Dong Kun Lee

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas.

The impacts of weather on tourist satisfaction and revisit intention: a study of South Korean domestic tourism

ABSTRACT This study explores the impacts of weather on tourist satisfaction and intention to revisit sites utilizing a survey of 1736 domestic tourists in South Korea. This study adds tourists’ perceived quality of weather to a path model that anticipates revisit intention and was originally based on the perceived quality of physical attributes and service, as well as tourist satisfaction. The results of this study show that the perceived quality of weather affects tourist satisfaction and revisit intention directly and indirectly, and that it correlates with the perceived quality of physical attributes and service. It was determined that the impact of weather perception on tourist satisfaction and revisit intention is higher in rainy weather conditions. The results of the study show that to sustain tourist satisfaction and revisit intention, efforts to moderate the negative impacts of uncomfortable weather conditions are required, especially in rainy weather.

The Role of Green Roofs on Microclimate Mitigation Effect to Local Climates in Summer

Although there is a large body of previous studies on the cooling effect of green roofs on urban heat islands (UHIs), more empirical studies with an experimental setting measuring the role of green roof on mitigating urban heat should be considered. The purpose of this research is to determine the air temperature difference between green and bare roofs located on two buildings in the same local climate zone, and calculate the expected cooling effect extended from the green roof to the local climate. The study site consisted of an extensive green roof and a bare roof which were close to each other and were located in a highly built-up same area. During the three and a half clean days of testing in the middle of summer, air temperature data were collected from each roof using an air temperature logger and a local automatic weather station close to both roofs; these data were then converted to hourly data. The data were analyzed by the t test, ANOVA test, and a regression analysis to determine the heat mitigation effect of green roofs. As a result, the green roof’s air temperature showed much lower than the bare roof’s and followed the local station’s air temperature change during the day. At night the air temperature difference between the two roofs was only slight and the green roof’s air temperature kept lower than the local station’s. Thus, even extensive green roofs can reduce air temperature through their cooling effects from their vegetation and substrates against solar radiation. The established models reported that the green roof contributed to lowering air temperatures in a local climate zone, while the bare roof made such zones more heated. The findings of this study contribute to the existing literature by proving that green roofs can be expected to help cool down not only at the small scale with building units, but also at the broader scale of urban district area. The study also gives field-experimented values to be used as updated variables to local climate simulation models.

Finding key vulnerable areas by a climate change vulnerability assessment

Extreme climate events such as typhoons, heat waves, and floods have increased in frequency with climate change. Many municipalities within the Republic of Korea (ROK) have experienced damage from these events, necessitating countermeasures. Vulnerability assessment has been suggested in the implementation of a national plan for reducing damage resulting from climate change. Thus, in this study, we assess the vulnerability of the ROK and identify key vulnerable municipalities in support of the national adaptation plan. We create a framework for assessing the vulnerability of all 232 municipalities of the ROK with respect to 32 items in 7 fields. The framework regards decision makers’ comprehension and availability of data as important factors. We assess the vulnerability index of each municipality by using variables of climate exposure, sensitivity, and adaptation capacity. The weights of variables are determined by the Delphi method. We used the representative concentration pathways 8.5 climate scenario to reflect future climate exposure for the vulnerability assessment. From the analysis, vulnerability maps are prepared for the 32 items of 7 fields, and key vulnerable municipalities are identified by aggregating the maps. The distribution of vulnerable municipalities changes with the future climate conditions. These maps provide a scientific and objective basis for the ROK government to establish adaptation plans and allocate resources. The ROK government can utilize the results to identify the characteristics of highly vulnerable areas, and municipalities can use the results as a basis for requesting support from the national government.