依据传统土壤数据应用数字土壤制图法对香港山地地区土壤建立土纲信息

Based on legacy soil data from a soil survey conducted recently in the traditional manner in Hong Kong of China, a digital soil mapping method was applied to produce soil order information for mountain areas of Hong Kong. Two modeling methods (decision tree analysis and linear discriminant analysis) were used, and their applications were compared. Much more eflort was put on selecting soil covariates for modeling. First, analysis of variance (ANOVA) was used to test the variance of terrain attributes between soil orders. Then, a stepwise procedure was used to select soil covariates for linear discriminant analysis, and a backward removing procedure was developed to select soil covariates for tree modeling. At the same time, ANOVA results, as well as our knowledge and experience on soil mapping, were also taken into account for selecting soil covariates for tree modeling. Two linear discriminant models and four tree models were established finally, and their prediction performances were validated using a multiple jackknifing approach. Results showed that the discriminant model built on ANOVA results performed best, followed by the discriminant model built by stepwise, the tree model built by the backward removing procedure, the tree model built according to knowledge and experience on soil mapping, and the tree model built automatically. The results highlighted the importance of selecting soil covariates in modeling for soil mapping, and suggested the usefulness of methods used in this study for selecting soil covariates. The best discriminant model was finally selected to map soil orders for this area, and validation results showed that thus produced soil order map had a high accuracy.     

长期施用堆肥对小麦根际及非根际土壤化学性质的影响

Compost of different rates was applied to artificial field plots of a low humic andosol at National Agriculture Research Center (NARC)of Japan for 15 or 28 years,and their effects on the chemical properties of wheat rhizosphere soil and nonrhizosphere soil were measured.Contiuous application of compost for 28 years resulted in raise of soil C,N,P,pH and exchangeable based.The building up of compost for 28 years resulted in raise of soil C,N,P,pH and exchangeale bases,The building up of organic matter in the soil occureed slowly.A residual effect of the compost on soil chemical properties was still present after 13 years of no application,but this effect was weaker in comparison with that of the continuous application treatments.In the rhizosphere soil,NaHCO3-extracted P and exchangeable Ca were higher than those in the bulk soil.The removal of free organic acid slightly affected the soil pH,especially,the rhizosphere soil pH.The raise of soil pH may result from the increase of exchangeable base by the application of compost.     

普通克里格法在土壤有机碳制图中的应用

The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understand many ecosystem processes.This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in a selected part of Zambia.A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method.An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram.The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero.The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25.The root mean square error of the predictions was 0.64,which represented 58.18％ of the mean observed data for SOC.It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported.Therefore,the maps produced can be used as guides for various uses including optimization of soil sampling.     

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.     

中国禹城土壤盐渍化的时空变异及其预测

This research used both geostatistics and GIS approach to compare temporal change of soil salt between 1980 and 2003, to analyze the spatial distribution of surface soil salt, to developed methods for predicting soil salinization potential based on recent improvements to the Dempster-Shafer theory, and to develop probability maps of potential salinization in Yucheng City, China. A semivariogram model of soil salt content was developed from the spherical model, and then employing kriging interpolation the spatial distribution of salt content in 2003 was obtained utilizing data from 100 soil sampling points. Potential salinization distribution was mapped using an approach that integrated soil data of the second general survey in 1980 in Yucheng City, which included groundwater salinity, groundwater depth, soil texture, soil organic matter content, and geomorphic maps. With the support of Dempster-Shafer theory and fuzzy set technique the factors that affected potential soil salinization were characterized and integrated;and then soil salinization was predicted. Finally a prognosis map of potential salinization distribution in the research area was obtained, with higher probability values indicating higher hazards to salinity processes. The distribution of the potential soil salinization probability was a successive surface.     

Effect of low-molecular-weight organic acids on Cl- adsorption by variable charge soils

Low-molecular-weight (LMW) organic acids exist widely in soils and have been implicated in many soil processes.The objective of the present paper was to evaluate effect of two LMW organic acids, citric acid and oxalic acid, on Cl^- adsorption by three variable charge soils, a latosol, a lateritic red soil and a red soil, using a batch method. The results showed that the presence of citric acid and oxalic acid led to a decrease in Cl- adsorption with larger decreases for citric acid. Among the different soils Cl- adsorption in the lateritic red soil and the red soil was more affected by both the LMW organic acids than that in the latosol.     

基于决策树模型的土壤有机质制图

Based on a case study of Longyou County, Zhejiang Province, the decision tree, a data mining method, was used to analyze the relationships between soil organic matter (SOM) and other environmental and satellite sensing spatial data.The decision tree associated SOM content with some extensive easily observable landscape attributes, such as landform,geology, land use, and remote sensing images, thus transforming the SOM-related information into a clear, quantitative,landscape factor-associated regular system. This system could be used to predict continuous SOM spatial distribution.By analyzing factors such as elevation, geological unit, soil type, land use, remotely sensed data, upslope contributing area, slope, aspect, planform curvature, and profile curvature, the decision tree could predict distribution of soil organic matter levels. Among these factors, elevation, land use, aspect, soil type, the first principle component of bitemporal Landsat TM, and upslope contributing area were considered the most important variables for predicting SOM. Results of the prediction between SOM content and landscape types sorted by the decision tree showed a close relationship with an accuracy of 81.1%.     

