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1.
Chile has more than half of the temperate forests in the southern hemisphere. These have been included among the most threatened eco-regions in the world, because of the high degree of endemism and presence of monotypic genera. In this study, we develop empirical models to investigate present and future spatial patterns of woody species richness in temperate forests in south-central Chile. Our aims are both to increase understanding of species richness patterns in such forests and to develop recommendations for forest conservation strategies. Our data were obtained at multiple spatial scales, including field sampling, climate, elevation and topography data, and land-cover and spectrally derived variables from satellite sensor imagery. Climatic and land-cover variables most effectively accounted for tree species richness variability, while only weak relationships were found between explanatory variables and shrub species richness. The best models were used to obtain prediction maps of tree species richness for 2050, using data from the Hadley Centre’s HadCM3 model. Current protected areas are located far from the areas of highest tree conservation value and our models suggest this trend will continue. We therefore suggest that current conservation strategies are insufficient, a trend likely to be repeated across many other areas. We propose the current network of protected areas should be increased, prioritizing sites of both current and future importance to increase the effectiveness of the national protected areas system. In this way, target sites for conservation can also be chosen to bring other benefits, such as improved water supply to populated areas. 相似文献
2.
Aki Niemi Carmen Fernández 《Journal of Agricultural, Biological & Environmental Statistics》2010,15(3):327-345
This paper develops a Bayesian approach for spatial inference on animal density from line transect survey data. We model the
spatial distribution of animals within a geographical area of interest by an inhomogeneous Poisson process whose intensity
function incorporates both covariate effects and spatial smoothing of residual variation. Independently thinning the animal
locations according to their estimated detection probabilities results into another spatial Poisson process for the sightings
(the observations). Prior distributions are elicited for all unknown model parameters. Due to the sparsity of data in the
application we consider, eliciting sensible prior distributions is important in order to get meaningful estimation results.
A reversible jump Markov Chain Monte Carlo (MCMC) algorithm for simulation of the posterior distribution is developed. We
present results for simulated data and a real data set of minke whale pods from Antarctic waters. The main advantages of our
method compared to design-based analyses are that it can use data arising from sources other than specifically designed surveys
and its ability to link covariate effects to variation of animal density. The Bayesian paradigm provides a coherent framework
for quantifying uncertainty in estimation results. 相似文献
3.
R. S. Fagundes M. A. Uribe-Opazo M. Galea L. P. C. Guedes 《Journal of Agricultural, Biological & Environmental Statistics》2018,23(2):276-296
This article studies the dependence of spatial linear models using a slash distribution with a finite second moment. The parameters of the model are estimated with maximum likelihood by using the EM algorithm. To avoid identifiability problems, the cross-validation, the Trace and the maximum log-likelihood value are used to choose the parameter for adjusting the kurtosis of the slash distribution and the selection of the model to explain the spatial dependence. We present diagnostic techniques of global and local influences for exploring the sensibility of estimators and the presence of possible influential observations. A simulation study is developed to determine the performance of the methodology. The results showed the effectiveness of the choice criteria of the parameter for adjusting the kurtosis and for the selection of the spatial dependence model. It has also showed that the slash distribution provides an increased robustness to the presence of influential observations. As an illustration, the proposed model and its diagnostics are used to analyze an aquifer data. The spatial prediction with and without the influential observations were compared. The results show that the contours of the interpolation maps and prediction standard error maps showed low changes when we removed the influential observations. Thus, this model is a robust alternative in the spatial linear modeling for dependent random variables. Supplementary materials accompanying this paper appear online. 相似文献
4.
Jonathan Acosta Felipe Osorio Ronny Vallejos 《Journal of Agricultural, Biological & Environmental Statistics》2016,21(3):407-425
This paper provides a framework for estimating the effective sample size in a spatial regression model context when the data have been sampled using a line transect scheme and there is an evident serial correlation due to the chronological order in which the observations were collected. We propose a linear regression model with a partially linear covariance structure to address the computation of the effective sample size when spatial and serial correlations are present. A recursive algorithm is described to separately estimate the linear and nonlinear parameters involved in the covariance structure. The kriging equations are also presented to explore the kriging variance between our proposal and a typical spatial regression model. An application in the context of marine macroalgae, which motivated the present work, is also presented. 相似文献
5.
