基于HSI模型的色彩合成技术在土壤模糊连续制图中的应用

RGB模型支持下的色彩合成技术能够实现模糊c-均值算法(FCM)输出结果的连续制图表达,但RGB模型本身的条形图例不能很好地反映出制图结果的空间信息和模糊不确定性信息。同时当模糊类别数量较多的情况下,容易出现新生成的类别色彩与原类别色彩相近甚至重复的现象,从而导致最终的模糊连续制图出现类别混乱的问题。研究表明基于HSI模型的轮式图例不仅可以反映出模糊类别之间的属性空间距离(亲疏关系),同时可以反映出制图结果的空间信息和模糊不确定性信息。两个色彩模型的有效结合较好地实现了色彩合成技术支持下的土壤模糊连续制图。     

基于多源环境变量和随机森林的橡胶园土壤全氮含量预测

土壤全氮与土壤肥力和土壤氮循环紧密相关。掌握土壤全氮详细的空间分布信息对提高土壤肥力管理效率和更好地了解土壤氮循环至关重要。该文以儋州国营橡胶园为研究区域,采集2511个土壤样品,利用随机森林(random forest,RF)、逐步线性回归(stepwise linear regression,SLR)、广义加性混合模型(generalized additive mixed model,GAMM)以及分类回归树(classification and regression tree,CART)结合多源环境变量(成土母质、平均降雨量、平均气温和归一化植被指数)对研究区橡胶园土壤全氮含量进行空间预测,并通过754个独立验证点比较了4种模型的预测精度。结果表明RF对土壤全氮的预测值和实测值的相关系数(0.82)明显高于SLR(0.68)、GAMM(0.70)和CART(0.69),而RF的预测平均绝对误差(0.08836 g/kg)和均方根误差(0.13090 g/kg)均低于SLR、GAMM和CART。此外,RF模型预测结果能反映更为详细的局部土壤全氮含量空间变化信息,与实际情况更为接近。可见,RF模型可作为橡胶园土壤全氮含量空间分布预测的高效方法,为其他土壤属性的空间分布预测提供了一种新的方法。     

河北省表层土壤有机碳和全氮空间变异特征性及影响因子分析

【目的】在陆地生态系统中, 土壤全氮和有机碳是重要的生态因子。本研究基于土壤调查获得大量土壤剖面的空间和属性信息,研究河北的土壤有机碳和全氮的空间分布特征,为河北的土壤养分监测和管理提供科学依据,同时也为其他类似地区土壤采样提供参考,减少采样成本。【方法】运用传统统计学和地统计学分析方法,以变异函数为工具,初步分析了河北土壤全氮和有机碳的空间变异特征,并应用普通克立格法和回归克里格法进行插值, 得出全氮和有机碳含量的分布格局。【结果】研究区土壤有机碳和全氮的平均值分别为15.25 g/kg和1.23 g/kg,变异系数分别为0.73和0.63,属于中等强度变异。经对数转换后,土壤有机碳和全氮均符合正态分布。选择球状模型作为土壤有机碳和全氮的半方差函数理论模型,土壤有机碳和全氮的块金值/基台值的比值分别为1.8%和1.2%,有机碳和全氮的块金系数均小于25%,表明有机碳和全氮具有强烈的空间相关性。有机碳和全氮空间变异的尺度范围不同,分别为50.400 km和59.200 km。研究区的有机碳总体空间分布规律是有机碳在北部较高、南部较低,呈自北向南递减趋势,土壤全氮与有机碳的空间分布趋势相似,但有机碳的空间变异特征较全氮明显,这种空间分布格局主要受环境因子、 土壤质地、 土壤类型以及土地利用类型等的影响,其中环境因子中的气温和海拔对有机碳和全氮的影响较大。通过比较普通克里格和回归克里格的预测结果,回归克里格能较好地反映东南部有机碳和全氮较低地区的局部变异外,对于西北部的山区也能更好地反映碳、 氮与地形及气候等因素的关系。【结论】河北土壤有机碳和全氮的空间变异和分布特征较为类似,受地形地貌、 气候等因素的影响。通过比较普通克里格法和回归克里格法的空间预测结果,回归克里格法可以消除环境因子的影响,从而得到更准确的空间预测结果,因此建议使用回归克里格法进行预测,以期获得一个更为准确的土壤有机碳和全氮的空间预测结果。     