扩散率与吸水率关系的解析方法

A simple method was developed to relate soil sorptivity to hydraulic diffusivity and water absorption experiments were conducted utilizing one-dimensional horizontal soil columns to validate the relationship. In addition, an estimation method for hydraulic diffusivity with disc infiltrometer was developed. The results indicated a favorable fit of the theoretical relation to the experimental data. Also, the experiment with disc infiltrometer for estimating the diffusivity showed that the new method was feasible.     

一个新的土壤重金属竞争吸附等温模型

A new competitive adsorption isothermal model (CAIM) was developed for the coexistent and competitive binding of heavy metals to the soil surface. This model extended the earlier adsorption isothermal models by considering more than one kind of ion adsorption on the soil surface. It was compared with the Langmuir model using different conditions, and it was found that CAIM, which was suitable for competitive ion adsorption at the soil solid-liquid surface, had more advantages than the Langmuir model. The new competitive adsorption isothermal model was used to fit the data of heavy metal (Zn and Cd) competitive adsorption by a yellow soil at two temperatures. The results showed that CAIM was appropriate for the competitive adsorption of heavy metals on the soil surface at different temperatures. The fitted parameters of CAIM had explicit physical meaning. The model allowed for the calculation of the standard molar Gibbs free energy change, the standard molar enthalpy change, and the standard molar entropy change of the competitive adsorption of the heavy metals, Zn and Cd, by the yellow soil at two temperatures using the thermodynamic equilibrium constants.     

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。     

基于小波包变换的农作物分类无人机遥感影像适宜尺度筛选

为寻找适宜分类的空间尺度,该文提出一种基于小波包的空间尺度选择方法。该文以无人机航拍农作物影像为数据源,针对高空间分辨率遥感影像农作物分类问题,基于小波包变换对影像分类特征进行多尺度定量分析。将七种农作物影像样本进行小波包分解,从高频部分获取均值,方差,能量,能量差四种纹理信息,从低频部分获取光谱信息,构建分类特征矢量,通过作物样本之间的J-M距离,分析在不同小波包分解层样本之间的可分性,并进一步通过农作物面向对象分类精度和分割耗时评价适宜尺度。该文选择位于河北的涿州农场为研究区,利用无人机航空影像,对提出的方法进行试验验证,结果显示:小波包分解到第三、四层级时,即空间分辨率为0.32~0.64 m时,适宜农作物面向对象分类;在适宜尺度下,基于小波包分解的面向对象分类总体分类精度可达到89%,Kappa系数可达到0.85。研究结果可为高空间分辨率遥感农作物精细识别提供支撑。     

Water erosion assessment using farmers' indicators in the West Usambara Mountains, Tanzania

The contribution of local knowledge to ecological sciences has not been fully exploited: there is still a gap between the recognition of farmers' knowledge as valid and an effective use of such knowledge in activities aimed at sustainable development. This study explores the use of farmers' indicators of erosion for developing a rapid tool for water erosion assessment at field level in the West Usambara Mountains (Tanzania). Two extensive field surveys were conducted in the research area concurrently. One survey consisted of applying an established erosion assessment method, the Assessment of Current Erosion Damage (ACED). According to the erosion features observed, fields were classified into five erosion classes, from very slightly eroded to very severely eroded. The second survey consisted of recording the type and number of indicators of erosion listed by farmers and present in the fields. The number of farmers' indicators per field increased with erosion intensity, from less than four in slightly eroded fields to more than eight in severely eroded fields. All farmers' indicators were positively correlated to the ACED erosion assessment classes. However, two groups of farmers' indicators could be distinguished in terms of erosion assessment: strong indicators, which were observed in more than 70% of cases in severely eroded fields, and weak indicators, which were observed more frequently in slightly and moderately eroded fields. Weak indicators appeared to be indicative of other land degradation phenomena, such as chemical fertility decline. Strong indicators and number of indicators were used to create a field erosion assessment tool in the form of a classification tree. The tree was built using one half of the field survey data and validated using the other half. The tree consisted of a hierarchical sequence of questions. Presence of rills and number of farmers' indicators were the most important factors of the tree. The validation yielded a highly significant Spearman's rho correlation coefficient (0.81). The contingency table showed that more than 80% of very severely eroded fields were correctly classified, whereas most misclassification occurred among slightly and moderately eroded fields. Farmers include land degradation phenomena and soil fertility decline in their definition of soil erosion. Soil and water conservation planning should address this broader farmers' perception by including, e.g., soil fertility improvements beside soil conservation. The distinction between strong and weak indicators of erosion is important in recommending the right intervention in the right spot, e.g., by counteracting soil erosion where strong indicators are present and by improving chemical fertility where weak indicators are present. The classification tree is of empirical nature and may need adaptation before being applied to other areas. The proposed methodology can be easily replicated and showed a high potential to provide extensionists with a field tool for erosion assessment. The classification tree was a successful example of integrating different types of knowledge for enhancing the co-operation between stakeholders involved in the erosion-control activities.     

