Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany

In Germany, a county-resolution data set that consists of 35 land-use and animal-stock categories has been used extensively to assess the impact of agriculture on the environment. However, because such environmental effects as emission or nutrient surplus depend on the location, even a county resolution might produce misleading results. The aim of this article is to propose a Bayesian approach which combines two sorts of information, with one being treated as defining the prior and the other the data to form a posterior, used to estimate a data set at a municipality resolution. We define the joint prior density function based on (i) remote sensing data, thus accounting for differences in county data and missing data at the municipality level, and (ii) the results of a cluster analysis that was previously applied to the micro-census, whereas the data are defined by official statistics at the county level. This approach results in a fairly accurate data set at the municipality level. The results, using the proposed method, are validated by the national research data centre by comparing the estimates to actual observations. The test statistics presented here demonstrate that the proposed approach adequately estimates the production activities.     

Selection of prior distributions and multiscale analysis in Bayesian temperature reconstructions based on fossil assemblages

It is possible to reconstruct the past variation of an environmental variable from measured historical indicators when the modern values of the variable and the indicators are known. In a Bayesian statistical approach, the selection of a prior probability distribution for the past values of the environmental variable can then be crucial and the selection therefore should be made carefully. This is particularly the case when the data are noisy and the statistical model used is complex since the influence of the prior on the results can then be especially strong. It can be difficult to elicit the prior probability distribution from the available information, since usually there are no measured data on the past values of the variable one wants to reconstruct and different reconstructions are typically consistent with each other only at a coarse level. To overcome these difficulties we propose to use a non-informative smoothing prior, possibly in combination with an informative prior, that simply penalizes for roughness of the reconstruction as measured by the variability of its values. We believe that it can sometimes be easier to set an overall prior distribution on the roughness than to agree on a prior for the actual values of the reconstructed variable. Note that by using a smoothing prior one incorporates into the model itself the smoothing step usually done before or after the actual numerical reconstruction. Another idea proposed in this paper is to integrate the reconstruction model with a multiscale feature analysis technique known as SiZer. Multiscale analysis of the posterior distribution of the reconstructed variable makes it possible to infer its statistically significant features such as trends, maxima and minima at several different time scales. While only temperature is considered in this paper, the technique can be applied to other environmental variables.     

基于SAIL模型的多角度多光谱遥感叶面积指数反演

随着多角度传感器的陆续出现及植被遥感传输机理研究的深入,多角度遥感逐渐成为地表信息反演的热点问题.以SAIL冠层反射率模型为基础,通过联合多角度和多光谱数据,可以从物理机理角度进行植被叶面积指数(LAI)反演的应用研究.首先通过计算得到多角度多光谱遥感影像的角度信息,并经6S模型纠正后得到多光谱多角度植被冠层反射率数据.然后将PROSPECT模型模拟出的植被叶片反射率和透过率,以及多角度观测数据、LAI和其它实测数据输入SAIL模型,模拟得到了多角度多光谱冠层反射率,进而建立多角度多光谱冠层反射率与LAI的查找表.最后,将影像的多角度多光谱冠层反射率与查找表进行匹配,实现植被LAI的反演.最后对反演结果进行了验证和分析,结果表明反演精度较高,误差均在合理范围之内.     

外商直接投资技术溢出空间效应及其变化——基于中国地级市的实证研究

FDI(外商直接投资)对内资企业的技术溢出效应是国内外研究的重要话题,但相关文献多为静态研究.本文认为FDI的技术溢出存在时间效应,即FDI对内资企业的技术溢出或挤出效应,随着FDI进入东道国的时间推移而变化.利用2003-2007年中国工业企业数据库,以通信设备、计算机和其他电子设备制造业为例,对城市面板数据建立门槛回归模型,研究FDI技术溢出效应随时间变化的动态过程.结果表明,随着FDI进入东道国的时间推移,技术溢出效应的变化呈扁S曲线:初始,FDI对内资企业表现为明显的挤出效应;3～4年后,逐渐由挤出效应向溢出效应转变;随着时间的进一步推移,溢出效应减缓下降,最终表现为不明显的溢出或挤出效应.此外,对不同所有制的内资企业,FDI技术溢出效应随时间变化的动态过程不同.