期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 8
页码：659-664
出版社：Copernicus Publications
摘要：Green vegetation plays rather important role in urban environment. Recently, re-vegetation in urban residential area is becoming important policy for urban planners from the view point of climate change measures. That is, by maintaining and regenerating green vegetation, both mitigation and adaptation can be achieved. In this regard, establishment of accurate assessment and evaluation methods for the green vegetation in urban environment is urgently needed. This research demonstrates such a method by using remote sensing technique and economic model with the case study of Tokyo metropolitan area. First, a high resolution green cover map is created by the classification of remotely sensed images (Landsat ETM+) using subspace method. Our past research have shown that the method performed better than conventional algorithms such as: Maximum Likelihood Classification (MLC), Self Organizing Map (SOM) neural network, and Support Vector Machine (SVM) methods. Then, we have projected the future distribution of green vegetation using a Computable Urban Economic (CUE) model developed recently. CUE model are often used for urban planning practitioners, but here we have developed a simplified CUE model focusing only on land-use changes but employing a higher ground resolution of the micro district level zones. This new model allows us to evaluate realistic/spatially finer green vegetation scenarios. We have created two extreme land-use scenarios: concentration and dispersion scenarios, and correspond changes as green cover map are created. The results show that the method demonstrated in this study has high applicability to the countries where conducting field survey of land cover is difficult
关键词：Satellite image classification; Subspace method; Computable urban economic model; Tokyo metropolitan area; ; Scenario analysis; Green vegetation; Land use prediction
