Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling. Part 2: Mapping of soil ecosystem services

A framework for estimating the distribution of soil ecosystem service (ES) supply is described that is based on the concept of matrix multiplication. This approach enables relationships between fundamental soil variables and associated environmental characteristics to be linked to soil processes, and hence to ecosystem functions and ecosystem services. The parameterization of these relationships was achieved using a combination of data from the Scottish Soils Database and expert knowledge. Baseline data to allow mapping of processes, functions and services across Scotland is given by digital maps of soil classes. The matrix multiplication approach constrains the relationship linkages to linear relationships and ignores potential synergies between factors at each stage, but does provide a mechanism for relating fundamental soil characteristics to ecosystem services. The approach has been tested by developing maps of selected ecosystem services in Scotland and comparing these with existing maps of the same or similar ESs. While the values and their ranges differ in each case, the spatial distribution of services is similar. The proposed mechanism is extensible at every level and can also be used to explore the impacts of land management options on environmental characteristics. This is demonstrated by using the model to estimate impacts of liming on three ecosystem services: Agricultural Capability, Carbon Sequestration and Drinking Water Provision. The model is shown to produce reasonable estimation of the impacts of this management option. Further discussion of improvements to the system and its potential applications is given.     

自动土壤图基于知识的分类

A machine-learning approach was developed for automated building of knowledge bases for soil resources mapping by using a classification tree to generate knowledge from training data. With this method, building a knowledge base for automated soil mapping was easier than using the conventional knowledge acquisition approach. The knowledge base built by classification tree was used by the knowledge classifier to perform the soil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal images and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on a field survey. The accuracy assessment mad maalysis of the resultant soil maps suggested that the knowledge bases built by the machine-learning method was of good quality for mapping distribution model of soll classes over the study area.     

Coping with imbalanced data problem in digital mapping of soil classes

An unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over- and under-sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost-sensitive learning and one-class classification (OCC) of the minority class combined with multi-class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost-sensitive decision tree (CSDT) within the C5.0 algorithm, and a one-class support vector machine combined with multi-class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold-out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study approach demonstrates that the three newly suggested methods can remarkably increase both overall and individual class accuracy for mapping.     

运用分类树进行土壤类型自动制图的研究

提供了一种基于机器学习的方法来自动建立针对土壤资源制图的规则库。以浙江省龙游县研究区为例,将已有的土壤图与地质图、土地利用现状图、DEM及其派生属性、双时相的TM卫星数据相结合,使用分类树算法从训练数据中生成该地区土壤制图的规则知识,并进行了研究区土壤类型的知识分类。这种建立土壤自动制图知识库的方法要比传统的知识获取方法更为简便易行。精度评价结果表明,所建立的知识库对于研究区的大部分土壤类型的预测是可行的。     

Classification of soil types using geographic object-based image analysis and random forests

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis (GEOBIA) partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests (RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values, the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of raster-geodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.     

水稻种植面积遥感估算的不确定性研究

利用研究区地物类别亚米级GPS详查数据及TM影像光谱数据,模拟生成1m分辨率的遥感模拟影像。用3种非参数分类法(最临近法KNN、误差后向传播神经网络BPN,模糊自适应网络FUZZY ARTMAP)和一种参数分类法(最大似然法MLC)对研究区TM影像进行硬分类估算水稻面积;还采用BPN全模糊分类、BPN和KNN模糊分类、抽象级结合和测量级结合的多分类器结合方法对遥感影像进行分类估算水稻面积;采用最多数法则的尺度扩展算法,实现由3m空间分辨率参考图提取30m空间分辨率影像像元纯度信息,讨论混合像元问题对遥感影像分类精度的影响。结果表明:非参数分类法精度均高于参数分类法,3种非参数分类法之间的差异较小,用最大似然法估算水稻面积的用户精度最高,用K最临近值分类法估算水稻面积的生产者精度最高;水稻类全模糊分类法的面积和真实面积最为接近,水稻类像元内的面积估测和真实面积无极显著差异;多分类器结合的分类法无论采用投票法还是测量级方法都能提高分类的总精度,能够提高水稻类面积提取的精度;研究区在30m空间分辨率的情况下,各类别分类总精度、Kappa系数随像元纯度升高而升高,4种硬分类方法没有对混合像元的分类表现出特别强的能力。本研究最终制作出分类影像像元的分类结果图、分类最大概率值、熵值图和水稻类概率值等4张图层,构成了对研究区分类结果不确定性的空间分布图不确定性图层,为采取进一步降低不确定性的措施提供了线索。     

Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran province,northern Iran

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km² grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.     