Jeroen Provoost Lucas Reijnders Frank Swartjes Jan Bronders Piet Seuntjens Johannes Lijzen 《Journal of Soils and Sediments》2009,9(1):62-73
Background, aim and scope During the last decade, soil contamination with volatile organic contaminants (VOC) received special attention because of
their potential to cause indoor air problems. Moreover, research has shown that people spend 64% to 94% of there time indoors;
therefore, the indoor air quality is of a primary importance for exposure to VOC. Human health risks to VOC—in cases of soil
contamination—are often dominated by the exposure route ‘inhalation of indoor air’. Exposure is often a result of vapour transport
from the soil or groundwater to the indoor air of the building. Within human health risk assessments, a variety of algorithms
are available that calculate transfer of soil gas to the indoor air. These algorithms suffer from a relatively high uncertainty
due to a lack of representation of spatial and temporal variability. For such an application, these algorithms need to be
further verified empirically against field observations so that they can be sufficiently reliable for regulatory purposes.
This paper presents the accuracy for seven algorithms by using observed and predicted soil and indoor air concentrations from
three sites, where the groundwater had been contaminated with aromatic and chlorinated VOC.
Materials and methods The algorithms for vapour intrusion that are frequently used in European countries were included in this study and were Vlier–Humaan
(Flanders), CSoil (Netherlands), VolaSoil (Netherlands), Johnson & Ettinger (USA), Risc (United Kingdom), and the dilution
factor (DF) algorithms from Sweden and Norway. Three sites were investigated in more detail and samples were taken synoptically
from the groundwater, soil and indoor air on four occasions. On the petroleum sites, the aromatic hydrocarbons benzene, toluene,
ethylbenzene and xylenes were analysed and, on the dry cleaning sites, the chlorinated hydrocarbons tetrachloroethylene, trichloroethylene
and cis 1,2-dichloroethene. To increase spatial resolution, measurements in groundwater and soil air were taken in three different
zones at each site, in the close proximity of or in the building. During sampling, several relevant soil properties were measured
like the bulk density, water and air filled porosity, soil temperature and depth of the groundwater. Also, building properties
like the dimensions of the building and the quality of the floor were registered. The seven algorithms were applied to compare
that observed with the predicted concentrations in soil and indoor air. The groundwater concentrations were used as a source
contamination. The results from the algorithms were compared by using performance criteria to assess the accuracy of each
algorithm.
Results All calculations are presented in a box plot that contains the predicted soil or indoor air versus the observed concentrations.
Results from the applied criteria are presented for each algorithm.
Discussion Differences between predictions and observations were up to three orders of magnitude and can be partially related to the
amount of parameters included in each algorithm and the mathematical concept used. For example, the inclusion or exclusion
of a capillary fringe or temperature correction for the Henry constant: it is not clear why all algorithms tend to over-predict
the soil air concentration. The prediction mostly starts with the calculation of a soil air concentration related to the Henry
constant, followed by diffusive and/or convective transport to the soil surface and zone of influence around the building
foundation. Further research is needed to investigate the over-predictions and the use of the Henry constant to calculate
the soil air concentration should be reviewed.
Conclusions The algorithms with the highest accuracy were the Johnson and Ettinger and the Vlier–Humaan algorithms. The DF algorithms
from Sweden and Norway resulted in higher over- and underpredictions than others. Results for the indoor air showed that all
the algorithms calculate high and low concentrations in the indoor air when compared to observations. The algorithms with
the highest accuracy were JEM, Vlier–Humaan and CSoil. The DF algorithm from Norway calculated concentrations that were frequently
higher than observed concentrations and the Swedish DF algorithm showed frequent higher and lower concentration than observed.
The conservatism of the most accurate algorithms is sufficient for regulatory purposes, and they can trigger an integrated
programme of field observations (monitoring) or/and modelling.
Recommendations and perspectives The dataset used for this paper was derived from three sites with groundwater contamination and further verification of these
algorithms should be done for other sites that have a vadose zone contamination. 相似文献
6.
