Showing papers on "Digital soil mapping published in 2007"

Digital soil mapping : an introductory perspective

Abstract: A. Introduction B. Digital soil mapping: current state and perspectives C. Conception and handling of soil databases D. Sampling methods for creating digital soil maps E. New environmental covariates for digital soil mapping F. Quantitative modelling for digital soil mapping F.i : Examples of predicting soil classes F.ii : Examples of predicting soil attributes G. Quality assessment and representation of digital soil maps

Soils: A Contemporary Perspective

Abstract: Soils are viewed in the context of ecosystem services, soil processes and properties, and key attributes and constraints. The framework used is based on the premise that the natural capital of soils that underlies ecosystem services is primarily determined by three core soil properties: texture, mineralogy, and soil organic matter. Up-to-date descriptions and geographical distribution of soil orders as well as soil attributes and constraints are given, along with the relationships between soil orders, properties, and biomes. We then relate ecosystem services to specific soil processes, soil properties, and soil constraints and attributes. Soil degradation at present is not adequately assessed and quantified. The use of an approach combining digital soil maps, pedotransfer functions, remote sensing, spectral analysis, and soil inference systems is suggested for simultaneous characterization of various chemical, physical, and biological properties to overcome the great limitations and costs of conventional ...

Digitally Mapping Gypsic and Natric Soil Areas Using Landsat ETM Data

Abstract: Mapping salt-affected soils in remote rangelands is challenging. We used Landsat 7 ETM data to facilitate digital mapping of gypsic and natric soil areas in the upper Colorado River drainage. Optimum index factor band combinations were used to explore the scene. Normalized difference ratio models and threshold values were developed by comparing spectral signatures with gypsic and natric soil areas verified in the field. Gypsic soil areas were mapped using the normalized difference ratio of Bands 5 and 7 with a threshold >0.11, probably related to the spectral reflectance of gypsum within a few centimeters of the surface. All sites predicted to be gypsic soil areas were determined to be gypsic by field assessment, and 87% of the field-observed gypsic soil areas were correctly predicted. Natric soil areas were mapped using the normalized difference ratio of Bands 5 and 4 with a threshold >0.19, possibly related to the co-occurrence of Fe-bearing minerals with natric soil areas. Most of the sites predicted to be natric were determined in the field to be natric (82%), but only half of the field-observed natric areas were correctly predicted, indicating that natric soils are harder to detect spectrally than gypsic soils. While the gypsic model may be transferred to other areas, particularly in the arid Colorado Plateau, transfer of natric models would be difficult. Normalized difference ratio models can be developed for other digital soil mapping areas where land surface features produce differences in Landsat spectral band reflectances.

Generating a Danish raster-based topsoil property map combining choropleth maps and point information

Abstract: Geografisk Tidsskrift, Danish Journal of Geography 107(2):1–12, 2007 The Danish environmental authorities have posed a soil type dependent restriction on the application of nitrogen. The official Danish soil map is a choropleth topsoil map classifying the agricultural land into eight classes. The use of the soil map has shown that the maps have serious classification flaws. The objective of this work is to compile a continuous national topsoil texture map to replace the old topsoil map. Approximately 45,000 point samples were interpolated using ordinary kriging in 250 m x 250 m cells. To reduce variability and to obtain more homogeneous strata, the samples were stratified according to landscape types. Five new soil texture maps were compiled; one for each of the five textural classes, and a new categorical soil type map was compiled using the old classification system. Both the old choropleth map and the new continuous soil maps were compared to 354 independent soil samples. 48% of the 354 indepe...

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Abstract: Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

Vnir diffuse reflectance spectroscopy for agricultural soil property determination based on regression-kriging

