土地利用与土地覆盖洪水影响评价的LANDSAT数据图像处理与监督分类

M. Kalidhas, R. Sivakumar
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引用次数: 0

摘要

洪水是世界上最常见的自然灾害之一,造成经济损失和人员损失。本研究采用图像处理技术对洪水前后的陆地卫星数据进行处理。对陆地卫星图像进行了初步的几何校正和辐射校正。同样,采用卫星图像的预处理技术。该GIS平台用于找出洪水前后对泰米尔纳德邦Chengalpattu taluk土地利用和土地覆盖变化的影响,用于监督分类技术。利用GIS平台将研究区划分为水、城市、森林、荒地和农业5类。利用Landsat 8高分辨率影像,将研究区划分为5类,并进行一级分类。监督分类在洪水前后对土地利用和土地覆盖影响的类别表示方面提供了更好的结果。2015年和2016年获得的图像分类总体准确率分别为94.25%和90.8%。由此证明,卫星数据具有通过图像分类技术分析洪水引起的LULC变化的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover
Floods are one of the most frequent natural disasters in the world, causing economic damage as well as human losses. In the present research Pre and Post flood Landsat satellite data was processed through image processing techniques. Landsat satellite image was initially correct the geometry correction and radiometric correction carried out. Similarly follow the pre-processing techniques on satellite image. The GIS platform used to find out the pre and post flood impact on land use and land cover changes for supervised classification techniques in Chengalpattu taluk, Tamil Nadu. The study Area which are classified into five classes in supervised classification for water, urban, forest, barren land and agriculture using GIS platform. Using high resolution Landsat 8 images, study area were categorised into five types in Level 1 classifications. Supervised classifications provide better result in the representation of classes for pre and post flood impact on land use land cover. The overall accuracy of image classification obtained in 2015 and 2016 is 94.25 % and 90.8%. Hence the result proves that satellite data has capability for analysing the changes in LULC through image classification techniques due to flood.
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