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类别辅助变量参与下的土壤无偏采样布局优化方法 总被引：3，自引：1，他引：2
为了提高采样点在地理空间和辅助变量特征空间中的代表性,该文提出特征空间偏离指数用以测度采样点在特征空间中的无偏性,采用类别型辅助变量参与下的多维特征空间构建方法,融合地理空间和特征空间均匀分布的多目标优化目标函数,并利用空间模拟退火的方法实现采样点布局优化。以北京顺义区农田土壤重金属采样为例,选取土地利用类型、土壤质地和母质为辅助变量进行样点布局优化,并与特征空间均匀和地理空间均匀采样方法比较,结果表明:用于区域变量总体估计时,地理空间均匀采样估计精度最低,在采样尺度大于0.275时以特征空间均匀采样估计精度最好,而在采样尺度小于0.275时,无偏采样能获得更好的估计结果;在特征空间代表性方面,采样尺度较大时特征空间均匀采样样点代表性最好,采样尺度小于0.302时,无偏采样与特征空间均匀采样的代表性基本一致,地理空间采样点的代表性最差;用于空间制图时,无偏采样总体上比其他2种方法具有更好的制图精度。可见,在辅助变量支持的采样优化中,当采样尺度大且样点数较少时,适合采用特征空间均匀方法,且只能用于总体估计;采样尺度较小,样点数多时,适合采用无偏采样方法。该研究为利用辅助变量设计区域采样布局提供参考。 相似文献
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Daniel D. SAURETTE Asim BISWAS Richard J. HECK Adam W. GILLESPIE Aaron A. BERG 《土壤圈》2024,34(3):530-539
In digital soil mapping (DSM), a fundamental assumption is that the spatial variability of the target variable can be explained by the predictors or environmental covariates. Strategies to adequately sample the predictors have been well documented, with the conditioned Latin hypercube sampling (cLHS) algorithm receiving the most attention in the DSM community. Despite advances in sampling design, a critical gap remains in determining the number of samples required for DSM projects. We propose a simple workflow and function coded in R language to determine the minimum sample size for the cLHS algorithm based on histograms of the predictor variables using the Freedman-Diaconis rule for determining optimal bin width. Data preprocessing was included to correct for multimodal and non-normally distributed data, as these can affect sample size determination from the histogram. Based on a user-selected quantile range (QR) for the sample plan, the densities of the histogram bins at the upper and lower bounds of the QR were used as a scaling factor to determine minimum sample size. This technique was applied to a field-scale set of environmental covariates for a well-sampled agricultural study site near Guelph, Ontario, Canada, and tested across a range of QRs. The results showed increasing minimum sample size with an increase in the QR selected. Minimum sample size increased from 44 to 83 when the QR increased from 50% to 95% and then increased exponentially to 194 for the 99% QR. This technique provides an estimate of minimum sample size that can be used as an input to the cLHS algorithm. 相似文献
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《Communications in Soil Science and Plant Analysis》2012,43(3-4):387-400
Abstract As the soil varies spatially, monitoring of temporal changes might be biased by spatial variation. Hence, there is a need to develop tools to identify spatial structures and to optimize sampling designs. This study documents the short‐range variability of lead (Pb), copper (Cu), and chromium (Cr) in a 1‐ha agricultural plot. The aims of this work were to identify the spatial structures of these metals, to discuss their origin, and to optimize sampling design for monitoring the long‐term changes of these characteristics. We used geostatistics and sampling simulations on a set of 65 individual samples. Results demonstrate that small changes in metal contents might be hidden by spatial variation if the sampling design for monitoring is not appropriate and that former land‐use history and old management practices may have long‐term significant effects on the distribution of some metals. 相似文献