Abstract: Visible and near-infrared diffuse reflectance spectroscopy has been widely applied in precision agriculture to develop soil property prediction models. This method assumes that residuals of prediction are independently and identically distributed. However, this assumption is violated by spatial dependence common in soil samples collected from agricultural fields, and subsequent prediction models are usually sub-optimal. In this article, the regression-kriging method was used to account for spatial dependence among soil samples and aid in prediction model development. A total of 273 soil samples were collected from an agricultural field in Quitman County, Mississippi. Particle size distribution (clay and sand) and chemical analysis (Ca, K, Mg, Na, P, and Zn) were performed in the laboratory. Soil reflectance spectra were measured with a spectroradiometer (250 to 2500 nm). Soil samples were divided into two groups: 245 samples in the calibration set, and 28 samples in the validation set. The calibration set was first used to develop the principal component regression (PCR) models for each soil property. Semivariance analysis of prediction residuals from PCR revealed strong spatial dependence in Na; medium spatial dependence in Ca, Mg, and sand; weak spatial dependence in K and P; and a pure nugget effect in Zn and clay. Fitted theoretical semivariograms were then used to develop the regression-kriging models. Both the PCR and regression-kriging models were tested with the validation set, and their prediction capability was evaluated by R2 and RMSE (root mean squared error). The results showed that the only two soil properties that could be predicted by the PCR models were Mg (R2 = 0.4 and RMSE = 25.4%) and Ca (R2 = 0.33 and RMSE = 16.6%). On the other hand, the regression-kriging models were able to predict most soil properties with reasonably high R2 (reaching 0.65) and low RMSE. Most impressively, substantial increases of R2 and decreases of RMSE were achieved by the regression-kriging models for Na (R2 = 0.65 and RMSE = 29.0%, compared to R2 = 0.10 and RMSE = 44.4% in the PCR model) and sand (R2 = 0.49 and RMSE = 19.8%, compared to R2 = 0.06 and RMSE = 26.0% in the PCR model). It is anticipated that the proposed method could be integrated into GIS packages for various precision agriculture applications, such as digital soil mapping based on remotely sensed hyperspectral images.

A GIS framework for surface-layer soil moisture estimation combining satellite radar measurements and land surface modeling with soil physical property estimation

Abstract: A GIS framework, the Army Remote Moisture System (ARMS), has been developed to link the Land Information System (LIS), a high performance land surface modeling and data assimilation system, with remotely sensed measurements of soil moisture to provide a high resolution estimation of soil moisture in the near surface. ARMS uses available soil (soil texture, porosity, K"s"a"t), land cover (vegetation type, LAI, Fraction of Greenness), and atmospheric data (Albedo) in standardized vector and raster GIS data formats at multiple scales, in addition to climatological forcing data and precipitation. PEST (Parameter EStimation Tool) was integrated into the process to optimize soil porosity and saturated hydraulic conductivity (K"s"a"t), using the remotely sensed measurements, in order to provide a more accurate estimate of the soil moisture. The modeling process is controlled by the user through a graphical interface developed as part of the ArcMap component of ESRI ArcGIS.

Optimization of sample configurations for digital mapping of soil properties with 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.

Establishing a spatial grouping base for surface soil properties along urban–rural gradient—A case study in Nanjing, China

Abstract: Conventional classification systems based on vegetation and land use are frequently used to characterize or describe urban soils to determine the influence of urbanization on soils. In this study, the sensitivity of different grouping methods in reflecting soil variations along an urban–rural gradient was compared. The objective of this study was to determine the most sensitive grouping system in depicting and explaining variations of soil attributes around an urban area. Grouping methods, including urban–rural division, in situ vegetation type, land use types in different scales and numerical clustering, were compared for both single soil attributes and “soil set” defined by multiple variables. The result shows urbanization has a strong impact on many soil properties, especially that of gravel content, sand content, pH, phosphorus and soil compaction. In terms of the variations of soil attributes, in situ vegetation type is the most sensitive in comparison with local land use types and district-viewed land use types. In other words, soil properties in this study are not sensitive to coarser spatial resolution. Therefore, it's hard to interpret the spatial variation of urban soil by regular methods using natural soil-landscape paradigm. Furthermore, vegetation would best proxy the delineation of single attribute of urban soils. Numerical clusters effectively reflect the land use types and their change during urbanization. All clusters were interpreted as different sets with practical meanings: soil in abandoned greenbelt, soil in ill-managed greenbelt, soil in new vegetable land, extreme urban conditioned soil, soil in well-managed greenbelt, soil in highly mellowed vegetable land, soil in common urban–peri-urban greenbelt and weak-urban-impacted soil. They can be used as bases for soil regionalization in urban and peri-urban environment.