Incorporating connectivity into reserve selection procedures 总被引：1，自引：0，他引：1
Robert A. Briers 《Biological conservation》2002,103(1):77-83
Methods for selecting sites to be included in reserve networks generally neglect the spatial location of sites, often resulting in highly fragmented networks. This restricts the possibility of dispersal between sites, which for many species may be essential for long-term persistence. Here I describe iterative reserve selection algorithms which incorporate considerations of reserve connectivity and evaluate their performance using a data set for macroinvertebrates in ponds. Methods where spatial criteria were only invoked when ties between sites occurred did not perform significantly better than a simple greedy algorithm in terms of reserve connectivity. An algorithm based on a composite measure of species added and changes in reserve connectivity produced a reserve network with higher connectivity, but needed more sites to represent all species. A trade-off between connectivity and efficiency may be inevitable, but the costs in terms of efficiency may be justified if long-term persistence of species is more likely. 相似文献
7.
基于改进空间引力模型的农作区遥感影像亚像元定位 总被引：1，自引：1，他引：0
针对空间引力模型在遥感影像亚像元定位中存在的不足,该文提出了一种基于改进空间引力模型的农作区遥感影像亚像元定位方法。研究首先分析了原始空间引力模型运行速度慢、定位精度低的原因。然后,分别改进了空间引力模型的初始化算法和优化算法,改进后的初始化算法使亚像元更具空间相关性;改进后的优化算法在初始化的基础上显著提高了模型的运行速度和定位精度。最后,以吉林省镇赉县农作区SPOT-5影像为例,在原图像空间分辨率退化4倍的尺度下进行遥感影像亚像元定位试验。结果表明,改进模型与原始模型相比亚像元定位精度提高了6.67个百分点,运行速度提高了10.69倍。因此,改进空间引力模型在地物类别相对复杂的农作区遥感影像亚像元定位中,可以更好的突破空间分辨率的限制,为确保农作物种植面积提取、区域产量遥感估测提供有力支撑。 相似文献
8.
Comparing sampling needs for variograms of soil properties computed by the method of moments and residual maximum likelihood 总被引：2，自引：0，他引：2
It has been generally accepted that the method of moments (MoM) variogram, which has been widely applied in soil science, requires about 100 sites at an appropriate interval apart to describe the variation adequately. This sample size is often larger than can be afforded for soil surveys of agricultural fields or contaminated sites. Furthermore, it might be a much larger sample size than is needed where the scale of variation is large. A possible alternative in such situations is the residual maximum likelihood (REML) variogram because fewer data appear to be required. The REML method is parametric and is considered reliable where there is trend in the data because it is based on generalized increments that filter trend out and only the covariance parameters are estimated. Previous research has suggested that fewer data are needed to compute a reliable variogram using a maximum likelihood approach such as REML, however, the results can vary according to the nature of the spatial variation. There remain issues to examine: how many fewer data can be used, how should the sampling sites be distributed over the site of interest, and how do different degrees of spatial variation affect the data requirements? The soil of four field sites of different size, physiography, parent material and soil type was sampled intensively, and MoM and REML variograms were calculated for clay content. The data were then sub-sampled to give different sample sizes and distributions of sites and the variograms were computed again. The model parameters for the sets of variograms for each site were used for cross-validation. Predictions based on REML variograms were generally more accurate than those from MoM variograms with fewer than 100 sampling sites. A sample size of around 50 sites at an appropriate distance apart, possibly determined from variograms of ancillary data, appears adequate to compute REML variograms for kriging soil properties for precision agriculture and contaminated sites. 相似文献
9.
北京地区改进SYNTAM算法反演AOT有效性验证 总被引：1，自引：1，他引：0
本文利用了改进的SYNTAM算法反演了北京地区气溶胶光学厚度,并利用AERONET站点观测值进行验证比对分析：（1）基于设定的卷积窗口统计计算反演的相应气溶胶光学厚度参量值;（2）利用匹配对应时间尺度范围的AERONET站点值进行相关性分析,合理评估双星协同反演算法的有效性。结果表明：在北京地区该算法具有良好的适用性和可用性,反演所得大气气溶胶光学厚度与地面监测值具有高度的一致性,其对该区大气质量环境监测和农业生产光照胁迫性分析具有很好的实用价值。 相似文献
10.
