美国基于GIS、专家系统和模糊推断的土壤制图方法

基于地理信系息统 (GIS)或专家系统的模糊土壤推断法 (土壤—土地推断模型 ,SoLIM) ,主要包括 3部分 :1)土壤相似性模型 ;2 )土壤相似性推断技术 ;3 )相似性描述的应用。相似性描述将土壤景观看作一个连续体 ,因而克服了传统土壤制图的概括性。此推断技术基于成土因素方程和土壤—景观模型。土壤—景观意味着只要知道某个地区土壤与环境的关系 ,则能通过评价某点的环境特征推断出特定景观下某处的土壤类型。在SoLIM中 ,成土环境特点可通过GIS或RS技术来确定。土壤与成土环境之间的关系可通过当地土壤专家或野外观察来确定。结合环境特点和土壤—环境关系推断某地土壤类型。在土壤调查方面 ,SoLIM与传统土壤调查相比 ,SoLIM得到的土壤信息的空间详细程度和属性准确度均相当高。另外 ,该方法显示出了在提高土壤调查效率进而更新调查时节省时间和代价方面的巨大前景。然而 ,SoLIM是否成功在很大程度上还依赖于环境资料的可用性和质量 ,以及对当地土壤—环境关系了解的程度     

诊断层和诊断特性与景观关系探讨 ——以江苏省句容某样区为例

土壤与所处的景观密切相关,诊断层和诊断特性是土壤系统分类中判定土壤类型的依据,诊断层和诊断特性与景观之间具有一定的联系,研究它们的关系有助于更好地辅助土壤调查。本文在景观理论的指导下,对江苏省句容市边城镇开展了样区土壤调查,调查采样的48个主要剖面归属于人为土、淋溶土、雏形土和新成土4个土纲、4个亚纲、9个土类、13个亚类;在此基础上,重点探讨了诊断层和诊断特性与景观之间的关系,结果表明景观能很好地指示某些诊断层和诊断特性,对景观的深入研究可以指导土壤调查与土壤系统分类类型的确定。     

诊断层和诊断特性与景观关系探讨1——以江苏省句容某样区为例

土壤与所处的景观密切相关,诊断层和诊断特性是土壤系统分类中判定土壤类型的依据,诊断层和诊断特性与景观之间具有一定的联系,研究它们的关系有助于更好地辅助土壤调查。本文在景观理论的指导下,对江苏省句容市边城镇开展了样区土壤调查,调查采样的48个主要剖面归属于人为土、淋溶土、雏形土和新成土4个土纲、4个亚纲、9个土类、13个亚类;在此基础上,重点探讨了诊断层和诊断特性与景观之间的关系,结果表明景观能很好地指示某些诊断层和诊断特性,对景观的深入研究可以指导土壤调查与土壤系统分类类型的确定。     

基于土壤系统分类的土壤类型和界线确定研究——以句容大顶山研究区为例

我国土壤系统分类的土壤调查技术仍处于研究阶段,借鉴传统的发生分类的土壤调查方法,本文主要研究了基于土壤系统分类的土壤类型与界线确定。土壤类型是通过常规布点法进行主要剖面点位置的设定,并从地形分级和制图精度方面分析确定其数量,对挖掘的土壤剖面利用诊断层和诊断特性定名得出。土壤界线则是利用内插法在调查路线上确定土壤类型分界点后,结合遥感图像将不同调查路线相同土壤类型分界点用平滑曲线连接最终确定。研究表明,土壤类型和土壤界线与母质、地形、植被和土地利用方式等多种景观因素有密切的联系,在土壤调查中要充分考虑景观因素的影响。     

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。     

面向土壤系统分类的土壤调查制图方法的初步研究

我国的土壤系统分类方案已经基本形成,但还没有相应的土壤调查方法技术研究。按照传统土壤调查的主要剖面、检查剖面、定界剖面的思想与办法,采用空间内插技术,在研究区的4条实验路线上共挖掘了64个剖面点,通过这些剖面点的诊断层和诊断特性确定了研究区的土壤类型并勾绘了土壤类型界线。再用1条检验路线对勾绘的土壤图进行检查,结果表明,在检验路线上设置的20个检查剖面点中,其中19个剖面点与实际情况相符,正确率95%,说明使用内插法进行面向土壤系统分类的土壤调查制图是可行的。再结合土壤景观可以辅助土壤调查工作者更高效地确定土壤类型、勾绘土壤界线。     

景观解译对小区域土系调查的作用研究

利用遥感(RS)和地理信息系统(GIS)技术能够快捷高效地进行景观解译。土系分布与景观特征密切相关,可以借助景观变异规律来辅助土系调查。本研究选择句容市某小区域为研究对象,首先以决策树分类方法对资源三号卫星影像进行土地利用类型的分类,将分类结果与原始影像叠加,并结合野外调查结果进行人工修正;精度达到要求后,再以修正后的土地利用类型图为底图,用决策树分类方法添加合适的坡度和高程信息,生成"高程-坡度-土地利用类型"的景观分类图;在此基础上,根据土壤-景观关系,结合数字高程模型(DEM)数据、资源三号卫星原始影像和景观分类图,对大比例尺土系调查进行调查路线的选择、剖面点的设置和土系界线的确定,并在研究区中选取核心区进行验证。结果证明:改进后的方法相对于传统土壤调查方法更加定量地考虑到景观变异情况,调查结果更具有代表性。     