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不同采样设计会对土壤呼吸空间变异特征的预测精度产生重要影响。本研究选取黄淮海平原北部潮土区1 km×1 km夏玉米样地,在7×7单元规则格网(样点间距167 m)、完全随机(样点平均间距433 m)以及3×3单元规则格网+完全随机(样点平均间距405m)3种布点方式的基础上,保持样本总量(49)不变,以占总样点2%～14%的短距离样点(样点间距4m)随机替换原方案相应样点个数的方法优化布点方式,应用普通克里金法插值,以均方根误差(RMSE)和确定系数(R2)作为验证指标,检验基于3种布点方式设置的短距离样点对土壤呼吸空间变异预测精度的影响。结果表明:研究区土壤呼吸平均速率为2.65μmol·m?2·s?1,空间分布均呈西高东低,表现出中等程度变异。采样设计对土壤呼吸空间分布的预测精度影响显著,基于3种布点方式设置短距离样点可提高预测精度7%～13%。无短距离样点替换时,规则格网+完全随机的布点方式最优,比完全随机布点和规则格网布点的空间插值预测精度分别提高10%和22%;设置短距离样点替换后,在最优布点方式(规则格网+完全随机)中,对土壤呼吸空间变异的预测精度可再提高4%～7%,其中短距离样点个数占样本总量10%对土壤呼吸空间变异预测精度的提高最为明显。研究发现,基于相同的样本数量设置短距离样点可增加区域范围内样点密度,提高土壤呼吸空间变异预测精度及试验结果的可靠性。因此,在黄淮海平原北部潮土区100 hm2尺度的夏玉米样地中,规则格网+完全随机+10%短距离样点的布点方式是预测土壤呼吸空间变异最适宜的采样布点方式。 相似文献
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Incorporating limited field operability and legacy soil samples in a hypercube sampling design for digital soil mapping 总被引：2，自引：0，他引：2 
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下载免费PDF全文 Felix Stumpf Karsten Schmidt Thorsten Behrens Sarah Schönbrodt‐Stitt Giovanni Buzzo Christian Dumperth Alexandre Wadoux Wei Xiang Thomas Scholten 《植物养料与土壤学杂志》2016,179(4):499-509
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 (cLHSadapt) 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 cLHSadapt with the original cLHS design (cLHSorig). With an optimized sample set size n = 30, the results show a similar representation of the cLHS covariate space between cLHSadapt and cLHSorig, while the correlation between the covariates is preserved (r = 0.40 vs. r = 0.39). Furthermore, we doubled the sample set size of cLHSadapt by adding available legacy samples (cLHSadapt+) and compared the prediction accuracies. Based on an external validation set cLHSval (n = 20), the coefficient of determination (R2) of the cLHSadapt predictions range between 0.59 and 0.71 for topsoil sand fractions. The R2‐values of the RF predictions based on cLHSadapt+, using additional legacy samples, are marginally increased on average by 5%. 相似文献
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Uncertainty analysis for pedotransfer functions 总被引：1，自引：0，他引：1
Both empirical and process‐simulation models are useful in predicting the outcome of agricultural management on soil quality and vice versa, and pedotransfer functions have been developed to translate readily available soil information into variables that are needed in the models. The input data are subject to error, and consequently the transfer functions can produce varied outputs. A general approach to quantifying the resulting uncertainty is to use Monte Carlo methods. By sampling repeatedly from the assumed probability distributions of the input variables and evaluating the response of the model, the statistical distribution of the outputs can be estimated. Methods for sampling the probability distribution include simple random sampling, the sectioning method, and Latin hypercube sampling. The Latin hypercube sampling is applied to the quantification of uncertainties in pedotransfer functions of soil strength and soil hydraulic properties. Hydraulic properties predicted using recently developed pedotransfer functions are also used in a model to analyse the uncertainties in the prediction of soil‐water regimes in the field. The uncertainties of hydraulic properties in soil‐water simulation show that the model is sensitive to the soil's moisture state. 相似文献
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土壤养分变异与合理取样数量 总被引：25，自引：2，他引：25
