Effect of flying altitude,scanning angle and scanning mode on the accuracy of ALS based forest inventory

Airborne laser scanning (ALS) is a widely used technology in the mapping of environment and forests. Data acquisition costs and the accuracy of the forest inventory are closely dependent on some extrinsic parameters of the ALS survey. These parameters have been assessed in numerous studies about a decade ago, but since then ALS devices have developed and it is possible that previous findings do not hold true with newer technology. That is why, the effect of flying altitudes (2000, 2500 or 3000 m), scanning angles (±15° and ±20° off nadir) and scanning modes (single- and multiple pulses in air) with the area-based approach using a Leica ALS70HA-laser scanner was studied here. The study was conducted in a managed pine-dominated forest area in Finland, where eight separate discrete-return ALS data were acquired. The comparison of datasets was based on the bootstrap approach with 5-fold cross validation. Results indicated that the narrower scanning angle (±15° i.e. 30°) led to slightly more accurate estimates of plot volume (RMSE%: 21–24 vs. 22.5–25) and mean height (RMSE%: 8.5–11 vs. 9–12). We also tested the use case where the models are constructed using one data and then applied to other data gathered with different parameters. The most accurate models were identified using the bootstrap approach and applied to different datasets with and without refitting. The bias increased without refitting the models (bias%: volume 0 ± 10, mean height 0 ± 3), but in most cases the results did not differ much in terms of RMSE%. This confirms previous observations that models should only be used for datasets collected under similar data acquisition conditions. We also calculated the proportions of echoes as a function of height for different echo categories. This indicated that the accuracy of the inventory is affected more by the height distribution than the proportions of echo categories.     

Accounting for high-order correlations in probabilistic characterization of environmental variables,and evaluation

Probabilistic characterization of environmental variables or data typically involves distributional fitting. Correlations, when present in variables or data, can considerably complicate the fitting process. In this work, effects of high-order correlations on distributional fitting were examined, and how they are technically accounted for was described using two multi-dimensional formulation methods: maximum entropy (ME) and Koehler–Symanowski (KS). The ME method formulates a least-biased distribution by maximizing its entropy, and the KS method uses a formulation that conserves specified marginal distributions. Two bivariate environmental data sets, ambient particulate matter and water quality, were chosen for illustration and discussion. Three metrics (log-likelihood function, root-mean-square error, and bivariate Kolmogorov–Smirnov statistic) were used to evaluate distributional fit. Bootstrap confidence intervals were also employed to help inspect the degree of agreement between distributional and sample moments. It is shown that both methods are capable of fitting the data well and have the potential for practical use. The KS distributions were found to be of good quality, and using the maximum likelihood method for the parameter estimation of a KS distribution is computationally efficient.     

Kolmogorov–Smirnov test for spatially correlated data

The Kolmogorov–Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov–Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size.     

Processing Italian damage data to derive typological fragility curves

Typological fragility curves have been derived from post-earthquake survey data on building damage, collected in the areas affected by the most relevant Italian earthquakes of the last three decades. A complex and time consuming codification and reinterpretation work has been done on a set of about 150,000 survey building records, in order to define empirical damage probability matrices for several building typologies, characteristic of the Italian building stock. The obtained data have then been processed by advanced nonlinear regression methods in order to derive typological fragility curves. These curves, organised in five damage levels, provide useful information both for relative comparisons among typologies and for seismic risk analyses at different scales. By combining hazard definitions, fragility curves and inventory data, complete earthquake risk scenario studies can be performed, but even the single convolution of hazard and fragility allows to obtain typological risk maps, both for single damage state definitions and for concise average loss parameters. The very high potential of these results is shown by some applications reported in the paper.     

2010年高雄地震震源参数的近远震波形联合反演

本文改进了传统基于近震波形数据的点源震源参数反演的Cut And Paste(CAP)方法,实现了近震Pnl波、面波和远震P波、SH波的联合反演的CAPjoint算法.对2010年3月高雄地震,分别进行单独反演以及联合反演,获得各自的震源机制解及深度,其中联合反演所得的最佳双力偶机制解参数为,节面1:走向317°,倾角36°,滑移角52°,节面2:走向181°,倾角62°,滑移角114°,深度为21 km.并对不同震中距波形对本次地震以及几种典型机制解断层几何参数的敏感性进行测试.为验证联合反演方法的可靠性,本文采用重抽样思想发展而来的Bootstrap方法,对近震数据的子集及其与远震数据的联合反演所得的参数进行统计,验证了在稀疏近台条件下联合反演中添加远震数据对地震震源参数约束的作用.     

