Digital soil map of the Ussuri River basin

On the basis of digital soil, topographic, and geological maps; raster topography model; forestry materials; and literature data, the digital soil map of the Ussuri River basin (24400 km²) was created on a scale of 1: 100000. To digitize the initial paper-based maps and analyze the results, an ESRI ArcGIS Desktop (ArcEditor) v.10.1 (http://www.esri.com) and an open-code SAGA GIS v.2.3 (System for Automated Geoscientific Analyses, http://www.saga-gis.org) were used. The spatial distribution of soil areas on the obtained digital soil map is in agreement with modern cartographic data and the SRTM digital elevation model (SRTM DEM). The regional soil classification developed by G.I. Ivanov was used in the legend to the soil map. The names of soil units were also correlated with the names suggested in the modern Russian soil classification system. The major soil units on the map are at the soil subtypes that reflect the entire vertical spectrum of soils in the south of the Far East of Russia (Primorye region). These are mountainous tundra soils, podzolic soils, brown taiga soils, mountainous brown forest soils, bleached brown soils, meadow-brown soils, meadow gley soils, and floodplain soils). With the help of the spatial analysis function of GIS, the comparison of the particular characteristics of the soil cover with numerical characteristics of the topography, geological composition of catchments, and vegetation cover was performed.     

Automated updating of medium-scale soil maps

An approach towards an automated updating of medium-scale soil maps via imitation of traditional mapping technologies is suggested. It is based on formulation of the rules of mapping in the form of classification trees for separating different soil cover patterns and on creation of the maps of soil-forming factors with the use of satellite data. Algorithms for mapping alluvial soils (Fluvisols), eroded (abraded), and anthropogenically transformed soils are presented. This approach was tested for the southern (Trans-Oka) part of Moscow oblast. The model for an automated soil mapping was realized using ILWIS software. The polygons of alluvial soils were mapped with a higher accuracy via the automated separation of floodplains according to the digital terrain model. The total area of alluvial soils shown on the medium-scale soil map decreased from 373 to 340 km². Calculations of slope angles according to digital terrain models allowed us to localize soil cover patterns with participation of eroded soils with a higher accuracy; their area decreased insignificantly: from 791 to 781 km². Anthropogenically transformed soils of building areas were mapped for the territory of Moscow oblast on the basis of satellite data for the first time. Their areas were delineated taking into account land use types and comprised 551 km², i.e., 15.4% of the total area (3570 km²) of the Trans-Oka part of Moscow oblast.     

基于土壤-环境关系的更新传统土壤图研究

传统土壤图是流域管理、生态水文模型所需土壤空间分布信息的主要数据源。然而,受传统制图技术和基础数据质量所限,传统土壤图的空间详细度和属性精确度并不高。随着地理信息技术的发展,如何利用可获取的高质量空间数据和现代空间分析技术来更新传统土壤图显得十分必要。基于传统土壤图中的土壤多边形与通过模糊聚类所得环境因子组合之间存在着对应关系这一假设,本文提出了一种从传统土壤图中提取土壤-环境关系知识并利用该知识更新传统土壤图的方法。该方法包括四个步骤:对环境数据进行模糊c均值聚类获取环境因子组合;利用传统土壤图建立环境因子组合与土壤类型间的对应关系;提取土壤-环境关系知识;进行土壤推理制图。将该方法应用于加拿大New B runsw ick省的W akefield研究区,以更新该区现有的1∶20 000的传统土壤图。应用结果表明:更新后的数字土壤图显示了更详细的空间分布信息;经野外独立验证点验证,所得土壤图(制图单元为土壤组合-排水等级)精度高出原土壤图约20%。因此,该方法是一种有效的更新传统土壤图的方法,可增加土壤图的空间详细度、提高土壤图的属性精确度。     

A map of the soil cover patterns in the western part of the Transbaikal region (1: 500000 scale)

The results of soil surveys in the Khorinsk district of the Buryat Republic have been generalized on a map of the soil cover patterns on a scale of 1: 500000. The map reflects the soil cover patterns in the zones of mountainous tundra and taiga, mountainous forest-steppes, sand massifs on piedmonts, and plain steppes and dry steppes. The areas of each component of the soil combinations shown on the map have been calculated. In the course of the generalization of large-scale soil maps, information on the genetic types and subtypes of soils has been preserved. The new map adequately displays the real pattern of the soil distribution in the studied area.     

