天津地区的水平形变特征

根据天津及周边地区GNSS连续观测站资料,在计算得到各站三维站坐标时间序列结果的基础上,文章分析了天津地区GNSS基线时间序列的变化情况和水平形变变化特征.结果显示,天津地区总体呈现压扭变化,不同地段存在差异.天津地区NE-SW向和NW-SE向GNSS基线的伸长和缩短变化呈北西向条带状相间分布.日本3·11地震使天津地壳出现近东西向拉张为主的活动,一定程度上释放消减了天津地区的挤压应变.     

Accurate mapping of forest types using dense seasonal Landsat time-series

An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales.     

Operational Parameters,Data Density and Benthic Ecology: Considerations for Image-Based Classification of Multibeam Backscatter

Efforts to develop a procedurally robust method for automated classification of multibeam backscatter have taken a variety of approaches (e.g., image-based, textural, angular range analysis). For image-based classification, little research has focused on the roles of operational parameters of vessel and sonar system in affecting the final classification. Repeat multibeam surveys (2005 and 2006) conducted at the same area with different sounding densities were classified using QTC-Multiview. Comparison of class areas revealed 78% agreement between classifications derived from the two surveys. Cross-tabulation of ground truth video and class demonstrate 71% agreement in the low-density survey and 77% for the high-density. Differences between classifications are primarily attributed to variation in along track data density, errors in the compensation process, and/ or insufficient quality control of the input data. Natural change detection at the scales observed was determined not to be practically discernable from the errors associated with the classification process.     

Localised intermittent upwelling intensity has increased along South Africa’s south coast due to El Niño–Southern Oscillation phase state

The El Niño–Southern Oscillation (ENSO) phase state is reported to drive interannual variability in sea temperatures along South Africa’s south coast through its influence on wind-induced upwelling processes. Whether ENSO drives the intensity of localised, abrupt, intermittent upwelling is less well known. To explore this relationship, we used an index of localised, extreme (>2 °C anomaly), intermittent upwelling intensity, derived from in situ sea temperature data within the Tsitsikamma National Park Marine Protected Area, and quantified the relationship between annual cumulative upwelling intensities (1991–2013) with an annual ENSO index, namely the Southern Oscillation Index. We found that ENSO phase state modulates the cumulative intensity of extreme intermittent upwelling events during an annual period, with more and greater events during La Niña phases compared with El Niño phases. Furthermore, these extreme upwelling events have increased with time along South Africa’s south coast as ENSO phase state becomes more intense and variable. Our findings support the emerging notion that the biological effects of climate change may be manifested through increased environmental variability rather than long-term mean environmental changes as ENSO is predicted to remain the dominant driver of local climate patterns in the future.     

基于MODIS EVI时序数据的云南高原山地森林物候特征识别

基于2000—2011年MOD13Q1产品的EVI时序,借助QA-SDS数据集消除云、阴影和冰雪等的影响后,采用非对称高斯函数拟合法进行时序重构,并运用动态阈值法提取云南高原山地典型森林植被的物候特征参数(即生长期开始时间、峰值时间、结束时间和生长期长度),进而分析了不同植被类型物候期规律及其主要控制因素。结果表明:1.从寒温性森林植被到热性森林植被的EVI值呈递增趋势;2.森林植被生长期开始时间、峰值时间和结束时间分别大致发生在3月中旬至4月中下旬、6月中旬至下旬和8月中旬至10月初,生长期长度为135～195 d;3.由寒温性植被向热性植被的生长期高峰时间和生长期结束时间总体呈延迟趋势,且生长期延长,生长期开始时间则由暖性植被向寒温性植被、暖性植被向热性植被双向提前;4.高原山地热量梯度决定了森林植被物候的空间格局,水分条件则主要控制着EVI和物候期的年际波动。     

基于小波消噪的时序分析改进法在GPS变形监测中的应用

考虑到小波技术在粗差探测、消噪方面有特殊的作用,本文结合实际算例,把小波分析与传统数据处理技术结合作为研究对象,提出一种基于小波消噪的时序分析改进法。并对这种处理方法在GPS监测中的应用作了充分地论证。结果表明,经过小波处理后,时序分析法在预报方面有明显的进步,小波处理后的时序分析法比小波处理前的精度高,从而说明了这种改进方法的可行性。     

基于时间序列模型的隧道涌水量反演与预测

鉴于使用确定性模型预测隧道涌水量时存在难以准确获取水文地质参数等诸多不便,本文将地下水系统视为"黑箱"模型,通过提取隧道涌水量历史观测数据本身蕴涵的趋势、周期和随机变化规律,建立了隧道涌水的时间序列预测模型。经用于铜锣山隧道实例,反演系列的平均绝对误差为14.67%,预测序列的平均绝对误差为14.34%,表明模型的整体预测精度较高。     

Annual variation in the nocturnal nekton assemblage of a tropical estuary

A nocturnal demersal nekton assemblage was sampled fortnightly for two years at five sites in the Labu estuary using a 3 m beam trawl with a 3·2 cm mesh net. Forty-eight species were caught, totalling 31 458 individuals with the five most abundant species comprising over 95% of the catch. Using multiple regression techniques with Fourier transformations, the mean number of species, S, the mean abundance, N, and mean weight, W, were found to conform to a regular annual cycle with maxima in April and May. Seven of the 11 most abundant species demonstrated regular annual cycles of abundance. S, N and W were greatest in the wider, middle sites and lowest in a shallow, stagnant side branch of the estuary. Catch weights and abundances were significantly correlated with physical data.Salinity and temperature values in the estuary exhibited an annual cycle with maxima occurring in February/March. The annual thermal variation of surface water outside the estuary followed a similar cycle. The salinity at the mouth of the Markham River is lowest during January/February, which corresponds with the rainy season in the Markham River catchment. Significant annual variation existed between years in estuarine bottom salinity and salinity values in Labu Bay.Several species exhibited a greater variation in abundance and mass between years than within years. This supports the hypothesis that in the tropics between-year variation in coastal marine biotic communities is greater than within-year variation.     

Short-run forecasting of United States coal production

Summary This paper focuses on short-range modelling and forecasting of aggregate US monthly coal production. The 1976–83 time-series data suggest a multiplicative autoregressive integrated moving average (ARIMA) model to replicate national level monthly coal production. The identified model required 12-month seasonal differencing and has an autoregressive component of lag 1 and a moving average component of lag 12. Model predictions for 1984 were very reasonable when compared with actual production: cyclical patterns were correctly replicated and the deterministic increasing trend was properly identified. The estimated model was enhanced by updating it with data for 1984. Intervention analysis was used to determine the impact of labour negotiations in coal production. Information relative to the identified ARIMA model was then used to model the intervening event of labour negotiations. Intervention modelling produced forecasts for 1984 superior to those identified by the ARIMA model. The mean predicted 1984 US monthly coal production of 1976–84 ARIMA and intervention models were 96.05 and 99.65% of the observed value of 74 178 thousand short tons per month, respectively. Simplicity of the ARIMA and intervention models, the realiability of their predictions, and the ease of updating make them very attractive when compared with large scale econometric models for use in short-term coal production forecasting.