大尺度采样下不同模型方法预测土壤全氮空间分布研究——以海南岛为例

在大区域尺度、有限土壤样点情况下,为探索准确预测土壤属性的方法,以海南岛为研究区,采用近似网格采样方法,采集130个样点,用多元线性回归(MLR)、普通克里格(OK)和回归克里格(RK)3种模型方法进行土壤全氮预测,并以29个验证点比较了预测精度。结果显示:1)对较大区域进行土壤全氮的空间分布的预测精度为OKRKMLR;2)3种模型对土壤全氮含量空间预测分布趋势基本一致,总趋势为岛内自东向西方向逐渐降低;3)0~5 cm土壤全氮含量与土地利用方式呈极显著相关关系,0~20 cm土壤全氮含量与归一化植被指数呈显著相关,20~40、40~60 cm土壤全氮含量与归一化植被指数、坡度呈极显著或显著相关。     

青海省表层土壤属性数字制图

对于土壤景观复杂的大区域,样点往往较为稀疏,如何准确地进行土壤预测制图仍是一个需要研究的问题。本文以青海省为研究区,基于近年采集205个土系调查点数据,采用随机森林模型,分别建立了表层(0～20 cm)土壤全氮、有机碳、粉粒含量和pH四个基本土壤属性与环境协同变量(海拔、坡度、地形湿度指数、年降水量、年平均气温、归一化植被指数、地表温度和地表反射率)之间的定量关系模型,对该地区进行了土壤多要素预测制图,分析了影响土壤空间变异的控制性因素。交叉验证结果显示,全氮、有机碳、粉粒含量和pH的R~2分别是0.61、0.53、0.47和0.54,这说明随机森林模型可解释47%以上的土壤空间变异。表层土壤全氮和有机碳空间分布趋势东南高,西北低,pH呈现出相反的空间模式;粉粒含量东高西低,预测结果高值出现在柴达木盆地和南部玉树、果洛地区。环境变量的重要性分析表明,年降水量对表层土壤全氮、有机碳、pH空间分布模式具有控制性影响,夜间地表温度与表层土壤粉粒含量空间变异具有较强的协同关系。     

基于土壤参数的冬小麦产量预测模型

为了实现冬小麦的精细田间管理,研究了基于土壤参数的冬小麦产量预测模型。采用灰色理论对冬小麦土壤电导率 EC值,全氮含量,K+、NO3-以及土壤pH值等因子进行灰色关联度分析,结果表明土壤EC值与土壤全氮含量,K+以及土壤 pH 值的灰色关联度较高。在分析不同生长时期土壤 EC 值,全氮含量,K+、NO3-以及土壤pH值和产量间的相关系数的基础上,采用土壤EC值,全氮含量以及K+作为模型的输入,产量作为输出,建立了冬小麦产量预测BP神经网络（BPNN）模型;采用土壤EC值,全氮含量,K+,灰色关联度作为输入,建立了小麦产量的模糊最小二乘支持向量机（FLSSVM）预测模型。建模结果表明,BPNN 模型的预测决定系数达0.8237,验证决定系数达0.7367;FLSSVM模型的预测决定系数达0.8625,验证决定系数达0.8003。BP神经网络以及FLSSVM预测模型的精度都较高,可以用来评估作物产量,为精细农业变量处方管理提供理论与技术支持。     

基于范畴型变量和贝叶斯最大熵的土壤有机质空间预测

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。     

北京典型耕作土壤养分的近红外光谱分析

为研究土壤养分含量分布信息,以从北京郊区一块试验田采集的72个土壤样品为试验材料,应用傅里叶变换近红外光谱技术分析了土样的全氮、全钾、有机质养分含量和pH值。采用偏最小二乘法(PLS)对光谱数据与土壤养分实测值进行回归分析,建立预测模型,以模型决定系数(R2)、校正标准差(RMSECV)、预测标准差(RMSEP)和相对分析误差(RPD)作为模型精度的评价指标。结果表明,利用该模型与光谱数据对土壤全氮、全钾、有机质养分含量和pH值进行预测,结果与实测数据具有较好的一致性,最高决定系数R2达到0.9544。偏最小二乘回归方法建立的养分预测模型能准确地对北京地区褐土土质全氮、有机质、全钾和pH值4种养分进行预测。     