结合多等级代表性采样和模糊隶属度的区域土壤制图方法

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales,could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon(SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results(environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error(RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.     

Digital soil mapping in Germany—a review

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.     

利用人工神经网络以及相关地形属性绘制数字土壤地图

Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks (ANNs) were developed to map soil units using digital elevation model (DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base (WRB) classification criteria than the Soil Taxonomy (ST) system, but more soil classes could be predicted when using ST (7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error (interpolation error) and validation error (extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.     

Automated predictive ecological mapping in a Forest Region of B.C., Canada, 2001–2005

Procedures for automated predictive thematic mapping were developed and applied to project areas totaling more than 3 million ha of forested land in British Columbia, Canada. The effective scale of mapping was 1:20,000 using data at a grid resolution of 25 m. The methods can be described as a form of automated feature extraction or object recognition where the objects of interest consist of ecological site types. The methods implement a hybrid of automated, semi-automated and manual procedures that develop and apply heuristic, rule-based conceptual models of ecological-landform relationships. The methods rely heavily upon terrain derivatives extracted from available digital elevation models (DEMs) in addition to satellite imagery and manually digitized maps of ancillary environmental conditions. The primary input has been the BC provincial Terrain Resource Information Management (TRIM) digital elevation model (DEM) surfaced to a regular grid of 25 m. Other input layers include manually interpreted maps of parent material texture, depth and ecological exception classes, manually prepared maps of the spatial distribution of ecological zones of the BC Biogeoclimatic Ecosystem Classification (BEC) system and, to a limited extent, LandSat7 digital satellite imagery. The procedures do not use any field sampling to develop or train classification rules. A knowledge-based approach is used to establish classification rules which are defined and implemented using a Semantic Import (SI) Model implementation of fuzzy logic. All rules are constructed by examining and deconstructing published field guides that define the required ecological output classes and that document the current expert understanding of the conditions and criteria that control the spatial distribution of these desired output classes. An iterative, trial and error, process is used to develop, apply, evaluate and revise object recognition rules that relate ranges of values of key input data layers to an expert-assigned likelihood of occurrence for each ecological class of interest. Local expert knowledge is used at each stage to evaluate each new set of output results and to guide refinement of the fuzzy SI model classification rules. Field sample observations obtained along randomly selected closed traverses were collected following a line intercept approach and used to assess the accuracy of the final predictions of ecological classification. Application of the procedures has progressed from an initial pilot project through projects to evaluate operational scale-up to full-scale commercial application to millions of hectares. Costs have been reduced from a high of $3.50/ha to less than $0.20/ha. Rates of progress increased from 150,000 ha per person year to more than 2.0 million ha per person year. Independent assessments of map accuracy produced results superior to the highest accuracies reported for all alternatives, including traditional manual mapping methods. We conclude that we have formalized and automated many of the concepts and techniques previously used to create thematic maps of ecosystems using manual interpretation of stereo air photos and ancillary data combined with field observations. We have shown that automated feature extraction is able to capture and apply the concepts of landform control referenced by typical landform-based ecological models and classification systems. We have demonstrated that it is possible to produce accurate and cost-effective ecological-landform maps by applying fuzzy and Boolean logic and automated landform analysis procedures to widely available spatial data.     

Quantification of soil properties based on external information by means of fuzzy‐set theory

The increasing demand for improved large‐scale thematic maps of soil properties for use in such areas as hydrological modeling and landscape planning requires the inclusion of external information in the computerized construction of soil maps. As this data is often purely qualitative, regionalization methods are called for, that permit consideration of uncertain empirical information. A method based on fuzzy‐set theory is presented, which enables a GIS‐based quantification of soil properties with no loss of content input when transforming ”︁soft” data. This approach, which is also applicable to other tasks, is demonstrated by the construction of maps of soil properties based on uncertain qualitative information from the Hessian Forestry Survey and the small‐scale soil map of Hesse (1:50.000). Quality and uncertainty can be judged from a separate map of uncertainty indices. The thematic output map of the available water capacity of the rooting zone in the Dietzhölztal (Hesse) research site prepared using the fuzzy approach provided an increase of 101 % in spatial resolution compared with the 1:50.000 soil map.     

