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。     

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 Tertiary wrench faulting appears to have been associated with rotations of crustal blocks at a plate margin, but was superseded in the Quaternary by extensional faulting of the Basin and Range province.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.     

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.     

ETM+卫星影像极地云层检测及分析

云一直是遥感图像预处理和分析中的一大难点,本文在分析南极地区成像特点的基础上,试图利用增强型主题成像传感器(ETM+)获取影像的多波段特点,对南极格罗夫山地区ETM+卫星影像进行云的检测。结果表明,该方法不仅能比较成功地检测极地云层像元,而且能有效地去除部分厚云的影响。     

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.     

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.     

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。     

Monitoring land changes in an urban area using satellite imagery,GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.     

Hydrocarbon Alteration Characteristics of Soils and Mechanism for Detection by Remote Sensing in East Sichuan Area,China

The type, strength, and spatial distributions of hydrocarbon alteration of the surface soil are studied in two sections in East Sichuan area through the simultaneous analysis of soil organic geochemistry, soil mineralogy, and soil chemistry. The spectral response and remote-sensing mechanism are studied through the soil spectral analysis in the range of VIS--NIR bands. The results of this study demonstrate that long-time hydrocarbon microseepage can induce mineral and chemical alteration of surface soil, including the increase of clay-mineral content and carbonate-mineral content, the increase of ferrous-iron content, and decrease of ferric-iron content. Soil mineral components related to hydrocarbon alteration have spatial coincidence with soil organic geochemical components. Increase of clay- and carbonate-mineral contents in the soil can cause decrease of reflectance in VIS–NIR bands and increase of Landsat band ratio TM5/TM7. Increase of ferrous-iron content and decrease of ferric-iron content in the soil may cause increase of reflectance in the range of 400 nm to 600 nm, and higher reflectance of Landsat band ratios of TM1/TM3 and TM2/TM3.