Usage of indices for extraction of built-up areas and vegetation features from landsat TM image: a case of Dar es Salaam and Kisarawe peri-urban areas, Tanzania

F. Mwakapuja, E. Liwa, J. Kashaigili
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引用次数: 19

Abstract

Abstrac t This paper address the use of Indices Co mbination with Supervision Classification methods to extract urban built-up areas, vegetation and water features fro m Landsat Thematic Mapper (TM7) imagery covering Dar es Salaam and Kisarawe peri-urban areas. The study uses three indices; Normalized Difference Bu ilt-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SA VI) to reduce the seven bands Landsat TM7 image into three thematic-oriented bands. Data correlation, spectral confusion and redundancy between original mu ltispectral bands were significantly reduced upon application of the techniques. As a result, the spectral signatures of the three urban land-use classes are mo re distinguishable in the new co mposite image than in the original seven-band image since the spectral clusters of the classes are well separated. Through a supervised classification on the newly formed image, the urban built-up areas, vegetation and water features were finally extracted effect ively; with the accuracy of 82.05 percent attained. The results show that the technique is effective and reliable and can be considered for use in other areas with similar characteristics.
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利用指数从landsat TM影像提取建成区和植被特征:以坦桑尼亚达累斯萨拉姆和基萨拉维城郊地区为例
摘要本文研究了利用指数结合监督分类方法,从达累斯萨拉姆和基萨拉维的Landsat Thematic Mapper (TM7)影像中提取城市建成区、植被和水体特征。本研究使用了三个指标;利用归一化差异水体指数(NDBI)、修正归一化差异水体指数(MNDWI)和土壤调整植被指数(SA VI)将Landsat TM7影像的7个波段简化为3个主题导向波段。应用该技术可显著降低原始多光谱波段之间的数据相关性、光谱混淆和冗余度。结果表明,与原7波段图像相比,新合成图像中3个城市土地利用类别的光谱簇分离较好,使得3个城市土地利用类别的光谱特征更容易区分。通过对新生成的图像进行监督分类,最终提取出有效的城市建成区、植被和水体特征;准确率达到82.05%。结果表明,该技术是有效可靠的,可以考虑在其他具有类似特点的领域推广应用。
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