Comparing mapping approaches at subcatchment scale in northern Thailand with emphasis on the Maximum Likelihood approach

This study compares different soil mapping approaches in three different petrographic areas in order to test their suitability for regional mapping in northern Thailand. Sampling was based on transects or grid-based randomization. Maps were created based on expert knowledge (eye fitting) or using Classification Tree (CART algorithm) or the Maximum Likelihood approach. In addition, local knowledge-based-soil maps were created. Validation was performed using soil reference maps and independent sampling points. The mapping approaches based on transects and grid-based randomization showed a very high correspondence with the respective reference soil map and a very high degree of matching with independent sampling points. Both methods are best suited for sub-watershed scale. Mapping larger areas is difficult due to the inaccessibility of the mountainous regions. The soil maps based on Maximum Likelihood showed a high correlation with the respective reference soil maps and the individual sampling points. Maximum Likelihood maps and Classification Tree maps showed similar levels of accuracy. The Maximum Likelihood approach is applicable to upscaling procedures; therefore, a calibration area is required which represents the target area. Local knowledge-based-soil mapping is very cheap and fast, but is restricted to village areas where classification often varies even within a village. Despite this, local knowledge is very useful for soil reconnaissance surveys, as well as to acquire an overview of the major distribution of soils and their properties. Upscaling of local knowledge due to its inherent inconsistency is not realistic.     

Analysis on pedodiversity and spatial subset representativity—the German soil map 1:1,000,000

Knowledge about spatial soil variation in terms of measured pedodiversity, as well as the spatial distribution of soils in terms of spatial subset representativity, offers the possibility to estimate the quality and variance within a soil map. Additionally, it can help to identify representative sample locations. Demonstrated at the German soil map at a scale of 1:1,000,000, this study describes a methodology to analyze the distribution of taxonomical pedodiversity using the Simpson index and a new approach to derive representative spatial subsets based on a modified χ²‐test (χ_m²), which can be used as monitoring areas. To analyze the spatial composition of the soil map and to detect differences in the underlying mapping schemes of the German soil map 1:1,000,000, three different spatial data structures were studied: (1) the entire soil map, (2) the soil map segmented into geomorphological regions, and (3) the soil map segmented into the Federal States of Germany. Representative patches of varying sizes were statistically derived for all spatial subsets as well as the entire soil map ranging from 20 km × 20 km up to 70 km × 70 km. The results show that the measured pedodiversity is linked to both the geomorphology as well as the political borders of the Federal States. On the one hand, this reveals the uncertainty of measuring pedodiversity on the basis of soil‐class maps as the spatial representation of pedodiversity is influenced by the different mapping traditions and methods applied in the 16 Federal States of Germany. On the other hand, it allows the analysis of the aggregation schemes of different landscapes. The presented approach helps to understand large soilscapes and to compare different soil maps of different states and countries as well as to enhance the soil map with additional information. Furthermore, the representative patches can be used to select soil‐monitoring areas.     

Assessment of the postagrogenic transformation of soddy-podzolic soils: Cartographic and analytic support

The results of experimental studies of the postagrogenic transformation of loamy soddy-podzolic soils on the southern slope of the Klin-Dmitrov Moraine Ridge are discussed. A chronosequence of soils (arable soils (cropland)-soils under fallow with meadow vegetation-soils under secondary forests of different ages-soils under a conventionally initial native forest) was examined, and the stages of the postagrogenic transformation of the automorphic soddy-podzolic soils were identified. The differentiation of the former plow horizon into the A1 and A1A2 horizons (according to the differences in the humus content, texture, and acidity) served as the major criterion of the soil transformation. A stage of textural differentiation with clay depletion from the uppermost layer was identified in the soils of the 20- to 60-year-old fallows. The specificity of the postagrogenic transformation of the soils on the slopes was demonstrated. From the methodological point of view, it was important to differentiate between the chronosequences of automorphic and semihydromorphic soils of the leveled interfluves and the soils of the slopes. For this purpose, a series of maps reflecting the history of the land use and the soil cover pattern was analyzed. The cartographic model included the attribute data of the soil surveys, the cartographic sources (a series of historical maps of the land use, topographic maps, remote sensing data, and a digital elevation model), and two base maps: (a) the integral map of the land use and (b) the map of the soil combinations with the separation of the zonal automorphic, semihydromorphic, and erosional soil combinations. This scheme served as a matrix for the organization and analysis of the already available and new materials.     

