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首页> 外文期刊>Indian Forester >LAND COVER CLASSIFICATION USING IRS LISS III SATELLITE IMAGE AND DIGITAL ELEVATION MODEL IN HILLY ENVIRONMENT - A CASE STUDY IN NONGKHYLLEM WILDLIFE SANCTUARY, MEGHALAYA
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LAND COVER CLASSIFICATION USING IRS LISS III SATELLITE IMAGE AND DIGITAL ELEVATION MODEL IN HILLY ENVIRONMENT - A CASE STUDY IN NONGKHYLLEM WILDLIFE SANCTUARY, MEGHALAYA

机译:利用IRS LISS III卫星图像和丘陵环境中的数字海拔模型对土地覆盖进行分类-以梅加拉州农凯勒姆·维尔利夫·圣克鲁斯(NongKylylm WILDLIFE SANCTUARY)为例

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摘要

IRS-LISS III satellite imagery corresponding to Nongkhyllem Wildlife Sanctuary area of Ri-Bhoi District, Meghalaya was used for remote sensing analysis of the landuse pattern and vegetation types occurring thereat. Maximum likelihood classifier algorithm of ERDAS Imagine 9.1 version was used to secure supervised classification of pixels into various landuse types and vegetation types among the forest class cover. For the purpose of preparation of training sets thematic maps of the area, and knowledge accruing from extensive personal field visits were taken aid of. Sample field plots were laid at 30 different locations of the Sanctuary to carry out accuracy assessment. Normalized Difference Vegetative Index value of the LISS III satellite imagery was also computed. Digital Elevation Model of the Sanctuary was erected in the GIS domain. Such GIS database was integrated with remote sensing data in proof of Integrated Geographic Information System capabilities to achieve higher accuracy in classification. There was indeed marked increase in classification accuracy on account of such integration. Bivariate correlation analysis was performed between spectral and DEM variables to cross check the results.
机译:对应于梅加拉亚邦里博伊区Nongkhyllem野生动物保护区的IRS-LISS III卫星图像用于遥感分析那里的土地利用方式和植被类型。使用ERDAS Imagine 9.1版本的最大似然分类器算法来确保将像素进行监督分类,以划分森林类别覆盖范围内的各种土地利用类型和植被类型。为了准备培训集,该地区的主题地图和从广泛的个人实地访问中积累的知识得到了帮助。在保护区的30个不同位置放置了样地图,以进行准确性评估。还计算了LISS III卫星图像的归一化植物营养指数值。在GIS领域中建立了保护区的数字高程模型。此类GIS数据库与遥感数据集成在一起,证明了“集成地理信息系统”的功能可以实现更高的分类精度。由于这种整合,分类准确度确实显着提高。在频谱变量和DEM变量之间进行了双变量相关分析,以交叉检查结果。

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