Soil erosion and overland flow in Japanese cypress plantations: spatio-temporal variations and a sampling strategy

ABSTRACT

Many studies have focused on soil erosion in unmanaged Japanese cypress plantations because the sparse understory vegetation and litter covering the forest ground enhance soil erosion. In this study, soil erosion, litter, and overland flow measurements were conducted over 14 months to identify the spatio-temporal variation and examine the optimal sample size. Fifteen traps (each 0.25 m wide) were installed in line along the bottom of a 15-m-wide slope. Soil erosion and overland flow had large spatial variations as compared to litter. The temporal coefficient of variation of soil erosion and overland flow was highest during dry seasons, while smaller during wet seasons. The random sampling analysis showed that the rate of decrease in spatio-temporal variation became moderate as the sample size increased beyond six. This result indicated that the optimal sample size was five, the total width of which was equivalent to about 8% of the monitored slope width.     

The integral of the semivariogram: A powerful method for adjusting the semivariogram in geostatistics

A good fining of the structural junction that describes the variability of a spatial phenomenon is an essential stage in the building of an accurate estimator by kriging. The technique of the integral of the semivariogram (ISV) makes it possible to find this structural function while overcoming the problem of grouping together the pairs of experimental points into classes of distances when the data are not sampled on a regular grid. The ISV is particularly useful when the dispersion of the values of the classical Semivariogram (SV) makes it difficult to fit a model. Since the ISV is composed of a large number of values, it is more continuous than a SV and therefore easier to fit analytically. In fact, when the general shape of the SV is known, the ISV method proves its worth in finding the parameters that best fit a given variogram model. The analytical models of ISV which will be used, are the integral expressions of the traditional analytical SV. In this paper and on the basis of hydrogeological examples, we propose a method to adjust all the parameters of each model. The first derivative of a filled ISV, used in the kriging equations, appears to be systematically the best SV for a cross-validation on the data. This is why we think that the ISV technique should be used when the strong spatial variability of a parameter spreads out the values of the experimental SV.     

Calculation of the variance of mean values for blocks in regional resource evaluation studies

Random sampling of a known covariance function can be used during the process of estimating the variance of means or totals for a spatial random variable in blocks of variable size. One advantage of this method is that the precision of any block variance can be determined at the same time as the integral itself. In two-dimensional space this approach yields sufficiently precise results for continuous spatial random variables with exponential, Gaussian, and spherical covariance functions, as well as for point patterns with exponential covariance density or power-law-type, second-order intensity function. Practical examples of application deal with the areal distribution of felsic volcanic rocks and gold deposits in the Abitibi Volcanic Belt, Canadian Shield. The exponential model yields good results in both cases, but, as an overall fit, the fractal (power-law) model performs better in the characterization of the two-dimensional distribution of the gold deposits.     

结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析

合理模拟城市内部的碳排放空间分布情况,是制定清晰明确的碳减排政策的重要前提。由于以往相关研究所用数据分辨率较低,且未考虑行业差异,因此所得结果较难精细地反映碳排放空间分布特征。为解决以上不足,本文提出一种更为合理的碳排放空间分布模拟方法。首先利用时间序列法预测2019年广州市各行业碳排放量;然后结合“珞珈一号”夜间灯光及城市功能分区数据,在精细尺度下实现分行业的碳排放空间化;在此基础上进行空间自相关分析,揭示广州市碳排放空间分布规律;最后采用随机森林模型分析影响广州市分行业碳排放的社会经济驱动因素。结果表明：① 广州市碳排放量在2011年后呈缓慢增长趋势,2019年碳排放量达83.12百万吨,其主要贡献来源为交通行业;② 与常用的ODIAC（1 km）、EDGAR（10 km）碳排放产品及基于NPP-VIIRS的碳排放空间化结果（500 m）相比,结合高分辨率（130 m）夜间灯光数据以及城市功能分区实现的碳排放空间化结果可以在更精细的尺度上呈现区域内部的空间碳排放差异;③ 广州市碳排放呈显著的全局空间正相关,形成了以第二和第三产业集中区域为依托的高高聚集区;④ 广州市2019年第二产业碳排放的主要影响因素是一般公共预算收入、第二产业GDP、一般公共预算支出、固定资产投资额;第三产业碳排放的主要影响因素是社会消费品零售额、第三产业GDP、各个行政区总GDP以及人口数量。综上,本研究从城市内部行业结构差异出发,结合高分辨率的夜间灯光数据,展现区域内部的碳排放分布格局,所得结果将有利于相关部门制定精准的碳减排和产业优化升级策略。     