基于传统土壤图的土壤—环境关系获取及推理制图研究

在数字土壤制图研究中,从历史资料中提取准确的、详细的土壤—环境关系对于土壤图的更新和修正十分重要。从传统土壤图中提取土壤类型并从地形数据中提取环境参数,采用空间数据挖掘方法建立土壤—环境关系,并进行推理制图和精度验证。以湖北省黄冈市红安县华家河镇滠水河流域为例,首先选取成土母质和基于地形数据提取的高程、坡度、坡向等7个环境因子;然后利用频率分布原理得到包含土壤类型与环境因子信息的典型样本数据1 410个;采用See5.0决策树方法进行空间数据挖掘,建立土壤—环境关系;将其导入So LIM中进行推理制图;最后利用270个实地采样点验证所得土壤图的精度。土壤图的精度提高了约11%,证明了本研究方法对土壤类型和空间分布推理的可靠性。     

半干旱沙区土类/亚类的遥感调查制图方法

传统土壤调查制图存在低时效性、低精度等问题。为了解决半干旱沙区土壤遥感调查制图问题,该文以科尔沁左翼后旗为例,基于野外实地调查和专家知识分析了半干旱沙区土壤类型分布特征与环境因素之间的关系,并探讨了基于多时相Landsat8 OLI影像数据的半干旱沙区土类/亚类遥感调查制图方法。结果表明:利用多时相Landsat8 OLI影像数据提取的归一化差异水体指数(modified normalized difference water index,MNDWI)、盐分指数(salt index,SI)、归一化差异湿度指数(normalized difference moisture index,NDMI)、归一化差异植被指数(normalized difference vegetation index,NDVI)等环境信息,可实现对沼泽土、盐碱土、草甸土、风沙土及其亚类等半干旱沙区主要土壤类型的遥感调查制图。应用本文提出的半干旱区土类/亚类遥感调查制图方法对科左后旗进行土壤遥感调查制图和精度验证,总体精度约为72.84%,Kappa系数为0.667 8。该方法可为半干旱沙区数字土壤调查制图提供思路和参考。     

The informativeness of coefficients <Emphasis Type="Italic">a</Emphasis> and <Emphasis Type="Italic">b</Emphasis> of the soil line for the analysis of remote sensing materials

The coefficients of the soil line are often taken into account in calculations of vegetation indices. These coefficients are usually calculated for the entire satellite image, or are taken as constants without any calculations. In both cases, the informativeness of these coefficients is low and insufficient for the needs of soil mapping. In our study, we calculated soil line coefficients at 8000 lattice points for the territory of Plavsk, Arsen’evsk, and Chern districts of Tula oblast on the basis of 34 Landsat 5, 7, and 8 images obtained in 1985–2014. In order to distinguish between the soil line calculated for a given image and the soil line calculated for lattice points on the basis of dozens of multitemporal images, we suggest that the latter can be referred to as the temporal soil line. The temporal soil line is described by a classical equation: NIR = RED a + b, where a is its slope relative to the horizontal axis (RED), and b is the Y-axis (NIR) intercept. Both coefficients were used to create soil maps. The verification of the maps was performed with the use of data on 1985 soil pits. The informativeness of these coefficients appeared to be sufficient for delineation of eight groups of soils of different taxonomic levels: soddy moderately podzolic soils, soddy slightly podzolic soils, soddy-podzolic soils, light gray forest soils, gray forest soils, dark gray forest soils, podzolized chernozems, and leached chernozems. The b coefficient proved to be more informative, as it allowed us to create the soil map precisely on its basis. In order to create the soil map on the basis of the a coefficient, we had to apply some threshold values of the b coefficient. The bare soil on each of Landsat scenes was separated with the help of the mask of agricultural fields and the notion of the spectral neighborhood of soil line (SNSL).     

