柴达木盆地东部微体化石丰度与天然气源岩

柴达木盆地东部三湖坳陷上新统第四系自生自储的天然气藏,已成为我国四大天然气产区之一。与柴达木盆地侏罗系、古近系一新近系原油伴生气与凝析气不一样,三湖坳陷生物成因气的气源岩母质主要是浅水湖泊动植物的有机质。在该区诸多门类的生物群落中,具有双壳易于保存为化石的微体动物介形类的丰度,与生物成因气源岩的分布密切相关,介形类化石丰度高低变化基本上反映了气源岩的好坏或有无。本文以三湖坳陷介形类化石丰度纵横向变化,提出了三湖坳陷进一步勘探天然气的方向：向坳陷北、东和南部比向西部红三旱四号方向较有利,因为该坳陷西部湖水含盐量太高,介形类等水生生物能生存者不多,生烃母质量少。     

近50年来松嫩平原西部土地利用变化及驱动力分析

在遥感和地理信息系统技术支持下,对松嫩平原西部土地利用方式、景观格局的动态变化进行定量研究。结果表明:过去近50年里,松嫩平原西部耕地净增加29.65×10⁴hm²,其中草地与湿地对耕地的增加贡献最大;草地减少70.29×10⁴ hm²,除部分草地转化为耕地外,退化为盐碱地、沙地也是草地大量减少的一个重要原因;松嫩平原西部现有沼泽湿地面积44.88×10⁴ hm²,近50年来减少62.54%。景观破碎化严重,不同土地利用类型间频繁转化,表明各种土地景观间存在不稳定因素,自然和人为因子在景观的转化中有不可忽略的作用,但人为因子的作用更显著。     

华北地台类的演化与煤层生成

以华北地台59处创面全涵盖式研究为基础,建立了华北石炭、二叠纪类生物群演化系列,并划分为6带13亚带,从中抽取4个与煤层生成关系密切的阶段加以详细讨论,分别称为：（1）Pr阶段;（2）T阶段;（3）Ps阶段和（4）S阶段。其中第1阶段反映煤盆地的构造启动,另外3个阶段分别代表太原组底部厚煤层、山西组主煤层、南华北区大风口组煤层的生成机制。     

三维地震神经网络反演在探明煤层富水灾害层的应用

在常规测井约束反演的的基础上,开展神经网络特征参数反演,将波阻抗等地震属性转化为与含水性更为密切的孔隙度、视电阻率数据体,使地震反演的地质属性与测井上的地质属性达到最优的相关性,从而实现应用三维地震对煤层顶板富水进行评价的目的。由于煤层顶板富水区的特殊性质,它同样也是地震后的易破坏层,因而对它的探明从抗震角度以及震害预测角度都是有价值的。以淮北某采区为例,通过孔隙度及电阻率的神经网络反演对研究区10#煤层顶板的富水性进行预测。反演结果表明采区北部发育一个强富水陷落柱,与钻孔揭示结果吻合。采区西部10#煤层顶板与第四系含水层呈不整合接触关系,神经网络反演结果预测为强富水区,同样与井下工程揭示富水特征吻合。利用多属性融合的神经网络反演可有效预测煤层顶板的富水特征,为煤矿安全生产以及抗震提供重要保障。     

Preliminary study on community structures of meiofauna in the middle and eastern Chukchi Sea

Sediment core samples were collected from 17 stations in the middle and eastern Chukchi Sea during the sixth Chinese National Arctic Research Expedition(CHINARE-Arctic) in summer 2014.The samples were analyzed for composition,abundance,biomass,vertical distribution,size spectra,and ecological indexes of meiofauna.A total of 14 meiofauna taxa were detected,and the free-living marine nematodes comprised the most dominant taxon,accounting for 97.21% of the average abundance.The abundance and biomass of meiofauna were within ranges of(218.12±85.83)-(7 239.38±1 557.15) ind./(10 cm~2) and(130.28±52.17)-(3 309.56±1 751.80) μg/(10 cm~2),with average values of(2 391.90±1 966.19) ind./(10 cm~2) and(1 549.73±2 042.85) μg/(10 cm~2)(according to dry weight)respectively.Furthermore,91.26% of the individuals were distributed in the top layer of 0-5 cm of surface sediment,and 90.84% had sizes of 32-250 μm.Group diversity index of meiofauna in the survey area was low,and the variation of abundance was the main difference in meiofauna communities among all stations.Abundance and biomass of meiofauna were not significantly correlated with environmental factors except concentration of nutrient Si in bottom seawater.Abundance of meiofauna in shallow water of marginal seas in the Pacific sector of the Arctic Ocean is likely at a same level and higher than that in most of China sea areas,suggesting that the shallow water of the summer Chukchi Sea is a continental shelf area with rich resources of meiofauna.The Chukchi Sea is important for studying the ecosystem of the Arctic Ocean and environmental responses.However,studies on meiofauna in the Chukchi Sea are still not enough,and in the future,natural and human disturbances may increase due to global warming,the Arctic channel opening,and other factors.Thus,more studies on meiofauna should be required,in order to know more about how the Arctic benthic community would alter.     

Discrimination and classification of mangrove forests using EO-1 Hyperion data: a case study of Indian Sundarbans

In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels.     

