Validation of topsoil texture derived from agricultural soil maps by current dense soil sampling

Agricultural soil maps were created to facilitate the management of fields. Such maps, at very large scale (1:5000), cover almost the entire agricultural land in Poland. These maps can be very useful for precision field management. However, they were prepared about 40–50 years ago using old mapping techniques with mainly field (organoleptic) examination of soil. For this reason it is necessary to verify agreement between such maps with the current soil status. In the case of detection of disagreements it is important to identify the causes and propose methods for improving soil maps. Only few studies were made in this aspect. The soil texture (ST) of the upper layer, as presented on the agricultural soil maps from the 1960s and 1970s, was compared with the actual ST of the same four fields in northern and central Poland. For laboratory ST determination, soil samples were collected in a dense sampling network. On average, the agreement of the ST presented on the agricultural soil maps and determined in this study was classified as medium for three fields and good for one field. However, the presence of field areas with poor agreement between the ST determined in this study and that shown on the agricultural maps was detected in all investigated fields. Identified causes for this comprised imprecision of ST determination using the organoleptic method, the generalization of the soil quality maps, and erosion processes during the years between soil mapping and this investigation. The improvement of ST presentation on the large scale soil maps might be achieved by quite denser soil sampling for ST analysis supported by ECa, yield and NDVI maps.     

基于GIS、模糊逻辑和专家知识的土壤制图及其在中国应用前景

详细的土壤空间与属性的信息已成为环境模型和土地管理的基本参数,传统的以类别多边形和手工编制为基础的传统土壤制图效率低精度也较差。本文基于GIS、模糊逻辑和专家知识,建立了土壤一环境推理模型(SoLIM),通过基于土壤一环境关系模型的土壤相似度模型与对该模型进行赋值的推理技术来编制土壤图,从而克服了传统土壤制图中的简化。通过两个小区的研究表明,与传统土壤制图相比,通过SoLIM得出的土壤信息在空间详细度和属性精确度都有较大的提高,也能够大量减少调查的时问和经费,从而大大提高土壤调查的效率。SoLIM方法在我国推广十分必要且具有一定的条件,但仍需要进一步完善。     

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。     

SOIL SEGMENT OVERLAP IN CHARACTER SPACE AND ITS IMPLICATIONS FOR SOIL CLASSIFICATION

A major problem in soil classification for soil survey is the lack, or uncertainty, of correspondence between mapping units in different localities. The problem is examined using multivariate soil data recorded at short regular intervals along transects in Oxfordshire and Aberdeenshire. The data for each transect were transformed to canonical variates, the first two of which were then used to locate soil boundaries and to show the relationships among the sampling points in that projection of the character space. With few exceptions, the sampling points in adjacent segments on all three transects lay in different parts of the canonical variate (CV) plane, showing that the boundaries were well defined. On the transect near Witney, Oxfordshire, distant segments either occupied distinct parts of the CV plane, or were superimposed on it. For the other two transects the portions of the CV plane occupied by some segments overlapped only partially the space occupied by one or more other segments that were not contiguous on the transects. The results show that on two of the three examples, lack of correspondence between mapping classes in different localities is already present in small tracts of country.     

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。     

面向数字土壤制图的土壤采样设计研究进展与展望

全球化土壤环境问题的出现对基础输入数据的精度、尺度和时序提出了更高要求,面向数字土壤制图的土壤采样研究得到了快速发展。首先利用文献计量学的方法定量化分析国内外土壤采样研究学科分布和研究热点变化;随后重点梳理了国内外土壤采样研究的文献,根据不同的土壤调查目的、调查区历史采样点将土壤采样设计分为:土壤全面采样设计、土壤补充采样设计、土壤验证采样设计和土壤监测采样设计;最后介绍了基于样点的推理制图方法。在此基础上,对未来在多尺度的土壤采样设计、土壤–环境因子关系的新型假设和采样设计中现实问题的量化等方面进行了展望,旨在为数字土壤调查工作的开展提供参考依据。     

一种基于样点代表性等级的土壤采样设计方法

采样设计是获取土壤空间分布信息的关键环节,直接影响到土壤制图的精度。目前常用的采样设计方法大多存在着设计样本量大、采样效率不高的问题。当可投入资源难以完成一次性大量采样时,采样往往需要多次、分批进行。然而现有分批采样方法多考虑各批采样点在地理空间的互补性,可能造成样本点在属性空间的重叠,影响采样资源的高效利用。鉴于此,本研究通过对与土壤在空间分布具有协同变化的环境因子进行聚类分析,寻找可代表土壤性状空间分布的不同等级类型的代表性样点,建立一套基于代表性等级的采样设计方法。将该采样方法应用于位于黑龙江省嫩江县鹤山农场的研究区,利用所采集的不同代表性等级的样点进行数字土壤制图并进行验证,探讨采样方案与数字土壤制图精度的关系,以评价本文所提出的采样方法。结果表明,通过代表性等级最高的少量样点可获取研究区的大部分主要土壤类型(中国土壤系统分类的亚类级别),且制图精度较高;随着代表性等级较低样点的加入,土壤图精度提高;但当样点增加到一定数量时,土壤图的精度变化不大。因此,与样点数相比,样点的代表性高低对制图精度的影响更大。该方法所提出的代表性等级可以为样点采集顺序提供参考,有助于设计高效的逐步采样方案。     