Integrated approach of using remote sensing and GIS to study watershed prioritization and productivity

Abstract: Soil data obtained from soil resource inventory, land and climate were derived from the remote sensing satellite data (Landsat TM, bands 1 to 7) and were integrated in GIS environment to obtain the soil erosion loss using USLE model for the watershed area. The priorities of different sub-watershed areas for soil conservation measures were identified. Land productivity index was also used as a measure for land evaluation. Different soil and land attribute maps were generated in GIS, and R,K,LS,C and P factor maps were derived. By integrating these soil erosion map was generated. The mapping units, found not suitable for agriculture production, were delineated and mapped as non-arable land. The area suitable for agricultural production was carved out for imparting the productivity analysis; the land suitable for raising agricultural crops was delineated into different mapping units as productivity ratings good, fair, moderate and poor. The analysis performed using remote sensing and GIS helped to generate the attribute maps with more accuracy and the ability of integrating these in GIS environment provided the ease to get the required kind of analysis. Conventional methods of land evaluation procedures in terms of either soil erosion or productivity are found not comparable with the out put generated by using remote sensing and GIS as the limitations in generating the attribute maps and their integration. The results obtained in this case study show the use of different kinds of data derived from different sources in land evaluation appraisals.

Use of ecosystem information to improve soil organic carbon mapping of a Mediterranean island.

Abstract: Detailed maps of soil C are needed to guide sustainable soil uses and management decisions. The quality of soil C maps of Italian Mediterranean areas may be improved and the sampling density reduced using secondary data related to the nature of the ecosystem. The current study was conducted to determine: (i) the improvements obtainable in mapping soil C over a Mediterranean island by using ecosystem features and (ii) the effect of different sampling densities on the map accuracy. This work relied on field sampling (n = 164) of soil properties measured over the island of Pianosa (Central Italy). Statistical analysis assessing the relationship between soil properties and ecosystem features revealed that the conceptual model of ecosystems defined on the basis of environmental features such as vegetation cover, land use, and soil type was mainly related to the variation of soil organic carbon (OC) content and to the type of Mediterranean environment. The distribution of ecosystems was used to improve the accuracy of soil OC maps obtainable by a simple interpolation approach (ordinary kriging). Substantial improvement was obtained by: (i) stratification into ecosystem types and (ii) applying locally calibrated regressions to satellite imagery that introduced both inter-ecosystem and intra-ecosystem information linked to vegetation features. This study showed that interpolation methods using information on ecosystem distribution can produce accurate maps of soil OC in Mediterranean environments, mostly because of the linkage between soil OC and vegetation types, which are spatially fragmented and heterogeneous.

Tasmanian organic soils

Abstract: The character, extent and location of Tasmanian organic soils have been largely overlooked in Australian soil classification and taxonomy, with only a loose interpretation of northern hemisphere organic soil classifications applied. The aim of present work is to produce a classification of the organic soils, based on measurable soil properties and to then relate the characteristic organic soil properties to environmental factors. The relationship between organic soil characteristics and environmental factors will enable predictive mapping of the occurrence and organic content of organic soils in Tasmania. Tasmanian organic soils were sampled across 127 sites yielding a total of 1159 soil pits. Soil and environmental characteristics were recorded for each soil pit. Unsupervised clustering of the soil characteristics from each soil pit distinguished 23 organic soil groups. A classification key for identifying the 23 clusters was produced using the soil characteristics, soil organic carbon, humification, soil total nitrogen and organic soil depth. Dominant environmental factors influencing the 23 clusters were found, through vector analysis, smooth plate spline contouring and multinomial log-linear modelling to be: vegetation, burn frequency, topography, geology, altitude and climate. In order to predict the location and occurrence of the soils and soil characteristics produced through unsupervised clustering, the dominant environmental factors were subsequently used to provide cluster centroids for a supervised clustering. The resulting 41 soil groups were found to be distinguishable in terms of vegetation type, geology, topography and microtopography. The supervised clusters were found to perform better than the available vegetation classifications in predicting the unsupervised clusters. Organic soil carbon, bulk density and depth were used to model organic soil carbon stocks in Tasmania and provide a geographic context for the supervised and unsupervised soil clusters. Stepwise regression of soil organic carbon, showed slope as the dominant predictor across organic soil producing vegetation types. The regression models allowed for mapping of organic soil areal extent and soil organic carbon stocks in Tasmania, producing a value of 3,072 Tg of soil organic carbon over 8, 974 km2. Suggested changes to the Australian Soil Classification for the order organosol include the addition of folic, lignic, arenic and argyllic to the differentiae. Suggested family criteria include: humification of surface tiers, organic horizon thickness, botanical composition of surface layers, botanical composition of dominant layers and acidity classes below pH cA 4.6. Changes to landform labels are also suggested.