基于GS+和ArcGIS的蒲江县耕地土壤养分空间变异分析

基于GS+7.0、ArcGIS10.2建立蒲江县全氮、碱解氮、有效磷、速效钾、有机质5种耕地土壤养分含量的最优半方差函数模型,利用该模型进行了空间变异和普通Kriging空间插值分析。研究区土壤全氮、碱解氮、有机质均具有中等空间变异性,有效磷、速效钾表现为弱空间变异性。全氮、碱解氮含量高低受地形、成土母质、土壤类型等自然条件和施肥、种植模式、耕作措施等人为活动的共同影响,有机质更多地受人为活动的影响,而有效磷、速效钾则主要决定于人为活动的作用。5种土壤养分含量的空间插值图可直观地表达县域耕地土壤养分的含量水平及其分布状况,使用该图件可方便地提出相应的平衡施肥建议。     

黄土高原县域土壤养分空间变异特征及预测——以陕西省横山县为例

研究土壤属性空间变异及其分布特征与环境因子的关系,对于了解生态系统的过程具有重要意义。以横山县为例,采集了254个耕层(0~20 cm)土样,利用数字地形与遥感影像分析技术,提取相关地形与遥感指数,分析不同土地利用、地形条件下土壤养分空间变异及分布特征,并结合回归分析与地统计学进行空间分布预测。结果表明,不同土地利用类型其养分含量差异显著,水田和川地的有机质和全氮含量明显高于其他土地利用类型,而全磷含量以梯田最高。不同坡度分析表明,"0~3°"坡度等级有机质和全氮含量显著高于其他坡度等级;不同坡向土壤养分含量差异均不显著,但存在一个明显的趋势,即阴坡有机质和全氮含量整体上要较阳坡高。土壤有机质与高程H呈现负相关,与坡向的余弦值cosα、复合地形指数CTI、修正后的土壤调节植被指数MSAVI及湿度指数WI呈正相关。土壤全氮与相关环境因子的关系基本与有机质的一致。而土壤全磷与修正后的土壤调节植被指数MSAVI及湿度指数WI正相关。土壤有机质和全氮用相关环境变量的多元线性逐步回归模型拟合预测较好,而对于全磷,预测结果较差;回归—克里格预测有效地减小了残差,消除了平滑效应,与实测值较为接近,预测精度高于多元线性逐步回归预测。     

基于样点个体代表性的大尺度土壤属性制图方法

大空间尺度范围的土壤属性分布信息是陆地表层过程模拟的基础信息.基于野外样点进行空间插值是获得土壤属性空间分布信息的重要手段.现有的空间插值方法通常要求所用样点对研究区土壤属性空间分布规律具有良好的全局代表性.然而,受采样经费和野外采样条件的限制,所采集的样点往往难以全面地反映研究区土壤属性的空间分布规律.基于这样的样点用现有空间插值方法得到的土壤属性分布图通常精度较低,并且由样点全局代表性差带来的推测不确定性也无法得到度量.为了合理利用这些已采集的但全局代表性不好的样点,本文提出了基于样点“个体代表性”推测土壤属性空间分布并度量推测不确定性的方法.该方法在两点环境条件越相似、土壤属性就越相似的假设下,认为每一样点可以代表与其环境条件相似的地区,并且代表程度可以由两点的环境相似度度量;通过分析环境相似度计算推测不确定性,并以环境相似度为权重计算样点可代表地区的土壤属性值.将该方法应用于推测新疆伊犁地区土壤表层有机质含量,经验证,本文方法能够有效地利用全局代表性差的样点推测样点能够代表地区的土壤属性空间分布,并且所得的推测不确定性与预测残差呈现正向关系,能够有效地指示推测结果的可靠程度.     

张家港土壤表层铜含量空间预测的不确定性评价研究

定量土壤重金属含量空间分布预测结果的不确定性对于控制土壤空间数据的应用风险具有重要意义。采用序贯指示模拟方法对张家港土壤表层(0~15 cm)Cu含量的空间不确定性进行了定量评价。结果表明,张家港东部的集约农业区及中北部的冶金和电子类企业聚集区土壤表层Cu含量较高,其空间预测的波动性也较大,而该市南部土壤Cu含量较低,空间表达的波动性也较小;序贯指示模拟的模拟实现图像没有平滑效应,当给定一个Cu含量阈值时,序贯指示模拟可以定量任一空间位置Cu含量大于该阈值的概率,同时序贯指示模拟也可以评价描绘Cu含量大于该阈值的区域的空间不确定性。     

