• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.529-536
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• 2007

As the Imperious surface is an important index for the estimation of urbanization and environmental change, the increase of impervious surfaces causes meteorological and hydrological changes like urban climate change, urban flood discharge increasing, urban flood frequency increasing, and urban flood modelling during the rainy season. In this study, the estimation of impervious surfaces is performed by using Landsat-7 ETM+ image in An-sung area. The construction of sampling data and checking data is used by IKONOS image. It transform to a tasselled cap and NDVI through the reflexibility rate of Landsat ETM+ image and analyze various variables that influence on impervious surface. Finally, the impervious surfaces map is accomplished by regression tree algorithm.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.6
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• pp.635-645
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• 2014

The changes of rainfall pattern and impervious covers have increased disaster risks in urbanized areas. Impervious covers such as roads and building roofs have been dramatically increased. So, it is falling the ability safety of flood defense equipments to exist. Runoff coefficient means ratio of runoff by whole rainfall which is able to directly contribute at surface runoff during rainfall event. The application of accurate runoff coefficients is very important in sewer pipelines design. This study has been performed to estimate runoff characteristics change which are applicable to the process of sewer pipelines design or various public facilities design. It has used the SHER model, a long-term runoff model, to analyze the impact of a rising impervious covers on runoff coefficient change. It thus analyzed the long-term runoff to analyze rainfall basins extraction. Consequently, it was found that impervious surfaces could be a important factor for urban flood control. We could suggest the application of accurate runoff coefficients in accordance to the land Impervious covers. The average increase rates of runoff coefficients increased 0.011 for 1% increase of impervious covers. By having the application of the results, we could improve plans for facilities design.

• 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.