Spatial assessment of erosion and its impact on soil fertility in the Tajik foothills

Abstract: Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Soil Information System of China (SISChina) and Its Application

Abstract: Soil is the material basis for human beings to live and develop on and it is the kernel of the terrestrial ecosystem. In order to solve problems in the fields of resources,environment and ecosystem,it is essential to establish a soil information system. A systematic review of researches of soil information systems home and abroad was presented here,introducing in detail sources of the soil data of the SISChina,soil spatial data,soil attribute data,China 1:1,000,000 soil database and its application. The introduction was of great practical significance to readers and researchers to get to know the developmental trend of the SISChina and make more effective use of the soil resources in agricultural production and eco-environment construction in China.

Development of predictive mapping techniques for soil survey and salinity mapping

Abstract: approved: Jay S. Noller Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data. ©Copyright by Abdelhamid A. Elnaggar June 5, 2007 All Rights Reserved Development of Predictive Mapping Techniques for Soil Survey and Salinity Mapping

Research on the Spatial Variability of Soil Moisture Based on GIS

Abstract: With the help of GPS and measuring instrument of soil moisture, soil moisture was measured and analyzed. As using Geo-statistics to the study of spatial variability of soil moisture and use ArcGIS 9.0, get the spatial distribution map of soil water property with Kriging interpolation. The research result showed that all soil spatial characters are normal distribution and the spatial distribution of soil water property accord with the fact. Geo-statistics Methods is the most appropriate methods in all of Mathematical Methods for Geostatistics. The spatial distribution map of soil water property what got with Kriging interpolation can make the spatial distribution of the entire plot, more accurate and reliable. Getting a veracious spatial distribution map of soil water speciality was very important and useful for adjusting precision fertilization and precision irrigation in time. It also offered the theoretical foundation of the connection studying between soil water speciality and enhancing the yield.

Enhanced quantification of soil variables linked with soil degradation using imaging spectroscopy

Abstract: Land degradation processes are complex and have profound effects on the biosphere and pedosphere. To understand these processes one has to describe quantitatively the soil and vegetation components and their interplay. We focus in our study on the enhanced quantitative determination of soil variables that are linked with land degradation and in particular soil degradation processes. The Natural Park Cabo de Gata-Nijar in SE Spain has been chosen as study site because it represents a fragile semi-arid environment highly sensitive to land and soil degradation processes. The site is characterized with a variable lithology and pedology where soils are exposed variously according to the different land cover types, ranging from crop fields (seasondependent, pure soil exposed) to natural shrubland vegetation (high plant cover on soils). The soil iron content has been shown to be most suitable as erosion indicator on the iron-rich soils dominating the study area. We developed a method for quantitative determination of soil iron that is based on an automated feature-based correlation technique. First, a soil mask is generated. Then chemical soil analyses and characteristic absorption features of the corresponding HyMap reflectance spectra are correlated using the spectral area feature of the blue iron absorption (r 2 = 0.98). Finally, the correlation result is applied on the hyperspectral image to retrieve the soil iron (Fed) content map which is then discussed in terms of validation with independent data (r 2 v= 0.57), and erosion processes in Cabo de Gata.

A Comparison of Regression and Regression-kriging for Soil Characterization Using Remote Sensing Imagery

Abstract: In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually makes the regression model suboptimal. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton field near Vance, Mississippi. The regression-kriging model was developed and tested by using 273 soil samples collected from the field. The result showed that by properly incorporating the spatial correlation information of regression residuals, the regression-kriging model generally achieved higher prediction accuracy than the stepwise multiple linear regression model. Most strikingly, a 50% increase in prediction accuracy was shown in Na. Potential usages of regression-kriging in future precision agriculture applications include real-time soil sensor development and digital soil mapping.