• Title/Summary/Keyword: Urban climate changes

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A Phenology Modelling Using MODIS Time Series Data in South Korea (MODIS 시계열 자료(2001~2011) 및 Timesat 알고리즘에 기초한 남한 지역 식물계절 분석)

  • Kim, Nam-Shin;Cho, Yong-Chan;Oh, Seung-Hwan;Kwon, Hye-Jin;Kim, Gyung-Soon
    • Korean Journal of Ecology and Environment
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    • v.47 no.3
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    • pp.186-193
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    • 2014
  • This study aimed to analyze spatio-temporal trends of phenological characteristics in South Korea by using MODIS EVI. For the phenology analysis, we had applied double logistic function to MODIS time-series data. Our results showed that starting date of phenology seems to have a tendency along with latitudinal trends. Starting date of phenology of Jeju Island and Mt. Sobeak went back for 0.38, 0.174 days per year, respectively whereas, Mt. Jiri and Mt. Seolak went forward for 0.32 days, 0.239 days and 0.119 days, respectively. Our results exhibited the fluctuation of plant phonological season rather than the change of phonological timing and season. Starting date of plant phenology by spatial distribution revealed tendency that starting date of mountain area was late, and basin and south foot of mountain was fast. In urban ares such as Seoul metropolitan, Masan, Changwon, Milyang, Daegu and Jeju, the phonological starting date went forward quickly. Pheonoligcal attributes such as starting date and leaf fall in urban areas likely being affected from heat island effect and related warming. Our study expressed that local and regional monitoring on phonological events and changes in Korea would be possible through MODIS data.

Study on the Current Status of Smart Garden (스마트가든의 인식경향에 관한 연구)

  • Woo, Kyung-Sook;Suh, Joo-Hwan
    • Journal of the Korean Institute of Landscape Architecture
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    • v.49 no.2
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    • pp.51-60
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    • 2021
  • Modern society is becoming more informed and intelligent with the development of digital technology, in which humans, objects, and networks relate with each other. In accordance with the changing times, a garden system has emerged that makes it easy to supply the ideal temperature, humidity, sunlight, and moisture conditions to grow plants. Therefore, this study attempted to grasp the concept, perception, and trends of smart gardens, a recent concept. To achieve the purpose of this study, previous studies and text mining were used, and the results are as follows. First, the core characteristics of smart gardens are new gardens in which IoT technology and gardening techniques are fused in indoor and outdoor spaces due to technological developments and changes in people's lifestyles. As technology advances and the importance of the environment increases, smart gardens are becoming a reality due to the need for living spaces where humans and nature can co-exist. With the advent of smart gardens, it will be possible to contribute to gardens' vitalization to deal with changes in garden-related industries and people's lifestyles. Second, in current research related to smart gardens and users' experiences, the technical aspects of smart gardens are the most interesting. People value smart garden functions and technical aspects that enable a safe, comfortable, and convenient life, and subjective uses are emerging depending on individual tastes and the comfort with digital devices. Third, looking at the usage behavior of smart gardens, they are mainly used in indoor spaces, with edible plants are being grown. Due to the growing importance of the environment and concerns about climate change and a possible food crisis, the tendency is to prefer the cultivation of plants related to food, but the expansion of garden functions can satisfying users' needs with various technologies that allow for the growing of flowers. In addition, as users feel the shapes of smart gardens are new and sophisticated, it can be seen that design is an essential factor that helps to satisfy users. Currently, smart gardens are developing in terms of technology. However, the main components of the smart garden are the combination of humans, nature, and technology rather than focusing on growing plants conveniently by simply connecting potted plants and smart devices. It strengthens connectivity with various city services and smart homes. Smart gardens interact with the landscape of the architect's ideas rather than reproducing nature through science and technology. Therefore, it is necessary to have a design that considers the functions of the garden and the needs of users. In addition, by providing citizens indoor and urban parks and public facilities, it is possible to share the functions of communication and gardening among generations targeting those who do not enjoy 'smart' services due to age and bridge the digital device and information gap. Smart gardens have potential as a new landscaping space.

Analysis of Human Thermal Environment in an Apartment Complex in Late Spring and Summer - Magok-dong, Gangseo-gu, Seoul- (아파트 단지의 늦봄·여름철 인간 열환경 분석 - 서울특별시 강서구 마곡동 -)

  • Park, Sookuk;Hyun, Cheolji;Kang, Hoon
    • Journal of the Korean Institute of Landscape Architecture
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    • v.50 no.1
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    • pp.68-77
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    • 2022
  • The human thermal environment in an apartment complex located in Seoul was quantitatively analyzed to devise methods to modify human heat-related stresses in landscape and urban planning. Microclimatic data (air temperature, relative humidity, wind speed, and short- and long-wave radiation) were collected at 6 locations [Apt-center, roof (cement), roof (grass), ground, playground, and a tree-lined road] in the late spring and summer, and the data were used to estimate the human thermal sensation, physiological equivalent temperature (PET) and universal thermal climate index (UTCI). As a result, the playground location had the highest thermal environment, and the roof (grass) location had the lowest. The mean difference between the two locations was 0.8-1.1℃ in air temperature, 1.8-4.0% in relative humidity, and 7.5-8.0℃ in mean radiant temperature. In open space locations, the wind speed was 0.4-0.5 ms-1 higher than others. Also, a wind tunnel effect happened at the Apt-center location during the afternoon. For the human thermal sensation, PET and UTCI, the mean differences between the playground and roof (grass) locations were: 5.2℃ (Max. 11.7℃) in late spring and 5.4℃ (Max. 18.1℃) in summer in PET; and 3.0℃ (Max. 6.1℃) in late spring and 2.6℃ (Max. 9.8℃) in summer in UTCI. The mean differences indicated a level change in PET and 1/2 level in UTCI, and the maximum differences showed greater changes, 2-3 levels in PET, and 1-1.5 levels in UTCI. Moreover, the roof (grass) location gave 4.6℃ PET reduction and a 2.5℃ UTCI reduction in late spring, and a 4.4℃ PET reduction and a 2.0℃ UTCI reduction in the summer when compared with the roof (cement) location, which results in a 2/3 level change in PET and a 1/3 level in UTCI. Green infrastructure locations [roof (grass), ground, and a tree-lined road] were not statistically significant in the reduction of PET and UTCI in thermal environment modifying effects. The implementation of green infrastructure, such as rooftop gardens, grass pavement, and street tree planting, should be adopted in landscape planning and be employed for human thermal environment modification.

GOCI-II Based Low Sea Surface Salinity and Hourly Variation by Typhoon Hinnamnor (GOCI-II 기반 저염분수 산출과 태풍 힌남노에 의한 시간별 염분 변화)

  • So-Hyun Kim;Dae-Won Kim;Young-Heon Jo
    • Korean Journal of Remote Sensing
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    • v.39 no.6_2
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    • pp.1605-1613
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    • 2023
  • The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R2), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R2 and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.