The purpose of the study was to review ecosystem service researches and show the trend to guide researchers who want to study ecosystem service. Ecosystem service has been a theoretical base for conservation of nature such as grand mountains, rivers and so on. Moreover, reviewed studies showed economical, social, environmental values of ecosystem service in nature as well as in urban to support ecosystem service theory. By leading metropolitan governments in America, Canada and european and asian countries, the urban tree canopy(UTC)s’ ecosystem services have been evaluated quantitatively and accumluated as urban forestry data. This global trend has an implication that the study of UTC and its ecosystem service can support the provision of urban forestry and green spaces and especially, Korean cities should realize UTCs’ values on ecosystem services and start to apply them institutionally to enhance their urban environment.

This paper presents a study to identify the relationship between vegetation phenology and landslide using remote sensing to access landslide prone areas in an event of an earthquake. A landslide triggered after the April 2015 earthquake in Manaslu Conservation Area in Gorkha District of Nepal was used as a study site. The method proposed in the paper uses pre- and post-event LANDSAT8 Operational Land Imager (OLI) images and uses Normalized Difference Vegetation Index (NDVI) for understanding the correlation between vegetation phenology of the study site to the landslide. Comparative study of the result shows lower NDVI mean value after the earthquake and shows that a slope with NDVI mean value lower than 0.247 could be landslide prone. Implication of the result, if confirmed, could aid in identifying landslide prone areas and implementing mitigation programs to either re-vegetate the slope or relocate residents directly under threat.

Tree canopy is a valuable component consisting of urban ecosystem. The purpose of this study was to classify urban tree canopy (UTC) by using high resolution imagery and object-oriented classification (OOC), which was used to classify the different land cover types. With an urban canopy mapping system based on OOC and Decision Tree Classification (DTC), a site mapping was carried out by merging spectral data of high resolution imagery. This methodological approach showed high classification accuracy to distinguish small patches and continuous UTC boundaries on the high resolution imagery. For shadow removal, decision tree classification with various environmental variables such as brightness channel and band combination could effectively work. Our proposed methodology can be successfully used for the assessment and restoration of fragmented urban ecosystem and offer an opportunity to obtain high classification accuracy for the distinction of UTC components in urban landscape areas.