应用土壤-景观定量模型预测土壤属性空间分布及制图

以土壤-景观定量模型为基础的土壤制图方法在世界范围内得到了广泛研究。本文在皖南宣城的丘陵地带内选择研究区,从该区的数字高程模型(DEM)中获取景观信息:地形因子,定量地分析了土壤属性与地形因子之间的相关关系,并建立基于该关系的线性土壤-景观定量模型,最后应用该模型来预测土壤属性在空间上的分布并制图。结果表明:土体厚度和表层有机质含量与地形因子之间有着显著相关性;建立的线性回归模型分别能解释土体厚度、表层有机质含量空间变异的32.2%和35.3%;依据该模型预测的土体厚度和表层有机质含量具有较高的准确度,并能制图表达土壤属性在空间上的自然连续性。     

土壤调查──建立在模式基础上的科学

本文介绍了ＴｈｏｍａｓＳ．Ｋｕｈｎ所揭示的科学领域中的模式原理，并提出土壤调查是以土壤－景观模式为基础的一门科学。土壤调查应该摆脱过分依赖非言传性知识（存留于经验中而不见诸于文献的知识）的局面。今后宜在完成土壤详力制作的同时，将编图时所判定的景观与土壤的关系记录下来，进行学术交流，不断丰富和完善土壤－景观模式的内容，并使其不仅见诸于土壤刊物，还能见诸于土壤教科书。     

黑土区小流域土壤速效磷空间分布模拟方法

为探索东北黑土小流域土壤AP空间分布模拟的最佳方法,综合考虑区域尺度、景观格局和采样方法等信息,选取两个典型黑土小流域(光荣和海沟河流域)作为研究对象,结合半变异分析、回归分析、主成分分析等方法,比较了不同空间插值方法(反距离权重法、径向基函数法、普通克里格、协同克里格、多元线性回归模型、地理加权回归模型、回归克里格和地理加权回归克里格)对AP空间模拟精度的影响。结果表明:(1)引入主成分分析后能够提高回归克里格和地理加权回归克里格方法的模拟精度(4.5%和2.4%);(2)地理加权回归克里格方法可以作为最优空间插值方法模拟黑土区小流域AP的空间分布格局。地理加权回归克里格方法相较于传统插值方法能在一定程度上提高黑土小流域土壤AP空间模拟精度,为小流域尺度黑土养分管理提供技术支撑。     

预测性土壤有机质制图中模糊聚类参数的优选

对数字高程模型(DEM)中的地形特征进行模糊C均值聚类,分别采用3种方法来选择最优模糊度和分类数组合及分类结果;在最优分类结果上,用多元线性回归方法建立土壤A层有机质含量与地形景观之间的定量关系,并应用该关系进行土壤制图应用.结果表明:3种方法选择的最优模糊度比较接近,主要为1.5,还包括1.4和1.6,但3种方法选择的最优分类数却有很大差别;尽管依据回归模型γ2选择的分类结果较多地解释了土壤A层有机质含量的变异,但基于这种分类结果的制图偏差较大,与实测值相比较的结果也说明基于这种分类结果的制图精度较低;用内部判据选择的分类结果在制图过程中产生的偏差较小,制图精度也较高.     

DEM栅格分辨率对丘陵山地区定量土壤-景观模型的影响

基于数字高程模型（Digital Elevation Model,DEM）的定量土壤-景观模型的精度依赖于DEM栅格分辨率,而DEM栅格分辨率如何影响土壤-景观模型及其预测精度目前研究较少。以西南丘陵山地区一典型汇水盆地为研究对象,以该区2.5、5、10、20和30 m DEM为基础,利用逐步线性回归方法建立起研究区不同分辨率下的定量土壤-景观模型,并应用这些模型预测研究区内土壤表层碱解氮含量的空间分布,进而比较DEM不同分辨率下土壤-景观模型及其预测精度。结果表明,随着DEM栅格分辨率的降低,比汇水面积、地形湿度指数的均值逐渐增加;平均坡度逐渐降低;曲率变化的范围逐渐减小。地形指数的这一变化规律对土壤-景观模型及其预测结果产生显著影响,模型的校正决定系数、平均绝对误差和均方根误差都以5 m栅格分辨率为转折点,分辨率低于5 m,模型的校正决定系数显著减小,平均绝对误差和均方根误差显著增加。     

Extrapolating regional soil landscapes from an existing soil map: Sampling intensity,validation procedures,and integration of spatial context

