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

• Journal of Sensor Science and Technology
• /
• v.10 no.2
• /
• pp.101-107
• /
• 2001

A highly selective, sensitive and reliable ozone sensing $In_2O_3$ thin film was fabricated by a sol-gel method. The fabricated film is operated at a relatively lower temperature than ever developed thin films and saved operating power. $In_2O_3$ films deposited by sol-gel technique has been recently attracted because it is an economical and energy saving method and precisely controlled microstructure. Indium alkoxide precursor was synthesized from the reaction between indium hydroxide and butanol. PVA binder was used to improve adhesion of the films. The $In_2O_3$ thin films were obtained by spin coating from 1 to 5 times followed by drying at $100^{\circ}C$ and calcining at $600^{\circ}C$ for 1h. The film thickness was controlled by the number of coating time. The morphology and the thickness of the $In_2O_3$ films were examined by a SEM and XRD. The $In_2O_3$ thin films show a high sensitive to ozone gas at operating temperature of $250^{\circ}C$. The $In_2O_3$ sensor has very good selectivity to $CH_4$, CO, $C_4H_{10}$ and ethanol.

• Journal of Digital Convergence
• /
• v.15 no.12
• /
• pp.323-331
• /
• 2017

In this paper, four types of deposition control processes such as temperature, pressure, input/output(I/O), and gas were replaced by the Internet of Things(IoT) to analyze the data load and sequence procedure before and after the application of it. Through this analysis, we designed the load balancing in the sensing area of the deposition process by creating the sequence diagram of the deposition process. In order to do this, we were modeling of the sensor I/O according to the arrival process and derived the result of measuring the load of CPU and memory. As a result, it was confirmed that the reliability on the deposition processes were improved through performing some functions of the equipment controllers by the IoT. As confirmed through this paper, by applying the IoT to the deposition process, it is expected that the stability of the equipment will be improved by minimizing the load on the equipment controller even when the equipment is expanded.