AN EXPERIMENTAL METHOD TO TEST THE EFFECTS OF LOCAL EMISSION REDUCTIONS DURING SUMMERSMOG

A method is presented that makes it possible to eliminate some of the meteorological influence on the change in air pollutant concentrations (mainly NO_χ and ozone) when considering the effects of local emission reductions. The relation of measurements outside the test area to values inside the area for normal conditions (i.e. defined weather type, no emission reduction in the test area) allows prediction of the values for the test area during emission reductions, but only if this relation shows a good correlation. This prediction includes the influence of meteorological factors that are distributed more or less homogeneously within an area of about 100 km × 100 km, which includes a large part of the meteorological influence. The comparison of predicted and measured values in the test area shows the effect of emission reduction, with the uncertainty of remaining meteorological effects, of course. The method is applied to the Heilbronn ozone experiment in summer 1994. Most precise results are obtained for ozone, while conclusions for nitrogen oxides are more uncertain.     

Emulation Engines: Choice and Quantification of Uncertainty for Complex Hydrological Models

Complex, mechanistic hydrological models can be computationally expensive, have large numbers of input parameters, and generate multivariate output. Model emulators can be constructed to approximate these complex models with substantial computational savings, making activities such as sensitivity analysis, calibration and uncertainty analysis feasible. Success in the use of an emulator relies on it making accurate and precise predictions of the model output. However, it is often unclear what type of emulation approach will be suitable. We present a comparison of reduced-rank, multivariate emulators built upon different ‘emulation engines’ and apply them to the Australian Water Resource Assessment System model. We examine first-order and second-order approaches which focus on specifying the mean and covariance, respectively. We also introduce a nonparametric approach for quantifying the uncertainty associated with the emulated prediction where this has bounded support. Our results demonstrate that emulation engines based on second-order approaches, such as Gaussian processes, can be computationally burdensome and may be comparable in performance to computationally efficient, first-order methods such as random forests.Supplementary materials accompanying this paper appear online.     

基于Elman动态神经网络的土壤墒情预测研究

土壤墒情是一个非线性、时空异质性和动态不确定过程,利用Elman动态神经网络对研究区临沂站和平邑站土壤水分含量进行了预测。结果表明,所建立的网络模型能够对土壤墒情进行成功模拟,预测的土壤水分值与观测值吻合得较好,模拟精度较高。临沂站和平邑站模拟土壤墒情的平均绝对误差分别为1.08%和1.07%,平均相对误差为10.2%和11.0%。Elman动态神经网络模型利用其独特的非线性、非凸性和适应时变特性的能力从时空变率复杂的土壤水分运移系统中找出一定的演变规律,为土壤水分预测提供了一种有效可靠的方法。为了更好地验证该方法的优越性,还需要更多的样本数据,更多的区域和更全面的敏感影响因素来验证,以及更深层次的理论研究和分析。     

Uncertainty analysis in near-surface soil moisture estimation on two typical land-use hillslopes

Purpose

Spatial prediction of near-surface soil moisture content (NSSMC) is necessary for both hydrologic modeling and land use planning. However, uncertainties associated with the prediction are always neglected and lack of quantitative analysis. The objective of this study was to investigate the influences of different sources of uncertainty on NSSMC estimation at two typical hillslopes (i.e., tea garden and forest).

Materials and methods

In this study, stepwise multiple regression models with terrain indices and soil texture were built to spatially estimate NSSMC on two typical land use hillslopes (tea garden and forest) at different dates. The uncertainties due to limited sample sizes used for developing regression models (uncertainty of model parameter), digital elevation model resolutions of 1, 2, 3, 4, and 5 m (uncertainty of terrain indices) and spatial interpolations of soil texture by kriging or cokriging with electromagnetic induction (uncertainty of soil texture), were investigated using bootstrap, resampling, and Latin hypercube sampling techniques, respectively.

Results and discussion

The accuracies of NSSMC predictions were acceptable for both tea garden (the Nash-Sutcliffe efficiency or NSE?=?0.34) and forest hillslopes (NSE?=?0.57). The model parameter uncertainty was more important on tea garden hillslope than on forest hillslope. A significant negative correlation (P?<?0.05) was observed between the model parameter uncertainty and the mean NSSMC of the hillslopes, indicating that the model parameter uncertainty was small when the hillslope was wet. The resolution uncertainty from digital elevation model had a minor effect on NSSMC predictions on both hillslopes. The texture uncertainty was weak on NSSMC estimations on tea garden hillslope. However, it was more important than the model parameter uncertainty on the forest hillslope.

