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81.
基于Landsat TM/ETM数据的锡林河流域土地覆盖变化 总被引:29,自引:2,他引:27
根据1987年、1991年、1997年和2000年4期Landsat TM/ETM影像的土地利用/土地覆盖分类结果,运用地理信息系统空间分析方法,分析了内蒙古锡林河流域1987~2000年间各土地利用类型及草甸草原、典型草原、荒漠草原的数量变化和空间变化特征。分析结果显示,锡林河流域土地利用/土地覆盖变化的主要特征为草甸草原、典型草原面积的大幅减少和荒漠草原、农田和沙漠化土地面积的大幅增加及城镇的扩张。其中面积增加最大的是荒漠草原,增加了2328 km2;相当于1987年荒漠草原面积的56 %。农田和城镇面积逐年增大,分别从1987年的114.3 km2和25.2 km2增加到2000年的332.1 km2和43.6 km2。面积减少最多的是羊草+丛生禾草、羊草+杂类草等优良高产温带典型草原类型,共减少2040 km2。草甸草原面积亦呈逐年减少的趋势,从1987年的1103 km2减少到2000年375 km2,面积减少了65.9 %。农田、沙化地及城镇等非草原土地利用类型面积增加了62.5 %。 相似文献
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A coastline is defined as the average spring tide line. Different types of seacoast, such as sandy, silty, and bio- logical coast, have different indicators of interpretation. It is very difficult to develop a universal method for interpreting all shorelines. Therefore, the sandy, the silty, and the biological coast are regarded as research objects, and with data mining technolog,found the rules of interpretation of those three types of coastlines. Then, an intelligent coastline interpretation method based on rules was proposed. Firstly, the rules for ex- tracting the waterline in Landsat TM/ETM+ (Thematic Mapper/Enhanced Thematic Mapper Plus) imagery were discovered. Then, through analyzing the features of sandy, silty and biological coast, the indicators of interpreting different types of shoreline were determined. According to the indicators, the waterline could be corrected to the real coastline. In order to verify the validity of the proposed algorithms, three Landsat TM/ETM+ imageries were selected for case studies. The experimental results showed that the proposed methods could interpret the coastlines of sandy; silty, and biological coasts with high precision and without human intervention, which exceeded three pixels. 相似文献
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基于多源遥感数据的水体信息提取研究 总被引:1,自引:0,他引:1
卫星遥感技术已被广泛应用到水体信息提取中,但目前基于遥感技术的水体信息提取多采用单一的遥感数据源,而没有充分利用多源数据的信息复合优势,因此,提取结果经常受天气气候或空间分辨率限制。本文研究了不同尺度、不同平台的多种遥感数据源的水体信息提取方法。首先,基于波谱间关系决策树分类算法对Landsat ETM+图像进行水体提取,利用其分辨率优势较准确地提取出水体范围;其次,在Radarsat SAR图像上利用阈值法粗提取水体信息后,结合DEM剔除阴影得到水体信息;最后,利用灾前Landsat ETM+图像水体信息提取结果和灾中Radarsat SAR图像水体信息提取结果,进行差值处理,得到洪水淹没范围。研究结果可以为洪水灾害监测与评估提供信息依据。 相似文献
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基于测量机器人目标自动寻找、识别和精确照准技术及Visual C++编程,本文从北二环高速K1+600至K1+900段变形监测数据入手,研究自由设站法和间接平差在变形监测数据处理中的应用,并对平差精度进行了探讨。结果表明,该方法精度高,便于实施。 相似文献
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Gary R. Watmough Peter M. Atkinson Craig W. Hutton 《International Journal of Applied Earth Observation and Geoinformation》2011
The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375 μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications. 相似文献
90.
Li LuoGiorgos Mountrakis 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):579-587
A classification model was demonstrated that explored spectral and spatial contextual information from previously classified neighbors to improve classification of remaining unclassified pixels. The classification was composed by two major steps, the a priori and the a posteriori classifications. The a priori algorithm classified the less difficult image portion. The a posteriori classifier operated on the more challenging image parts and strived to enhance accuracy by converting classified information from the a priori process into specific knowledge. The novelty of this work relies on the substitution of image-wide information with local spectral representations and spatial correlations, in essence classifying each pixel using exclusively neighboring behavior. Furthermore, the a posteriori classifier is a simple and intuitive algorithm, adjusted to perform in a localized setting for the task requirements. A 2001 and a 2006 Landsat scene from Central New York were used to assess the performance on an impervious classification task. The proposed method was compared with a back propagation neural network. Kappa statistic values in the corresponding applicable datasets increased from 18.67 to 24.05 for the 2006 scene, and from 22.92 to 35.76 for the 2001 scene classification, mostly correcting misclassifications between impervious and soil pixels. This finding suggests that simple classifiers have the ability to surpass complex classifiers through incorporation of partial results and an elegant multi-process framework. 相似文献