondary information for spatial prediction alleviated some of the loss in precision. Using SOC L or SOC R estimates of SOC decreased map precision by 10% to 14% in OK or by 7% to 10% with RK as compared to the SOC C method. Regardless of the laboratory method chosen, secondary information should be used in SOC mapping to reduce sampling cost and/or increase map precision. However, the relative improvement of hybrid geostatistical techniques over OK largely depends on the strength of the correlation between SOC and ancillary variables.
TL;DR: The Latin hypercube sampling (LHS) as mentioned in this paper is a sampling strategy on existing data layers that provides a full coverage of the range of each variable by maximally stratifying the marginal distribution.
Abstract: Prediction of soil attributes (properties and classes) in digital soil mapping (DSM) is based on the correlation between primary soil attributes and secondary environmental attributes. These secondary attributes can be obtained relatively cheaply over large areas. In the presence of these environmental covariates, a strategic sampling design needs to ensure the coverage of the full range of environmental variables. This could enhance the full representation of the expected soil properties or soil classes. This chapter presents the Latin hypercube sampling (LHS) as a sampling strategy on existing data layers. LHS is a stratified-random procedure that provides an efficient way of sampling variables from their multivariate distributions. It provides a full coverage of the range of each variable by maximally stratifying the marginal distribution. This method is illustrated with examples from DSM of part of the Hunter Valley of New South Wales. Comparison is made with other methods: random sampling, equal spatial strata and principal component (PC). Results showed that the LHS is the most effective way to replicate the distribution of the variables.
TL;DR: In this paper, the authors advocate that pedological knowledge of soil landscape distribution, soil-forming factors and soil processes is essential to modern soil spatial analysis and may be rigorously integrated into soil mapping.
Abstract: Classical soil survey usually integrated existing pedological knowledge to enhance its efficiency and compensate for very low standard sampling densities. This approach has been criticised because the information taken into account was not explicitly specified and validation procedures were not developed. We advocate that pedological knowledge of soil landscape distribution, soil-forming factors and soil processes is essential to modern soil spatial analysis and may be rigorously integrated into soil mapping. Basic reasons for such integration are an increase in prediction efficiency and also the necessity to link soil maps to dynamic modelling, enabling risk assessment and impact studies. Several approaches are reviewed including spatial prediction techniques using existing soil maps and spatial modelling based on soil-forming factors. Combination in the near future of space and time modelling demands additional integration of the dynamics of physical and biochemical soil processes.
01 Jan 2006
TL;DR: The relation between humus forms and general features of the soil faunal community is well founded knowledge in soil science and soil biology as mentioned in this paper, and mapping of humus form can be a tool for upscaling and prediction of soil biodiversity at regional or land-scape scales.
Abstract: The relation between humus forms and general features of the soil faunal community is well founded knowledge in soil science and soil biology. As the direct explora-tion of soil communities is limited to the local scale, mapping of humus forms can be a tool for upscaling and prediction of soil biodiversity at regional or land-scape scales. Existing inconsistencies in classification systems that hamper an easy translation of humus forms into soil biodiversity are addressed.
TL;DR: The state of the art of the Brazilian soil survey and mapping, including a brief history of soil surveys in Brazil, a summary description of survey methods and techniques, mapping paradigms, as well as the present-day needs and current challenges are discussed in this paper.
