黄土高原沟壑区小流域土壤养分空间变异特征及其影响因素

了解表层土壤有机质与全量养分的空间变异规律及其影响因素,能够为黄土高原生态脆弱带的土壤养分管理提供参考。基于王东沟的93个野外采样点,综合多种地统计学方法,分析了土壤养分的空间变异特征及其影响因素。结果表明:研究区表层土壤养分含量处于中等水平,空间变异大小依次为全磷>全氮>有机质>全钾,均由结构性因素主导;有机质、全氮较全磷、全钾变异尺度小、空间自相关性弱、空间复杂程度高。有机质、全氮的分布格局为南高北低,主要影响因素包括海拔、坡度、曲率和土地利用;全磷呈现相反的北高南低分布,海拔、坡度、土地利用、土壤机械组成和人类活动对其影响较大;全钾则为内部高四周低,分布较破碎,坡向和土壤机械组成作用较强。据此建立了9个环境因子与土壤养分之间的回归预测模型,以期为研究区土壤资源的可持续利用提供数据支持。     

重庆市酉阳县南部农田土壤重金属污染评估及来源解析

为了探究矿业活动及地质背景对土壤环境的影响,在重庆市酉阳县南部农用地中采集了土壤组合样品156件,分析了土壤中重金属的含量及pH,基于地统计法、单因子指数法及内梅罗指数法对土壤重金属污染状况进行了评价,利用主成分分析/绝对主成分分数（PCA/APCS）受体模型讨论土壤重金属的来源.结果表明,土壤Cd污染呈面状分布,而土壤Hg主要表现为点状污染,土壤中-重度污染主要分布在涂市乡、麻旺镇及龙潭镇三镇交界处,土壤以酸性为主,出现农作物污染的风险较高;指示克里格法评价结果显示,三镇交接处及涂市乡北部有较高的土壤污染概率;主成分分析/绝对主成分分数（PCA/APCS）受体模型分析结果显示,土壤As、Cd、Cr及Ni的来源主要受到地质背景的控制,土壤Hg与Pb、Zn主要受到矿业活动的控制,土壤Cu同时受到地质背景和矿业活动的影响;此外,农业活动也是土壤As、Cd、Pb、Cu及Zn等元素来源之一.研究区土壤中-重度污染主要是由矿业活动引起的,而地质背景导致的土壤重金属污染主要为轻度污染.该研究可为典型区域土地安全利用及土壤污染防治提供理论基础.     

黑土区小流域土壤氮素空间分布及主控因素研究

在经典统计学和地统计学的基础上,结合"3S"技术,对黑土区海沟河小流域土壤表层(0~20 cm)中全氮(TN)、碱解氮(AN)的空间变异、分布特征及主控因素进行深入探讨.结果表明:海沟河小流域土壤TN含量处于较高水平,AN含量为中等水平;TN和AN的变程分别为900 m和1282 m,其空间变异均受地形、成土母质等结构性要素影响较大,在东-西(E-W)方向的空间变异相对剧烈;TN含量与坡度等地形指标显著性相关,AN含量与高程等地形要素显著性相关;回归协同克里格插值结果显示,TN自东向西呈现"高-低"交替的带状格局,与土地利用方式在东西方向上的演替相近,AN的高值在东部山区,低值在中部旱地分布集中的区域,呈现"两边高,中间低"的特征;水系、居民点等环境要素对TN和AN的空间分布存在明显的作用距离,土地利用方式及坡位对TN和AN含量分布影响显著,且存在较大差异.     

吉林省农田黑土中Cd、Pb、As含量的空间分布特征

利用地统计学方法研究了吉林省黑土区耕层土壤(0～20cm)中Cd、Pb、As含量的空间结构及其分布特征.结果表明,研究区土壤Pb含量属中等程度的空间相关,而Cd、As含量属空间弱相关.3种元素含量的空间变异主要受人为的随机性因素的影响.利用半方差函数的分析结果经普通克立格插值得到了3种元素含量的空间分布图.结果显示,在空间分布上,受不同污染源的影响,土壤Cd、Pb、As含量表现出不同的分布特征.土壤Cd含量较高的地区主要分布于煤矿区周围;土壤Pb含量高的样点主要分布于城郊及公路两侧;煤矿区周围及城郊农田土壤中As含量相对较高.     

Interpolating SRTM Elevation Data to Higher Resolution to Improve Hydrologic Analysis

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) has been a valuable resource for hydrological analysis, providing elevation data at a consistent resolution on a near‐global scale. However, its resolution (three arc‐second or 90 m) is sometimes too low to obtain the desired level of accuracy and precision for hydrologic analysis. We evaluated the performance of several methods for interpolating SRTM three arc‐second data to a 30‐m resolution grid to better represent topography and derive terrain characteristics of the landscape. STRM data were interpolated to 30‐m DEMs on a common grid using spline, inverse distance weighting (IDW), kriging (KR), natural neighbor methods, and cubic convolution (CC) resampling. Accuracy of the methods was assessed by comparing interpolated and resampled 30‐m grids with the reference data. Slope, aspect, sinks, and stream networks were derived for the 30‐m grids and compared on a cell‐by‐cell basis to evaluate their performance in reproducing the derivatives. The comparisons identify spline and KR as the most accurate interpolation methods, of which spline is preferred because of its relative simplicity. IDW provided the greatest bias in all methods with artifacts evident in slope and aspect maps. The performance of CC projection directly to a 30‐m resolution was comparable to spline interpolation, thus is recommended as the most convenient method for interpolating SRTM to a higher resolution.     