Unearthing the role of biological diversity in soil health

The soil provides a great variety of microhabitats for myriad organisms of different size, physiological activity, behavior and ecosystem function. Besides abundance of participating soil organisms, their species diversity facilitates maximum exploitation of the resources available in the different habitats. At various levels of resolution, species can be categorized into classes performing ecosystem functions and, within each functional class, into guilds of species with similar life course characteristics. Measurement of the diversity and abundance of species within a functional class provides insights into the nature of ecosystem functions and services and to the health of the soil. At higher resolution, species diversity within guilds of a functional class may infer the degree of exploitation of available resources and the complementarity of an ecosystem service; diversity among the guilds of a functional class may indicate successional complementarity of the services. A diversity of guilds within a functional class expands the range of conditions over which ecosystem services are performed while species diversity within a functional class and its guilds contributes to the magnitude of the services. Consequently, diversity of species within functional classes is a key element of the biological component of soil health. In the context of ecosystem services and soil health, the biomass or metabolic activity of species are more useful measures of their abundance than numbers of individuals. Thus, understanding of soil health and ecosystem function requires, besides knowledge of species diversity within functional classes, assessment of the range of functions currently performed in the system and the abundances of organisms by which they are performed. We propose a diversity-weighted abundance product for comparison of the functional magnitude of different assemblages of like organisms.     

模糊c-均值算法在区域土壤预测制图中的应用

基于模糊c-均值算法和地统计学空间插值,在面积约为1km2的研究区内进行区域土壤预测制图。研究结果表明:根据研究区123个剖面和土钻样点,通过分析它们在形态学上的特征和定量属性,建立了9类诊断特征土层。通过FCM算法模型,获得4类最佳分类数,模糊指数为1.7。类别数目与研究区受地形、母质和土地利用方式影响的主要成土过程决定的土纲下土壤类型数目一致。将经过对称对数比转换的隶属度成分数据进行单一模糊类别隶属度土壤预测制图,4种类别土壤在空间上具有明显的渐变过渡特征,制图结果较理想。在单一类别隶属度土壤图的基础上生成最大隶属度土壤图,与常规土壤调查土壤图具有共同参比的基础。     

Large‐scale assessment of soil erosion using a neuro‐fuzzy model combined with GIS: A case study of Hubei Province,China

Reliable and cost‐effective soil erosion assessment is an important precondition for soil conservation measures, which remains a major challenge at large scale. Considering that the neuro‐fuzzy model has the special advantage in multi‐index comprehensive assessment and GIS technology is adept at geo‐spatial information processing, through the combination of them, it is possible to provide an effective approach for this difficult problem. Taking Hubei Province as a case study area, five evaluating indicators were selected for the large‐scale assessment, in which the GIS technology was used to construct the classification maps of evaluating indicators and to divide basic assessment units, and the neuro‐fuzzy model was adopted to extract fuzzy rules for individual units assessment from available ground truth data. According to the optimized assessment criteria generated by the neuro‐fuzzy model, the soil erosion state of the entire study area was then assessed. To represent the spatial distribution of soil erosion, a detailed map was produced by statistical mapping, which was represented with six erosion levels (from slight to severe) at a map scale of 1:250 000. The resulting map showed that about 30.1% of the total land area in Hubei was affected by different levels of soil erosion problem. Western high mountains and eastern low mountains suffered from the most serious erosion damage, a strong level of soil erosion was widely observed in these mountains. Large areas of moderate level erosion occurred in the northern hills. In contrast, most of the central plains were characterized as slight level erosion effect. The validation indicated that an overall accuracy of 88% and a κ of 0.89 were achieved, proving that the resulting map was in conformity with actual conditions, which indicates this assessment approach was reasonable and applicable. Copyright © 2009 John Wiley & Sons, Ltd.     

