• Korean Journal of Environment and Ecology
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• v.29 no.6
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• pp.936-946
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• 2015

This study is aimed to analyze the reduction performance of building energy consumption according to planting base types of panel-type green walls which can be applied to existing buildings. The performance was compared to the general performance of green walls that have demonstrated effects of improving the thermal environment and reducing building energy consumption in urban areas. The number of planting base types was 4 in total, and simulations were conducted to analyze the thermal conductivity, thermal transmittance, and overall building energy consumption rate of each planting base type. The highest thermal conductivity by the planting base type was Case C (0.053W/mK), followed by Case B (0.1W/mK) and Case D (0.17W/mK). According to the results of energy simulation, the most significant reduction of cooling peak load per unit area was Case C (1.19%), followed by Case B (1.14%) and Case D (1.01%) when compared to Case A to which green wall was not applied; and the most significant reduction of heating peak load per unit area was estimated to be Case C (2.38%), followed by Case B (1.82%) and case D (1.50%) when compared to Case A. The amount of yearly cooling and heating energy use per unit area showed 3.04~3.22% of reduction rate. The amount of the 1st energy use showed 5,844 kWh/yr of decrease on average for other types when compared to Case A. The amount of yearly $CO_2$ emission showed 996kg of decrease on average when compared to Case A to which the green wall was not applied. According to the results of energy performance evaluation by planting location, the most efficient energy performance was eastward followed by westward, southward and northward. According to the results of energy performance evaluation by planting location by green wall ratio, it was found that as the ratio of green wall increased, the energy performance displayed better results, showing approx. double reduction rate in energy consumption at 100% of green wall ratio than the reduction rate at 20% to 80% of green wall ratio.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.