Abstract: In this chapter, we shall discuss about the state of the art of the Brazilian soil survey and mapping, including a brief history of soil surveys in Brazil, a summary description of survey methods and techniques, mapping paradigms, as well as the present-day needs and current challenges. Digital soil mapping is viewed as an opportunity to recover the unaccomplished soil mapping program in Brazil. We also focus on several attempts to make a national soil database, starting at the beginning of the 1980s with SisSolos, followed by SigSolos, and lately, SigWeb “Iniciativa Solos br”, available at http://www.cnps.embrapa.br/soilsbr and the country's new challenges to improve soil mapping, as well as some insights into digital soil mapping. Traditional soil surveys in Brazil have covered almost the whole country; these soil surveys are mainly in small-scale mapping, except for the Amazon region, which is poorly provided with soil surveys. Four main governmental institutions, Brazilian Agricultural Research Corporation (EMBRAPA), Brazilian Institute of Geography and Statistics (IBGE), Agronomic Institute of Campinas (IAC) and Geological Survey of Brazil (CPRM), execute soil surveys at the national and state levels. Private consultants also perform soil surveys, particularly on larger scales, under private contracts. Consequently, there is much dispersed information about soil surveys, but at least the methods and procedures are kept reasonably uniform all over the country. The systematic, governmental-supported soil mapping of the entire country, as initially planned, has been cancelled for a long time, although the demand for soil survey information continues at the same or even higher levels in some regions. At present, complete soil mapping covers 17 states out of 26, and the Federal District, at scales ranging from 1:100,000 to 1:600,000, covers approximately 35% of Brazilian soil, as well as a full uniform cover at scales of 1:1,000,000 and 1:5,000,000. Extensive zones still lack complete soil information at suitable scales and survey levels, needed to face the current problems of use, management, conservation, prevention and recovery of agriculturally and nonagriculturally degraded areas. Nowadays, soil surveys are made only on governmental demands, to support agroecological zonings and evaluation of environmental-impact projects, precision agriculture, degraded-area reclamation, planning of rural settlements and land-use planning, and are always linked to multidisciplinary activities. Soil and environmental data organisation, structuring and availability is imperative to perform digital soil mapping, which will certainly generate demands of quality databases as well as of the necessary tools for institutions involved in soil surveys in Brazil.
TL;DR: In this paper, the authors collate and integrate various land feature digital layers to the same resolution and coordinate system, and develop spatial prediction models based on scorpan and scorpan-kriging methods, for predicting selected soil attributes.
Abstract: The application of statistical techniques to spatially predicting soil attributes from ancillary variables evolved from Jenny's factors of soil formation, termed “Climate, Organisms, Relief, Parent material and Time” or “ corpt ” The corpt approach was recently extended to include other soil attributes ( s as surrogates) and space ( n ), and thus it is termed scorpan with time factor t in corpt replaced as age ( a) The main objectives of this chapter are to collate and integrate various land feature digital layers to the same resolution and coordinate system, and to develop spatial prediction models based on scorpan and scorpan -kriging methods, for predicting selected soil attributes Existing analogue maps were first digitised and transformed into digital maps These, along with other existing digital information, were reprojected in the same coordinate system, the Geocentric Datum of Australia (1994) and Map Grid of Australia (1994), namely GDA-94 and MGA-94 Further these digital map layers, along with Landsat bands and digital terrain attributes, were used to predict soil attributes and thus producing different soil attribute maps for a number of soil depths The spatial prediction methods used were scorpan methods, such as multiple linear regressions (MLR) and scorpan -kriging (SK), which combines simple kriging with MLR While MLR was good enough model to predict a number of soil attributes, SK was more appropriate for electrical conductivity for two layers: 0–10 and 70–80 cm layers and was equally good, if not slightly better than the scorpan technique of MLR However, the results of scorpan- kriging exhibit more detailed variation across the extent of the study area compared with kriging or MLR The application of generalised linear and generalised additive models did not improve the prediction accuracy Two of the soil attributes – clay content and electrical conductivity – for both the topsoil (0–10-cm depth) and subsoil (70-80-cm depth) are illustrated here Both soil attributes have significant influence on the hydrological processes shaping the landscape Finally, the digital land feature maps, including those of soil and digital terrain attributes, are displayed as geographical information systems (GIS) layers, which could potentially be useful for various environmental and catchment modelling
TL;DR: In this article, the authors developed and tested a methodology that incorporates geographic information systems (GIS), remote sensing and modelling to predict and map soil distribution in the Powder River Basin of Wyoming.
