利用logistic模型对MODIS数据进行湿地提取

MODIS是近年来较常用于宏观土地利用/土地覆盖的一种遥感数据来源。为了克服MODIS空间分辨率的限制,在建立提取模型的时候使用了多期遥感数据,并且加入了地形辅助数据。Logistic模型是一种非线性的回归分析模型,它通常是用于预测和判定未知单元的类别属性。先划出训练样区,计算出湿地分布与各因子之间的关系式,进而得到湿地分布概率图,选取一定的阈值(0.5),最终提取出湿地。研究结果显示,Logistic模型用于遥感分类,分类精度高于最大似然法。另外,加入了地形因子(dem,slope,tpi)后,分类精度得到明显的提高。

The study of banded spherulite patterns by coupled logistic map lattice model

Banded spherulite patterns are simulated in two dimensions by means of a coupled logistic map lattice model. Both target pattern and spiral pattern which have been proved to be existent experimentally in banded spherulite are obtained by choosing suitable parameters in the model. The simulation results also indicate that the band spacing is decreased with the increase of parameter mu in the logistic map and increased with the increase of the coupling parameter epsilon, which is quite similar to the results in some experiments. Moreover, the relationship between the parameters and the corresponding patterns is obtained, and the target patterns and spiral patterns are distinguished for a given group of initial values, which may guide the study of banded spherulite.

Simulation of Banded Spherulite Patterns by Coupled Logistic Map Lattice Model in Three Dimensions

Banded spherulite patterns are simulated in three dimensions by means of a Coupled Logistic map lattice model. The patterns obtained by numerical calculation are consistent with those in experiments. The simulation results also indicate that the hand spacing is decreased with the increase of parameter mu in the Logistic map and increased with the increase of the coupling parameter e for cube lattices, and increased with the increase of the thickness of the lattice for polymer film, which is quite similar to the results in some experiments. Spiral pattern in three dimensions is also shown in this paper, which helps us understand the form of banded spherulite in polymers.

AFLP-based genetic linkage maps of the Pacific oyster Crassostrea gigas Thunberg

Amplified fragment length polymorphisms (AFLPs) were used for genome mapping in the Pacific Oyster Crassostrea gigas Thunberg. Seventeen selected primer combinations produced 1106 peaks, of which 384 (34.7%) were polymorphic in a backcross family. Among the polymorphic markers, 349 were segregating through either the female or the male parent. Chi-square analysis indicated that 255 (73.1%) of the markers segregated in a Mendelian ratio, and 94 (26.9%) showed significant (P < 0.05) segregation distortion. Separate genetic linkage maps were constructed for the female and male parents. The female framework map consisted of 119 markers in 11 linkage groups, spanning 1030.7 cM, with an average interval of 9.5 cM per marker. The male map contained 96 markers in 10 linkage groups, covering 758.4 cM, with 8.8 cM per marker. The estimated genome length of the Pacific oyster was 1258 cM for the female and 933 cM for the male, and the observed coverage was 82.0% for the female map and 81.3% for the male map. Most distorted markers were deficient for homozygotes and closely linked to each other on the genetic map, suggesting the presence of major recessive deleterious genes in the Pacific oyster.

Phylogenetic relationships of five pika species (genus Ochotona) based on mitochondrial DNA restriction maps

Restriction site mapping of mitochondrial DNA (mtDNA) with 16 restriction endonucleases was used to examine the phylogenetic relationships of Ochotona cansus, O. huangensis, O. thibetana, O. curzoniae and O. erythrotis. A 1-kb length variation between O. erythrotis of subgenus Pika and other four species of subgenus Ochotona was observed, which may be a useful genetic marker for identifying the two subgenera. The phylogenetic tree constructed using PAUP based on 61 phylogenetically informative sites suggests that O. erythrotis diverged first, followed by O. cansus, while O. curzoniae and O. huangensis are sister taxa related to O. thibetana, The results indicate that both O. cansus and O. huangensis should be treated as independent species. If the base substitution rate of pikas mtDNA was 2% per million years, then the divergence time of the two subgenera, Pika and Ochotana, is about 8.8 Ma ago of late Miocence, middle Bao-dian of Chinese mammalian age, and the divergence of the four species in subgenus Ochotona would have occurred about 2.5 - 4.2 Ma ago, Yushean of Chinese mammalian age. This calculation appears to be substantiated by the fossil record.

