多源多时相遥感影像相对辐射归一化方法研究

为满足多源、多时相遥感影像定量化信息提取的应用需求,本文发展了一种半自动化的相对辐射归一化方法。将多源遥感影像的相对辐射归一化分为传感器辐射校正与针对光照等外部因素的辐射归一化2个过程。首先,基于晴空影像,采用分类回归的方式获取传感器辐射校正系数;然后,利用样本传递再分类的方法实现多源影像的半自动分类和传感器辐射偏差校正;最后,基于NDVI差值直方图和类别约束的PIFs自动选取方法,实现影像的相对辐射归一化。采用准同期的GF1-WVF1和Landsat8-OLI影像以及多源时序影像对方法进行了验证,结果表明,本文方法可以对传感器间的辐射偏差进行有效纠正,并在整体上获得比传统方法更好的辐射归一化精度;同时,多源时序影像的辐射校正结果也表明,本文方法能够有效地消除时序影像间的辐射特征波动,使植被等地类的季相变化信息得到更准确地表达,为多源时序影像的协同利用提供了借鉴方法。     

内蒙古中部地区中-新元古代黑色岩系中的金矿床及找矿远景

内蒙古中部地区中—新元古代黑色岩系发育,形成于渣尔泰-白云鄂博裂陷槽内,命名为渣尔泰群和白云鄂博群。目前,仅在白云鄂博群的黑色岩系中发现并勘查了一批原生金矿。矿石主要类型为细脉型和蚀变变质岩型。金矿受层位和构造破碎带控制,金平均品位低,可形成大型或超大型矿床。因此,内蒙古中部中—新元古代黑色岩系中金矿资源潜力大,找矿远景好。     

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The analysis of turbulence time series: Statistical and correlation approaches using the minitab package

The theoretical basis and use of statistical and correlation approaches for the analysis of turbulence time series are described. In particular, the analysis of data from high frequency current meter measurements using MINITAB is presented and the data files and analytical routines are included on the software disk.     

Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation

Iraq contains the Great Mesopotamian alluvial plain of the Euphrates and Tigris rivers. Its regional vegetation phenological patterns are worthy of investigation because relatively little is known about the phenology of semi-arid environments, and because their inter-annual variation is expected to be driven by uncertain rainfall and varied topography. The aim of this research was to assess and map the spatial variation in key land surface phenology (LSP) parameters over the last decade and their relation with elevation. It is the first study mapping land surface phenology during last decade over the whole of Iraq, and one of only a few studies on vegetation phenology in a semi-arid environment. Time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference vegetation index (NDVI) data at 250 m spatial resolution and 8 day temporal resolution, were employed to map the spatial variation in three LSP parameters for the major vegetation types in Iraq during 2001–2012. LSP parameters were defined by inflection points after smoothing the vegetation phenological signals using the Fourier technique. The estimated key LSP parameters indicated that the relatively shorter length of season (LOS) in the north of Iraq resulted from a delayed start of season (SOS). Greater spatial variation occurred in the SOS than end of season (EOS), which may be due to the spatial distribution of rainfall and temperature as a function of elevation. A positive correlation was observed for SOS and EOS with elevation for all major land cover types with EOS producing the largest positive correlation (R² = 0.685, R² = 0.638 and R² = 0.588, p < 0.05 in shrubland, cropland and grassland, respectively). The magnitude of delay in SOS and EOS increased in all land cover types along a rising elevation gradient where for each 500 m increase, SOS was delayed by around 25 or more days and EOS delayed by around 22 or more days, except for grassland. The SOS and EOS also varied temporally during the last decade, particularly the SOS in the lowland, north of the country where the standard deviation was around 80 to 120 days, due mainly to the practice of crop rotation and the traditional biennial cropping system. Thus, the results of this research emphasize the effect of elevation on key LSP parameters over Iraq, for all major vegetation types.     

涟源富硒土壤研究与湖南省富硒土壤分布初探

在部省合作"湖南省娄邵盆地多目标地球化学调查"项目的资助下,系统地采集测试了涟源市耕地和园地的表层土壤样品。调查发现该地区发育了大量的富硒土壤,富硒土壤的来源主要与震旦系上统—寒武系下统和二叠系中上统的黑色岩系有关。上述黑色岩系分布区域的表层土壤具有全区最高的硒含量,均大于1 mg/kg,部分甚至达到硒过量的水平。经文献研究和综合评价,推断黑色岩系风化物质扩散堆积形成的山间盆地是湖南省快速高效的寻找富硒土壤的理想靶区。     

Multi‐depth monitoring of electrical conductivity and temperature of groundwater at a multilayered coastal aquifer: Jeju Island,Korea

To supplement conventional geophysical log data, this study presents temporal variations in electrical conductivity (EC) and temperature with depth in a multilayered coastal aquifer, on the eastern part of Jeju Island, Korea. One‐month time‐series data obtained at eight points from a multi‐depth monitoring system showed that semidiurnal and semimonthly tidal variations induced dynamic fluctuations in EC and temperature. At some depths, EC ranged from 1483 to 26 822 µS cm^?1, while some points showed no significant variations. The results of EC log and time‐series data revealed that a sharp fresh‐saltwater interface occurred at low tide, but the diffusion zone broadened to 20 m at high tide. EC, temperature, and tide level data were used for the cross‐correlation analysis. The response time of EC and temperature to tide appears to range from less than 30 min to 11 h. Using end‐member mixing analysis (EMMA), the fraction of variations of chloride concentration in the multilayered aquifer was explained, and a conceptual model was developed which subdivided the coastal aquifer into four vertical zones. The percentage of water derived from seawater varied from 2 to 48 at specific depth, owing to tidal fluctuations. Continuous observations of EC and temperature at multiple depths are powerful tools for quantifying the transport of saline water by tidal variations in multilayered coastal aquifers. Copyright © 2008 John Wiley & Sons, Ltd.     

Clustering spatial–temporal precipitation data using wavelet transform and self-organizing map neural network

A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data (1982–2003) for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial–temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of clusters based on the Haar wavelet, which captures the occurrence of extreme hydrological events, appear in blocks while those classified by the Morlet wavelet, which indicates periodicity changes and describes fine structures, appear in strips that cross the island of Taiwan. Principal component analysis is applied to the precipitation data of each cluster. The first principal components explain 62–90% of the total variation of data. Characteristics of precipitation data for each cluster are explored using scalogram analysis. The results show that both extreme hydrological events and periodicity changes appear in the spatial and temporal precipitation data but with different characteristics for each cluster. Recognizing homogeneous hydrologic regions and identifying the associated precipitation characteristics improves the efficiency of water resources management in adapting to climate change, preventing the degradation of the water environment, and reducing the impact of climate-induced disasters. Measures for countering the stress of precipitation variation for water resources management are provided.     

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