This paper aims to investigate the potential of using soil-landscape pattern extracted from a soil map to predict soil distribution at unvisited location. Recent machine learning advances used in previous studies showed that the knowledge embedded within soil units delineated by experts can be retrieved and explicitly formulated from environmental data layers However, the extent to which the models can yield valid prediction has been little studied. Our approach is based on a classification tree analysis which has underwent a recent statistics advance, namely, stochastic gradient boosting. We used an existing soil-landscape map to test our methodology. Explanatory variables included classical terrain factors (elevation, slope, curvature plan and profile, wetness index, etc.), various channels and combinations of channels from LANDSAT ETM imagery, land cover and lithology maps. Overall classification accuracy indexes were calculated under two validation schemes, either taken within the training area or from a separated validation area. We focused our study on the accuracy assessment and testing of two modelling parameters: sampling intensity and spatial context integration. First, we observed strong differences in accuracy between the training area and the extrapolated area. Second, sampling intensity, in proportion to the class extent, did not largely influence the classification accuracy. Spatial context integration by the use of a mean filtering algorithm on explanatory variables increased the Kappa index on the extrapolated area by more than ten points. The best accuracy measurements were obtained for a combination of the raw explanatory dataset with the filtered dataset representing regional trend. However, the predictive capacity of models remained quite low when extrapolated to an independent validation area. Nevertheless, this study offers encouragement for the success of extrapolating soil patterns from existing soil maps to fill the gaps in present soil map coverage and to increase efficiency of ongoing soil survey.     

结合统计和数字地形数据的可视化方法预测土壤深度

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models (DEMs) with geographic information systems (GIS) and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the Al-Muwaqqar Watershed (70 km²) in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.     

土壤时空变化研究的进展与未来

理解和表征土壤的时空变化是土壤学的基本任务,也是评估和合理发挥土壤功能的重要前提。土壤的时空变化与气候环境变迁、岩石圈风化、地表物质迁移、生物地球化学循环等圈层变化过程相耦合。围绕土壤时空变化研究的新近进展,本文综述并展望了土壤形成和演变过程、土壤形态学、土壤调查、土壤分类、数字土壤制图与土壤退化的发展态势。未来土壤时空变化研究的关键科学问题主要包括:地球表层系统中土壤与环境要素之间的多过程耦合机理与模拟、多尺度土壤-环境关系与模拟、多元土壤信息的融合机理与数据同化。未来重点研究领域将涉及到关键带科学引领的土壤形成和演变研究、多尺度数字土壤制图与时空变化预测、基于多传感器的土壤综合观测原理与技术、完整和详尽的国家和全球土壤资源清单及共享平台建设、区域土壤资源退化机理及其功能恢复。     

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。     

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.     

Soil formation on the Wallagaraugh Adamellite, southeastern N.S.W., Australia

Soil development on Wallagaraugh Adamellite has been studied within Yambulla State Forest, southeastern Australia. Twenty-five soil profiles have been described and soil chemical and physical properties determined for selected horizons. A geomorphological model is described for elucidating processes affecting soil development on this adamellite parent material. Three main geomorphic environments have been incorporated into this model: residual surfaces found on interfluves and broad hillcrests, transportational surfaces associated with ridges and hill slopes, and depositional surfaces associated with foot-slopes and valley floors. Residual surfaces have developed duplex primary profile forms (yellow podzolics) with structured B horizons of high clay content where exchangeable Al dominates the exchange complex. Transportational surfaces have soils with shallow, uniform to gradational profiles with minimal profile development, and highest, but most variable, concentrations of the exchangeable Ca, K, Na, and total P. Depositional surfaces are characterized by a thick accumulation of uniform, coarse-sandy colluvium-alluvium. Where this surface is stable and well drained, podzols have formed. In contrast, areas of impeded drainage on this depositional surface have humic gley profiles. Concentrations of total P, exchangeable Ca and Mg are low throughout these aggraded soils.The soil-landscape units derived from the geomorphological model are discriminated by multivariate analysis using soil chemical and physical properties. Surface soil chemical properties are shown to be superior in discriminating soil-landscape units to subsoil properties.The mineralogy, geochemistry and texture of the adamellite soil parent material are critical in determining the distinctive geomorphology which, in turn, affects the development of soils in this forest environment.     

基于环境因子和联合概率方法的土壤有机质空间预测

<正>土壤连续属性(如土壤中养分含量、重金属含量等)的空间分布特征和定量分布信息是进行土壤质量评价和区域环境综合评估的基础。精准农业战略的实施和各种区域生态评价均需要更详细更精确的土壤属性信息作为依据[1-2]。因此,土壤属性空间预测一直是土壤学研究的热点问题。经典地统计学以各种克里格插值法为代表,是土壤属性空间预测中的常用方法。但该方法缺乏对辅助信息(如环境信息)的有效利用[3-4],导致预测精度降低[5]。而土壤景观定量模型的理论依据就是土壤与环境的关系,但该法忽略了采样点之间的空间相