基于MRF随机场的多光谱遥感影像最优化分级聚类

有限混合模型FM的分级聚类已广泛应用于不同领域,然而,由于它的计算复杂度与观测数据量平方成正比,致使在遥感影像方面应用受到了限制。另外,多光谱图像能提供空间和光谱两类信息详细的数据,但是,大多数多光谱图像聚类方法是基于像素的聚类,仅使用了其光谱信息而忽视了空间信息。本文定义一个相对混合密度函数,通过引入一个q-参数来调节各成分密度对其混合分布的贡献,提出一种广义有限混合模型GFM．设计一种新的适用于多光谱遥感影像的GFM分级聚类算法。该算法把MRF随机场和GFM模型结合在了一起,分类数通过PLIC准则自动确定。最后,利用仿真结果验证该算法的有效性,同时通过与K均值聚类、FM分级聚类以及SVMM分级聚类的比较说明本文算法的优越性。     

基于改进混沌果蝇优化小波阈值法地震信号随机噪声压制

由于野外采集地震资料往往带有较多的随机噪声,给资料解释造成困难。针对小波阈值去噪的阈值选取通常需要对信号进行先验估计,带有较强猜测性,阈值选取难以获得最优结果。本文提出基于改进混沌果蝇优化的小波阈值法,将基于广义交叉验证(GCV)函数设定为阈值选取目标函数,在混沌果蝇优化算法中引入调节系数实现对该目标函数的迭代寻优,在无先验信息前提下,获取最优小波阈值。通过将本文算法用于合成地震记录和实际地震记录进行去噪处理,并对比常用小波阈值去噪算法,证明了本文算法的有效性。     

Effect of scouring in sand on monopile-supported offshore wind turbines

This paper analyzes the influence of scour on the overall response of monopile-supported offshore wind turbines (OWTs) in 20-m water depth. Scouring effects on OWTs have been often studied within the geotechnical domain, considering static loads at the mudline. The present work attempts to address the scour-induced problems in OWTs by making use of an integrated aerodynamic–hydrodynamic load approach in sandy soils. The OWT analysis is simulated for operational and shut-down (parked) condition. Under parked situations, the OWT blades are feathered, and power production is suspended, owing to structural safety concerns. The 50 Monte Carlo responses of stochastic sea-state condition (wind speed with turbulence, significant wave height, and peak spectral period) are generated. Irregular, long-crested waves are generated using the Joint North Sea Wave Project (JONSWAP) spectrum. Then from each simulation, the ensemble response is obtained. Sandy soils of varying densities are considered. Results indicate that OWTs founded on loose sands suffer significant stiffness (and hence natural frequency) reductions, shifting the structure into the resonance regime. Lateral responses also show an escalation with reduction in density of sandy soil.     

基于随机森林算法的近地表气温遥感反演研究

近地表气温是城市热环境的重要表征,是改变和影响城区气候的重要因素。为获得空间上连续的近地表气温,本文以北京市为研究区,利用Landsat5/TM数据计算分别得到地表温度、归一化植被指数、改进的归一化差异水体指数、地表反照率、不透水面盖度,并结合气象站点气温和高程作为输入参数建立随机森林模型反演近地表气温。结果表明,随机森林反演的近地表气温平均绝对误差（MAE）为0.80 ℃,均方根误差（RMSE）为1.06 ℃,与传统多元线性气温回归方法相比,平均绝对误差（MAE）和均方根误差（RMSE）分别提高0.06 ℃和0.09 ℃。研究表明,利用随机森林模型反演近地表气温是可行的,并且具有一定的优越性。此外,对随机森林模型的输入参数进行重要性分析,地表温度对气温反演模型的影响最大,其次为高程。     

基于动态总方差的随机信号动态特性分析

动态Allan方差法是分析输出信号随机误差的一种新方法,但是窗函数截取数据会使数据减少、自由度降低,从而导致Allan方差估计值置信度低、误差变大等。本文设计了一种可以跟踪信号变化和减小估计值误差的动态总方差法,首先以确定的步长用固定的窗函数对原始信号进行截取,然后将窗函数内的数据用倒镜像映射进行延拓来提高置信度,最后将拓展后数据的方差值再用二维或三维图表达出来。仿真数据表明,动态总方差法既能及时地跟踪信号的动态变化,也可以克服估计值置信度低、误差大的问题。