利用地统计学方法、地理信息系统技术 ,结合土壤养分状况系统研究法对一定条件下的土壤合理取样数量作了细致的研究。结果表明 ,大部分土壤养分都具有较为良好的半方差结构 ,空间自相关距都比较大。在平衡取样成本和精确度的前提下必须考虑土壤养分的空间变异程度。利用地理信息系统等手段可以充分表现土壤养分变异的分布情况 ,从而为设置取样点提供依据。在本研究条件下 ,利用分层取样的最适分配法获得 34.5hm2耕地上的最佳取样数量 ,针对土壤速效钾的取样以 95%的置信水平 10%的相对误差为宜 ,取样数量为 24个 ;针对土壤速效磷的取样以 95%的置信水平 20%的相对误差为宜 ,取样数量 10个。 相似文献
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秦巴山地县域土壤碱解氮空间变异与合理采样数的确定 总被引：2，自引：1，他引：2
[目的]了解秦巴山地县域土壤碱解氮的空间分布规律,确定合理的采样密度,为研究区农田养分管理提供科学依据。[方法]运用地统计学与GIS结合的方法,随机抽取不同采样密度的样点数据,进行插值分析,采用交叉验证法对插值精度进行评价。[结果](1)土壤碱解氮的变异系数为42.95%,属于中等变异;(2)块金值与基台值的比值约为1/2,具有中等强度的空间相关性,空间最大相关距离为9 171m;(3)样点数目从1 060个到742个时,变程以及块金值与基台值之比出现明显偏差,其相对误差分别为152.32%和36.1%,均方根误差(RMSE)、相关系数(R)同样出现明显偏差。[结论]汉滨区土壤碱解氮空间连续性较好,适当地减少采样密度,仍可以满足插值分析的需要,考虑到土壤碱解氮的空间变异评价的精度分析结果,县域土壤碱解氮的合理采样数应该控制在1 060个以上,即最大以345.5hm2为一个采样单元。 相似文献
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基于方差四叉树法的滨海盐土电导率采样布局研究 总被引：3，自引：0，他引：3
利用土壤空间变异特性和空间分布特征进行采样设计是当前土壤采样研究的重要内容。采用方差四叉树法（Variance quad-tree method，简称VQT），结合半方差函数，设计滨海盐土采样的最优布局。并利用普通克立格法对传统网格采样法与方差四叉树采样法所得到的不同的样点数目进行插值，计算估值误差并进行精度比较。结果发现，同样的样本数目，利用方差四叉树法得到的克立格估值误差明显地较利用网格采样法得到的克立格估值误差小，其采样效率提高约16％一25％。该方法的优势在于，可设计在土壤特性变异大的区域密集采样而在变异较小的区域稀疏地采样，从而在有效表达土壤空间变异性的同时，提高了采样效率，减少了采样成本。 相似文献
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不同采样密度下土壤特性的空间变异特征及其推估精度研究 总被引：12，自引：3，他引：12
对3种采样密度下的土壤特性进行了空间变异结构研究,并在此基础上对土壤特性的推估精度进行了比较。土壤空间变异结构在不同采样密度下的变化随土壤的特性而定,土壤pH、NH4 -N、P及S的变异分别符合不同的半方差模型,土壤OM、Ca及Mg则均符合同一模型;采样密度与模型的拟合度、变异的有效变程及空间相关性均没有必然联系。对模型拟合度均达显著水平的土壤特性进行推估,土壤OM及S的推估精度随采样密度的提高而增加,但土壤NH4 -N及Ca没有表现出同样的规律。 相似文献
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采用2 m×2 m均匀栅格取样方式和等级次序地统计学方法，研究了面积为200 m2褐土耕层土壤机械组成的空间变异性。统计特征量表明褐土耕层土壤的机械组成存在高度空间变异性，样本数据不服从正态分布和对数正态分布，无法直接应用普通克里格法研究其空间变异规律。采用重叠移动窗口统计数据可削弱局部偏差值带来的影响，经等级次序标准化转换后用半方差函数分析其等级次序空间结构，再根据普通克里格法对标准化等级次序空间进行估值。对所得的标准等级次序估计值用中位值模型进行逆转换，较清晰地阐明了褐土农田机械组成带状各向异性的空间分布规律。 相似文献
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《Communications in Soil Science and Plant Analysis》2012,43(9-10):1674-1687
Spatial variability in soil tests for essential plant nutrients influences how well producers accurately sample their fields for fertilization and compliance with environmental regulations. The objective of this study was to determine the effect of spatial variability on soil tests in a pasture system. Soil samples were collected from a 6‐ha and a manured 4‐ha pasture (study sites 1 and 2, respectively). Samples were analyzed for soil pH, organic matter, and plant‐available nitrogen, phosphorus, and potassium. Semivariance parameters from a geographical information system (GIS) were used to quantify spatial variability, and “R” computer program was used to optimize the number of soil subsamples sufficient to form a representative composite sample. Spatial variability was obtained in some of the soil properties, and approximately 22 soil subsamples were sufficient to form a representative composite sample in a pasture system. 相似文献
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利用空间建模思维对地价空间进行定量模拟分析成为了地价研究中值得深入探讨的问题。文章以南京市为研究区域,以南京市住宅地价为研究对象,选取了影响住宅地价空间分布的地物驱动要素,然后基于空间抽样和多元回归模型,建立空间因子与住宅地价的多元回归模型,并利用栅格GIS模拟了城市住宅地价的空间分布。研究显示模拟结果具有较好的效果,能真实反映各类地物要素对住宅地价空间的影响大小,尤其是轨道交通对住宅地价的影响明显。因此,通过住宅地价空间模拟研究,能够为提高城市土地价值,实现城市土地高效集约利用提供借鉴价值。 相似文献
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一种基于样点代表性等级的土壤采样设计方法 总被引：10，自引：1，他引：10
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。 相似文献
