Recultivation of abandoned agricultural lands in Ukraine: Patterns and drivers

The recent rise in agricultural commodity prices and the expectation that high price will persist have triggered a wave of farmland expansion in regions where land resources are still available. One such region is the former Soviet Union, where the collapse of socialism caused massive agricultural abandonment and where some of these lands are now being brought back into production. Yet, the extent and spatial patterns of recultivation, and what determines these patterns, remains unclear. We examined the extent of recultivation of abandoned agricultural land in Ukraine since 2007 using a new, satellite-based recultivation map and assessed the effect of biophysical and socioeconomic determinants on recultivation patterns using boosted regression trees. We found key predictors of recultivation to be related to the suitability of land for agriculture (i.e., soil quality, temperature). Accessibility to major cities was also important, with most recultivation happening closer to settlements, but this influence varied across Ukraine. Variables related to agricultural management (fertilizer input, mechanization) and demography were negligible in explaining recultivation in our analyses. These factors suggest that recultivation patterns were primarily driven by factors related to land productivity, with recultivation focusing on the most promising areas. Given the remaining large amount of unused agricultural land in Eastern Europe and the former Soviet Union, and considering that much abandonment occurred in areas only marginally suited to agriculture, our findings provide important insights into where recultivation can be expected to happen and thus for assessing the potential socioeconomic and environmental impacts of recultivation.     

遥感模式分类中的空间统计学应用——以面向对象的遥感影像农田提取为例

如何有效地从遥感图像中提取所需信息,是遥感图像处理和应用的关键,而尺度选择问题一直是影响遥感信息提取精度的关键问题之一。本文论述了利用空间统计学方法解决遥感影像模式分类中的尺度问题的理论基础。针对面向对象影像分析问题,将影响遥感影像多尺度分割的尺度分割参数概括为空间属性分割参数、光谱属性分割参数和影像对象面积阈值参数,并分别提出了基于统计学的尺度参数估计方法。以SPOT-5影像面向对象农田提取为例,基于变异函数方法进行了尺度优选试验,系列尺度分类试验结果表明基于空间统计学尺度估计得到的尺度分割结果进行分类能得到最高的精度,进而证明了基于空间统计学方法进行面向对象信息提取尺度估计的有效性。该方法是完全数据驱动的方法,基本不需要先验知识参与。不同于以往分割后评价的尺度选择方法会占用大量计算资源且耗费大量时间,本文提出的方法不仅能在一定程度上保证面向对象信息提取的精度,而且在一定程度上也提高了面向对象信息提取的效率和自动化程度。     

部分变量误差模型的整体抗差最小二乘估计

部分变量误差模型(partial EIV model)的加权整体最小二乘(weighted total least-squares,WTLS)估计不具备抵御粗差的能力。鉴于粗差可能同时出现在观测值和系数矩阵中,本文在提出部分变量误差模型WTLS估计的两步迭代解法的基础上,运用抗差M估计的等价权方法,发展了一种整体抗差最小二乘(TRLS)估计方法,并采用一致最大功效统计量确定降权因子。针对WTLS估计两步迭代解法的特点,设计了两个不同的降权方案:第1个方案是在估计系数矩阵元素时,不对观测值降权,仅对系数矩阵降权;第2个方案是在估计系数矩阵元素时,既对系数矩阵降权,同时也对观测值降权。通过对模拟2D仿射变换和线性拟合实例进行计算和分析,结果表明第1方案优于第2方案,并且优于基于残差和验后单位权方差的抗差估计和现有的变量误差模型抗差估计。     

干涉图像第二类统计Goldstein自适应滤波方法

干涉图滤波是InSAR数据处理中的关键步骤之一,滤波结果的优劣会直接影响到相位观测的质量和最终产品精度。本文结合干涉图滤波算法的研究进展,对Goldstein频率域滤波及其经典改进算法进行了系统分析和比较,在此基础上提出了一种基于第二类统计的稳健相干性估计量的Goldstein自适应滤波方法。本文采用模拟数据和Envisat ASAR真实数据与现有方法进行了验证,试验结果表明,新的滤波方法在保持细节和抑制噪声方面优势更加明显。     

地理国情普查试点成果综合分析

城镇服务设施布局的评价与分析是地理国情普查综合统计分析城镇化发展的重要内容。针对当前城镇化发展不平衡,生活服务设施空间布局不合理的情况,该文利用克拉玛依市独山子区地理国情普查试点成果,结合专题数据,采用GIS网络分析方法,在独山子区城镇综合设施之间配置最短最优路径。耦合结果表明:老城区卫生资源分布较合理,但是教育资源和整体服务设施分布却过于集中。这种方法还可以进一步用于优化城镇功能单元布局与服务保障设施点的建设,完善居民出行、教育、日常生活和文化服务等方面。     

Estimation of transit trip production using Factorial Kriging with External Drift: an aggregated data case study

Studies in transportation planning routinely use data in which location attributes are an important source of information. Thus, using spatial attributes in urban travel forecasting models seems reasonable. The main objective of this paper is to estimate transit trip production using Factorial Kriging with External Drift (FKED) through an aggregated data case study of Traffic Analysis Zones in São Paulo city, Brazil. The method consists of a sequential application of Principal Components Analysis (PCA) and Kriging with External Drift (KED). The traditional Linear Regression (LR) model was adopted with the aim of validating the proposed method. The results show that PCA summarizes and combines 23 socioeconomic variables using 4 components. The first component is introduced in KED, as secondary information, to estimate transit trip production by public transport in geographic coordinates where there is no prior knowledge of the values. Cross-validation for the FKED model presented high values of the correlation coefficient between estimated and observed values. Moreover, low error values were observed. The accuracy of the LR model was similar to FKED. However, the proposed method is able to map the transit trip production in several geographical coordinates of non-sampled values.     

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.     

A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.     

Nonparametric Estimation of Undrained Shear Strength for Normally Consolidated Clays

For decades, various well-known empirical correlations have been established and commonly used to estimate the undrained shear strength of normally consolidated clays in geotechnical practice. However, there are still many contradictory cases regarding the validity of these correlations, so their applicability and reliability are questionable. In this study, an extensive geotechnical database for normally consolidated clays has been compiled. Next, a Bayesian nonparametric method is exploited to identify the most critical soil index properties in inferring the undrained shear strength and a new predictive model is proposed. Finally, the performance of the proposed predictive model is assessed and compared with two well-known correlations using an independent testing database. It confirms that the proposed model not only possesses high predictability, but also provides superior performance over other existing empirical correlations.     

西藏“一江三河”地区地下水环境背景值初步研究

在分析对比国内外研究方法的基础上,选择数理统计方法,通过最广泛的数理统计法的应用原则、数据检验、背景值取值等问题的论述,对西藏"一江三河"地区地下水水质指标TDS,Ca²+,Mg²⁺,Na⁺环境背景值进行研究,并初步探讨了它们的空间分布规律及控制因素.结果表明,地形地貌、区域水动力条件等因素对研究区地下水指标的空间分布起主要控制作用.该结果为评价西藏"一江三河"地区地下水污染状况提供科学的依据.