首页> 外文会议>1st ACM SIGSPATIAL international workshop on data mining for geoinformatics 2010 >PhD Showcase: Land Use Analysis using GIS, Radar and Thematic Mapper in Ethiopia
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PhD Showcase: Land Use Analysis using GIS, Radar and Thematic Mapper in Ethiopia

机译:博士展示:埃塞俄比亚使用GIS,雷达和专题测绘仪进行土地利用分析

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Land degradation, and poverty issues are very common in our world, especially in developing countries in Africa. There are fewer adaptation strategies for climate change in these countries. Ethiopia is a tropical country found in the horn of Africa. The majority of the population live in rural areas and agriculture is the main economic sector. Extensive agriculture has resulted in an unexpected over-exploitation and land degradation. The project locations are Southwestern and Northwestern Ethiopia. The main objectives are to analize the accuracy of land use classification of each sensors, classification algorithms and analyze land use change. Thematic Mapper (TM) and Radar data will be used to classify and monitor land use change. Two consecutive satellite images will be used to see the land use change in the study area (1998, 2008). ERDAS Imagine will be used to resample and spatially register the Radar and TM data. The image classification for this research study is supervised signature extraction. The Maximum likelihood decision rule and C4.5 algorithm will be applied to classify the images. TM and Radar data will be fused by layer staking. The accuracy of the digital classification will be calculated using error matrix. Land change modeler will be used for analyzing and predicting land cover change. The impact of roads, urban and population density on land use change will be analayzed using GIS.
机译:土地退化和贫困问题在我们的世界中非常普遍,尤其是在非洲的发展中国家。这些国家针对气候变化的适应策略较少。埃塞俄比亚是在非洲之角发现的热带国家。人口的大多数生活在农村地区,农业是主要的经济部门。广泛的农业造成了意想不到的过度开发和土地退化。项目地点在埃塞俄比亚西南部和西北部。主要目标是分析每个传感器的土地利用分类的准确性,分类算法并分析土地利用变化。专题测绘器(TM)和雷达数据将用于分类和监控土地使用变化。将使用两个连续的卫星图像来观察研究区域内的土地利用变化(1998年,2008年)。 ERDAS Imagine将用于对Radar和TM数据进行重新采样并在空间上进行注册。这项研究的图像分类是监督签名提取。最大似然决策规则和C4.5算法将应用于对图像进行分类。 TM和Radar数据将通过图层放样融合。数字分类的准确性将使用误差矩阵进行计算。土地变化建模器将用于分析和预测土地覆被变化。道路,城市和人口密度对土地利用变化的影响将使用GIS进行分析。

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