基于遥感和地统计分析的滑坡监测研究评述

在滑坡监测过程中,常规技术方法往往会受到气候与地形的限制,且连续观测能力较差,实施困难较大.而遥感技术可以克服观测条件的限制,并可针对滑坡不同阶段实行监测.此外,地统计分析方法可充分利用遥感数据本身具有的空间相关性,可以提高影像信息提取的准确性.因此,遥感与地统计方法集成用于滑坡监测具有广阔的应用前景.     

塞罕坝华北落叶松人工林生产力及其空间分布预测

准确预测森林立地生产力是进行高效森林经营的关键。立地指数是森林生产力可靠的评价指标之一。基于地形、气候和土壤因子以及220块样地解析木数据,采用回归克里格（RK）模型对塞罕坝机械林场华北落叶松（Larix principis-rupprechtii）人工林立地指数（SI）进行空间插值预测,并分析了不同半变异函数对RK模型精度的影响。拟合结果表明：基于高斯半变异函数的RK模型精度优于球状和指数RK模型,且具有较小的残差（RMSE=0.82 m,MAE=0.66 m）,表明高斯RK模型具有很强的预测SI能力;高斯半变异函数分析表明研究区华北落叶松人工林SI存在较强的空间自相关性,且在724.89 m变程内差异显著;影响华北落叶松立地指数分布的主要环境因子有土壤全氮、土壤pH、夏季降水量和春季降水量;立地生产力较高区域一般分布在春季降水适中、夏季降水较多、土壤为中性及偏酸性且全氮含量较高的东南部地区,占研究区总面积的32.00%,而在春、夏季降水量少或者春季降水量过多、土壤全氮含量过低且偏碱性的北部边缘地区立地生产力较低,仅占研究区总面积的8.90%。研究区土壤、气候因子与树木生长习性共同决定了华北落叶松人工林生产力的分布格局。通过降低土壤酸碱度和适当施加氮肥等措施,可以提高华北落叶松人工林生产力。     

Spatial interpolation of air temperature using environmental context: Application to a crop model

The air temperature is one of the main input data in models for water balance monitoring or crop models for yield prediction. The different phenological stages of plant growth are generally defined according to cumulated air temperature from the sowing date. When these crop models are used at the regional scale, the meteorological stations providing input climatic data are not spatially dense enough or in a similar environment to reflect the crop local climate. Hence spatial interpolation methods must be used. Climatic data, particularly air temperature, are influenced by local environment. Measurements show that the air above dry surfaces is warmer than above wet areas. We propose a method taking into account the environment of the meteorological stations in order to improve spatial interpolation of air temperature. The aim of this study is to assess the impact of these corrected climatic data in crop models. The proposed method is an external drift kriging where the Kriging system is modified to correct local environment effects. The environment of the meteorological stations was characterized using a land use map summarized in a small number of classes considered as a factor influencing local temperature. This method was applied to a region in south-east France (150×250 km) where daily temperatures were measured on 150 weather stations for two years. Environment classes were extracted from the CORINE Landcover map obtained from remote sensing data. Categorical external drift kriging was compared to ordinary kriging by a cross validation study. The gain in precision was assessed for different environment classes and for summer days. We then performed a sensitivity study of air temperature with the crop model STICS. The influence of interpolation corrections on the main outputs as yield or harvest date is discussed. We showed that the method works well for air temperature in summer and can lead to significant correction for yield prediction. For example, we observed by cross validation a bias reduction of 0.5 to 1.0°C (exceptionally 2.5°C for some class), which corresponds to differences in yield prediction from 0.6 to 1.5 t/ha.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).     

Application of geostatistical methods to arsenic data from soil samples of the Cova dos Mouros mine (Vila Verde-Portugal)

A total of 286 soil samples were collected in the Cova dos Mouros area. All samples were dry sieved into the <200 mesh size fraction and analysed for Fe, Cu, Zn, Pb, Co, Ni, Bi and Mn by atomic absorption spectrometry (AAS) and for As, Se, Sb and Te by atomic absorption spectrometry-hydrid generation (AAS-HG). Only the results of arsenic are discussed in this paper although the survey was extended to all analysed chemical elements. The purpose of this study was to make a risk probability mapping for arsenic that would allow better knowledge about the vulnerability of the soil to arsenic contamination. To achieve this purpose, the initial variable was transformed into an indicator variable using as thresholds the risk-based standards (intervention values) for soils, as proposed by [Swartjes 1999. Risk based assessment of soil and groundwater quality in the Netherlands: Standards and remediation. J. Geochem. Explor.73 1–10]. To account for spatial structure, sample variograms were computed for the main directions of the sampling grid and a spherical model was fitted to each sample variogram (arsenic variable and indicator variables). The parameters of the spherical model fitted to the arsenic variable were used to predict arsenic concentrations at unsampled locations. A risk probability mapping was also done to assess the vulnerability of the soil towards the mining works. The parameters of the spherical model fitted to each indicator variable were used to estimate probabilities of exceeding the corresponding threshold. The use of indicator kriging as an alternative to ordinary kriging for the soil data of Cova dos Mouros produced unbiased probability maps that allowed assessment of the quality of the soil.