Digital map of salt-affected soils of Khakassia

A new methodological approach aimed at the creation and correction of digital soil maps on the basis of systematized and georeferenced available cartographic and attribute information arranged into a GIS project has been tested. The particular stages and methods of the creation of a digital map of salt-affected soils of Khakassia with the use of GIS technologies in the ArcInfo format are described. All the available cartographic materials, including the Soils of Russia GIS project, digital elevation models, remote sensing data, and analytical data on 82 soil pits (including the authors?? materials and previously published data), have been used for the creation of this new map.     

Small-scale soil maps and the scientific heritage of N.N. Rozov

The comparative analysis of small-scale maps of the USSR has revealed the characteristics of their contents, concepts, and ways of compilation and cartographic representation. The facies-zonal concept is reflected both in the map legends and the patterns of the mapping units. The differences between the maps reflect the time of their creation: the degree of knowledge on the soils, the contribution of the geographic forecast, and the objective of the maps and their scale. Especial importance in soil cartography and geographic education belongs to the map of 1954 compiled by Rozov. Forty years later, it was replaced by a map of similar purpose, scale, and mapped area. This map was compiled at the Faculty of Geography (Moscow State University), and it has inherited many features of its predecessor—the map of Rozov.     

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.     

Match method and its application for the development of a large-scale soil map

A method for interpolating field soil data to obtain the maps of soil taxa is suggested. It is based on representation of categorical data in the form of Voronoi map with barriers limiting the areas with particular combinations of indicative soil-landscape relationships. The predictive capacity of the proposed model depends on the level of the taxon and exceeds 80% for soil types and subtypes in the study area. At different levels of detail of the indicative soil-landscape relationships, the accuracies of prediction are different. The proposed method significantly reduces the time of soil mapping and opens new possibilities for investigating the soil-landscape relationships.     

Long-term salinization dynamics in irrigated soils of the Golodnaya Steppe and methods of their assessment on the basis of remote sensing data

A set of salinization maps (1983–2000) for the former Yusupov State Farm in the irrigated area of the Golodnaya Steppe (Uzbekistan) were developed with the use of aerial and satellite images taken in the early autumn season. A unified approach based on the analysis of the mottled patterns of the cotton fields was used. The soil cover of the farm consists of irrigated hydromorphic moderately saline soils with the participation of nonsaline and strongly saline soils. The long-ter m dynamics of the soil salinity were studied via superposing two or more maps for different years. It was shown that the long-term salinization dynamics could not be determined on the basis of the comparison of only two maps for different years. Maps of the trends of the salinization for the entire period of the observations, a map of the salinization dynamics showing the degree of changes in the soil salinity for the particular areas, a map of the areas with stable salinization through the entire period, and some other maps were developed. A considerable part of the investigated area was characterized by highly unstable soil salinity and active salinization-desalinization processes. The degree of the soil salinity varied from slight to strong and vice versa. For the entire period, the soils were mainly medium saline in the upper meter with a weakly expressed tendency for further salinization and a drop in the area of nonsaline and slightly saline soils.     

Spatial prediction of USDA‐ great soil groups in the arid Zarand region,Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types

The main objectives of this study were to compare binary logistic regression as an indirect approach and multinomial logistic regression as a direct approach to produce soil class maps in the Zarand region of southeast Iran. With indirect prediction, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel‐wise classification by combining the presence or absence of diagnostic horizons. In direct prediction, the dependent variable was the great group itself, so the probability distribution of the great soil groups was directly predicted. Among the predictors, the geomorphology map was identified as an important tool for digital soil mapping approaches as it helped to increase the accuracy. The results of prediction showed larger mean probability values for each great soil group in the areas actually covered by the great soil groups compared with other areas, indicating the reliability of the prediction. In most predictions, the global purity was slightly better than the actual purity for the models; however, both models provided poor predictions for Haplocambids and Calcigypsids. The results showed that soils with better prediction were those much influenced by topographical and geomorphological characteristics and soils with very poor accuracy of prediction were only slightly influenced by topographical and geomorphological characteristics. An advantage of the indirect method is that it gives insight into the causes of errors in prediction at the scale of diagnostic horizons, which helps in the selection of better covariates.     

