马四剖面揭示的共和盆地12 ka以来的环境演变

以共和盆地东部风成沉积剖面(MS剖面)磁化率为依据,运用四次回归方程重建了剖面形成时的年均气温和降水量,确定近12 ka以来共和盆地年均温度–0.37℃～5.09℃,多年平均温度为2.5℃,降水量267.91～550.77 mm,多年平均降水量为418.62 mm。对常量元素氧化物(SiO_2、Al_2O_3和Fe_2O_3)含量进行分析,结果表明:1)古土壤层SiO_2含量均低于风成砂层, Al_2O_3和Fe_2O_3含量均高于风成砂层; 2)风成砂层SiO_2含量S3>S5>S1,Al_2O_3和Fe_2O_3含量S5>S3>S1,揭示S3层形成时期冷干, S5层次之, S1层干旱程度较弱;土壤层SiO_2含量S4>S2>S6, Al_2O_3和Fe_2O_3含量为S6>S2>S4,表明S6形成时期气候温湿, S2层次之, S4层温湿程度较低; 3)分层氧化物变异系数大小表明S2和S6层形成时气候波动剧烈, S1和S3层形成时期气候较稳定。以常量元素氧化物含量、温度和降水为依据,结合14C测年,重建了近12 ka以来共和盆地环境演变过程:11.9 ka BP以前为凉干期,11.9～9.7 ka BP为温湿期, 9.7～8.0 ka BP为冷干期, 8.0～4.8 ka BP为暖湿期, 4.8～2.9 ka BP为凉干期, 2.9 ka BP以来为温润期。马四剖面记录的环境演变过程与青藏高原其他区域环境演变过程具有相似性,表明共和盆地环境演变与区域环境变化具有同步性。     

九江红土的磁学特征及其环境意义

为了解南方红土磁性变化的机理,采用磁化率仪、扫描电子显微镜及X荧光光谱分析对九江红土剖面的磁性特征、磁性矿物的特征及风化程度进行了深入研究。研究结果表明,九江剖面上部砂土经历的风化改造强度弱于下部的网纹红土,而磁性强度高于下部网纹红土,磁性强度的增加与超顺磁颗粒及多畴磁铁矿的含量密切相关;风化强度的增加导致磁铁矿的晶形遭到破坏,同时导致红土中强磁性矿物向弱磁性矿物转化,这是造成网纹红土磁性强度低于砂土的主要原因;红土中的多畴磁铁矿并不是形成于风化成土作用过程中,而是来自红土原始母质的风化残留。从中更新世到晚更新世气候由湿热向冷干转变,风化作用强度逐渐减弱,导致上部砂土相对于网纹红土保留了更多的强磁性矿物。因此,红土中主要的载磁矿物并不是风化成土过程中的产物,其磁性特征不能反映气候环境的变化。     

绿洲生态预警信息系统初步设计

绿洲生态预警信息系统是实现绿洲区域生态环境评价、预警及动态监测的先进手段．将为绿洲区域可持续发展提供辅助决策信息。论文对系统的网络拓扑结构、系统总体逻辑结构、系统功能、分级权限等进行了初步设计,以期为绿洲生态保护与社会可持续发展提供一个可行的辅助决策系统实现方案。     

Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.     

A sapping erosion susceptibility model for the southern Cantabrian Range, North Spain

A susceptibility model of sapping-related depressions has been produced by analysing the statistical relationship between the location of depressions and topographical variables. Sapping erosion plays an important role in the growth of gullies developed over poorly-consolidated quartzite conglomerates in the northern sector of the Duero Tertiary Basin (the Cantabrian Mountain Range of Spain). Gullies, topsoil subsidence depressions related to sapping processes, and shallow slides have been identified over sandy soils in an area of 37 km². This area underwent major changes in land use during the 20th century, leading to the expansion of gully erosion processes. On the surface, depressions of several meters in diameter, caused by internal erosion related to sapping processes, can be identified. Using the topographical location of these topsoil depressions and several topographical variables, 14 discriminate functions were derived to provide information about the influence of topography on the development of sapping erosion processes. The functions have accuracy levels varying from 56 to 81%. The most accurate function was regionalized by means of a Geographical Information System and was used to complete a land classification method which is based on the Mahalanobis distance to the multivariate average (centroid) of a sapping erosion form group. The classification of this model allows the identification of areas with different relative probabilities of sapping processes and the resultant growth of gullies.     

Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey)

Considering damage to man-made structures by natural hazards in Turkey, landslides are the second most important hazard after earthquakes. For this reason, a large-scale study titled Turkish Landslide Inventory Project, has been carried out since 1998. During this project, some special, susceptibility, hazard and risk assessments have been performed. In this study, a landslide susceptibility map of a part of tectonic Kelkit Valley in the north of central Turkey was produced, employing binary logistic regression analyses. To achieve the most appropriate results some sensitivity analyses were also carried out. For this purpose, four different data sets were constructed considering conditioning factors used and sampling strategies applied for the training data sets in this study. As a consequence of the analyses, the most proper outcomes were obtained by using the data set in which continuous topographical parameters and lithological dummy variables were implemented together and 50% of training data set was taken from seed cells at random. Correct classification percentage and Root Mean Square Error (RMSE) values for the validation data for that case were estimated as 84.16% and 0.36, respectively. This prediction capability shows that the landslide susceptibility map produced in this research paper can be used for the planning of protective and mitigation measures in the region.     

武汉市土地利用覆被变化与生态环境效应研究

利用1996年和2006年两期TM遥感影像解译图获取武汉市土地利用变化数据。基于GIS空间分析与数理统计方法对1996~2006年武汉市土地利用/覆被变化特征进行了定量分析。运用区域生态环境质量指数和区域土地利用变化类型生态贡献率,对研究时段内武汉市土地利用变化的生态环境效应以及导致武汉市生态环境质量变化的土地利用变化类型进行了分析与评价。研究结果表明:① 1996~2006年期间武汉市土地利用变化主要表现为耕地、草地和未利用土地的减少,林地、建设用地和水域面积的增加的态势。② 1996年~2006年武汉市的区域生态环境质量指数从0.451 上升至0.468。生态环境在一定程度上维持着相对平衡,并呈现出一种上升趋势。但生态环境改善和恶化的两种趋势并存。③ 局部地区生态环境呈负向发展,其中城市化引起的空间扩张对区域土地利用/覆被变化产生影响最为深刻。     

影响中国黄土磁化率差异的多因素评述

 通过查阅最近30 a来对黄土古土壤磁化率与气候的关系等方面的研究结果,认为：成壤作用过程中由生物作用和风化作用产生的超顺磁粒级的磁铁矿、磁赤铁矿等强磁性矿物是导致古土壤磁化率增强的主要原因。另外,磁化率值高低也不容忽视其他因素的影响,如黄土古土壤本底值的区别、淋溶作用、有机质对磁性矿物老化的阻碍及其本身的磁化率等。因此,黄土古土壤磁化率的高低是在气候条件为主导的作用下多种因素参与的结果。     

滇东北拱王山地区全新世以来的气候环境变化

对滇北拱王山狐狸房峰南侧老碳房村附近冰蚀湖相沉积剖面进行粒度、磁化率分析,结合^14C年代,建立了8．4ka至今的古气候环境变化序列。全新世早期以来经历5次较明显的气候波动：8．4—7．7ka,磁化率（xlf）变化在1．62-3．88μm^3kg^-1,粒度平均值为5．46φ,气候凉湿;7．7～6．5ka,粒度均值为5．83φ,磁化率降低,变化在1．44-2．28μm^3kg^-1。之间,气候冷湿;6．5-4．7ka,粒度均值为5．95φ,磁化率值变化在1．53—3．65μm^3kg^-1之间,气候冷干;4．7-2．1ka,粒度均值为5．91φ,磁化率增加,变化在2．0—5．2μm^3kg^-1。之间,气候凉湿;2．1ka以来,粒径平均值为5．63φ,磁化率值显著增加,介于3．44-6．33μm^3kg^-1。之间,平均值为4．80μm^3kg^-1,气候以暖湿为主。     

基于粗糙集的支持向量机滑坡易发性评价

区域滑坡易发性评价对灾害中长期预测预报具有重要意义。以三峡库区秭归至巴东段为研究区,利用粗糙集理论对20个初始评价因子进行属性约简,去掉冗余或干扰信息,得到13个核心评价因子,并以此作为支持向量机的输入特征集,构建支持向量机模型,实现滑坡易发性评价。在易发性分区图中高易发区占8.2%,主要分布在童庄河右岸、归州河沿岸、青干河左岸、树坪至范家坪长江右岸、牛口到东壤口长江左岸和巴东附近;不易发区占 52.7%,主要分布于店子湾至巴东旧城以及远离长江水系及植被覆盖度高的区域。通过验证与分析,粗糙集-支持向量机模型在高中易发区中的预测精度为85.6%,其预测能力优于支持向量机模型;与野外调查对比,预测结果与实际情况吻合较好。研究表明,应用粗糙集和支持向量机相结合进行滑坡易发性评价具有预测能力强、计算效率高等优点。