联合Terra/Aqua MODIS多角度多光谱数据反演冬小麦叶面积指数 总被引：1，自引：1，他引：0
Terra与Aqua双星搭载的MODIS传感器可实现每日上下午分别对同一地点观测一次,并且由于卫星轨道漂移形成累积连续多天的多角度观测特点,加上多通道的光谱响应,极大地丰富了地表目标的观测信息,为LAI等地表参数的实时准确反演提供了可能。该文利用MODIS双星高质量的连续多天的多波段地表反射率数据,结合PROSAIL(PROSPECT+SAIL,properties spectra+scattering by arbitrarily inclined leaves)模型和查找表方法反演冬小麦LAI,并与MODIS LAI产品及野外采样点实测LAI对比,结果表明,联合双星高质量的多角度多波段数据能够较准确反演冬小麦LAI,其反演结果无论从空间分布还是时序变化特征来讲,较MODIS LAI产品更符合实际情况,也更接近地面实测值。该文的研究为充分利用MODIS数据的角度和光谱信息反演小麦等农作物的LAI提供了一定的借鉴。 相似文献
11.
As environmental monitoring data are collected successively in time, the data are suitable for sequential analysis. An earlier article proposed a refined sequential probability ratio test (SPRT) to test against a minimal relevanttrend, assuming no serial correlations and without modeling the spatial covariance matrix. As the model parameters are unknown in advance, a minimal number of observations(n min) is required for estimation prior to analysis. Leaving the spatial covariance matrix unstructured, n min increases if the number of sampling locations increases. Therefore, assumptions on the spatial covariance matrix are proposed, thereby reducing the number of nuisance parameters, thus reducingn min. This article studies. three simple types of spatial covariance matrix structures and derives an adjusted SPRT for each of these types. Furthermore, we examine the robustness against deviations from the assumed spatial covariance matrix structure. Simulation studies show that adjusted SPRTs can be derived rather easily and that they are in general robust against deviations from the assumed type of spatial covariance matrix. Sequential analysis of simulated data, which are based on monitoring data of bats in the Netherlands, illustrates the use of one of the derived SPRTs. 相似文献
12.
苹果采摘路径规划最优化算法与仿真实现 总被引：3，自引：0，他引：3
采摘路径规划对苹果采摘机器人的工作效率有很大的影响,为了提高苹果采摘机器人采摘效率,研究了采摘路径规划最优化方法。将苹果采摘的路径规划问题转化为三维的旅行商问题进行求解,结合图像识别得到的苹果位置特征,提出了有限域信息素自适应更新的改进蚁群算法,避免了基本蚁群算法求解过程中的早熟和局部收敛的问题,研究了三维模型的建模和驱动方法。试验结果表明将蚁群算法用于解决苹果采摘路径规划问题,当苹果数量达到250个时,改进蚁群算法迭代次数是基本算法的25.3%,而搜索到的最优路径是其94.3%,可见改进算法在搜索次数和最优结果上都有明显的优势。本研究为苹果采摘机器人采摘路径规划的提供理论参考。 相似文献
13.
由于现有的用于农业作物生长监测数据的特征优化方法—局部保持投影(locality preserving projection,LPP)只保留局部信息,同时存在未考虑样本类别信息导致特征提取时误分类,准确率与数据优化效率并不理想。针对上述问题,提出了改进型LPP方法,并将其用于作物生长特征的优化。首先将样本利用二维主成分分析(two-dimensional principal component analysis,2DPCA)进行初步降维,保留原样本数据中的整体空间信息;然后提出优化的2类子图—聚集子图和分离子图,用来描述不同类别数据之间的关联信息;然后提出优化的2类子图对不同类别数据间的远近关系进行描述;最后采用改进型LPP算法,将数据进一步投影到低维空间,提取样本的局部信息,完成样本特征优化。试验分析表明,改进型LPP具有很好的适应性,最高支持向量机(support vector machine,SVM)分类准确率能够达到96%以上,使精度达到最高的最优维数比主成分分析(principal component analysis,PCA)和二维主成分分析2种算法降低了25%以上,同时算法运行效率比PCA与2DPCA算法提升32.4%与8.3%,整体性能比基本LPP算法更为优越,能够适应农作物多维数据的优化处理。研究结果为现代精准农业信息监测过程中的数据处理与分析提供了参考。 相似文献
14.