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气候变化效应评估、土壤固碳潜力和肥力管理等,迫切需要详尽的土壤有机质（soil organic matter, SOM）空间分布信息。该文以江苏省第二次土壤普查的1 519个典型土壤剖面的表层（0～20 cm）SOM含量为例,选择1 217个样本为建模集,302个为验证集,选取年均温度、年均降雨、物理性黏粒和土壤pH值等因子进行SOM的地理加权回归（geographically weighted regression, GWR）建模。从建模集中分别随机抽取100%（1 217个）、80%（973个）、60%（730个）、40%（486个）,20%（243个）的样点,对比不同样点数量下GWR和传统全局回归模型的精度差异,并选择最优模型进行SOM空间预测制图。结果表明：1）江苏省SOM含量在不同空间尺度上存在极显著的空间自相关性。不同样点数量的建模集的全局自相关性和局部空间自相关聚类图结果相似。全局Moran''s I值介于0.25～0.61（P<0.001）。SOM含量空间分布以空间聚集特征为主,"高-高"聚集区主要分布在苏中和苏南地区,"低-低"聚集区主要分布在苏北地区。2）GWR建模结果均优于传统的传统全局回归建模,其残差在不同的空间尺度上均不存在空间自相关性。不同建模集的GWR的R2adj较全局建模均提高0.15～0.20,其AIC和RSS均比全局模型有大幅降低,为56.08～360.19和17.40～76.67。不同建模样本数量的GWR模型对SOM的解释能力差异较小。3）建模样点数量（除建模样本n=243）对GWR预测制图结果的精度影响不大,RMSE介于5.56～5.75 g/kg之间,MAE介于3.87～4.05 g/kg之间,R2介于0.52～0.48之间,均优于全部建模样点的普通克里格插值验证结果。该研究可为样点数较少的省级尺度地区SOM空间建模与制图提供借鉴。 相似文献
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Soil biodiversity varies through space as influenced by habitat features and land-use history. The performance of any sampling strategy highly depends on its relevance with regards to this pattern. We surveyed the soil macrofaunal species richness in the pastures of the Benfica Field Station (Eastern Amazonia, State of Pará, Brazil) and described its variability in 4 independent replicate plots. We designed a within-plot sampling scheme that accounted for the soil spatial variation (stratified sampling). Replicated pasture plots had different species richness (49-65) corresponding to a low proportion (40-53%) of the total number of species (123). Pairs of replicated plots showed an outstandingly low number of shared species (28-41% of the species pool). Likewise, different classes of soil thickness, corresponding to a Ferralsol-Cambisol sequence, had different species richness (12-44) and exhibited a very low proportion of shared species (15-29%). The proportion of rare species, i.e. singletons, ranged from 40-51% of the total species richness depending on the plot considered. We used the abundance-based coverage estimator of species richness (ACE) and the Chao shared species estimator that provides a correction based on the relative abundance of rare species. These indices also showed both a high between plots dissimilarity and a substantial within plot variability of species composition. Because of the high proportion of rare species, the rarefaction curves failed to reach any asymptote in all replicated plots. Bootstrap resampling showed that less than 5 samples per stratum (class of soil thickness) provided inconsistent species richness values. We simulated the efficiency of sampling strategies that included our 4 replicate plots and the 3 classes of soil thickness but with varying sampling effort within each stratum. The results indicated that a fairly large (74%) proportion of species would be recorded if strata were sampled using 5 sampling units (hence 15 samples per plot for a total of 4×15=60 samples). This study showed the need for adequate plot replication in soil macrofaunal biodiversity studies. Also, the main relevant factors of within-replicate plot spatial heterogeneity (e.g. soil, vegetation) should be accounted for through stratified sampling. The results showed that there is no way of reducing the local sampling effort below a certain level (here, 5 sampling units per stratum). 相似文献
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采样尺度对土壤养分空间变异分析的影响 总被引：10，自引：2，他引：10
以高密度土壤养分采样数据为数据源,通过随机抽取生成不同采样尺度的样点数据,分析采样尺度对土壤养分空间变异特征分析的影响。研究结果表明:区域土壤养分预测均值随采样尺度减小呈下降趋势,而变异系数增加;养分空间分布的全局趋势随采样尺度增大而增强,但不影响半方差模型;当采样尺度较大,样点间自相关较弱时,相对较少的样点也能满足区域统计参数估测分析需要,但不能用于空间变异特征和插值分析;当样点数大于最佳采样数时,养分统计参数、空间变异特征和插值分析随着采样尺度减小而精度提高,当采样尺度达到0.2左右时,能够满足中等空间变异的土壤养分空间插值分析需要;样点空间布局对相关距和空间插值分析精度的影响比采样尺度本身更为显著。 相似文献