中国主要牧区雪灾遥感监测评估模型研究

该文以中国雪灾高发区的西藏那曲地区为例，主要介绍了在试验区雪灾背景数据库支持下，利用ＮＯＡＡ／ＡＶＨＲＲ卫星遥感资料对雪灾进行判别，预测及对灾情损失进行综合评估的一些技术和方法，其内容包括：（１）用于雪灾危险等级分析与判定的判别模型；（２）用于灾情发展趋势模拟与预测的预测模型；（３）用于灾情损失综合评估与估算的评介模型。并运用这些模型对１９９５年２－３月发生在西藏那曲的雪灾进行实时分析，其结果和实     

Biogeochemical aromatic markers in the sediments from mahakam delta (Indonesia)

Analysis of the tri-, tetraand pentaaromatic hydrocarbon fractions of several sediments (0–600 m) from the Mahakam Delta (Indonesia), indicate the predominance of polycyclic aromatic hydrocarbons (PAH) diagenetically related to triterpenoid natural precursors. High resolution Shpol'skii spectrofluorometry (HRS) proved to be a valuable analytical tool, in conjunction with high performance liquid chromatography (HPLC) and capillary gas chromatography coupled with mass spectrometry (CGC/MS), for the identification of individual components in Chromatographic fractions obtained after a high resolution reverse phase liquid chromatography.     

辽河盆地西部凹陷下第三系原油和生油岩的生物标记化合物研究

本文应用色谱-质谱和气相色谱等技术分析鉴定了辽河断陷盆地西部凹陷下第三系原油和生油岩饱和烃中的甾、萜烷等生物标记化合物。并利用甾、萜烷等生物标记化合物和其它地质、地球化学资料,讨论了油、岩的生物先体性质,将其做了分类对比,确定了沙河街组、东营组和前中生界潜山原油的原生性和次生性。     

基于数学形态学的IKONOS多光谱图像分割方法研究

利用数学形态学方法,研究与探讨了IKONOS多光谱图像的分割技术.提出一种结合图像边缘特征和纹理特征的混合分割新算法.在高分辨率多光谱遥感图像K-L变换的基础上,采用多尺度多方向形态学梯度算子提取边缘特征.应用数学形态学滤波及局部方差统计特征对图像对象进行标记,最后采用强制最小过程,进行标记控制的分水岭分割.研究结果表明,提出的分割算法优于仅利用边缘特征的分水岭分割算法,同时,该算法能较好地解决分割过程中存在的过分割与欠分割问题,是一种适合高分辨率多光谱遥感图像的分割算法.     

Remotely-sensed phenology of Italian forests: Going beyond the species

Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.     

翘嘴鳜(Siniperca chuatsi)EST-SSR标记与生长性状相关性及4个选育群体遗传结构研究

采用翘嘴鳜(Siniperca chuatsi)转录组高通量测序并筛选得到的13对多态性EST-SSR引物,对60尾翘嘴鳜的体长、体高、体厚及体重4种生长性状进行标记-性状相关性研究,并对4个翘嘴鳜选育群体各30尾个体进行遗传结构分析。结果显示,与其生长性状显著相关的微卫星分子标记有5个,包括:分别与体厚和体长极显著相关(P0.01)和显著相关(P0.05)的标记C10127,AA型为体长、体厚的优势基因型,而AC型为劣势基因型;与体重显著相关(P0.05)的标记C19016,AA型为体重的优势基因型,AB型为体长的劣势基因型;与体厚显著相关(P0.05)的标记C14847,BB型为体厚的优势基因型;分别与体长、体高和体重、体厚极显著相关(P0.01)和显著相关(P0.05)的标记C12350,BC型为体长、体厚、体高和体重性状的优势基因型;分别与体厚、体重和体高极显著相关(P0.01)和显著相关(P0.05)的C18691标记,BB型为体厚、体重和体高性状的优势基因型。4个翘嘴鳜群体遗传多样性高低依次为太湖选育群体长江口选育群体洪泽湖选育群体鄱阳湖选育群体,其平均的等位基因数、有效等位基因数、观测杂合度、期望杂合度和多态信息含量分别为2.2115、1.9405、0.55513、0.5925和0.3775,属于中度多态水平。     

Application of Bayesian model and discriminant function analysis to the estimation of sediment source contributions