基于最大熵模型的玉米冠层LAI升尺度方法 总被引：1，自引：1，他引：0
叶面积指数(leaf area index,LAI)是表达农作物冠层结构的关键参数之一,准确获取LAI对于农作物长势监测、估产等研究具有非常重要的意义。由于地物空间复杂性、数据源的不同以及遥感反演模型的非线性,LAI的反演结果会存在尺度效应,因此需要进行尺度转换研究。理想的升尺度转换应该只是数据空间分辨率的降低,而数据内在信息应保存到低分辨率中。最大熵(maximum entropy,Max Ent)模型是基于多种环境因子的广义学习模型,对分析因子的空间分布具有较高的估算精度,因此,该研究利用最大熵模型进行玉米冠层LAI升尺度方法研究,从而将野外实测的LAI点数据扩展到空间分辨率为30 m的面数据,所使用的数据源是Landsat8 OLI遥感影像、气象数据和野外样点上测量的LAI数据。研究结果表明:利用最大熵模型升尺度转换结果与实测LAI相比,R2为0.601、RMSE为0.898,说明两者的相关性较高;由于玉米冠层叶片之间的相互遮挡,导致整体结果偏低,但偏低误差在可接受范围内。因此,Max Ent模型可用于农作物LAI点数据到面数据的升尺度转换。 相似文献
15.
David P. Larsen Anthony R. Olsen Donald L. Stevens 《Journal of Agricultural, Biological & Environmental Statistics》2008,13(3):243-254
The need for aquatic resource condition surveys at scales that are too extensive to census has increased in recent years.
Statistically designed sample surveys are intended to meet this need. Simple or stratified random sampling or systematic survey
designs are often used to obtain a representative set of sites for data collection. However, such designs have limitations
when applied to spatially distributed natural resources, like stream networks. Stevens and Olsen proposed a design that overcomes
the key limitations of simple, stratified random or systematic designs by selecting a spatially balanced sample. The outcome
of a spatially balanced sample is an ordered list of sampling locations with spatial distribution that balances the advantages
of simple or stratified random samples or systematic samples. This approach can be used to select a sample of sites for particular
studies to meet specific objectives. This approach can also be used to select a “master sample” from which subsamples can
be drawn for particular needs. At the same time, these individual samples can be incorporated into a broader design that facilitates
integrated monitoring and data sharing. 相似文献
16.
基于四维变分和集合卡尔曼滤波同化方法的冬小麦单产估测 总被引：1，自引：5，他引：1
为了通过数据同化方法提高冬小麦的估产精度,以陕西省关中平原为研究区域,采用标定的CERES-Wheat模型模拟8个典型样点冬小麦整个生育期的叶面积指数(LAI),通过四维变分(4DVAR)和集合卡尔曼滤波(En KF)2种同化算法同化CERES-Wheat模型模拟的LAI和遥感数据反演的LAI,获得单点尺度的LAI同化数据,将单点尺度的LAI同化值扩展到区域尺度,对两种同化方法的单点尺度和区域尺度的同化结果进行对比与分析。结果表明,两种同化方法均能综合遥感反演LAI和模型模拟LAI的优势,使LAI同化值更符合冬小麦LAI的实际变化规律;在单点尺度和区域尺度上,En KF-LAI均更能反映关中平原冬小麦的实际生长状况。采用En KF-LAI构建关中平原冬小麦估产模型估测2008年和2014年的冬小麦单产,通过实测单产对估产模型进行验证,结果表明,2008年样点估测单产与实测单产的相对误差均小于15%,部分县估测单产与实测单产的相对误差均小于10%;与2014年模拟单产与实测单产间的相对误差相比,估测单产与实测单产间的相对误差降低0.57%~9.30%,RMSE降低217 kg/hm2,其中,8个样点的估产精度达到94%以上,表明组合估产模型的估产精度较高。 相似文献
17.