Abstract: Vast areas of the earth need new or updated soil survey data, but traditional methods of soil survey are inefficient, expensive and often inaccurate. We developed and tested a methodology that incorporates geographic information systems (GIS), remote sensing and modelling to predict and map soil distribution in the Powder River Basin of Wyoming. Based on conceptual models in which unique soils are the products of unique sets of soil-forming factors, topographic data derived from digital elevation models (DEMs) and Landsat 5 spectral data were selected to represent soil-forming factors. These digital data were analysed using commercially available GIS and image processing software. Unsupervised, supervised and simple knowledge-based classifications were used in the preliminary stage to develop visual representations of soil-landscape patterns and to plan for field data collection. As more was learned about the survey area from data collection, a knowledge-based decision-tree classification model was built and refined. The resulting maps were evaluated qualitatively by local experts and quantitatively using accuracy assessment, and showed good agreement between predicted and observed map units. Continued technological advancements in spatial data and improved GIS and modelling expertise of soil scientists should increase the accuracy and efficiency of the soil survey process.
TL;DR: In this paper, the authors describe the steps followed to build and improve the Soil Geographical Database of Eurasia at scale 1:1,000,000 and suggest automatic soil mapping techniques to improve it.
Abstract: In this Chapter, we describe the steps followed to build and improve the Soil Geographical Database of Eurasia at scale 1:1,000,000 and suggest automatic soil mapping techniques to improve it. The work started in 1952 with the compilation of materials provided by the contributing countries to publish the Soil Map of the European Communities at scale 1:1,000,000 in 1985. From then on, it was computerised, geo-referenced, structured to form a geographic database, enriched using archives of the original materials, extended geographically and thematically, harmonised over country borders, updated and documented. Despite this streamline of development and enhancements, it has limitations inherent to the initial objective of publishing a paper map and to the wide variety of views on soil mapping that were brought together from the community of contributors. The next logical step would thus be to improve the Soil Database by using existing detailed soil information and newly European-wide available satellite data and digital elevation models (DEMs). Existing soil maps at larger scale available in some parts of Europe could be used as training references to assess the morphometric (relief) and spectral characteristics of the Soil Mapping Units (SMUs) from DEM and satellite images, and the results extrapolated beyond to the surrounding areas and compared with the present delineation included in the 1:1,000,000 Soil Database. The Limousin Region of France was chosen as a test area but other geomorphologically diverse and representative areas in Europe should be included as well. This work is in progress but more partners will be needed in a collaborative project effort to establish a multi-scale European Soil Information System.
01 Mar 2006
TL;DR: In this article, the accuracy of traditional crisp soil maps can be improved in several ways: with the refinement of soil contours; with the subdivision of mapping units taking into consideration smaller, within patch inhomogeneities; and with refinement of attribute information (more recent data, more precise measurement, upto-date methodology, more appropriate classification etc.).
Abstract: A key issue of the applicability of both traditional soil maps and soil information systems (SSISs) is their accuracy. Essentially, the main practical aim of soil surveys/mapping and spatial soil information is prediction. A traditional tool of this information extension is the classical (crisp) soil map (using soil mapping units), which generally constitute the geometric basis of SSISs, too. Numerous novel methods have been developed for producing more accurate soil maps, however traditional crisp soil maps are still extensively applied, as they offer the most easily interpretable results for the majority of users. On the other hand, accuracy of this kind of soil maps can be increased in several ways: with the refinement of soil contours; with the subdivision of mapping units taking into consideration smaller, within patch inhomogeneities; and with the refinement of attribute information (more recent data, more precise measurement, up-to-date methodology, more appropriate classification etc.). The GIS ad...
01 Mar 2006
TL;DR: In this paper, a group estimation method was developed to predict the mean soil hydrophysical properties, and the estimation efficiency of the worked out prediction procedures was controlled on a test database, and on a dataset of a study area.
Abstract: According to the Hungarian Soil Information and Monitoring System's (HSIMS) database a group estimation method was developed to predict the mean soil hydrophysical properties. The estimation efficiency of the worked out prediction procedures was controlled on a test database, and on a dataset of a study area. It can be established that the water retention and the hydraulic conductivity of soils are sufficiently predictable from the category data of soil maps. The 10-digit map codes of the PWW mapping method were created by different estimation methods, and as a result the PWW map was drawn. However, it is not always possible to estimate the necessary soil hydrophysical properties from the available map information for preparing the PWW map. Sometimes the knowledge gained from the field reports is needed as well. Further studies are planned for integrating these morphological information into our estimations.