煤田火区特征的土地覆盖分类方法 ——以乌达煤田火区为例

土地覆盖变化是土地分析与评价和生态环境变化预测的重要科学基础, 通过精确的土地覆盖分类方法 获取高精度的土地覆盖图是研究煤田火区生态环境变化的必要手段。本文以最大似然法、光谱角度法、面向对象 分类法和基于复合分区的分层分类法进行乌达煤田火区土地覆盖分类的方法研究。研究结果表明, 基于复合分区的 分层分类方法分类精度较高, 总体分类精度为92.97%, kappa 系数为0.9155。该方法通过基于地表热辐射特征、热 异常状况、地貌类型, 以及对生态系统扰动状况等的划分, 减少了地物信息的混淆度, 即通过提     

一种基于马尔可夫随机场的影像纹理分类方法

针对纹理影像先估计出其马尔可夫随机场参数,后运用多元统计分析中模糊聚类分析的数学方法进行定量分类,从而为解决划分上的不确定性现象找出描述方法,获得客观的分类结果。     

页岩油的储层特征及其影响因素分析——以沾化凹陷罗家地区为例

为查明沾化凹陷罗家地区古近系沙河街组三段下亚段页岩油的储层特征及其影响要素,通过岩心、薄片、扫描电镜等多种资料综合分析,开展页岩油储层岩性、储层空间类型、影响因素及页岩油储层评价参数的研究。结果表明: 综合矿物成分和沉积构造2个因素,可将研究区页岩油储层岩性划分为7种类型; 不同岩性储集空间发育有较大差异,纹层状泥质灰岩和纹层状灰岩储集空间最为发育,纹层状灰质泥岩储集空间较发育,块状泥岩储集空间发育一般,块状泥质灰岩、块状灰质泥岩及纹层状粉砂岩储集空间发育较差; 研究区页岩油储层储集空间的发育主要受矿物成分、沉积构造、有机质含量和赋存方式以及成岩作用的影响。以此为基础,选取方解石含量、纹层状构造、总有机碳(Total Organic Carbon,TOC)含量、镜质体反射率(R_o)及孔隙度作为页岩油储层评价参数,将沾化凹陷罗家地区页岩油储层分为优质储层、有利储层和不利储层3类。     

Conservation status of New Zealand marine invertebrates, 2009

A re-evaluation of the threat status of New Zealand's marine invertebrates was undertaken in 2009, following earlier review of New Zealand's Threat Classification System and subsequent refinement of the national criteria for classifying threat of extinction to New Zealand's flora and fauna. Sufficient information was available to enable 295 marine invertebrate taxa to be fully evaluated and assigned to a national threat category. The 10 taxa at most risk of extinction (‘nationally critical’) were the giant seep clam Calyptogena sp., the primitive acorn barnacle Chionelasmus crosnieri, O'Shea's vent barnacle Volcanolepas osheai, the stalked barnacle Ibla idiotica, the four-blotched umbrella octopus Cirroctopus hochbergi, the roughy umbrella octopus Opisthoteuthis chathamensis, the giant squid Idioteuthis cordiformis, the large-egged polychaete Boccardiella magniovata and two gravel maggots, Smeagol climoi and Smeagol manneringi. The key threatening processes identified for marine invertebrates were fishing and land-use associated impacts such as sedimentation. We identified no taxa that had improved in threat status as a result of past or ongoing conservation management action, nor any taxa that had worsened in threat status because of known changes in their distribution, abundance or rate of population decline. We evaluated a small fraction of New Zealand's marine invertebrate fauna for their threat status. Many taxa remain ‘data deficient’ or unlisted. In addition to the most threatened taxa, we recommend these taxa and their habitats as priorities for further survey and monitoring.     

IDENTIFICATION AND ANALYSIS OF NATURAL CHANNEL NETWORKS FROM DIGITAL ELEVATION MODELS

The identification and analysis of natural channel networks from digital elevation models are discussed from the point of view of their environmental applications. An interactive, graphical software package that implements some of the most widely used techniques for the automatic recognition of channel networks and for the computation of some useful geomorphologic indices and functions is presented.     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.     

Geomorphological site classification for the Fujian province

Geomorphological classification of the Fujian province was done based on remote-sensing imaging, digital elevation maps, and slope gradient data acquired by ArcGIS 9.2. The engineering geological units in the Fujian province were divided into five types by the geomorphologic shape and genesis. The relationship among geomorphological type, engineering geological unit, and site category was determined using engineering geological data and site category data. Then, after making a preliminary site classification adjusted by drill hole data, the site was classified into four types: I₀, I₁, II, and III according to site classification standards of equivalent shear-wave velocity and overburden thickness. The results showed that the site categories in the Fujian province mainly consist of type II, which accounts for 85.26 % of the land area. The percentage of I₀ was the smallest, which accounts for only 2.44 % of the total area.     

基于四叉树算法的POI分级优化显示方法研究

为了使"天地图"中大数据量、高密度的POI数据在各显示比例下达到清晰易读的表达效果,本文在考虑POI权重属性的情况下,提出了一种基于四叉树算法进行自动过滤的方法,达到各级比例尺优化显示的效果,并通过实例验证该方法的可行性、稳定性,达到了对POI数据高效过滤和图面表达美观的双重效果。     

基于CART集成学习的城市不透水层百分比遥感估算

利用Landsat ETM^＋遥感数据，提出了一种基于CART集成学习的ISP遥感亚像元估算方法，将Boosting重采样技术引入CART分析中，用于提高ISP估算的精度。实验结果表明，该方法的ISP估算性能优于传统的单一CART学习算法，从ETM^＋影像中估算的ISP值与真实值之间的相关系数达到0．91，平均偏差为11．16％。     

花岗岩类土质边坡工程特性及加固方法研究

华南、华东南地区具有厚度可达上百米的花岗岩风化残积层。高等级公路、铁路的规模建设在该地区形成了大量的花岗岩类土质高边坡。深入认识该类边坡的工程特性并提出有效的针对性边坡加固方法,对该地区公路、铁路的安全建设和正常运营具有重要意义。为了更有效指导该类边坡的稳定性分析及边坡设计,本文深入调查分析了广东和广西境内4条高等级公路沿线的44处花岗岩类土质边坡特征及其稳定性,认为控制该类边坡稳定性的因素主要有:土体的均匀性、岩脉(墙)及花岗岩球状硬核的分布情况、坡体中保留的结构面是否对坡体稳定性起控制作用。继而依据这些因素把花岗岩类土质边坡划分为岩脉(墙)隔挡型边坡、网状结构型边坡、外倾结构型边坡、浮石型边坡4种类型。深入分析了每种类型边坡的坡体结构特征、稳定特性,针对性地提出了边坡稳定性分析中应重点考虑的因素。认为花岗岩类土质边坡的土质及坡体结构特征,造成了该类边坡开挖临时自稳性较好,但具有较强的开挖卸荷效应和较弱的抗冲刷能力。最后针对每种类型的花岗岩类土质边坡,提出了相应的设计方案建议。基于花岗岩类土质边坡良好的临时自稳性,认为可进行陡开挖强支护设计,同时强调逐级开挖逐级支护、加强坡面防冲刷、边坡护脚及压顶、边坡截排水等设计建议。