GIS支持下豫东地区土壤野外采样布点方法探索

以河南省通许县潮土区中低产田为例,介绍了一种GIS支持下的土壤野外采样布点方法,并对所取得的结果进行了讨论。根据要求覆盖研究区中低产田潮土的面积、涵盖所有土种类型、表层采样与剖面采样相结合、网格点与类型控制点相结合等原则,确定采用2 km×2 km网格布点方法。室内初步布点157个,涵盖了研究区16种土种类型,其中普通网格点141个,类型网格采样点6个,类型参考采样点10个。将布点图层与土壤图进行叠加分析,建立了空间数据库,完善布点属性表,作为野外精确采样的依据。实际采集普通网格点135个,剖面点15个。将室内布点与实际采样点位信息在数据库中分层管理,方便查询与更新。特别在剖面采样过程中发现实际采样与第二次土壤普查土种图比较,存在某些差异,应该根据实际采样诊断土壤类型并在GIS界面修正原始土壤分布图,将此改动作为建议提出,以便完善当地的土壤图。     

基于聚类和最大似然法的汶川灾区泥石流滑坡易发性评价

选择坡度、相对高差、地表径流深和地震烈度4个震后地质灾害的主控因素作为评价影响因子,采用聚类分析和最大似然法评价汶川极重灾区(四川省部分)震后地质灾害的易发性.结果表明:聚类分析和最大似然法这种非监督分类的方法适用于没有足够训练数据的情况,可以快速对一个大区域的地质灾害易发性进行评估;从得到的易发性评价图中可知,在大块的高易发区内存在一些小的极低易发区,这些区域可以作为安全的居民点使用,在极高易发区和高易发区以及高易发区与极低易发区分界线附近,地质灾害点分布比较密集.     

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.     

Integrating geospatial and multi‐depth laboratory spectral data for mapping soil classes in a geologically complex area in southeastern Brazil

Soil mapping across large areas can be enhanced by integrating different methods and data sources. This study merges laboratory, field and remote sensing data to create digital maps of soil suborders based on the Brazilian Soil Classification System, with and without additional textural classification, in an area of 13 000 ha in the state of São Paulo, southeastern Brazil. Data from 289 visited soil profiles were used in multinomial logistic regression to predict soil suborders from geospatial data (geology, topography, emissivity and vegetation index) and visible–near infrared (400–2500 nm) reflectance of soil samples collected at three depths (0–20, 40–60 and 80–100 cm). The derived maps were validated with 47 external observations, and compared with two conventional soil maps at scales of 1:100 000 and 1:20 000. Soil suborders with and without textural classification were predicted correctly for 44 and 52% of the soil profiles, respectively. The derived suborder maps agreed with the 1:100 000 and 1:20 000 conventional maps in 20 and 23% (with textural classification) and 41 and 46% (without textural classification) of the area, respectively. Soils that were well defined along relief gradients (Latosols and Argisols) were predicted with up to 91% agreement, whereas soils in complex areas (Cambisols and Neosols) were poorly predicted. Adding textural classification to suborders considerably degraded classification accuracy; thus modelling at the suborder level alone is recommended. Stream density and laboratory soil reflectance improved all classification models, showing their potential to aid digital soil mapping in complex tropical environments.     

Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu,China

The rapid developments in the acquisition of data on soil should enable pedologists to update existing digital soil maps readily. The methods by which that is done must take into account temporal change in soil properties and local differences in spatial variation. The common mapping techniques will have to be modified to make full use of digital data. We show what can be achieved with a case study on updating maps of soil organic matter (SOM) in Jiangsu Province, China, with three sets of soil data collected in the 1980s, 2000 and 2006. Our results showed that temporal changes in SOM between the three sampling periods occurred in only very small parts of the regions. Models of spatial variation of SOM based on the data collected in the 1980s and 2006 for the whole region differed somewhat, whereas models based on the data collected in the 1980s, 2000 and 2006 for the Taihu region (south Jiangsu) were significantly different. As updating with Bayesian maximum entropy continued, the accuracy of prediction increased and that of the prediction variance decreased. Finally, our study leads us to suggest improved technologies for updating digital soil maps with new data.     

