• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Korean Society of Environmental Engineers
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• v.37 no.2
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• pp.126-135
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• 2015

Increase of delivery effect of pollutant loads and surface runoff due to urbanization of catchment area results in serious environmental problems in receiving urban streams. This study aims to develop integrated stormwater management system to assist efficient urban stream flow and water quality control using information from the Storm Water Management Model (SWMM), real time water level and quality monitoring system and remote or automatic treatment facility control system. Based on field observations in the study site, most of the pollutant loads are flushed within 4 hours of the rainfall event. SWMM simulation results indicates that the treatment system can store up to 6 mm of cumulative rainfall in the study catchment area, and this means any type of normal rainfall situation can be treated using the system. Relationship between rainfall amount and fill time were developed for various rainfall duration for operation of stormwater treatment system in this study. This study can further provide inputs of river water quality model and thus can effectively assist integrated water resources management in urban catchment and streams.