基于高分5号影像的东北典型黑土区土壤分类

高精度的土壤分类及制图结果有助于更好地制定土地环境保护和土地资源利用策略。为探究星载高光谱影像实现区域尺度高精度土壤分类及制图的可能性,该研究获取东北黑土区拜泉县、明水县共计4幅高分5号(GF-5)星载高光谱遥感影像。首先,将原始反射率数据(Original Reflectance,OR)进行包络线去除处理获得去包络线数据(Continuum Removal,CR);其次,对OR和CR进行主成分分析(Principal Component Analysis,PCA)处理,分别得到反射率主成分信息(OR-PCA)和去包络线主成分信息(CR-PCA),并在OR-PCA和CR-PCA的基础上结合地形因子(Terrain,TA)。最后,OR、CR、OR-PCA、CR-PCA、OR-PCA-TA、CR-PCA-TA分别作为输入量结合随机森林分类模型,进行土壤分类并实现数字土壤制图。结果表明:1)包络线去除法可有效地提高星载高光谱土壤分类精度,与OR相比,CR的总精度提高了5.48%,Kappa系数提高了0.12。2)PCA可有效地降低高光谱数据的冗余性,提高模型的运算效率以及分类精度;与CR作为输入量相比,CR-PCA的土壤分类总精度提高了3.67%,Kappa系数提高了0.02。3)TA的引入显著提升了土壤分类精度,以CR-PCA-TA作为输入量的土壤分类精度最高,总精度为81.61%,Kappa系数为0.72,实现了高精度的土壤分类模型及土壤制图。研究结果可为大范围、高精度的土壤分类及制图提供新的思路。     

Digital soil mapping using artificial neural networks

In the context of a growing demand of high‐resolution spatial soil information for environmental planning and modeling, fast and accurate prediction methods are needed to provide high‐quality digital soil maps. Thus, this study focuses on the development of a methodology based on artificial neural networks (ANN) that is able to spatially predict soil units. Within a test area in Rhineland‐Palatinate (Germany), covering an area of about 600 km², a digital soil map was predicted. Based on feed‐forward ANN with the resilient backpropagation learning algorithm, the optimal network topology was determined with one hidden layer and 15 to 30 cells depending on the soil unit to be predicted. To describe the occurrence of a soil unit and to train the ANN, 69 different terrain attributes, 53 geologic‐petrographic units, and 3 types of land use were extracted from existing maps and databases. 80% of the predicted soil units (n = 33) showed training errors (mean square error) of the ANN below 0.1, 43% were even below 0.05. Validation returned a mean accuracy of over 92% for the trained network outputs. Altogether, the presented methodology based on ANN and an extended digital terrain‐analysis approach is time‐saving and cost effective and provides remarkable results.     

Automated soil map compilation

Abstract

A soil map is eonventionally prepared by an experieneed surveyor via the following three steps; (1) establishment of taxonomie class, (2) alloeation of sam pie into one of the classes, and (3) delineation of homogeneous areas in terms of mapping unit. These steps involve some degree of arbitrariness; thus soil maps prepared by two surveyors are never identical. The aim of this study is to define a eertain proeedure of soil map eompilation, by introdueing numerical handling of soil data, to obtain a reproducible and easy-to-prepare soil map, with the help of the funetions of the eomputer-based Soil Data Management System (COSMAS).

The authors applied Hayashi's theory of quantification No. 3 to numerical representation of soil profiles based on the pattern of eombination of various soil attributes relevant to soil classification. The following four soH types were recognized in the seattergram plotted using numerical va lues assigned to the soH profiles; Gley Lowland SoH, Gray Lowland Soil, Brown Lowland Soil and Pseudogley SoH. Then, using these numerical values, diseriminant analysis was carried out to classify each profile into one of the above-defined soil types. As a result, 89.7% of the observed profiles were assigned to the same soil types as assigned by a surveyor in the filed. Area delineation for each mapping unit on the basis of soil type assignment and probabHity of membership of a respective soil type group at a sampled si te was automatieally performed by an "AUTOMAP" program whieh was newly developed for COSMAS for graphic representation of soil data. The numerically prepared soi! map showed reasonable agreement with the surveyor's. A wide range of users of soil survey data can prepare various maps using the procedure proposed in this paper.