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1.
综合BME和BNN法的农田土壤水分与养分分布空间插值   总被引:2,自引:2,他引:0  
徐英  夏冰 《农业工程学报》2015,31(16):119-127
掌握农田土壤水分和养分的空间分布特征是实现农田土壤精确管理及实施精确农业的重要依据。以有限的采样信息为基础,通过多种空间分析理论的融合,形成优势互补的综合方法,对提高土壤变量空间分布模拟和绘图精度具有重要意义。该文将贝叶斯最大熵法(Bayesian maximum entropy,BME)和贝叶斯人工神经网络方法(Bayesian neural networks,BNN)结合形成一种空间插值新方法,即用BNN法表达估值的不确定性,并将其结果融入现代地质统计学BME法中,用融入BNN法结果的BME法(Bayesian maximum entropy method combined with Bayesian neural networks,BMENN)模拟土壤变量的空间分布。以江苏省扬州市区北部某田块的土壤水分、有机质、全氮、碱解氮、速效钾和速效磷6种土壤特性的采样数据为例,运用交叉验证法,将BMENN法对土壤变量的估值精度与BNN法、普通克立格法(ordinary Kriging,OK)进行了比较。结果表明:与OK法和BNN法相比,BMENN法将估计方差(mean squared error,MSE)缩小2.26%~23.54%,具有最小的估计方差和接近于0的平均绝对误差(mean error,ME);BMENN法的估计值与实测值相关系数更大(r=0.62~0.89),具有更高的相关程度;MSE的组成分析表明,BMENN法再现变量波动程度和波动大小的能力更强;从模拟的空间分布图来看,BMENN法绘制的空间分布图更连续,"牛眼"较少,更符合土壤变量的地学规律。BMENN法对于利用有限数据信息提高土壤变量空间分布模拟精度具有重要意义,并可为土壤管理、精准农业的实施以及区域环境规划等提供科学依据。  相似文献   

2.
基于时空克里格的土壤重金属时空建模与预测   总被引:5,自引:0,他引:5  
土壤重金属或其他生态环境属性在时间和空间上均存在连续性和变异性,而目前的研究忽略了它们在时间维的变异。为了在预测时使用多时期采样数据,该文提出使用时空克里格方法对土壤重金属进行时空建模及预测,着重介绍了经验半方差值的计算、理论变异模型的形式及参数拟合、时空克里格估值算法、估值方差和精度随邻近点数量的变化及时空克里格制图。以武汉市青山区土壤重金属为例介绍了时空克里格建模及预测的流程。结果表明,时空克里格方法能够很好地描述土壤重金属在空间、时间和时空上3个部分的变异特征,能够利用其他时期的数据对预测时间点的属性进行插值,而多时期的属性空间分布图能够很好地反映土壤重金属的分布变化规律。该研究可为资源环境生态时空建模及预测研究提供参考。  相似文献   

3.
田块尺度上土壤重金属污染地统计分析及评价   总被引:3,自引:1,他引:2       下载免费PDF全文
该文以北京市某生态农场的大田为例,应用地统计学分析方法进行了变异函数的计算和模型拟合,建立了计算土壤重金属含量的最适空间插值理论模型,随后运用克立金估值方法绘制了田块尺度上土壤重金属的空间分布图,并与土壤重金属污染标准进行比较,土壤重金属污染评价结果表明:研究范围内的土壤重金属含量较低,未发现污染。该研究结果表明地统计学分析方法可为田块尺度上的农业优化管理提供决策支持。  相似文献   

4.
该文以北京市某生态农场的大田为例,应用地统计学分析方法进行了变异函数的计算和模型拟合,建立了计算土壤重金属含量的最适空间插值理论模型,随后运用克立金估值方法绘制了田块尺度上土壤重金属的空间分布图,并与土壤重金属污染标准进行比较,土壤重金属污染评价结果表明:研究范围内的土壤重金属含量较低,未发现污染。该研究结果表明地统计学分析方法可为田块尺度上的农业优化管理提供决策支持。  相似文献   

