• Ecology and Resilient Infrastructure
• /
• v.2 no.4
• /
• pp.311-316
• /
• 2015

This study defined and classified no-net-loss-of-greenness (NNLG) based on the law, and then assessed the NNLG index by metropolitan cities and provinces in Korea after estimating NNLG evaluation indicators for the introduction of NNLG for health improvement of urban ecosystems. The results are as follows. First, NNLG was the comprehensive meaning that was included in the greenbelt and park greenbelt and the green area which was defined by the Act on Urban Parks, Greenbelts, etc. and the National Land Planning and Utilization Act respectively. Second, NNLG was classified as a park greenbelt which was included urban parks and greenbelts such as buffer greenbelts, scenic greenbelts, and connecting greenbelts, green areas which was included in green conservation areas, green production areas, green natural areas, and green coverage which is included forests, grasslands, and wetlands that were occupied by vegetation such as trees, shrubs, and plants. Third, NNLG index by cities and provinces was assessed based on the estimation of NNLG evaluation indicators, which included parks and greenbelt areas per capita, green areas per capita, green coverage per capita, ratio of parks and greenbelts, ratio of green areas, and ratio of green coverage. As a result, Sejong city got the highest point of NNLG index and Seoul and Daegu got lowest points of NNLG index among metropolitan cities in Korea. Chungbuk got the highest point of NNLG index and Kyonggi and Jeju got lowest points of NNLG index among provinces in Korea.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.16 no.2
• /
• pp.88-100
• /
• 2013

Urban green space is one of most important aspects of urban infrastructure for improving the quality of life of city dwellers as it reduces the heat island effect and is used for recreation and relaxation. However, no systematic management of urban green space has been introduced in Korea as past practices focused on efficient development. A way to calculate the amount of green space needed to complement an urban area must be developed to preserve urban green space and to determine 'regulations determining the total amount of greenery'. In recent years, various studies have quantified urban green space and infrastructure using remotely sensed data. However, it is difficult to detect a myriad small green spaces in a city effectively when considering the spatial resolution of the data used in existing research. In this paper, we quantified small urban green spaces using CASI-1500 hyperspectral imagery. We calculated MCARI, a vegetation index for hyperspectral imagery, to evaluate the greenness of small green spaces. In addition, we applied image-classification methods, including the ISODATA algorithm and Spectral Angle Mapper, to detect small green spaces using supervised and unsupervised classifications. This could be used to categorize land-cover into four classes: unclassified, impervious, suspected green, and vegetation green.