Wrench faults,pull-apart basins,and volcanism in central Oregon: A new tectonic model based on image interpretation

A tectonic study of the Newberry Crater region of central Oregon has been based on the interpretation of Landsat Thematic Mapper imagery. Two major faults, the Brothers-Tumalo and Eugene-Denio Faults, pass NW-SE through the region and step to the right at the eastern margin of the Cascades Range. Dextral wrench faulting on these structures during the Tertiary controlled the formation of the La Pine Basin, a pull-apart structure containing Tertiary and Quaternary sediments and volcanics. Tertiary wrench faulting appears to have been associated with rotations of crustal blocks at a plate margin, but was superseded in the Quaternary by extensional faulting of the Basin and Range province. Newberry Crater and other major bimodal volcanic centres in the NW Cordillera (Crater Lake, Medicine Lake, Mt. St. Helens) seem to have a similar tectonic setting in crustal pull-aparts. A relationship between magma type and fault trend at Newberry and Medicine Lake is suggested.     

基于Global Mapper的地貌晕渲图设计与制作

地貌晕渲图是指利用数字高程模型（ DEM）来生成,可以很好地反映地形、地势的变化,具有较好立体感的地图。本文以地貌晕渲基本理论为出发点,利用Global Mapper软件实现对制图数据的地貌晕渲图自动生成。对制图区域内的地形地势特征,地貌晕渲图在计算机上实现的关键技术,整体性考虑对地貌晕渲图效果的影响,合理分析设置Global Mapper软件中的各类参数,并利用Photoshop软件对地貌晕渲图进行调整。     

基于Global Mapper的DEM检查方法

Global Mapper是一款地图绘制软件。本文结合辽宁省测绘局1∶10000 DEM制作项目,阐述了利用Global Mapper软件对DEM最终成果进行质量检查的具体检查方法。     

铁路测量中坐标系的建立与转换

在铁路测量中,长度投影变形要求小于2．5cm／km。但是由于铁路为典型的线性工程,通常横跨多个投影带,因此采用国家标准3。带难以满足铁路测量精度要求,本文通过分析高斯投影长度变形,提出分段建立独立坐标系的方法,控制长度投影变形,满足精度要求,最后介绍利用GlobalMapper软件实现两个独立坐标系的转换。     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity

The influence of illumination effects on the optimality of the dNBR (differenced Normalized Burn Ratio) was evaluated for the case of the 2007 Peloponnese (Greece) wildfires using a pre/post-fire Landsat TM (Thematic Mapper) image couple. Well-illuminated pixels (south and south-east facing slopes) exhibited more optimal displacements in the bi-spectral feature space than more shaded pixels (north and north-west exposed slopes). Moreover, pixels experiencing a small image-to-image difference in illumination obtained a higher optimality than pixels with a relatively large difference in illumination. To correct for illumination effects, the c-correction method and a modified c-correction technique were applied. The resulting median dNBR optimality of uncorrected, c-corrected and modified c-correction data was respectively 0.58, 0.60 and 0.71 (differences significant for p < 0.001). The original c-correction method improved the optimality of badly illuminated pixels while deteriorating the optimality of well-illuminated pixels. In contrast, the modified c-correction technique improved the optimality of all the pixels while retaining the prime characteristic of topographic correction techniques, i.e. detrending the illumination–reflectance relationship. For a minority of the data, for shaded pixels and/or pixels with a high image-to-image difference in illumination, the original c-correction outperformed the modified c-correction technique. In this study conducted in rugged terrain and with a bi-temporal image acquisition scheme that deviated up to two months from the ideal anniversary date scheme the modified c-correction technique resulted in a more reliable change detection.     

Global Mapper系统在海洋调查中的应用

Global Mapper是一个简单、实用的地理信息管理与应用软件,文中介绍了该软件的基本功能和开发应用.在海洋地质调查工作中,通过对Global Mapper软件功能的转换与灵活应用,结合动态GPS,实现了地形导航和历史信息、综合信息的实时显示与查询,并改进了多波束水深测量现场监控、工程磁法测量成果数据处理等技术方法...     

极端干旱区荒漠稀疏河岸林遥感分类研究

研究以位于极端干旱区的塔里木河干流中下游地区为例,基于Landsat TM影像,结合决策树分类、几何光学模型与光谱角匹配,解决混合像元信息分解,实现干旱区稀疏荒漠河岸林类别识别。首先从遥感视角的角度,将地物分解为目标和背景,提出塔里木河干流荒漠河岸林植被分类系统;其次以多变量决策树法将非荒漠植被信息剔除,采用几何光学模型模拟各类荒漠植被的像元光谱,最后以光谱角匹配的方法将荒漠植被进一步进行分解,得到塔里木河干流中下游地区典型研究区的植被分类专题图,分类精度结果表明:基于混合像元分解与几何光学模型的分类方法总精度达到了79.43%,Kappa系数为0.718,表明分类质量良好。     

Monitoring wetland ditch water levels in the North Kent Marshes,UK, using Landsat TM imagery and ground-based measurements

Abstract

An understanding of hydrology is a prerequisite for ensuring the successful management, conservation and restoration of wetland environments. Frequently, however, little is known about historical hydrological conditions, such as water levels, within wetlands. Moreover, many channel and ditch systems in wetlands are not routinely monitored, except perhaps for research purposes. A methodology is presented herein which makes use of satellite imagery to indirectly provide remotely sensed observations of water levels within channels and ditches. Using multi-temporal Landsat Thematic Mapper (TM) imagery and simultaneous ground-based measurements of water levels, statistical relationships are established between satellite-derived effective wet ditch widths and measured water levels in the drainage system of the Elmley Marshes, southeast England. These relationships can be used subsequently to estimate historical ditch water levels and to monitor contemporary ditch water levels in the wetland. The study shows that satellite imagery has much to offer in monitoring changes in the hydrological regime of wetlands and in providing complimentary approaches to field monitoring.     

Clustering based on eigenspace transformation – CBEST for efficient classification

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.