5.
地理加权回归及其在土壤和环境科学上的应用前景   总被引:14,自引:1,他引:13       下载免费PDF全文
瞿明凯  李卫东  张传荣  黄标 《土壤》2014,46(1):15-22
地理加权回归(GWR)是近些年来出现的一种新的空间局部回归技术。它是将数据的空间位置嵌入线性回归模型中,以此来探测空间关系的非平稳性。在运用于空间数据分析方面,相对于传统的普通最小二乘回归法,具有明显的优势。本文首先介绍了GWR的理论起源并描述了该方法的基本原理、存在的不足以及后来的扩展;然后为了更准确地了解GWR的应用状况和研究进展,进行了一个文献调查;接着回顾了GWR在土壤和环境科学上的初步应用情况;最后对该方法在土壤和环境科学上的应用前景作了展望。目的是为我国土壤和环境科学领域的同行了解和应用GWR提供参考。经过国内外研究者多年的研究和实践,GWR方法已被证明是一个理论上较为成熟,能够应用到实际研究中的优秀空间统计学方法。因此,GWR在土壤和环境科学上将会有着广泛的应用前景。  相似文献   

6.
空间变异理论在土壤特性分析中的应用研究进展   总被引:4,自引:0,他引:4  
空间变异理论是研究随机变量空间变异性的理论.该理论是建立在地统计学基础之上,其研究的主要对象就是那些在空间上具有一定变异性的随机变量.空间变异理论被广泛应用到土壤学、生态学、地质学、水文学、气象、资源环境及其存在"空间变异性"的领域.对空间变异理论的研究方法进行简要的介绍,并且对空间变异理论在土壤的物理、化学特性及微量元素中的应用研究进行介绍.在对空间变异理论的研究方法和在土壤特性中应用的归纳和分析中,指出空间变异理论在理论和应用中存在的问题并为其提出展望.  相似文献   

7.
基于范畴型变量和贝叶斯最大熵的土壤有机质空间预测   总被引:1,自引:1,他引:0  
选取湖北省沙洋县为研究区域,以土壤质地与土壤有机质定量关系为辅助信息,利用贝叶斯最大熵(BME)方法对沙洋县土壤有机质含量进行空间预测,并与以土壤质地和土壤全氮为辅助变量的协同克里格方法预测结果精度作对照,探讨两种方法的预测效果。结果表明,协同克里格方法和BME方法均能较好反映研究区有机质空间分布特征。在辅助变量与土壤养分存在显著相关性条件下,BME方法能更好地利用范畴型变量等多种类型辅助信息。比较极值误差范围、平均绝对误差、均方根误差等方面,BME方法在土壤属性空间预测方面具有更高精度,且能有效降低数据获取成本和难度,在县域尺度土壤属性空间预测上具有更大优势。  相似文献   

8.
该文利用人工神经网络的BP模型建立了具有类似普通Kriging(OK)法和条件模拟(CS)运算目标的人工神经Kriging(NK)方法,在黄河河套平原进行了耕地和盐荒地初冻期、最大冻深期和融通期土壤水盐时空变异性的模拟和估值,通过NK法与OK法、CS法模拟、估值、检验结果及3种方法的理论变异函数、统计参数与实验变异函数的对比,结果表明NK法在消除滑动平均影响方面优于OK法,并以类似于CS法的空间变异性进行模拟,而且NK法有自身独特的优点,它不需要协方差函数的估计和变异函数的推求,对于含有一定特异值和一维到三维空间的扩展有更强的适应性,是对空间变异性应用研究方法的一种补充,同时拓宽了ANN的应用领域,具学科融合的优势。  相似文献   

9.
对点源时间序列数据缺失值进行有效估值能提升其数据质量。为探究不同估值方法对点源时间序列数据缺失值的估值效果及其影响因素,以亚热带典型小流域长期定位观测的每日气象和水文数据(最高气温、最低气温、太阳辐射量、降雨量及地表径流量)为例,以均方根误差(RMSE)、绝对平均误差(MAE)和Pearson相关系数(r)为性能验证指标,比较了线性内插法(LIM)、K-最近邻插值法(KNNM)、样条插值法(SIM)、多项式插值法(PIM)和核密度估值法(KDEM)5种估值方法的估值性能差异及其主要影响因素。结果表明:(1)LIM、SIM和KDEM的估值性能总体上优于其它2种方法;(2)5种估值方法对气象数据(最高气温、最低气温和太阳辐射量)缺失值估值的RMSE为1.81~6.35,MAE为1.30~4.20,r为0.70~0.98(P0.05),而对水文数据(降雨量和地表径流量)缺失值估值的RMSE为12.54~26.28,MAE为3.60~14.21,r为0.07~0.72。可见,各估值方法对气象数据的估值性能强于对水文数据;(3)上述数据集的变异系数(CV)与估值评估指标(RMSE、MAE及r)线性相关(P0.05),是影响估值性能的重要因素。  相似文献   

