A Fused Lasso Approach to Nonstationary Spatial Covariance Estimation

Spatial data are increasing in size and complexity due to technological advances. For an analysis of a large and diverse spatial domain, simplifying assumptions such as stationarity are questionable and standard computational algorithms are inadequate. In this paper, we propose a computationally efficient method to estimate a nonstationary covariance function. We partition the spatial domain into a fine grid of subregions and assign each subregion its own set of spatial covariance parameters. This introduces a large number of parameters and to stabilize the procedure we impose a penalty to spatially smooth the estimates. By penalizing the absolute difference between parameters for adjacent subregions, the solution can be identical for adjacent subregions and thus the method identifies stationary subdomains. To apply the method to large datasets, we use a block composite likelihood which is natural in this setting because it also operates on a partition of the spatial domain. The method is applied to tropospheric ozone in the US, and we find that the spatial covariance on the west coast differs from the rest of the country.     

Modeling Bromus diandrus Seedling Emergence Using Nonparametric Estimation

Hydrothermal time (HTT) is a valuable environmental index to predict weed emergence. In this paper, we focus on the problem of predicting weed emergence given some HTT observations from a distribution point of view. This is an alternative approach to classical parametric regression, often employed in this framework. The cumulative distribution function (cumulative emergence) of the cumulative hydrothermal time (CHTT) is considered for this task. Due to the monitoring process, it is not possible to observe the exact emergence time of every seedling. On the contrary, these emergence times are observed in an aggregated way. To address these facts, a new nonparametric distribution function estimator has been proposed. A bootstrap bandwidth selection method is also presented. Moreover, bootstrap techniques are also used to develop simultaneous confidence intervals for the HTT cumulative distribution function. The proposed methods have been applied to an emergence data set of Bromus diandrus.     

Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions

The evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. Selecting the wrong control strategy has costly economic and social consequences. The objective comparison of the means and variances of modeled air pollution concentrations with the ones obtained from observed field data is the common approach for the assessment of model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled air pollution distribution. Improving the ability of these numerical models to characterize high pollution events is of critical interest for air quality management.     

Estimation and Prediction in the Spatial Linear Model

Often in environmental monitoring studies interesting ecological factors will be observed at several locations repeatedly over time. Generally these space-time data are subject to a sequential spatial data analysis. In geostatistics, spatial data describing an environmental phenomenon like the pH value in precipitation at several locations are regarded as a realisation from a stochastic process. Component models are used to interpret the spatial variation of the process. Decomposing the spatial process into single components is based on the theory of linear models. Trend surface analysis is seen to be the geostatistical method for best linear unbiased estimation (BLUE) of the trend component, whereas universal kriging is equivalent to best linear unbiased prediction (BLUP) of the realisation of the spatial process. Furthermore trend surface analysis and universal kriging are shown to agree with the estimation of fixed effects and prediction of fixed and random effects in mixed linear models. Since estimation and prediction for spatial data result in different interpolations the differences are explained also graphically by example. The example uses acid-precipitation monitoring data. The extension of these spatial methods for application to space-time problems by combination with dynamic linear models is treated in the discussion.     

湿地生态系统服务功能及价值评估

生态系统服务功能是指生态系统与生态过程所形成及所维持的人类赖以生存的自然环境条件与效用。湿地生态服务功能不仅包括大量物质资源，而且具有巨大的环境调节功能和环境效益，在调节气候、控制土壤侵蚀，均化洪水，降解污染物、美化环境等方面发挥着重要作用。同时，湿地还是一个丰富的遗传基因库，特别是珍稀濒危物种的存续具有难以估量的重要价值。近年来生态系统服务功能已引起了人们的广泛重视，现已成为当前生态学与生态经济学研究的前沿课题。系统地分析了湿地生态系统服务功能及评价方法，并探讨生态服务功能淤其可持续发展研究的关系。     

云南省土壤有机碳储量估算及空间分布

根据云南省第二次土壤普查资料,采用土壤类型法估算了云南省主要土壤类型的有机碳(SOC)密度和储量,并对云南省土壤有机碳密度的空间分布差异和影响土壤有机碳储量的主要因子进行了分析。结果表明,云南省0-20 cm土层平均SOC密度为59.77 t/hm²,SOC储量为2.30×10⁹ t;0-100 cm土层平均SOC密度为159.95 t/hm²,SOC储量为6.15×10⁹ t,占全国储量的7.28%,占全球陆地生态系统SOC储量的0.41%;其中SOC储量占前4位的土壤类型为红壤、黄棕壤、赤红壤、棕壤,不同深度下4者之和约占云南省总储量的60%。在土壤有机碳密度空间分布上,SOC密度分布最高的区域为云南省西北部和东北部地区,其次是西部的横断山脉和东部的云南高原地区,而以紫色土为主的中北部地区SOC密度则最低。由于降雨量、温度、海拔和土地利用类型的共同影响,导致了区域内的SOC密度分布不均,其中降雨量、温度和海拔等自然因素是影响SOC密度分布的主要因子。     

Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999?C2008.     