《Geoderma》2005,124(3-4):235-252
Efficient intervention to control soil erosion in rural tropical landscapes requires accurate models for predicting the spatial location and intensity of degradation. The Universal Soil Loss Equation (USLE) has commonly been applied for spatial erosion risk assessment in the tropics, but has rarely been validated using ground observations of soil degradation. As with any empirical model, application in new regions requires calibration before results are used for decision support. We evaluated USLE effectiveness for predicting erosion in a small watershed in western Kenya based on 420 georeferenced ground observations of ordinal erosion class (three categories) systematically collected from throughout the basin. Relativized model factors were parameterized using standard remote assessment methods based on interpolated spatial data layers. Inference of degradation status at cultivated sites was estimated by calibration to near infrared diffuse reflectance spectra obtained from sampled soils; diagnostic models based on spectra produced validation accuracies of 78% for three categories. Association between USLE predicted risk and observed erosion, estimated using mixed effects logistic regression to control for within-site variability, correctly classified only 38% of sites into three degradation classes and model sensitivity for delineating regions of severe degradation was only 28%. Graphical modeling was used to identify those USLE risk factors that were conditionally associated with observed degradation, and an ordinal logistic regression model, employing only these factors was developed. This alternative model, which allowed statistical flexibility in estimating effect direction and strength, correctly predicted ordinal degradation class at 54% of field sites, with 55% sensitivity for the severe degradation class. This result suggests a critical need for efficient ground-based sampling schemes to be used in conjunction with flexible statistical models based on USLE factors for future investments in erosion risk assessment in the tropics. 相似文献
18.
精准获取葡萄种植区分布信息对其精细化管理和优质基地建设具有重要意义，通常大区域种植区识别主要基于遥感影像完成，但葡萄种植区空间位置的分散性和背景环境的复杂性，使得种植区识别的精度不高。该研究基于DeepLabv3+网络，改进网络输入通道数使其能够接受更多的光谱信息，同时构建波段信息增强模块（Band Information Enhancement，BIE），利用各波段特征图之间的相关性生成综合特征，提出了波段信息增强的葡萄种植区识别方法（BIE-DeepLabv3+）。在2016和2019年高分二号影像葡萄种植区数据集上训练网络，在2020年影像上测试其性能，结果表明，改进模型输出结果的平均像素精度和平均交并比分别为98.58%和90.27%，识别效果好于机器学习SVM算法，在深度学习DeepLabv3+模型的基础上分别提高了0.38和2.01个百分点，比SegNet分别提高了0.71和4.65个百分点。BIE-DeepLabv3+模型拥有更大的感受野和捕获多尺度信息特征的同时放大了地物间的差异，能够解决影像中葡萄种植区存在类间纹理相似性、背景和环境复杂等问题，在减少模型参数的同时预测出的葡萄种植区更加完整，且边缘识别效果良好，为较大区域内背景复杂的遥感图像葡萄种植区识别提供了有效方法。 相似文献
19.
基于MODIS数据的黄淮海夏玉米高温风险空间分布 总被引：3，自引：1，他引：3
近年来中国高温灾害频发,对黄淮海地区的玉米生产造成较大影响。目前已有的高温风险研究多用的是气象站点的点源数据,针对气象站点数据对大范围区域代表性较差的问题,该文使用搭载在Aqua卫星上的MODIS陆地表面温度产品(MYD11A1),在研究其与气象日最高温度间具有显著相关性的基础上,使用遥感温度数据获取黄淮海夏玉米花期的高温风险空间分布,并结合高程、水体等地理环境因素分析高温风险的成因。结果表明:气象日高温数据与遥感温度数据间的决定系数R2为0.51,P0.001,存在显著的正相关性。通过遥感温度计算发现近年高温风险主要分布在秦岭山区北部以及城镇、村庄的周边地区,与实际情况相符。该研究对于大范围高温风险研究与玉米生产管理具有参考作用。 相似文献
20.
A novel computer model is presented which describes the flow of C and N in the soil. It employs a structure with conceptual compartments. Organic matter is represented by seven different compartments, two for added matter, two for soil microbial biomass, one for microbial residues, one for native (‘humified’) organic matter, and one for inert organic matter. The latter pool represents both truly inert matter, and matter with negligible turnover in a time-span of decades to a century. This paper describes the parameterisation and performance of this model on selected long-term field carbon and radiocarbon data from United Kingdom, Sweden and Denmark. Previously unpublished radiocarbon data series from Denmark are included. Statistical methods were employed to estimate parameters, and obtain proximate confidence intervals for these parameters. Simulations in good agreement with measured values could be achieved, using the same set of parameters on all sites. It was demonstrated that the inert pool might constitute any amount between approx. 10 and 50% of total soil C, so that modelling cannot be used as a tool to obtain narrow estimates for this pool. 相似文献