10.
空间变异理论在土壤特性分析中的应用研究进展   总被引:2,自引:0,他引:2  
空间变异理论是研究随机变量空间变异性的理论。该理论是建立在地统计学基础之上,其研究的主要对象就是那些在空间上具有一定变异性的随机变量。空间变异理论被广泛的应用到土壤学、生态学、地质学、水文学、气象、资源环境以及其它存在“空间变异性”的领域。对空间变异理论的研究方法进行了简要的介绍,并且对空间变异理论在土壤的物理、化学特性及微量元素中应用的研究进行了介绍。在对空间变异理论的研究方法和在土壤特性中应用的归纳和分析中,指出了空间变异理论在理论和应用中的存在的问题并为其提出了展望。  相似文献   

11.
The Bayesian maximum entropy (BME) method is a valuable tool, with rigorous theoretical underpinnings, with which to predict with soft (imprecise) data. The methodology uses a general knowledge base to derive a joint prior distribution of the data and the prediction by the criterion of maximum entropy; the hard (precise) and soft data are then processed using this prior distribution to yield a posterior distribution that provides the BME prediction. The general knowledge base commonly consists of the mean and covariance functions, which may be extracted from the data. The common method for extracting the mean function from the data is a generalized least squares (GLS) approach. However, when the soft data take the form of intervals of plausible values, this method can result in errors in the BME predictions. This paper suggests a maximum likelihood (ML) method for fitting the local mean. The two methods are compared in terms of their predictions, firstly on simulated random fields and then on a case study to predict the depth of soil using some censored data. The results show that the ML method can result in more accurate BME predictions; the degree of improvement over the GLS method depends on the parameters of the spatial covariance model.  相似文献   

12.
Categorical variables such as water table status are often predicted using the indicator kriging (IK) formalism. However, this method is known to suffer from important limitations that are most frequently solved by ad hoc solutions and approximations. Recently, the Bayesian Maximum Entropy (BME) approach has proved its ability to predict categorical variables efficiently and in a flexible way. In this paper, we apply this approach to the Ooypolder data set for the prediction of the water table classes from a sample data set. BME is compared with IK using global as well as local criteria. The inconsistencies of the IK predictor are emphasized and it is shown how BME permits avoiding them.  相似文献   

13.
Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi‐point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was used as soft information (S). The category with the maximum estimated probability was used as the predicted category. The quality of the resulting BME(HS)‐map was compared with that of the BME(H)‐map obtained by using only the hard data in BME‐estimation, and with the existing soil map. Validation with a probability sample showed that the use of the soft information in BME‐estimation leads to a considerable and significant increase of map purity by 15%. This increase of map purity was due to the high purity of the existing soil map (71.3%). The purity of the BME(HS) was only slightly larger than that of the existing soil map. This was due to the small correlation length of the soil categories. The theoretical purity of the BME‐maps overestimated the actual map purity, which can be partly explained by the biased estimates of the one‐point bivariate probabilities of hard and soft categories of the same label. Part of the hard data is collected to describe characteristic soil profiles of the map units which explains the bias. Therefore, care must be taken when using the purposively selected data in soil information systems for calibrating the probability model. It is concluded that BME is a valuable method for spatial prediction and simulation of soil categories when the number of categories is rather small (say < 10). For larger numbers of categories, the computational burden becomes prohibitive, and large samples are needed for calibration of the probability model.  相似文献   

14.
Soils encompass a huge diversity of organisms which mostly remains to be characterized due to a number of methodological and logistical issues. Nonetheless, remarkable progress has been made in recent years toward developing strategies to characterize and describe soil biodiversity, especially thanks to the development of molecular approaches relying on direct DNA extraction from the soil matrix.Metabarcoding can be applied to DNA from any environment or organism, and is gaining increasing prominence in biodiversity studies. This approach is already commonly used to characterize soil microbial communities and its application is now being extended to other soil organisms, i.e. meso- and macro-fauna.These developments offer unprecedented scientific and operational opportunities in order to better understand soil biodiversity distribution and dynamics, and to propose tools and strategies for biodiversity diagnosis. However, these opportunities also come with challenges that the scientific community must face. Such challenges are related to i) clarification of terminology, (ii) standardisation of methods and further methodological development for additional taxonomic groups, (iii) development of a common database, and (iv) ways to avoid waste of information and data derived from metabarcoding. In order to facilitate common application of metabarcoding in soil biodiversity assessment, we discuss these opportunities and challenges and propose solutions towards a more homogeneous framework.  相似文献   