基于Google Earth Engine的红树林年际变化监测研究

遥感技术已广泛应用于红树林资源调查与动态监测中,但仍然存在遥感数据获取困难、数据预处理工作量大、监测时间长而周期过大等问题,影响了学者对红树林演变过程的精细刻画与理解。本文基于Google Earth Engine（GEE）云遥感数据处理平台,选取Landsat系列卫星数据,生成长时间序列年际极少云影像集（云量少于5%）,利用3个红外波段反射率（NIR、SWIR1、SWIR2）和3个特征指数（NDVI、NDWI、NDMI）建立阈值规则集,实现对实验区越南玉显县红树林、红树林-虾塘、不透水面-裸地、水体4种目标地物的专家知识决策树分类和土地覆盖的制图,并基于分类结果监测该区域1993-2017年的红树林年际动态变化。结果表明：GEE平台可满足多云多雨地区红树林的长时间序列年际变化监测需求;本文阈值分类方法可以有效提取红树林及红树林-虾塘,实验区有86%年份的影像分类精度达到80%以上;年际变化监测可精细刻画实验区红树林面积先增后减再增的变化过程,也能准确反映红树林与红树林-虾塘养殖系统面积之间的负相关关系。红树林年际动态监测结果可以降低红树林演变分析的不确定性,并能更精细地量化红树林与其他土地覆盖类型的转化过程,从而评估经济发展、政策等因素对红树林演变的影响。     

Characterizing the Spatio-temporal Dynamics and Variability in Climate Extremes over the Tibetan Plateau during 1960-2012

Extreme climate events play an important role in studies of long-term climate change. As the Earth’s Third Pole, the Tibetan Plateau (TP) is sensitive to climate change and variation. In this study on the TP, the spatiotemporal changes in climate extreme indices (CEIs) are analyzed based on daily maximum and minimum surface air temperatures and precipitation at 98 meteorological stations, most with elevations of at least 4000 m above sea level, during 1960-2012. Fifteen temperature extreme indices (TEIs) and eight precipitation extreme indices (PEIs) were calculated. Then, their long-term change patterns, from spatial and temporal perspectives, were determined at regional, eco-regional and station levels. The entire TP region exhibits a significant warming trend, as reflected by the TEIs. The regional cold days and nights show decreasing trends at rates of -8.9 d (10 yr)^-1 (days per decade) and -17.3 d (10 yr)^-1, respectively. The corresponding warm days and nights have increased by 7.6 d (10 yr)^-1 and 12.5 d (10 yr)^-1, respectively. At the station level, the majority of stations indicate statistically significant trends for all TEIs, but they show spatial heterogeneity. The eco-regional TEIs show patterns that are consistent with the entire TP. The growing season has become longer at a rate of 5.3 d (10 yr)^-1. The abrupt change points for CEIs were examined, and they were mainly distributed during the 1980s and 1990s. The PEIs on the TP exhibit clear fluctuations and increasing trends with small magnitudes. The annual total precipitation has increased by 2.8 mm (10 yr)^-1 (not statistically significant). Most of the CEIs will maintain a persistent trend, as indicated by their Hurst exponents. The developing trends of the CEIs do not show a corresponding change with increasing altitude. In general, the warming trends demonstrate an asymmetric pattern reflected by the rapid increase in the warming trends of the cold TEIs, which are of greater magnitudes than those of the warm TEIs. This finding indicates a positive shift in the distribution of the daily minimum temperatures throughout the TP. Most of the PEIs show weak increasing trends, which are not statistically significant. This work aims to delineate a comprehensive picture of the extreme climate conditions over the TP that can enhance our understanding of its changing climate.     

1960-2016年黄土高原多尺度干旱特征及影响因素

明晰黄土高原干旱特征对于生态工程建设和社会经济可持续发展具有至关重要的作用。基于1960—2016年黄土高原59个气象台站数据和标准化降水蒸散指标（SPEI）,本文分析了黄土高原多尺度干旱时空变化特征,并探讨了遥相关指数对黄土高原干旱变化的影响。结果表明：① 黄土高原SPEI指数呈下降趋势,其中70年代初和90年代末为显著干旱时期,80—90年代初为较湿润时期;② 年尺度SPEI呈下降趋势的区域遍布整个黄土高原,以山西西部、宁夏北部和甘肃中东部最为显著,而黄土高原西北和西南部则表现为变湿趋势;③ 春、夏和秋三季SPEI均呈下降趋势,且夏、秋季下降趋势较大,趋势系数均为-0.03/10a。春季干旱趋势与年际变化较为一致,秋季干旱趋势范围较大,占总面积的64.53%,冬季干旱范围较小且不显著;④ 多尺度干旱同时受IOD、NAO、PDO、AMO和ENSO3.4等遥相关指数的共同影响,且该影响存在明显的年际和年代际相位转换特征。多元回归分析显示,IOD和NAO对黄土高原干旱解释率较高,分别为22.98%和12.23%,而ENSO3.4解释率较低,表明黄土高原降水变化与西南季风具有较好的关联性。     

Spatial considerations when monitoring reef fishes

Spatial dependence can obscure relationships between response and explanatory variables because of structuring within the residuals reducing variance and biasing coefficient estimates. Here, we highlight the influence of the spatial component, in the presence of spatial dependence, on abundance trends. This is illustrated using abundance data for a Critically Endangered reef fish, dageraad Chrysoblephus cristiceps, which were obtained from a long-term monitoring programme in the Tsitsikamma National Park marine protected area, South Africa. Correlograms illustrate distinct spatial structuring in the abundance data, and spatial variables were determined as more important than temporal variables when ranked according to predictive power using a random forest analysis. A generalised additive model (GAM) that did not account for spatial dependencies was compared to a generalised additive mixed model (GAMM) that incorporated a spatial residual correlation structure. Results derived from the spatially explicit GAMM differed considerably from the GAM lacking a spatial component, with the latter deemed to produce over-precise and partially biased abundance trends. The study emphasises the importance of space in accurately modelling abundance estimates, particularly temporal trends, and provides an introduction to the minimal statistical requirements necessary to address the violations associated with spatial autocorrelation.