土壤制图中多等级代表性采样与分层随机采样的对比研究

采样设计是土壤地理研究中备受关注的重要问题。本文以区域尺度土壤属性制图为例,将多等级代表性采样与经典采样中的分层随机采样进行对比研究。以安徽宣城研究区的表层砂粒含量为目标要素,采集数量均为59个的两套样点,设计不同数量(46、58和59)的样点分组,采用两种制图方法进行制图并利用独立验证点进行评价。结果表明:1)无论是采用多元线性回归方法还是基于环境相似度的制图方法,在同等样点数量下,利用代表性样点所得土壤图精度均高于利用随机样点所得精度,并且利用少量代表性样点(46个)所得土壤图精度也高于利用多量随机样点(59个)所得精度;2)随着代表性较低样点的增加,土壤制图精度基本有一个提高的趋势,而采用随机样点所得土壤图的精度波动较大。因此,可认为多等级代表性采样方法是一种可用于区域尺度土壤调查的有效采样方法,且比分层随机采样高效、稳定。     

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。     

基于1:50 000土壤数据库并结合PKB方法估算中国浙江省土壤有机碳储量的研究

Soil organic carbon(SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change. A newly built 1:50 000 soil database of Zhejiang Province containing 2 154 geo-referenced soil profiles and a pedological professional knowledge-based(PKB) method were used to estimate SOC stock up to a depth of 100 cm for the Province. The spatial patterns of SOC stocks stratified by soil types,watershed(buffer analysis), topographical factors, and land use types were identified. Results showed that the soils in Zhejiang covered an area of 100 740 km2 with a total SOC stock of 831.49 × 106 t and a mean SOC density of 8.25 kg m-2, excluding water and urban areas. In terms of soil types, red soils had the highest SOC stock(259.10 × 106t), whereas mountain meadow soils contained the lowest(0.15 × 106t). In terms of SOC densities, the lowest value(5.11 kg m-2) was found in skel soils, whereas the highest value(45.30 kg m-2) was observed in mountain meadow soils. Yellow soils, as a dominant soil group, determined the SOC densities of different buffer zones in Qiantang River watershed because of their large area percentage and wide variation of SOC density values.The area percentages of various soil groups significantly varied with increasing elevation or slope when overlaid with digital elevation model data, thus influencing the SOC densities. The highest SOC density was observed under grassland, whereas the lowest SOC density was identified under unutilized land. The map of SOC density(0–100 cm depth) and the spatial patterns of SOC stocks in the Province would be helpful for relevant agencies and communities in Zhejiang Province, China.     

运用模糊隶属度进行土壤属性制图的研究——以黑龙江鹤山农场研究区为例

通过传统土壤类型图所得的土壤属性图已不能满足精准农业和生态环境模型所需土壤属性的精度,而目前应用较多的统计方法和地统计方法均存在一定的局限性。鉴于此,本文探索了一种采用模糊聚类获取模糊隶属度进行土壤属性制图的方法。首先,采用模糊c均值聚类(Fuzzyc-means clustering,FCM)方法对环境因子进行聚类,通过野外采样(称为建模点)建立土壤-环境关系知识;然后,计算区域内各像元点对土壤类型的模糊隶属度;最后,对模糊隶属度采用加权平均的方法获取土壤属性值。将该方法应用于黑龙江鹤山农场老莱河流域的研究小区,以土体厚度和表层有机质为例进行土壤属性制图。为了评价该方法的有效性,将其与采用环境因子所建立的多元线性回归模型进行比较,通过野外验证点集评价两种模型所得的土壤属性,评价指标为观测值和预测值的相关系数、平均绝对误差(MAE)、均方根误差(RMSE)和准确度(AC)。结果表明,尽管通过建模点建立的多元线性回归方程R2较大,但该方程并不适用于研究区内的其他样本点,这表明多元线性回归方法在该区具有一定的局限性。与之相比,模糊隶属度加权平均的方法则可以通过较少的建模点得到更好的预测效果。     

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     

不同插值方法对成分数据空间预测结果的影响——以土壤连续分类模糊隶属度值为例

地球科学中成分数据(compositional data)非常普通,其在进行空间插值时必须满足4个条件:每一位置各组分之和为常数,每一组分为非负,插值结果无偏最优.本文以土壤连续分类模糊隶属度值为例,数据经对数正态变换、非对称对数比转换、对称对数比转换后进行普通克里格插值结果和成分克里格插值(compositional kriging)结果进行比较.结果表明,对原始数据和经对数正态变换后数据进行插值,每一位置预测结果隶属度之和不能满足常数1.经非对称对数比转换后,插值结果虽然满足各个位置组分之和为1,但是预测结果精度较低,且预测结果空间分布连续性不明显.数据经对称对数比转换后插值结果和成分克里格插值结果,都能满足成分数据空间插值的4个条件,但二者各有优势.相比较而言,对称对数比转换方法得到的预测结果更能体现土壤空间连续渐变特征,而成分克里格插值结果能保证隶属度本身是最优无偏估计.     