滑坡泥石流信息遥感获取与空间预测

On the issue of geological hazard evaluation（GHE）, taking remote sensing and GIS systems as experimental environment, assisting with some programming development, this thesis combines multi-knowledges of geo-hazard mechanism, statistic learning, remote sensing (RS), high-spectral recognition, spatial analysis, digital photogrammetry as well as mineralogy, and selects geo-hazard samples from Hong Kong and Three Parallel River region as experimental data, to study two kinds of core questions of GHE, geo-hazard information acquiring and evaluation model. In the aspect of landslide information acquiring by RS, three detailed topics are presented, image enhance for visual interpretation, automatic recognition of landslide as well as quantitative mineral mapping. As to the evaluation model, the latest and powerful data mining method, support vector machine (SVM), is introduced to GHE field, and a serious of comparing experiments are carried out to verify its feasibility and efficiency. Furthermore, this paper proposes a method to forecast the distribution of landslides if rainfall in future is known baseing on historical rainfall and corresponding landslide susceptibility map. The details are as following: (a) Remote sensing image enhancing methods for geo-hazard visual interpretation. The effect of visual interpretation is determined by RS data and image enhancing method, for which the most effective and regular technique is image merge between high-spatial image and multi-spectral image, but there are few researches concerning the merging methods of geo-hazard recognition. By the comparing experimental of six mainstream merging methods and combination of different remote sensing data source, this thesis presents merits of each method ,and qualitatively analyzes the effect of spatial resolution, spectral resolution and time phase on merging image. (b) Automatic recognition of shallow landslide by RS image. The inventory of landslide is the base of landslide forecast and landslide study. If persistent collecting of landslide events, updating the geo-hazard inventory in time, and promoting prediction model incessantly, the accuracy of forecast would be boosted step by step. RS technique is a feasible method to obtain landslide information, which is determined by the feature of geo-hazard distribution. An automatic hierarchical approach is proposed to identify shallow landslides in vegetable region by the combination of multi-spectral RS imagery and DEM derivatives, and the experiment is also drilled to inspect its efficiency. (c) Hazard-causing factors obtaining. Accurate environmental factors are the key to analyze and predict the risk of regional geological hazard. As to predict huge debris flow, the main challenge is still to determine the startup material and its volume in debris flow source region. Exerting the merits of various RS technique, this thesis presents the methods to obtain two important hazard-causing factors, DEM and alteration mineral, and through spatial analysis, finds the relationship between hydrothermal clay alteration minerals and geo-hazards in the arid-hot valleys of Three Parallel Rivers region. (d) Applying support vector machine (SVM) to landslide susceptibility mapping. Introduce the latest and powerful statistical learning theory, SVM, to RGHE. SVM that proved an efficient statistic learning method can deal with two-class and one-class samples, with feature avoiding produce ‘pseudo’ samples. 55 years historical samples in a natural terrain of Hong Kong are used to assess this method, whose susceptibility maps obtained by one-class SVM and two-class SVM are compared to that obtained by logistic regression method. It can conclude that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (e) Predicting the distribution of rainfall-induced landslides by time-series analysis. Rainfall is the most dominating factor to bring in landslides. More than 90% losing and casualty by landslides is introduced by rainfall, so predicting landslide sites under certain rainfall is an important geological evaluating issue. With full considering the contribution of stable factors (landslide susceptibility map) and dynamic factors (rainfall), the time-series linear regression analysis between rainfall and landslide risk mapis presented, and experiments based on true samples prove that this method is perfect in natural region of Hong Kong. The following 4 practicable or original findings are obtained: 1) The RS ways to enhance geo-hazards image, automatic recognize shallow landslides, obtain DEM and mineral are studied, and the detailed operating steps are given through examples. The conclusion is practical strongly. 2) The explorative researching about relationship between geo-hazards and alteration mineral in arid-hot valley of Jinshajiang river is presented. Based on standard USGS mineral spectrum, the distribution of hydrothermal alteration mineral is mapped by SAM method. Through statistic analysis between debris flows and hazard-causing factors, the strong correlation between debris flows and clay minerals is found and validated. 3) Applying SVM theory (especially one-class SVM theory) to the landslide susceptibility mapping and system evaluation for its performance is also carried out, which proves that advantages of SVM in this field. 4) Establishing time-serial prediction method for rainfall induced landslide distribution. In a natural study area, the distribution of landslides induced by a storm is predicted successfully under a real maximum 24h rainfall based on the regression between 4 historical storms and corresponding landslides.