利用VP型垂直摆倾斜仪观测数据检测2011日本M_w9.0级地震激发的低频地球自由振荡

甚长周期VP型垂直摆倾斜仪对某些地球物理信号有特殊的敏感性,除了在观测小地震、慢地震等方面有优势之外,还可有效应用于低频地球自由振荡信号的探测.利用安置在武汉大学珞珈山的我国自行研制的VP型垂直摆倾斜仪在2011日本Mw9.0级大地震之后不同长度的观测数据,联合EEMD方法、自回归估计(AR)方法和bootstrap法,本文不仅检测到该地震激发的频率小于4.7 mHz的零阶球型振荡(0S2至0S38)和环型振荡(0T4至0T35)几乎所有振型以及15个谐频振型,还检测到5个其频率低于1mHz的低阶球型多线态(0S2、2S1、0S3、0S4和1S2)的部分或全部谱峰分裂现象,并给出了所有检测结果的精度评估.此外,本文分析了某些球型和环型振荡之间的耦合效应,结果表明耦合效应将显著影响地球自由振荡信号的相关参数.     

Streamflow simulation and skewness preservation based on the bootstrapped stochastic models

The stochastic model has been widely used for the simulation study. However, there was a difficulty in the reproduction of the skewness of observed series and so the stochastic model for the skewness preservation was appeared. While the skewness in the residuals of the stochastic model has been considered for the skewness preservation this study uses a random resampling technique of residuals from the stochastic models for the simulation study and for the investigation of the skewness coefficient. The main advantage of this resampling scheme, called the bootstrap method is that it does not rely on the assumption of population distribution and this study uses the combined model of the stochastic and bootstrapped models. The stochastic and bootstrapped stochastic (or combined) models are used for the investigations of skewness preservation and of the reproduction of probability density function between the simulated series. The models are applied to the annual and monthly streamflows of Yongdam site in Korea and Yakima river, Washington, USA for the streamflow simulation study then the statistics and probability density functions for the observed and simulated streamflows are compared. As the results the bootstrapped stochastic model reproduces the skewness and probability density function much better than the stochastic model. This evidences suggest that the bootstrapped stochastic model might be more appropriate than the stochastic model for the preservation of skewness and for simulation purposes of the series.     

Analysis of extreme rainfall using the log logistic distribution

Computer-intensive methods are used to examine the fit of the log logistic distribution to annual maxima of Irish rainfall. The characteristics of the L-moment solutions are examined by using the conventional bootstrap on the data and by random sampling within the ellipse of concentration of the parameter estimates. A statistical method of examining uncertainty is provided by the maximum product of spacings solution. Factors derived from random division of an interval are proposed for estimation of short-duration falls for which no data are available.     

Detection of spatial variation in risk when using CAR models for smoothing relative risks

In this paper we derive score tests for spatial independence in mortality or incidence risk in the framework of hierarchical spatial models where different Gaussian Markov random field (MRF) priors are given for modelling the area random effects (specifically, two non-intrinsic Gaussian priors and a convolution Gaussian prior). The techniques used to test the practically relevant and important simplifying hypotheses of an absence of spatial variation in risk will provide a guidance for practitioners to select an adequate model (i.e., a model with an exchangeable-independent-prior, an intrinsic prior, a convolution prior or a non-intrinsic prior, for the area-specific random effects distribution). The proposed methodology is illustrated by analyzing the well-known data set of lip cancer in Scotland and female mortality due to cerebrovascular disease in Navarra, Spain.     

考虑抽样不确定性的水文设计值估计

水文频率分析计算过程中,水文极值样本系列容量一般都较小、代表性不高,使得水文设计值估计具有不确定性。利用Bootstrap方法,研究样本抽样不确定性对水文设计值的影响。与传统水文频率分析方法相比,基于Bootstrap方法不仅可提供设计值的点估计和区间估计,同时能够对设计值的不确定性进行定量评价。此外,基于Bootstrap技术,结合矩法、权函数法及线性矩法,设置3套方案,分析了该方法在不同参数估计方法间的有效性。以南通市1970-2011年共42年的年降雨量数据资料为例,对所提方法进行实例应用分析,结果表明,从期望设计值、90%置信区间及最终设计值角度而言,基于所提方法的设计成果受参数估计方法的选取影响不大,且可回避规范中B值诺莫图通用性较差及误差显著问题。