基于RAGA的PPE模型在土壤质量等级评价中的应用研究

以往的土壤分类与等级综合评价多是建立在模糊数学 (Fuzzy)基础上的模糊聚类分析与模糊综合评判 ,因此不可避免的涉及权重矩阵人为干扰 ,导致分类与评价结果的人为倾向。本文将高维降维技术——投影寻踪评价模型 (PPE)应用到土壤学科领域 ,利用改进的加速遗传算法 (RAGA)优化投影方向 ,将多维数据指标转换到低维子空间 ,通过寻求最优投影方向及投影函数值来实现对土壤的分类与等级评价 ,避免了主观赋权的人为干扰 ,取得了较好效果 ,可为土壤分类与等级评价研究提供一条新思路     

基于HJ-CCD数据和决策树法的干旱半干旱灌区土地利用分类

为了实现干旱半干旱灌区地表信息低成本、高效率的动态监测,利用HJ-CCD数据的多时相和多光谱信息,探讨了平罗县土地利用遥感分类方法。首先建立研究区内典型地物的NDVI时间序列曲线,提取反映该区物候模式的时序特征参数;然后对土壤信息丰富的3月份多光谱影像进行主成分变换,选取第1主成分(PC1)作为光谱特征参数,最后基于分类回归树(classification and regression tree,CART)算法进行决策树监督分类。总体分类精度达到92.26%,Kappa系数为0.91,比最大似然法分类结果精度提高了2.58%。研究表明:构建的NDVI时间序列曲线对研究区内的地类具有较强的代表性,提取的时间维和光谱维的分类参数对各地类均有很好地区分性,CART决策树算法分类结果清晰准确且精度较高。该方法为HJ小卫星在干旱半干旱区等区域的深入应用提供科学依据和实证基础。     

矿山土壤特性及其分类研究进展

矿山土分类是认识矿山土壤的基础,对矿山土壤系统分类研究是进行矿山土壤改良和植被重建的重要基础。本文从物理、化学、生物和污染4个方面阐述了矿山土壤特性的变化及其对矿山土壤系统分类的影响,并对分类名称、依据、指标体系和分类方法的相关研究进展进行了综述。目前,矿山土壤系统分类大多以其特有属性作为分类指标,运用光谱技术、模糊均值法和神经网络模型等方法完成分类。但这些方法都有其自身的优缺点,建议今后因地制宜地选取定量化分类指标,探索将主成分分析等数学模型与光谱等技术相结合的方法进行矿山土壤系统分类,以期为今后矿山土地复垦与利用提供科学依据。     

基于数据挖掘分类法的农用地分等

应用决策树模型、BP神经网络和Logistic回归模型等分类法,对龙川县农用地分等进行了实证研究,并对各方法的分等结果有效性进行了评价,同时利用混淆矩阵探讨了样本数量对3种模型分类精度的影响。结果表明,样本数量对模型影响有差异,其中对BP神经网络和决策树模型影响较大,在较多训练样本时,模型的精度较高。在较多样本支持下,BP神经网络精度最高,但训练模型的时间较长,可解释性差;决策树模型既具有较高的精度又具有良好的可解释性;Logistic回归模型表现较差。决策树模型最适合龙川县农用地分等工作。研究结果表明,数据挖掘分类法是有效而准确的土地评价方法,有助于提高土地评价的精度和准确性,对农用地分等方法的优化具有一定的借鉴意义。     

基于裸土期多时相遥感影像特征及最大似然法的土壤分类

运用单时相遥感数据进行土壤分类及制图,其数据本身易受到其他因素干扰而出现误差,存在一定的局限性,导致制图精度不高。为了提高制图精度,以松嫩平原林甸县为研究区,利用裸土时期多时相Landsat 8遥感影像、DEM数据和全国第二次土壤普查数据,从所有单时相遥感影像中提取出多种分类特征,按照分类特征类型进行压缩处理,得到新的多时相分类特征,将不同分类特征进行组合并分别进行最大似然法分类,得到不同分类特征组合下的土壤类型图,通过不同土壤类型图精度来判断各分类特征对于制图的影响。研究表明,该文所提取的分类特征均可以实现土壤制图,使用压缩处理后得到的多时相遥感数据分类特征完成制图的精度更高,总体精度达到91.0%,研究可为土壤精细制图提供依据。     

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。