Small scale digital soil mapping in Southeastern Kenya

Digital soil mapping techniques appear to be an interesting alternative for traditional soil survey techniques. However, most applications deal with (semi-)detailed soil surveys where soil variability is determined by a limited number of soil forming factors. The question that remains is whether digital soil mapping techniques are equally suitable for exploratory or reconnaissance soil surveys in more extensive areas with limited data availability. We applied digital soil mapping in a 13,500 km² study area in Kenya with the main aim to create a reconnaissance soil map to assess clay and soil organic carbon contents in terraced maize fields. Soil spatial variability prediction was based on environmental correlation using the concepts of the soil forming factors equation. During field work, 95 composite soil samples were collected. Auxiliary spatially exhaustive data provided insight on the spatial variation of climate, land cover, topography and parent material. The final digital soil maps were elaborated using regression kriging. The variance explained by the regression kriging models was estimated as 13% and 37% for soil organic carbon and clay respectively. These results were confirmed by cross-validation and provide a significant improvement compared to the existing soil survey.     

对全国第二次土壤普查中土类、亚类划分及其调查制图的辨析

按照中国发生分类对新采集的68个北京市山区的土壤剖面进行了分类命名,并与剖面点所在土壤普查图上的分类名称进行比较,结果是只有18个剖面的分类名称一致。造成分类名称不一致的原因:1发生分类以区域典型土壤剖面分类命名,而区域内很多土壤不同于典型土壤剖面;2发生分类往往以现代生物气候带为主要分类标准命名区域土壤,而不是根据土壤性质;3分类不一致的最大原因可能是制图精度不够。研究认为,土壤分类必须依据土壤性质本身,而不是土壤形成因素;采取野外单土壤性质调查制图,室内叠加单土壤性质图形成多属性图斑,根据分类系统对它们进行综合分类,以提高分类制图精度。     

基于ArcEngine的土壤侵蚀抽样调查单元的底图制作

土壤侵蚀普查是第一次全国水利普查的主要目标之一,为获得野外调查单元数据,需充分利用密度抽样方法和大量的野外调查单元底图以辅助野外调查,并采用空间分析方法从已获取的调查数据中计算各侵蚀因子。但我国幅员辽阔,土壤侵蚀范围广,抽样调查点的数量也随之增大,制图过程中数据量大,制图流程较为繁杂。运用C^#语言、Visual Studio 2008开发工具和ArcEngine组件式二次开发实现了土壤侵蚀抽样调查单元底图的自动制图。详细介绍了自动制图过程中各部分功能的设计和实现思路。通过编程实现地图数据的自动裁剪,计算与分类,地图排版以及打印输出,极大减轻了制图工作量。     

Maps of averaged spectral deviations from soil lines and their comparison with traditional soil maps

The analysis of 34 cloudless fragments of Landsat 5, 7, and 8 images (1985–2014) on the territory of Plavsk, Arsen’evsk, and Chern districts of Tula oblast has been performed. It is shown that bare soil surface on the RED–NIR plots derived from the images cannot be described in the form of a sector of spectral plane as it can be done for the NDVI values. The notion of spectral neighborhood of soil line (SNSL) is suggested. It is defined as the sum of points of the RED–NIR spectral space, which are characterized by spectral characteristics of the bare soil applied for constructing soil lines. The way of the SNSL separation along the line of the lowest concentration density of points on the RED–NIR spectral space is suggested. This line separates bare soil surface from vegetating plants. The SNSL has been applied to construct soil line (SL) for each of the 34 images and to delineate bare soil surface on them. Distances from the points with averaged RED–NIR coordinates to the SL have been calculated using the method of moving window. These distances can be referred to as averaged spectral deviations (ASDs). The calculations have been performed strictly for the SNSL areas. As a result, 34 maps of ASDs have been created. These maps contain ASD values for 6036 points of a grid used in the study. Then, the integral map of normalized ASD values has been built with due account for the number of points participating in the calculation (i.e., lying in the SNSL) within the moving window. The integral map of ASD values has been compared with four traditional soil maps on the studied territory. It is shown that this integral map can be interpreted in terms of soil taxa: the areas of seven soil subtypes (soddy moderately podzolic, soddy slightly podzolic, light gray forest. gray forest, dark gray forest, podzolized chernozems, and leached chernozems) belonging to three soil types (soddy-podzolic, gray forest, and chernozemic soils) can be delineated on it.