基于多源数据和模糊k-均值方法的农田土壤管理分区研究

以近地传感器(EM38)快速获取的土壤表观电导率数据(ECa)、高分遥感影像提取的归一化植被指数(NDVI)和雷达影像提取的后向散射系数为数据源,以滨海滩涂区为研究对象,利用聚类法进行农田土壤管理分区研究.在对不同的数据源进行空间变异分析的基础上,结合滨海滩涂区土壤盐分的空间变异规律,利用非监督分法一模糊k-均值聚类方法进行特征相似性归类,划分不同的管理分区.结果表明,最佳的分区数目为3个.根据每一分区的特点,可以采取相应的管理措施进行土壤改良和农田精确管理.分区结果不但可以指导采样,而且可用于实施变量投入和精确施肥推荐,为样区大面积农田土壤管理提供科学的决策依据.     

基于样点的数字土壤属性制图方法及样点设计综述

土壤剖面数据与土壤类型图按照某种原则进行连接是目前获取土壤属性空间分布信息的主要方法,这种传统的土壤属性制图方法以土壤专家的“经验”和手工描绘为基础,耗费资本高、生产周期长.数字土壤制图通过借鉴先进的空间信息处理技术和高分辨率地形数据的优势,能够快速地获取高精度、高分辨率的土壤属性空间变化信息,是一种精细、高效、经济的土壤属性制图技术.本文详细介绍了基于样点进行数字土壤属性制图的3种方法:①基于空间自相关的方法:②基于空间自相关和土壤-环境关系混合相关的方法:③基于土壤-环境关系的方法.同时,为保证样点能够全面地捕捉到研究区内土壤属性空间变异特征,以上3种方法都对样点的数量、分布或典型性提出了较为严格的要求,即样点应具有全局代表性.因此,如何设计样点成为数字土壤属性制图中的一个重要问题.依据样点设计过程中是否能够整合已有样点,本文将样点设计方案分为采样设计方案和补样设计方案两种,并对其分别进行了详细的综述.     

稻田土壤养分空间变异与合理取样数研究

以嘉善县陶庄农场内一块0.52hm2的水稻田为研究区,采用地统计方法和常规统计方法进行土壤pH及养分(全氮、全碳、速效钾、速效磷、速效镁、速效锰、速效铜、速效锌)的空间变异及其合理取样数目研究。结果表明,各土壤养分在田间尺度均存在一定的空间变异,全氮、全碳、速效钾、速效磷等主要养分的空间格局呈南北向条带状,与农户经营田块的方向基本一致;不同农户耕种、管理下的4田块的土壤养分差异显著;反映土壤肥力的土壤全碳、全氮等养分的合理取样数目在95%的置信度和5%的相对误差条件下为45～个,基本上每个农户经营田块为1个,即可反映土壤养分的真实情况。因此,试验区以N素管理为主的精确田间管理的土壤采样可以采用以农户经营地块为单位的混合样采样方式,以减少土壤采样量和分析成本。     

Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping

Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples to cope with usually scarce resources for field sampling and laboratory analysis. The study focuses on the development and application of an efficiency improved DSM sampling design that (1) applies an optimized sample set size, (2) compensates for limited field accessibility, and (3) enables the integration of legacy soil samples. The proposed sampling design represents a modification of conditioned Latin Hypercube Sampling (cLHS), which originally returns distinct sample sites to optimally cover a soil related covariate space and to preserve the correlation of the covariates in the sample set. The sample set size was determined by comparing multiple sample set sizes of original cLHS sets according to their representation of the covariate space. Limited field accessibility and the integration of legacy samples were incorporated by providing alternative sample sites to replace the original cLHS sites. We applied the modified cLHS design (cLHS_adapt) in a small catchment (4.2 km²) in Central China to model topsoil sand fractions using Random Forest regression (RF). For evaluating the proposed approach, we compared cLHS_adapt with the original cLHS design (cLHS_orig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHS_adapt and cLHS_orig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHS_adapt by adding available legacy samples (cLHS_adapt+) and compared the prediction accuracies. Based on an external validation set cLHS_val (n = 20), the coefficient of determination (R²) of the cLHS_adapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R²‐values of the RF predictions based on cLHS_adapt+, using additional legacy samples, are marginally increased on average by 5%.     

Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region,Iran

This study evaluates the performances of a combination of genetic programming and soil depth functions to map the three-dimensional distribution of cation exchange capacity (CEC) in a semiarid region located in Baneh region, Iran. Using the conditioned Latin hypercube sampling method, the locations of 188 soil profiles were selected, which were then sampled and analyzed. In general, results showed that equal-area quadratic splines had the highest R², 89%, in fitting the vertical CEC distribution compared to power and logarithmic functions with R² of 81% and 84%, respectively. Our findings indicated some auxiliary variables had more influence on the prediction of CEC. Normalized difference vegetation index (NDVI) had the highest correlation with CEC in the upper two layers. However, the most important auxiliary data for prediction of CEC in 30–60 cm and 60–100 cm were topographic wetness index and profile curvature, respectively. Validation of the predictive models at each depth interval resulted in R² values ranging from 66% (0–15 cm) to 19% (60–100 cm). Overall, results indicated the topsoil can be reasonably well predicted; however, the subsoil prediction needs to be improved. We can recommend the use of the developed methodology in mapping CEC in other parts in Iran.     

流域尺度土壤厚度的模糊聚类与预测制图研究

基于土壤厚度与景观位置和特征之间的关系,运用模糊c均值聚类(FCM)方法对西苕溪流域的土壤厚度分布进行了空间预测。选取高程、坡度、平面曲率、剖面曲率、径流强度系数和地形湿度指数6个地形因子进行模糊聚类,根据相应的聚类参数将流域地形组合分为8类。利用部分调查获得的土壤剖面数据,结合样点属性和专家经验为典型区赋值,最后由加权平均得到流域土壤厚度预测图。验证结果表明,FCM方法可以对地形因子组合进行有效合理的分级,其预测精度较高,模型的稳定性较好,是一种低成本高效率的制图方法。该方法在土壤厚度预测方面具有一定的可靠性。     

Soil nitrogen dynamics after slurry injection in field trials: Evaluation of a soil sampling strategy

Slurry injection below maize seeds is a rather new application technique developed to improve the nitrogen use efficiency of liquid organic manure. To enable the characterization of the spatial and temporal soil mineral nitrogen (SMN) dynamics after slurry injection, the present study aims to develop an appropriate soil sampling strategy. Three consecutive experiments were conducted. The first testing of the soil sampling approach was conducted in an existing field trial where the slurry was injected down to a depth of 12 cm (upper rim) below the soil surface. The soil profile (75 cm wide) centered below the maize row was sampled grid‐like to a depth of 90 cm. Around the injection zone, soil monoliths (SM) were sampled using a purpose‐built soil shovel. Below the SMs and in the interrow space (15 and 30 cm distance to the row) a standardized auger procedure was performed. The second experiment aimed at improving the sampling strategy with a focus on sample homogenization quality and necessary sample sizes per pooled sample. Furthermore, the risk of a carryover of slurry components along the soil core due to drilling an auger through a slurry band was analyzed. In the third experiment this improved sampling strategy was validated. Results from the first testing of the sampling procedure showed that the strategy is suitable, although some problems occurred (especially the high spread in values among the replications causing high coefficients of variation (CV) of mostly 40–60%). The improvement trial revealed that due to the high gradient of SMN concentration in the direct range of the injection zone an intensive homogenization of these samples is required. Suitable sample sizes (twelve auger samples and six soil monolith samples per pooled sample) have to be collected to obtain reliable SMN values. Drilling an auger through a slurry band to sample subjacent soil layers has to be avoided. Following this enhanced sampling strategy, in the final validation trial the spread in values were considerably reduced and resulted in CV values of mostly < 20%. The developed sampling strategy enables the characterization of the spatial and temporal SMN dynamics when slurry has been band‐injected below a maize row. The method can be transferred to other row crops and different slurry injection spacing.     

Geospatial modeling of surface soil texture of agricultural land using fuzzy logic,geostatistics and GIS techniques

ABSTRACT

Soil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process.     

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.