漓江上游植被类型的水文特征与功能评价

漓江上游植被包括针叶林、阔叶林、竹林、灌丛和草丛等 5种类型。不同植被类型之间的水文特征与功能有较大差异 ,主要表现在对降水再分配、水量平衡和水文综合调节功能等的差别上。森林对河川径流的调节作用随森林水文功能的变化而变化。在评价各种植被类型水文功能的基础上 ,指出常绿阔叶林是亚热带水文功能最佳的植被类型。针对漓江面临的生态问题和漓江上游森林植被功能现状 ,提出相应的生态恢复对策     

基于TRMM数据的巴基斯坦降雨侵蚀力估算及空间降尺度模拟

中巴经济走廊是一带一路建设的旗舰项目。降雨侵蚀力是反映区域资源环境承载能力的重要指标,开展境外高精度降雨侵蚀力遥感估算研究对中巴经济走廊建设具有重要意义。基于TRMM(Tropical Rainfall Measuring Mission)卫星3B42降雨数据估算了巴基斯坦的降雨侵蚀力,并结合气象、植被、地形等因子构建了空间降尺度模型,将降雨侵蚀力的空间分辨率提高到1km。结果表明:(1)空间降尺度模型模拟的降雨侵蚀力与TRMM 3B42降雨数据估算的降雨侵蚀力显著相关(R~2=0.94),降雨侵蚀力的空间降尺度结果能更加详细地刻画巴基斯坦降雨侵蚀力的空间分布格局,尤其是地形起伏较剧烈的北部山区和俾路支东缘;(2)巴基斯坦境内2010年的年降雨侵蚀力平均值为524.15(MJ·mm)/(hm~2·h·a),分布范围为0.58~5 929.85(MJ·mm)/(hm~2·h·a),整体上呈现出东部高西部低的空间格局,且高值区主要分布在伊斯兰堡、门盖拉镇、锡亚尔科特-拉合尔-萨希瓦尔沿线,以及卡拉奇等城镇附近区域。     

线性非平稳型农田土壤水分信息空间变异性及预测研究

以中国东北春旱秋涝严重的西辽河灌区为背景，用线性地质统计学理论探索１ｍ深农田土壤层内非平稳型（含有漂移）水分信息的空间分布特征。在采样区土壤水分信息空间结构性揭示基础上，对土壤水分信息的空间漂移数据与观测尺度间的效应关系、区域化变量变差函数γ_Ｚ（ｈ）与剩余变差函数γ_R（ｈ）间的差异性、普通克立格法与泛克立格法用于待估区域水分信息值的差异性等进行了较深入研究。结果表明在最大滞后距范围（ｈ≤ａ）之内，两种估值方法得到的结果与实验值均较吻合，因而可用计算简单的普通克立格法对线性非平稳型土壤水分信息进行大面积区域墒情预测     

秦岭生态功能区森林水源涵养功能的经济价值估算

从水源涵养功能的角度,利用影子工程法和市场价值法等,估算了2010年秦岭生态功能区水源涵养的经济价值。结果得出,2010年秦岭生态功能区现有水源涵养功能总价值为3.17×1010元,相当于安康市2010年国内生产总值(GDP),比商洛市2010年国内生产总值高出5.70×109元。纵向比较,2010年秦岭水源涵养价值比2002年高出8.80×109元,平均每年增长近1.00×109元,在水源涵养方面功能改善是显著的。目前生态功能经济价值仅为一种潜在价值,为此提出通过加快建立与市场经济相适应的生态补偿机制,实现水源涵养功能经济价值向现实经济价值的转化。     