Conclusions

Improving the regression model structure and the hillslope soil texture mapping are critical in the accurate spatial prediction of NSSMC on tea garden and forest hillslopes, respectively. This study presents techniques for analyzing three different uncertainties that can be used to identify the main sources of uncertainties in soil mapping.

     

Pedometric mapping of soil organic matter using a soil map with quantified uncertainty

This paper compares three models that use soil type information from point observations and a soil map to map the topsoil organic matter content for the province of Drenthe in the Netherlands. The models differ in how the information on soil type is obtained: model 1 uses soil type as depicted on the soil map for calibration and prediction; model 2 uses soil type as observed in the field for calibration and soil type as depicted on the map for prediction; and model 3 uses observed soil type for calibration and a pedometric soil map with quantified uncertainty for prediction. Calibration of the trend on observed soil type resulted in a much stronger predictive relationship between soil organic matter content and soil type than calibration on mapped soil type. Validation with an independent probability sample showed that model 3 out‐performed models 1 and 2 in terms of the mean squared error. However, model 3 over‐estimated the prediction error variance and so was too pessimistic about prediction accuracy. Model 2 performed the worst: it had the largest mean squared error and the prediction error variance was strongly under‐estimated. Thus validation confirmed that calibration on observed soil type is only valid when the uncertainty about soil type at prediction sites is explicitly accounted for by the model. We conclude that whenever information about the uncertainty of the soil map is available and both soil property and soil type are observed at sampling sites, model 3 can be an improvement over the conventional model 1.     

滨海盐土盐分空间分布的条件模拟和不确定性研究

以海涂围垦区为例,利用普通克立格插值法和序贯高斯条件模拟方法对土壤盐分的空间分布进行估值和模拟,并利用序贯指示条件模拟进行不确定性评价。结果表明,样区东南区域土壤盐分含量较高而北部区域盐分含量低。由普通克立格法得到的土壤盐分的空间分布整体比较连续,具有明显的平滑效应,估值结果数据的分布频率趋于平缓。序贯高斯条件模拟结果整体分布相对离散,突出了原始数据分布的波动性,其模拟结果数据的分布频率相对集中。预测精度上,序贯高斯条件模拟的预测结果精度相对较高。以评价标准204mSm-1作为土壤盐分含量的阈值进行的序贯指示条件模拟结果显示,在土壤盐分含量较高的东南部地区,超过阈值的概率超过75%,而北部很多盐分相对含量低的地区,超过阈值的概率值都低于25%。以超阈值概率为0.9、0.85和0.8三个值来选取盐分的高值风险区进行空间不确定评价,结果表明,联合概率比单点统计的概率更为严格,在划分较大范围高盐风险区域时,最好同时采用联合概率来进行信度评价。     

Predictive soil mapping with limited sample data

Existing predictive soil mapping (PSM) methods often require soil sample data to be sufficient to represent soil–environment relationships throughout the study area. However, in many parts of the world with only a limited quantity of soil sample data to represent the study area, this is still an issue for PSM application. This paper presents a method, named ‘individual predictive soil mapping’ (iPSM), which can make use of limited soil sample data for PSM. With the assumption that similar environmental conditions have similar soils, iPSM uses the soil–environment relationship at each individual soil sample location to predict soil properties at unvisited locations and estimate prediction uncertainty. Specifically, the environmental similarities of an unvisited location to a set of soil sample locations are used in a weighted average method to integrate the soil–environment relationships at sample locations for prediction and uncertainty estimation. As a case study, iPSM was applied to map soil organic matter (SOM) content (%) in the topsoil layer using two sets of soil samples. Compared with multiple linear regression (MLR), iPSM produced a more accurate SOM map (root mean squared error ( RMSE) 1.43, mean absolute error ( MAE) 1.16) than MLR (RMSE 8.54, MAE 7.34) the ability of the sample set to represent the study area is limited and achieved a comparable accuracy (RMSE 1.10, MAE 0.69) with MLR (RMSE 1.01, MAE 0.73) when the sample set could represent the study area better. In addition, the prediction uncertainty estimated by iPSM was positively related to prediction residuals in both scenarios. This study demonstrates that iPSM is an effective alternative when existing soil samples are limited in their ability to represent the study area and the prediction uncertainty in iPSM can be used as an indicator of its prediction accuracy.