Incorporating spectral data into logistic regression model to classify land cover: a case study in Mt. Qomolangma (Everest) National Nature Preserve

Only few models for land-cover classification incorporated spectral data into ordinary logistic regression (OL model) in the Mt. Qomolangma (Everest) National Nature Preserve (QNNP) in China. In this study, spectral variables were incorporated into OL model and autologistic regression (AL) model to classify six main land covers. Twelve environmental variables and seven spectral variables of 10,000 stratified random sites in the QNNP were quantified and analyzed; OL model, AL model, OL model with spectral data (OLM model), and AL model with spectral data (ALM model) were estimated. The OLM and ALM models produced better estimates of regression coefficients and significantly improved model performance and overall accuracy for the grassland, sparsely vegetated land, and bare land compared with OL and AL models.     

加权频谱相干法在识别地层各向异性类型中的应用

利用交叉偶极声波数据可以提取出快慢弯曲波曲线,在此基础上利用加权频谱相干法可以稳定的提取出快慢弯曲波频散曲线.利用快慢弯曲波频散曲线的形态能够区分各向异性的类型.若产生交叉现象,则证明应力作用明显.若两者重合,则证明该点地层属于各向同性地层.当地层中存在页岩、裂缝等地层固有的各向异性特征时,快慢弯曲波曲线在整个频段分开,并在低频处逼近地层真实慢度.     

频谱激电法在安徽沙溪斑岩铜矿中的应用

为探查安徽沙溪斑岩型铜矿区深部矿体及隐伏矿体,同时为了试验频谱激电法在此类矿产探测中的应用效果,在安徽沙溪斑岩铜矿区布置了一条2 km的频谱激电法(SIP)测线,经过一系列的数据处理及反演,得到7个不同电性参数断面图,对比分析并结合钻孔地质剖面,视电阻率和相位断面图较好的反映了矿体的形态,极化率和金属因子断面图较好的反映了矿体,电性异常与实际矿体的位置及埋深有较好的一致性.试验结果表明,频谱激电法在斑岩铜矿探测中可以取得较好的效果,该方法在高电阻率、高极化率的金属矿的探测中同样可以发挥重要的作用.     

Approximation approach to the SRM based on root decomposition in the simulation of spatially varying ground motions

The spectral representation method(SRM) is widely used to simulate spatially varying ground motions.This study focuses on the approximation approach to the SRM based on root decomposition,which can improve the efficiency of the simulation.The accuracy of the approximation approach may be affected by three factors: matrix for decomposition,distribution of frequency interpolation nodes and elements for interpolation.The influence of these factors on the accuracy of this approach is examined and the following conclusions are drawn.The SRM based on the root decomposition of the lagged coherency matrix exhibits greater accuracy than the SRM based on the root decomposition of the cross spectral matrix.The equal energy distribution of frequency interpolation nodes proposed in this study is more effective than the counter pith with an equal spacing.Elements for interpolation do not have much of an effect on the accuracy,so interpolation of the elements of the decomposed matrix is recommended because it is less complicated from a computational efficiency perspective.     

A spatial cross‐correlation model of spectral accelerations at multiple periods

Many seismic loss problems (such as disruption of distributed infrastructure and losses to portfolios of structures) are dependent upon the regional distribution of ground‐motion intensity, rather than intensity at only a single site. Quantifying ground‐motion over a spatially‐distributed region therefore requires information on the correlation between the ground‐motion intensities at different sites during a single event. The focus of the present study is to assess the spatial correlation between ground‐motion spectral accelerations at different periods. Ground motions from eight well‐recorded earthquakes were used to study the spatial correlations. On the basis of obtained empirical correlation estimates, we propose a geostatistics‐based method to formulate a predictive model that is suitable for simulation of spectral accelerations at multiple sites and multiple periods, in the case of crustal earthquakes in active seismic regions. While the calibration of this model and investigation of its implications were somewhat complex, the model itself is very simple to use for making correlation predictions. A user only needs to evaluate a simple equation relying on three sets of coefficients provided here to compute a correlation coefficient for spectral values at two periods and at a specified separation distance. These results may then be used in evaluating the seismic risk of portfolios of structures with differing fundamental periods. Copyright © 2012 John Wiley & Sons, Ltd.     

施甸盆地三维地震动研究

本文利用三维施甸盆地模型以及川滇地区的地壳速度结构,采用谱元法和并行计算技术,考虑地形的影响,模拟了盆地外围中等设定地震作用下施甸盆地地震波的传播,分析了面波的产生和传播,研究了该盆地不同位置剖面地震动的差异和原因.研究表明,小型盆地在中等地震的作用下盆地内部地震动空间差异很大,盆地中较深的小型凹陷更容易汇聚面波,形成更强的振动,位于覆盖层厚度剧烈变化区域的地震动强度也会很大,甚至大于覆盖层厚的区域.较基岩地震动,盆地内地震动持续时间明显增加,地震动的放大作用明显,这也是盆地震害严重的主要原因之一.     

Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi‐scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust‐producing sources. The representation of intra‐annual and inter‐annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter‐annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas. Copyright © 2012 John Wiley & Sons, Ltd.     

Turbidity dynamics in karstic systems. Example of Ribaa and Bittit springs in the Middle Atlas (Morocco)

Abstract

The identification of the turbidity mechanisms in two karstic springs (Ribaa and Bittit) located in the Middle Atlas Plateau in Morocco was performed by means of correlation and spectral analyses applied to time series of rainfall, flow rates, and turbidity. Time series analyses of rainfall and discharges revealed high inertia and storage capacities of the karstic systems. However, the occurrence of turbidity in the springs proved independent of discharges. A causal relationship between rainfall and the occurrence of turbidity in the form of waves was established. Accordingly, turbidity was assumed to be related to the hydrodynamic conditions prevailing in the karst. Turbulent quickflows in the karst transmissive conduits, following heavy rainfall, are thought to provoke the resuspension of solid particles deposited in the conduits, as well as their transport towards outlets. An external origin has also been contemplated, concerning infiltration waters may be loaded with suspended matters washed from the watershed.     

Spatiotemporal reflectance blending in a wetland environment

To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency, this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area, China, using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005. The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM. Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance. It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is <2.68 m to achieve blending accuracy with a mean average absolute difference of 4% between the observed and blended reflectance in all spectral bands.     

Introducing sensor spectral response into the classification process

Many sensors have their bands overlapped and therefore do not set a normal space. If a spectral distance is measured, as in first-order statistical classifiers, the direct consequence is that the result will not be the most accurate. Image classification processes are independent of the spectral response function of the sensor, so this overlap is usually ignored during image processing. This paper presents a methodology that introduces the spectral response function of sensors into the classification process to increase its accuracy. This process takes place in two steps: first, incident energy values of the sensors are reconstructed; second, the energy of the bands is set in an orthonormal space using a matrix singular value decomposition. Sensors with and without overlapping spectral bands were simulated to evaluate the reconstruction of energy values. The whole process was implemented on three types of images with medium, high and very high spatial resolution obtained with the sensors ASTER, IKONOS and DMC camera, respectively. These images were classified by ISODATA and minimum distance algorithms. The ISODATA classifier showed well-defined features in the processed images, while the results were less clear in the original images. At the same time, the minimum distance classifier showed that overall accuracy of the processed images increased as the maximum tolerance distance decreased compared to the original images.