面元细分处理资料的分辨率定量评价

以永新工区高精度采集的地震资料为例,对高精度勘探的面元细分方法及细分处理资料的分辨率进行了评价。首先给出了面元细分的方法及面元细分对地震资料分辨率的影响,介绍了现有的一些分辨率计算方法。对于纵向分辨率,主要从λ/4传统薄层分辨率和高截频二个方面进行了讨论;对于横向分辨率,从菲涅耳带半径、防空间假频、Lindsey分辨率定义、考虑偏移孔径的分辨率等四个方面进行了分析、比较。通过研究,得出了有一定借鉴意义的结论:①在现有常规检波器条件下,面元细分处理对地震资料的分辨率影响较小,其变化不大;②在单道记录信噪比已较高的情况下,想通过提高覆盖次数来提高资料的分辨率,其提升空间有限;③就横向分辨率而言,考虑偏移孔径的计算方法,结果比较准确;④对于东部地区,从性价比上考虑,覆盖次数150,200已能满足要求,太高的覆盖次数没有从本质上改进资料的质量。     

基于广义S变换的地震资料信噪分离方法

基于S变换具有良好的时频聚焦性和分时分频性,将可灵活选取窗函数的广义S变换引入到地震信号去噪处理中,系统研究了广义S变换在地震资料信噪分离中的应用。首先对地震数据进行广义S变换,然后对含噪频率剖面选取适当阈值压制噪声干扰,提取有效信号,最后重构得到去噪后的记录。经合成记录和实际地震资料处理实验证明,该方法能有效地进行信噪分离,提高地震剖面信噪比和分辨率。     

复杂山区小流域径流模拟影响因素分析

为探讨子流域划分与DEM分辨率对于地形和地表状况比较复杂的山区小流域径流模拟的影响,利用分布式水文模型SWAT(Soil and Water Assessment Tool)对复杂山区小流域进行径流模拟,从不同的子流域划分条件下分析了子流域划分对径流模拟的影响及其变化趋势.然后通过对原有的DEM(Digital Elevation Model)进行重采样生成不同格网大小的DEM作为模型输入,研究不同DEM分辨率对复杂山区小流域径流模拟的影响.研究表明:(1)存在一个比较合理的子流域划分方案使径流深比较稳定;(2)存在一个合适的DEM分辨率使模拟得到的径流深比较稳定;(3)山区复杂流域环境,要求更加精细的子流域划分才能获得(1)中的稳定径流深.     

遥感影像分辨率分析技术在滑坡研究中的应用

采用定性与定量相结合的方法对滑坡要素进行了深入分析。将滑坡要素划分为空间要素、时间要素和岩土性质要素3个方面,并分别讨论它们与遥感影像空间分辨率、时间分辨率和波谱分辨率的关系。首先对各滑坡要素进行了定性分析,然后提出了判识滑坡空间要素的定量依据;提出了通过遥感影像计算滑动速率的公式,以此作为按照时间分辨率选择遥感数据的依据;最后提出为判识岩土性质要素应采用高波谱分辨率遥感影像的观点。     

Terrestrial surface-area increment: the effects of topography,DEM resolution,and algorithm

The area increment of land surface compared with its projected area is an effect of topographic relief and is also a source of environmental variations. To examine the effects of topography and data resolution on surface area calculation, we calculated incremental area coefficients (IACs), based on two different algorithms, for a DEM of China at a series of spatial resolutions. Sampling the DEM with a regional network of 50?km?×?50?km cell size, we explored the relationships among the two IACs and topographic features. Both IACs studied were exponential functions of resolution. At 30-m resolution, the IACs were 4.31 and 4.89% over China, respectively. The largest increment for a 50?km?×?50?km cell was >45%. Between the IACs there was a linear relationship that varied with DEM resolution. Hierarchical variation partitioning revealed that the factors included contributed in a very similar percentage composition to the two IACs, mean slope (37.5 or 38.7%) and standard deviation of slope (22.3 or 19.6%) at local scale dominated the area increment, followed by regional elevation range. Data resolution contributed about 10%, while the deviation of slope exposure only had minimal (1.4 or 1.7%) impact on surface-area increment. For a specific type of geomorphology, a threshold resolution of DEM can be determined, below which the surface-area increment (i.e., IAC) is negligible. Our results provided the first comprehensive estimate of the contributions of the topographic features, DEM resolution, and algorithms for the surface-area increment, and indicated the scale-related properties and potential environmental consequences of topographic heterogeneity in various estimates of natural resources and ecosystem functions when area needs to be taken into account.     

Estimating agricultural production in marginal and food insecure areas in Kenya using very high resolution remotely sensed imagery

Regularly monitoring the amount of food produced in food insecure, isolated, subsistence farming areas can be used to help identify households or communities who may be in need of additional food resources. Measuring seasonal food production in developing countries, particularly at a sub-national level, is complicated by lack of data. In this study we use high resolution remotely sensed data to calculate cultivated area in two different growing areas, during two different seasons in Kenya. The results of the research support the usefulness of this approach for agricultural monitoring in the developing world and suggest that monitoring cultivated area requires attention to the specific growing characteristics of an area.     

渤海风暴减水特征及其对深水航路影响的数值模拟

利用环渤海9个沿岸站近10a潮位资料分析渤海海域的风暴减水特征,结果表明:渤海年均出现50cm和100cm风暴减水分别超过30d和6d,每年的9月至翌年4月份风暴减水最为频繁;建立了一套精细化天文潮-风暴潮耦合模型用于渤海深水航路的潮位预报,各站天文潮模拟验证的平均均方根误差为18.5cm,由此计算得到航路代表点的潮汐特征值并作潮汐预报;后报模拟了近10a重大风暴减水过程,模拟与实测吻合较好,说明该耦合模型可为该航路的潮位预报提供有益参考。     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.     

Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland

Recognising the importance of the timing of image acquisition on the spectral response in remote sensing of vegetated ecosystems is essential. This study used full wavelength, 350–2500 nm, field spectroscopy to establish a spectral library of phenological change for key moorland species, and to investigate suitable temporal windows for monitoring upland peatland systems. Spectral responses over two consecutive growing seasons were recorded at single species plots for key moorland species and species sown to restore eroding peat. This was related to phenological change using narrowband vegetation indices (Red Edge Position, Photochemical Reflectance Index, Plant Senescence Reflection Index and Cellulose Absorption Index); that capture green-up and senescence related changes in absorption features in the visible to near infrared and the shortwave infrared. The selection of indices was confirmed by identifying the regions of maximum variation in the captured reflectance across the full spectrum. The indices show change in the degree of variation between species occurring from April to September, measured for plant functional types. A discriminant function analysis between indices and plant functional types determines how well each index was able to differentiate between the plant functional groups for each month. It identifies April and July as the two months where the species are most separable. What is presented here is not one single recommendation for the optimal temporal window for operational monitoring, but a fuller understanding of how the spectral response changes with the phenological cycle, including recommendations for what indices are important throughout the year.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.