Earthquake-induced landslide hazard and vegetation recovery assessment using remotely sensed data and a neural network-based classifier: a case study in central Taiwan

A catastrophic earthquake with a Richter magnitude of 7.3 occurred in the Chi-Chi area of Nantou County on 21 September 1999. Large-scale landslides were generated in the Chiufenershan area of Nantou County in central Taiwan. This study used a neural network-based classifier and the proposed NDVI-based quantitative index coupled with multitemporal SPOT images and digital elevation models (DEMs) for the assessment of long-term landscape changes and vegetation recovery conditions at the sites of these landslides. The analyzed results indicate that high accuracy of landslide mapping can be extracted using a neural network-based classifier, and the areas affected by these landslides have gradually been restored from 211.52 ha on 27 September 1999 to 113.71 ha on 11 March 2006, a reduction of 46.24%, after six and a half years of assessment. In accordance with topographic analysis at the sites of the landslides, the collapsed and deposited areas of the landslide were 100.54 and 110.98 ha, with corresponding debris volumes of 31,983,800 and 39,339,500 m³. Under natural vegetation succession, average vegetation recovery rate at the sites of the landslides reached 36.68% on 11 March 2006. The vegetation recovery conditions at the collapsed area (29.17%) are shown to be worse than at the deposited area (57.13%) due to topsoil removal and the steep slope, which can be verified based on the field survey. From 1999 to 2006, even though the landslide areas frequently suffered from the interference of typhoon strikes, the vegetation succession process at the sites of the landslides was still ongoing, which indicates that nature, itself, has the capability for strong vegetation recovery for the denudation sites. The analyzed results provide very useful information for decision-making and policy-planning in the landslide area.     

基于ETM影像的喀斯特石漠化调查研究——以重庆南川市为例

以遥感资料为主要信息源,以地理信息系统(GIS)为支撑,采用经验模型法确定植被指数与植被覆盖度之间的关系;结合由地形图派生的坡度图,由区域地质图派生的纯碳酸盐岩、不纯碳酸盐岩以及非碳酸盐岩区的岩性背景图,参考降水量和降雨强度等有关因素,建立基于专家分类的石漠化定量分析模型,对南川市石漠化强度进行了分级制图.经实地检验,Kappa指数均超过最低允许的判别精度要求,为石漠化信息的获取提供了一个实用而高效的方法.     

基于误差分析的组合分类器研究

提出了一种基于误差分析的组合分类器,通过结合两种监督分类方法,提出的算法分别估计了两种监督分类方法在计算过程中的误差,给出了规则输出的置信区间,再根据置信区间的大小对两种分类方法的输出结果进行加权平均,从而得到更精确的规则输出.利用该方法对遥感图像进行分类实验,在不同训练样本分布与不同训练样本数量的情况下,比较新的组合分类器与单一分类器的精度.结果表明新的组合分类器能够取得比单一的分类器更高的分类精度.结果还显示出,两个分类器的独立性越强,组合分类器的效果越好.另外一个实验比较了新的组合分类器与和式规则组合分类器的分类精度,结果仍显示出了新方法的优越性.     

基于高斯混合模型的抗差Bayes滤波在动态导航中的应用

在动态环境下导航定位受到很多因素影响,且异常噪声会严重影响导航滤波结果。通过研究Bayes滤波的抗差方法,在导航观测方程中采用高斯混合模型,对混合模型不确定度参数采用层次模型实时估计。借助指示变量进行模型变换,削弱导航过程中异常噪声的影响。并采用Rao-Blcakwellized粒子采样方法,求取复杂、非标准形式的状态后验分布。最后通过卫星导航以及组合导航实验算例,分析验证了基于高斯混合模型的Bayes滤波在动态导航定位中的抗差性能。     

A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification

Tree species information is crucial for digital forestry, and efficient techniques for classifying tree species are extensively demanded. To this end, airborne light detection and ranging (LiDAR) has been introduced. However, the literature review suggests that most of the previous airborne LiDAR-based studies were only based on limited kinds of tree signatures. To address this gap, this study proposed developing a novel modular framework for LiDAR-based tree species classification, by deriving feature parameters in a systematic way. Specifically, feature parameters of point-distribution (PD), laser pulse intensity (IN), crown-internal (CI) and tree-external (TE) structures were proposed and derived. With a support-vector-machine (SVM) classifier used, the classifications were conducted in a leave-one-out-for-cross-validation (LOOCV) mode. Based on the samples of four typical boreal tree species, i.e., Picea abies, Pinus sylvestris, Populus tremula and Quercus robur, tests showed that the accuracies of the classifications based on the acquired PD-, IN-, CI- and TE-categorized feature parameters as well as the integration of their individual optimal parameters are 65.00%, 80.00%, 82.50%, 85.00% and 92.50%, respectively. These results indicate that the procedures proposed in this study can be used as a comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification.     

Inferring trip purposes and uncovering travel patterns from taxi trajectory data

Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched. The probability of points of interest to be visited is modeled by Bayes’ rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our results closely approximate the residents’ travel survey data in Shanghai. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions. In the era of big data, we encounter the dilemma of “trajectory data rich but activity information poor” when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals’ trajectories.     

Informal likelihood measures in model assessment: Theoretic development and investigation

Within hydrology performance criteria such as the Nash–Sutcliffe efficiency have been used to condition the parameter space of a model. Their use is motivated by the fact that the stochastic error series between a model output and corresponding observations is the result of the composite effect of multiple error sources which cannot be described, even in form, a priori. This paper formalises the use of such performance criteria within a Bayesian framework, such as Generalised Likelihood Uncertainty Estimation (GLUE), by introducing the concept of Informal Likelihoods. Informal Likelihoods are used to characterise desirable features in the relationship between the model output and corresponding observed data. A number of common performance criteria are considered as Informal Likelihoods. Analytical results and a simulation indicate all of the performance criteria considered as Informal Likelihoods in this paper have one or more properties which may be considered undesirable, but may perform no less well in conditioning model parameters than formal likelihoods for which the assumptions are only mildly incorrect.     

岩体结构面倾向参数概率分布函数改进的Bayes推断方法

三峡船闸是世界上最大的船闸。该地区断层及节理比较发育,且具有一定的随机统计性,它对岩体力学性质起重要的控制作用。为了研究节理倾向的概率分布特征,本文引入以Bayes 最小熵优度比较检验为基础的概率分布的改进Bayes 统计推断方法,基于三峡工程永久船闸节理岩体3373 条结构面的实测参数,就对岩体力学性质起控制作用的各组结构面的倾向参数的概率分布进行了研究。文章最后还讨论了推断的最优分布参数,估计了结构面参数的检验误差范围。     

Examining Risk in Mineral Exploration

Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin. Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.     

华北水氡异常与地震关系的Bayes判别分析

选取华北地区１９９０￣１９９８年８月较完整的水氡观测资料,笔者采用ｘ＾２统计检验法识别前兆异常,利用笔者建立的Ｂａｙｅｓ判别分析方法,对该地区水氡异常与中强震活动性的关系进行了内符检验和外推预测。在风险代价比Ｋｄｎ取４的情况下,内符检验的有震报准率ｃ为０．７１,预报占时率ｂ为０．３３,Ｒ值可达０．３８;外推有震报效率ｃ为０．５,时空占有率０．０５,Ｒ值为０．４５,能够正确预测１９９８年１月１０日张