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首页> 外文期刊>GIScience & remote sensing >Detecting fractional land-cover change in arid and semiarid urban landscapes with multitemporal Landsat Thematic mapper imagery
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Detecting fractional land-cover change in arid and semiarid urban landscapes with multitemporal Landsat Thematic mapper imagery

机译：使用多时态Landsat专题制图仪图像检测干旱和半干旱城市景观中的土地覆盖率变化

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摘要

Pixel-based approaches are commonly used for urban land-cover classification and change detection, but the results are often inaccurate in arid and semiarid urban landscapes due to the mixed-pixel problem and similar spectral signatures between impervious surface areas (ISAs) and bare soils. This research proposes a subpixel-based approach to examine land-cover change in Urumqi and Phoenix urban landscapes using multitemporal Landsat Thematic Mapper (TM) imagery. Linear spectral mixture analysis (SMA) was used to unmix TM multispectral imagery into four fractions -high-albedo object, low-albedo object, green vegetation (GV), and soil. ISA was determined from the sum of high-albedo and low-albedo fraction images after removal of non-ISA in both fraction images. The ISA, vegetation abundance, and soil images at different dates were used to examine their change over time. The results indicate that this subpixel-based approach can successfully detect small changes of urban land covers in medium spatial resolution images which pixel-based approaches cannot.
机译：基于像素的方法通常用于城市土地覆盖物的分类和变化检测，但是在干旱和半干旱的城市景观中，由于混合像素问题以及不透水表面区域（ISA）和裸露土壤之间的相似光谱特征，结果常常不准确。 。这项研究提出了一种基于亚像素的方法，可以使用多时态Landsat Thematic Mapper（TM）影像来检查乌鲁木齐和凤凰城城市景观的土地覆盖变化。线性光谱混合分析（SMA）用于将TM多光谱图像分解为四个部分-高反照率物体，低反照率物体，绿色植被（GV）和土壤。从两个分数图像中去除非ISA后的高反照率和低反照率分数图像之和确定ISA。 ISA，植被丰度和不同日期的土壤图像用于检查其随时间的变化。结果表明，这种基于亚像素的方法可以成功检测中等空间分辨率图像中城市土地覆盖的微小变化，而基于像素的方法则无法。

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  • 来源
    《GIScience & remote sensing》 |2015年第6期|700-722|共23页
  • 作者

    Zhang Chi; Chen Yaoliang; Lu Dengsheng;

  • 作者单位

    Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Xinjiang, Peoples R China;

    Zhejiang Univ, Sch Publ Affairs, Dept Land Management, Hangzhou 310058, Zhejiang, Peoples R China;

    Zhejiang A&F Univ, Sch Environm & Resource Sci, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Linan 311300, Zhejiang, Peoples R China|Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48823 USA;

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  • 正文语种 eng
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  • 关键词

    arid and semiarid urban landscapes; fractional land cover; change detection; multitemporal Landsat imagery;

    机译：干旱和半干旱的城市景观;局部土地覆盖;变化检测;多时相Landsat影像;

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