{"title":"基于QuickBird图像分析的公路建设生态环境影响评价","authors":"Xiaolei Yao, Jingshan Yu, Hua Li","doi":"10.1109/ICSAI.2012.6223467","DOIUrl":null,"url":null,"abstract":"Highway, as human made construction on land surface, has influence on natural environment in some geographical region. Ecological environment monitoring is fundamental for highway environmental protection, which is one of the main tasks of traffic environmental monitoring. Conventional methods of the ecological environment monitoring are time-consuming and are not feasible for large area monitoring. Assessment based on remote sensing and GIS can dynamically detect the impacts on ecological environment before and after highway construction. And it can also provide useful information for making decision about the strategy of highway environmental protection. In this study, an assessment based on analyzing Quick Bird image before and after construction period was carried out. After fusing multispectral and panchromatic Quick Bird images based on principal component analysis (PCA) and geometric correction using ERDAS software, the Normalized Difference Vegetation Index (NDVI) and Vegetation Coverage (VC) was computed from the fused image. By utilizing the eCognition software, land use classification was detected. The NDVI, VC and land use classification before and after construction was compared. The results indicate that the vegetation density, area of farmland and woodland are reduced, which is consistent with the results obtained from field survey.","PeriodicalId":164945,"journal":{"name":"2012 International Conference on Systems and Informatics (ICSAI2012)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impact assessment of highway construction on ecological environment based on QuickBird images analysis\",\"authors\":\"Xiaolei Yao, Jingshan Yu, Hua Li\",\"doi\":\"10.1109/ICSAI.2012.6223467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Highway, as human made construction on land surface, has influence on natural environment in some geographical region. Ecological environment monitoring is fundamental for highway environmental protection, which is one of the main tasks of traffic environmental monitoring. Conventional methods of the ecological environment monitoring are time-consuming and are not feasible for large area monitoring. Assessment based on remote sensing and GIS can dynamically detect the impacts on ecological environment before and after highway construction. And it can also provide useful information for making decision about the strategy of highway environmental protection. In this study, an assessment based on analyzing Quick Bird image before and after construction period was carried out. After fusing multispectral and panchromatic Quick Bird images based on principal component analysis (PCA) and geometric correction using ERDAS software, the Normalized Difference Vegetation Index (NDVI) and Vegetation Coverage (VC) was computed from the fused image. By utilizing the eCognition software, land use classification was detected. The NDVI, VC and land use classification before and after construction was compared. The results indicate that the vegetation density, area of farmland and woodland are reduced, which is consistent with the results obtained from field survey.\",\"PeriodicalId\":164945,\"journal\":{\"name\":\"2012 International Conference on Systems and Informatics (ICSAI2012)\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Systems and Informatics (ICSAI2012)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2012.6223467\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Systems and Informatics (ICSAI2012)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2012.6223467","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Impact assessment of highway construction on ecological environment based on QuickBird images analysis
Highway, as human made construction on land surface, has influence on natural environment in some geographical region. Ecological environment monitoring is fundamental for highway environmental protection, which is one of the main tasks of traffic environmental monitoring. Conventional methods of the ecological environment monitoring are time-consuming and are not feasible for large area monitoring. Assessment based on remote sensing and GIS can dynamically detect the impacts on ecological environment before and after highway construction. And it can also provide useful information for making decision about the strategy of highway environmental protection. In this study, an assessment based on analyzing Quick Bird image before and after construction period was carried out. After fusing multispectral and panchromatic Quick Bird images based on principal component analysis (PCA) and geometric correction using ERDAS software, the Normalized Difference Vegetation Index (NDVI) and Vegetation Coverage (VC) was computed from the fused image. By utilizing the eCognition software, land use classification was detected. The NDVI, VC and land use classification before and after construction was compared. The results indicate that the vegetation density, area of farmland and woodland are reduced, which is consistent with the results obtained from field survey.