15.
无人机遥感在农田信息监测中的应用进展   总被引:11,自引:2,他引:9       下载免费PDF全文
快速实时地掌握农田信息是实施精准农作的基础。以无人机为平台的低空遥感探测技术,具有空间分辨率高、时效性强和成本低等特点,可填补地面监测和高空遥感间的测量尺度空缺,因此在农田信息精准监测领域具有广泛的应用前景。近年来,随着无人机飞行平台稳定性增强、操作难度降低,机载遥感设备的轻量化和多样化,以及遥感数据处理技术的进步,无人机遥感在农田信息监测领域得到了快速发展。本文对国内外相关研究成果进行了总结,对常用遥感技术类型和数据处理方法以及具体应用方向和实施效果进行了综述,并提出了当前存在的突出问题和未来的发展方向,以期为推动无人机遥感在农田信息监测和精准农业中更广泛的应用提供依据。  相似文献   

16.
融合面向对象与缨帽变换的湿地覆被类别遥感提取方法   总被引:2,自引:2,他引:0  
为了有效提取湿地覆被类别遥感信息,该文基于国产环境星影像(HJ-CCD)和Landsat7遥感影像(ETM)提出了一种融合面向对象技术和缨帽变换的提取湿地覆被信息的方法,并对东洞庭湖区的湿地进行提取。遥感提取结果的总体精度90.02%,Kappa系数0.88,高于传统的分类方法分类的量化结果;获得的结果没有"椒盐现象"且比较紧致。试验结果表明融合面向对象和缨帽变换的方法能够有效的提取湿地覆被类别,精度高,效果好。研究结果为有效地利用遥感手段提取湿地覆被信息提供参考。  相似文献   

17.
贝叶斯最大熵地统计方法研究与应用进展   总被引:1,自引:0,他引:1       下载免费PDF全文
杨勇  张若兮 《土壤》2014,46(3):402-406
以克里格估算为基础的插值和随机模拟为代表的经典地统计方法是目前研究地理属性空间分布的主要方法,但仍存在精度不高及不能有效利用其他有价值信息的缺陷。近年来贝叶斯最大熵地统计方法在国外逐渐流行,该方法能够在有效利用多源数据的基础上,提高空间分布研究精度,是一种新的非线性方法。本文详细阐述了贝叶斯最大熵方法的数据内容、实施步骤、一般算法及计算结果,并介绍了该方法的应用情况,最后对该方法的优点和不足作出了评价。  相似文献   

18.
Zein, the prolamin of corn, is attractive to the food and pharmaceutical industries because of its ability to form edible films. It has also been investigated for its application in encapsulation, as a drug delivery base, and in tissue scaffolding. Zein is actually a mixture of proteins, which can be separated by SDS-PAGE into α-, β-, γ-, and δ-zein. The two major fractions are α-zein, which accounts for 70-85% of the total zein, and γ-zein (10-20%). γ-Zein has a high cysteine content relative to α-zein and is believed to affect zein rheological properties. The aim of this study was to investigate the effect of γ-zein on the often observed phenomena of zein gelation. Gelation affects the structural stability of zein solutions, which affects process design for zein extraction operations and development of applications. The rheological parameters, storage modulus (G') and loss modulus (G″), were measured for zein solutions (27% w/w solids in 70% ethanol). β-Mercaptoethanol (BME) was added to the solvent to investigate the effect of sulfhydryl groups on zein rheology. Modulus data showed that zein samples containing γ-zein had measurable gelation times under experimental conditions, contrary to samples with no γ-zein, where gelation was not detected. Addition of BME decreased the gelation time of samples containing γ-zein. This was attributed to protein unfolding. SEM images of zein microstructure revealed the formation of microspheres for samples with relatively high content of α-zein, whereas γ-zein promoted the formation of networks. Results of this work may be useful to improve understanding of the rheological behavior of zein.  相似文献   

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