Land use analysis using GIS, radar and thematic mapper in Ethiopia: PhD showcase

Haile K. Tadesse
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Abstract

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.
利用地理信息系统、雷达和专题绘图仪在埃塞俄比亚进行土地利用分析:博士展示
土地退化和贫困问题在我们的世界非常普遍,特别是在非洲的发展中国家。这些国家对气候变化的适应策略较少。埃塞俄比亚是一个位于非洲之角的热带国家。大多数人口生活在农村地区,农业是主要的经济部门。粗放农业导致了意想不到的过度开发和土地退化。该项目位于埃塞俄比亚西南部和西北部。主要目的是分析各传感器的土地利用分类精度、分类算法以及土地利用变化分析。专题绘图仪(TM)和雷达数据将用于分类和监测土地利用变化。将使用两张连续的卫星图像来观察研究区域的土地利用变化(1998年,2008年)。ERDAS Imagine将用于雷达和TM数据的重新采样和空间注册。本研究的图像分类是监督签名提取。使用极大似然决策规则和C4.5算法对图像进行分类。TM和Radar数据将通过层桩融合。采用误差矩阵计算数字分类的精度。土地变化建模器将用于分析和预测土地覆盖变化。利用GIS分析道路、城市和人口密度对土地利用变化的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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