基于CSLE和抽样调查的泰国土壤水蚀评价

通过对泰国区域土壤侵蚀的定量评价,掌握泰国土壤水蚀特征,以期为泰国土壤侵蚀防控和相关研究提供技术和数据支撑。采用CSLE模型,基于30 m分辨率区域侵蚀因子综合运算完成泰国土壤水蚀速率计算(地图代数法制图),基于亚米级分辨率抽样调查完成抽样单元水蚀速率计算,再以抽样单元计算结果为参考,对地图代数制图结果进行直方图匹配,最终获得研究区土壤水蚀速率专题图。结果表明:(1)直方图匹配制图结果既保留了原有的空间分布特征,又具有准确的统计特征。(2)泰国平均土壤水蚀速率为687.9 t/(km²·a),是全球平均土壤水蚀速率的2.4倍,个别地区达到1 000 t/(km²·a)以上(占面积13.2%,占侵蚀总量72.0%),与全球平均水蚀速率相比,土壤水蚀较为严重,0.6%的区域年侵蚀量约占研究区侵蚀总量的21.5%,局部侵蚀剧烈。(3)在各土地利用类型中,耕地水蚀最为严重,平均水蚀速率高达1 020.2 t/(km²·a),水蚀速率>2 500 t/(km²·a)的热点地区84.1%区域为耕地。由此可知,泰国局部区域的土壤水蚀较为剧烈,耕地对区域水土流失的贡献较大。     

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。     

Repeated measurements on permanent plots using local variability sampling for monitoring soil cover

On US military installations, training activities such as vehicle use disturb ground and vegetation cover of landscapes, and increase potential rainfall runoff and soil erosion. In order to sustain training lands, soil erosion is of major concern. Thus there is a need for sampling designs to monitor degradation and recovery of land conditions. Traditionally, permanent plots are used to obtain the change of land conditions. However, the permanent plots often provide less information over time in characterizing the land conditions because of the fixed number and locations of plots. In this paper, we analyzed the sufficiency of a permanent plot sample and developed a method to improve the re-measurements of the permanent plots over time for a monitoring system of soil erosion based on spatial and temporal variability of a random function. We first applied a local variability based sampling method to generate reference samples that have sampling distances varying spatially and temporally to monitor a soil erosion relevant cover factor for an installation, Fort Riley, USA. Then, we compared a permanent sample with the reference samples annually over 13 years to determine additional sampling in the areas with high variability and temporarily suspending measurements of the permanent plots in the areas with low variability. The local variability based sampling provides estimates of local variability of the cover factor and thus is more cost-efficient than random sampling. By comparison with a reference samples, the re-measurements obtained should more accurately characterize the dynamics of the land conditions.     

基于地统计的土壤养分采样布局优化

传统的土壤养分采样布置方法都是基于采样区土壤特征状态空间随机变异的假设。而地统计学研究表明,土壤特征状态在空间上有关联性,因此利用传统方法来制定采样方案并不是最优的,因为它没有考虑土壤特性的空间相关性,不能反映其局部的变化特征。该文在分析土壤肥力空间变异的基础上,研究利用经典统计学方法确定合理的采样点数目,并基于地统计学的半方差函数拟合与Kriging方法确定合理的采样点布局的方法,选择典型地区的土壤肥力进行空间变异分析和采样点布置的优化设计。研究结果表明：在合理的位置布置14个采样点就可以满足典型基地种植区绘制施肥处方图进行变量施肥决策的要求;利用经典统计学与地统计分析相结合的方法进行农田尺度的土壤肥力采样布点优化分析具有良好的可行性。     

复杂景观环境下土壤厚度分布规则提取与制图

复杂景观环境下,土壤—环境关系知识的获取是预测性土壤制图的基础。为了探究复杂景观下土壤厚度分布与环境条件的关系,该文以黑河上游祁连山区典型小流域为研究区,应用模糊c均值聚类(fuzzy C-means cluster,FCM)和决策树(decision Tree,DT)方法,建立了一套获取土壤厚度分布与环境间关系知识的方法。利用2种方法结合获得流域内土壤厚度各分布等级的环境要素关键阈值与土壤-环境关系知识集,将所得环境阈值和知识集进行预测性制图,并通过野外独立样点对制图结果进行精度评价。结果表明:土壤厚度图的总体精度为74.2%,Kappa系数为0.659。该研究将2种方法结合获得了土壤厚度分布对应的土壤环境关键阈值和土壤-环境关系知识集,为复杂景观环境下土壤厚度的预测性制图提供了一种有效的解决方案。     

The elucidation of soil pattern in the Wyre Forest of the West Midlands, England. II. Spatial distribution

The spatial distribution of soil in the Wyre Forest of England was analysed in two phases. In the first the soil was examined at sites chosen using a five-stage nested design with spacings increasing geometrically from 6 m to 600 m. Some 80% of the variance was contributed by components for the spacings between 6 m and 60 m. Measurements were then made on transects at 5 m intervals and semi-variograms estimated to 70 m. Except for those of pH the semi-variograms of the soil properties had the same general transitive form and a common effective range of about 40 m. This short range meant that very intensive sampling, approximately one point per 400 m², is needed to map the soil variation. A survey was made of a small portion of the forest on a 20m × 25 m grid to test the inference. Maps of clay and sand content were made successfully by kriging from the data. The mutual correlations between soil properties and the common range of their semi-variograms enabled a general purpose and spatially coherent soil classification to be created from the data. Its mapping confirmed the intricacy of the soil pattern in the Forest.