Bayesian and discriminant function analysis (DFA) models have recently been used as tools to estimate sediment source contributions. Unlike existing multivariate mixing models, the accuracy of these two models remains unclear. In the current study, four well-distinguished source samples were used to create artificial mixtures to test the performance of Bayesian and DFA models. These models were tested against the Walling-Collins model, a credible model used in estimation of sediment source contributions estimation, as a reference. The artificial mixtures were divided into five groups, with each group consisting of five samples with known source percentages. The relative contributions of the sediment sources to the individual and grouped samples were calculated using each of the models. The mean absolute error (MAE) and standard error of (SE) MAE were used to test the accuracy of each model and the robustness of the optimized solutions. For the individual sediment samples, the calculated source contributions obtained with the Bayesian (MAE = 7.4%, SE = 0.6%) and Walling-Collins (MAE = 7.5%, SE = 0.7%) models produced results which were closest to the actual percentages of the source contributions to the sediment mixtures. The DFA model produced the worst estimates (MAE = 18.4%, SE = 1.4%). For the grouped sediment samples, the Walling-Collins model (MAE = 5.4%) was the best predictor, closely followed by the Bayesian model (MAE = 5.9%). The results obtained with the DFA model were similar to the values for the individual sediment samples, with the accuracy of the source contribution value being the poorest obtained with any of the models (MAE = 18.5%). An increase in sample size improved the accuracies of the Walling-Collins and Bayesian models, but the DFA model produced similarly inaccurate results for both the individual and grouped sediment samples. Generally, the accuracy of the Walling-Collins and Bayesian models was similar (p > 0.01), while there were significant differences (p < 0.01) between the DFA model and the other models. This study demonstrated that the Bayesian model could provide a credible estimation of sediment source contributions and has great practical potential, while the accuracy of the DFA model still requires considerable improvement.     

美国4 个凡纳滨对虾(Litopenaeus vannamei)种群形态差异与判别分析

采用3种多元分析方法,对来源于美国的4个凡纳滨对虾种群的13个形态比例参数进行比较研究.聚类分析结果表明,Kona Bay种群与Molokai种群形态差异很小,而SIS种群、OI种群趋异程度逐渐增加.判别分析结果显示,4种群的13个形态比例参数有着极显著的差异(P>0.01);4个种群雌虾的判别准确率P1为61.76%-97.14%,判别准确率P2为62.03%-96.43%,综合判别率为80.25%;4个种群雄虾的判别准确率P1为75.76%-97.06%,判别准确率P2分别为67.57%-97.06%,综合判别率为80.71%,各种群的判别函数判别准确率均较高;主成分分析,构建的雌体的5个主成分方差贡献率分别为,主成分1:23.594%,主成分2:19.534%,主成分3:15.748%,主成分4:13.653%,主成分5:8.653%.累积贡献率为81.182%.而在构建的雄体的5个主成分中,方差贡献率分别为主成分1:22.095%,主成分2:19.270%,主成分3:15.430%,主成分4:15.245%,主成分5:7.885%.累积贡献率为79.925%.综上所述,Kona Bay种群与Molokai种群应是同一种群,与SIS种群亲缘关系较为接近,SIS种群和OI种群为独立的种群.     

利用脂肪酸标记法对南极罗斯海桡足类优势物种的食性研究

脂肪酸标记法是浮游动物食性研究的一种重要方法.将提取的脂质区分为中性脂和极性脂,可以进一步查明不同脂质在浮游动物食性研究中的作用.本研究通过分析不同脂质的脂肪酸构成,对2013年1月中国第29次南极考察期间在罗斯海采集的5种优势种桡足类的食性进行了研究.结果显示:5种桡足类的极性脂脂肪酸具有相似的组成,无法给出桡足类食性差异信息,食性信息主要体现于中性脂脂肪酸组成的差异.通过分析中性脂脂肪酸的组成信息,将5种桡足类的食性分为三类:Cala-noides acutus和Calanus propinquus主要摄食浮游植物,Metridia gerlachei及小型桡足类Oncaea curvata为杂食性种,Paraeuchaeta antarctica为肉食性种.本文的结果说明,在应用脂肪酸作为浮游动物食性标记的研究中,应尽量区分不同脂质种类的脂肪酸信息,从而得到更为精确的浮游动物食性信息.