基于地理权重回归模型的土壤有机质空间预测

准确了解土壤有机质的空间分布是合理施肥的重要前提,也是水土流失控制及保护环境的重要基础。利用113个土壤有机质样点数据,以海拔高度、土壤侵蚀强度、土地利用、比值植被指数、样点至河流的欧氏距离、亚铁矿物指数及坡度为参考因子,来尝试利用GWR(Geographically Weighted Regression)模型探索多重因素作用下的土壤有机质空间分布,并通过与普通线性回归(ordinary least squares,OLS)相比较,来了解GWR模型的精度,进而进行了土壤有机质的空间制图,并对其制图效果进行了评价。结果表明,与OLS模型相比,GWR预测模型它能显著降低AIC(Akaike Information Criterion)值,较大程度地提高模型的决定系数,并有效地减少模型的回归残差值。从制图的总体效果看,GWR模型的预测结果与实测值的吻合程度要优于OLS模型。文章还对利用GWR模型进行回归时的样点数量、因子筛选及因子定量化等方面进行了相应的讨论。     

赣南地区土壤保持功能的时空变化特征

[目的]分析赣南地区土壤保持功能时空特征,为提升该区土壤保持能力和维护南方丘陵生态安全屏障提供科学依据。[方法]利用InVEST模型,计算2000,2010,2018年赣南地区不同土地利用类型、不同高程、不同坡度、不同县域的土壤保持强度,分析土壤保持强度与NDVI空间相关性和赣南地区土壤保持功能时空特征。[结果](1)2000—2018年,研究区耕地、灌林地、水域和未利用土地面积都处于减少趋势,城镇用地和农村居民地面积都有较大的增长。(2)土壤保持强度由189.93 t/(hm²·a)上升为190.50 t/(hm²·a),其中有林地土壤保持强度最高,并由259.85 t/(hm²·a)上升为262.03 t/(hm²·a)。(3)赣南地区土壤保持强度呈现四周高、中部低的分布格局,随海拔升高先升高再降低,随坡度升高而升高。(4)土壤保持强度与NDVI在双变量局部空间自相关分析中呈现显著正相关(p<0.01),高高集聚区在赣南地区北部、西部及南部;高低集聚区为中部地区(于都县);低低集聚区为高经...     

Kernel Intensity Estimation of 2-Dimensional Spatial Poisson Point Processes From k-Tree Sampling

To estimate the spatial intensity (density) of plants and animals, ecologists often sample populations by prespecifing a spatial array of points, then measuring the distance from each point to the k nearest organisms, a so-called k-tree sampling method. A variety of ad hoc methods are available for estimating intensity from k-tree sampling data, but they assume that two distinct points of the array do not share nearest neighbors. However, nearest neighbors are likely to be shared when the population intensity is low, as it is in our application. The purpose of this paper is twofold: (a) to derive and use for estimation the likelihood function for a k-tree sample under an inhomogeneous Poisson point-process model and (b) to estimate spatial intensity when nearest neighbors are shared. We derive the likelihood function for an inhomogeneous Poisson point-process with intensity λ(x,y) and propose a likelihood-based, kernel-smoothed estimator \(\hat{\lambda}(x,y)\) . Performance of the method for k=1 is tested on four types of simulated populations: two homogeneous populations with low and high intensity, a population simulated from a bivariate normal distribution of intensity, and a “cliff” population in which the region is divided into high- and low-intensity subregions. The method correctly detected spatial variation in intensity across different subregions of the simulated populations. Application to 1-tree samples of carnivorous pitcher plants populations in four New England peat bogs suggests that the method adequately captures empirical patterns of spatial intensity. However, our method suffers from two evident sources of bias. First, like other kernel smoothers, it underestimates peaks and overestimates valleys. Second, it has positive bias analogous to that of the MLE for the rate parameter of exponential random variables.     

ArcGIS空间分析功能在流域坡度分析中的应用

坡度对水土保持规划设计具有决定性的作用,是土地利用规划和治理措施规划首先要考虑的因素。用传统的方法计算流域坡度组成和平均坡度,效率低、精度低、误差大,而应用ArcGIS软件的空间分析功能分析流域坡度组成情况,不但效率高、精度高,而且所产生的分析结果具有唯一性,避免了人为因素对数据分析造成的影响。     

基于EOF的陕西省降水变化时空分异研究

应用经验正交函数法（empirical orthogonal function,EOF）对陕西省23 a来96个气象站点的数据进行EOF分解,分别计算出年降水场的特征向量分布和时间系数序列。结果表明,应用EOF方法可以很好地揭示降水场的时空分布特征,且对陕西省而言前3个特征向量揭示了3种典型的分布场,其累计贡献率达81.10%。陕西省降水的空间分布有3种典型的模式,即全局型、南北型和东西型。总体上3种场的特征向量分布值表现为南大北小,且高值中心都出现在陕西省的最南端,这说明南部虽然多雨,但年际变化量较大,北方少雨,但年际变化量较小。对特征向量所对应的时间系数分析发现,陕西省的降水场主要表现为6种类型。从23 a来的统计结果可以看出它与贡献率所反映的降水场的典型程度基本一致。     

