{"title":"基于监督分类、遥感和GIS的印度安得拉邦Kadapa地区Mandavi河流域土地利用/覆被变化检测分析","authors":"R. Raju, G. Raju, M. Rajasekhar","doi":"10.37591/.V9I3.249","DOIUrl":null,"url":null,"abstract":"Assessment, development, and management of watershed strategy require exact calculations reports of present and past land use/cover data and its change determine the ecological and hydrological process taking place in a watershed. In this study, we have to adopt supervised classification with maximum likelihood algorithm in ERDAS imagine to notice land use/cover changes (LU/LCC) analyzed in Mandavi river basin, Kadapa district, Andhra Pradesh, India using multispectral satellite data gained from Landsat satellite series for the years 2006 and 2018. These satellite data is intended for land use/cover through supervised classification in ERDAS 2014, software. In the result, we could identify six land use/land cover (LU/LC) classes, namely agricultural land, built-up land, fallow land, forest land, river and water bodies. The results shown that during the 2006 and 2018, built-up land fallow land have been increased about 0.84% (such as 12.30 km 2 ) and 2.92% (42.82 km 2 ), respectively, whereas the area under other land categories such as agricultural land, forest land, river and water bodies have decreased about 1.86% (27.30 km 2 ), 1.34 (19.66 km 2 ), 0.26 (3.87 km 2 ) and 0.29 (4.28 km 2 ), respectively. Finally, accuracy assessment has been carried out and their result shows that overall accuracy of classified images of the year 2006 and 2018 are 86.62% and 91.85% respectively. The overall Kappa coefficient values of classified images of the year 2006 and 2018 are 0.8343 and 0.8987. Hence, these values indicate that acceptable accuracy of the classified LU/LC features. Keywords: Supervised classification, land use/cover, change detection, accuracy assessment, RS and GIS Cite this Article R. Siddi Raju, G. Sudarsana Raju, M. Rajasekhar. Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India. Journal of Remote Sensing & GIS. 2018; 9(3): 46–54p.","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India\",\"authors\":\"R. Raju, G. Raju, M. 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The results shown that during the 2006 and 2018, built-up land fallow land have been increased about 0.84% (such as 12.30 km 2 ) and 2.92% (42.82 km 2 ), respectively, whereas the area under other land categories such as agricultural land, forest land, river and water bodies have decreased about 1.86% (27.30 km 2 ), 1.34 (19.66 km 2 ), 0.26 (3.87 km 2 ) and 0.29 (4.28 km 2 ), respectively. Finally, accuracy assessment has been carried out and their result shows that overall accuracy of classified images of the year 2006 and 2018 are 86.62% and 91.85% respectively. The overall Kappa coefficient values of classified images of the year 2006 and 2018 are 0.8343 and 0.8987. Hence, these values indicate that acceptable accuracy of the classified LU/LC features. Keywords: Supervised classification, land use/cover, change detection, accuracy assessment, RS and GIS Cite this Article R. Siddi Raju, G. Sudarsana Raju, M. Rajasekhar. Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India. 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引用次数: 4
摘要
流域战略的评估、发展和管理需要精确的计算,现在和过去土地利用/覆盖数据的报告及其变化决定了流域发生的生态和水文过程。在本研究中,我们采用ERDAS想象中的最大似然监督分类算法,对印度安得拉邦Kadapa地区Mandavi河流域的土地利用/覆盖变化(LU/LCC)进行了分析,使用了2006年和2018年Landsat卫星系列的多光谱卫星数据。这些卫星数据旨在通过ERDAS 2014软件中的监督分类用于土地利用/覆盖。结果表明,土地利用/覆被(LU/LC)可划分为6类,即农用地、建设用地、休耕地、林地、河流和水体。结果表明:2006年和2018年,建成区和休耕地面积分别增加了0.84% (12.30 km 2)和2.92% (42.82 km 2),而农用地、林地、河流和水体等其他土地类型面积分别减少了1.86% (27.30 km 2)、1.34 km 2 (19.66 km 2)、0.26 km 2 (3.87 km 2)和0.29 km 2 (4.28 km 2)。最后进行了准确率评估,结果表明,2006年和2018年分类图像的总体准确率分别为86.62%和91.85%。2006年和2018年分类图像的Kappa系数总体值分别为0.8343和0.8987。因此,这些值表明分类的LU/LC特征具有可接受的准确性。关键词:监督分类,土地利用/覆被,变化检测,精度评估,RS和GIS基于监督分类、遥感和GIS的印度安得拉邦Kadapa地区Mandavi河流域土地利用/覆被变化检测分析遥感与地理信息系统学报。2018;9 (3): 46-54p。
Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India
Assessment, development, and management of watershed strategy require exact calculations reports of present and past land use/cover data and its change determine the ecological and hydrological process taking place in a watershed. In this study, we have to adopt supervised classification with maximum likelihood algorithm in ERDAS imagine to notice land use/cover changes (LU/LCC) analyzed in Mandavi river basin, Kadapa district, Andhra Pradesh, India using multispectral satellite data gained from Landsat satellite series for the years 2006 and 2018. These satellite data is intended for land use/cover through supervised classification in ERDAS 2014, software. In the result, we could identify six land use/land cover (LU/LC) classes, namely agricultural land, built-up land, fallow land, forest land, river and water bodies. The results shown that during the 2006 and 2018, built-up land fallow land have been increased about 0.84% (such as 12.30 km 2 ) and 2.92% (42.82 km 2 ), respectively, whereas the area under other land categories such as agricultural land, forest land, river and water bodies have decreased about 1.86% (27.30 km 2 ), 1.34 (19.66 km 2 ), 0.26 (3.87 km 2 ) and 0.29 (4.28 km 2 ), respectively. Finally, accuracy assessment has been carried out and their result shows that overall accuracy of classified images of the year 2006 and 2018 are 86.62% and 91.85% respectively. The overall Kappa coefficient values of classified images of the year 2006 and 2018 are 0.8343 and 0.8987. Hence, these values indicate that acceptable accuracy of the classified LU/LC features. Keywords: Supervised classification, land use/cover, change detection, accuracy assessment, RS and GIS Cite this Article R. Siddi Raju, G. Sudarsana Raju, M. Rajasekhar. Land Use/Land Cover Change Detection Analysis Using Supervised Classification, Remote Sensing and GIS In Mandavi River Basin, YSR Kadapa District, Andhra Pradesh, India. Journal of Remote Sensing & GIS. 2018; 9(3): 46–54p.