高寒草地植被与土壤水源涵养功能的时空耦合关系

探究草地植被格局与土壤水源涵养功能的关系,对于评价和有效利用土壤及草地植被资源起着重要的作用。以黄河源区的高寒草地生态系统为研究对象,运用MODIS数据构建土壤水源涵养功能模型并探究其时空变化特征,明确草地植被的时空变化规律,以耦合协调度理论为基础探讨二者耦合关系的季节性变化和空间分布特征。结果表明：(1)2000—2021年土壤水源涵养功能指数和综合植被指数在各个季节上均呈增加趋势,22年的季平均值表现为夏季和秋季高于春季和冬季;(2)2000—2021年各个季节土壤水源涵养功能—植被(SWCF—VEG)的耦合协调度基本上均呈增加趋势,同时各土层深度耦合协调度值的空间分布差异明显,且在季节上表现为夏季>秋季>春季>冬季;(3)2000—2021年季节上各草地类型的SWCF—VEG耦合协调度均为增加趋势;各季节温性草原不同土层深度耦合协调度的平均值均最大,且夏季、秋季和冬季各草地类型的值均表现为10—20 cm>0—10 cm;各草地类型不同季节的耦合协调发展类型各异。研究结果为黄河源区草地植被与土壤水源涵养功能的耦合关系提供高效定量的时空分析方法,可为草地植被...     

长江经济带坡面土壤保持服务功能时空格局及其变化特征

[目的]分析长江经济带2010—2015年坡面土壤保持服务功能的时空格局及其变化特征。[方法]采用修正的通用水土流失方程(RUSLE)分析长江经济带剖面土壤保持服务功能变化。[结果]2010—2015年,长江经济带年均剖面土壤保持量为1.88×1010 t/a,单位面积土壤保持量为91.54t/(hm2·a)。森林、草地、农田生态系统的单位面积坡面土壤保持量依次为129.09,111.47,40.38t/(hm2·a)。5a间,长江经济带坡面土壤保持服务功能总体呈现上升态势。国家级优化开发区、重点开发区、农产品主产区和重点生态功能区的坡面土壤保持服务功能依次增强。[结论]森林、草地、农田生态系统的坡面土壤保持能力依次降低。主体功能区战略的实施对于提升长江经济带生态安全保障能力具有重要意义。     

水田生态系统空间格局演变规律与服务价值评估

在GIS技术的支持下,利用三峡库区重庆段1986、1995、2000和2007年4期土地利用资料及相关数据,分析了库区20多年来水田生态系统服务价值的变化情况。结果表明:1986—2007年,库区水田面积减少了40 128 hm2,间接服务价值呈减少趋势,但生态系统服务价值仍增加了12.12亿元。出现这种情况的原因是因价值系数增大造成的水田生态系统服务总价值变化大于因水田面积减少所产生的价值变化。由于重庆三峡库区特殊的地理条件,水田生态系统还具有巨大的环境功能价值和维持区域生态安全价值,因此保护和合理利用水田资源是我们义不容辞的责任。     

陕西省土壤有机碳密度空间分布及储量估算

土壤有机碳是反映土壤质量以及土壤缓冲能力的一个重要指标,对于温室效应与全球气候变化具有重要的控制作用。研究以陕西省第二次土壤普查数据为主要资料,结合多年的相关研究数据,对陕西省土壤碳密度及储量进行估算,并分析了陕西省土壤有机碳密度的空间分布特征。结果表明:榆林、延安地区北部以及商洛等地区分布的风沙土、黄绵土、石质土等土壤有机碳密度最小,小于4 kg m-2;陕西南部地区的土壤有机碳密度主要在4~9 kg m-2之间;咸阳地区及渭南地区土壤有机碳密度也较高,主要在9~12 kg m-2之间;关中地区渭河及其两岸、秦岭部分山区土壤有机碳密度最大,最高达30 kg m-2以上;陕西省平均土壤有机碳密度为6.87 kg m-2,在全省的22个土壤类型中,有13个土壤有机碳密度低于全国平均水平,占全省土壤总面积的77.42%,全省土壤有机碳储量约为1.41×1012kg。本研究为我国土壤碳库和碳平衡的研究提供基础数据。