[논문]Evaluating Tropical Night by Comparing Trends of Land cover and Land Surface Temperature in Seoul, Korea

Sarker, Tanni; Huh, Jung Rim; Bhang, Kon Joon

doi:10.7848/ksgpc.2020.38.2.123

Evaluating Tropical Night by Comparing Trends of Land cover and Land Surface Temperature in Seoul, Korea 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.38 no.2, 2020년, pp.123 - 130

Sarker, Tanni (Dept. of Geography, George Washington University) , Huh, Jung Rim (Social Eco Tech Institute, Konkook University) , Bhang, Kon Joon (Dept. of Civil Engineering, Kumoh National Institute of Technology)

Abstract ▼ AI-Helper

The impact of urbanization on LST (Land Surface Temperature) and TN (Tropical Night) was observed with the analyses of land cover change and LST by associating with the frequency of TN during the period of 1996 to 2016. The analyses of land cover and LST was based on the images of Landast 5 and 8 for September in 1996, 2006, and 2016 at a 10 year interval. The hourly-collected atmospheric temperatures for the months of July and August during the period were collected from AWSs (Automatic Weather Stations) in Seoul for the frequency analysis of TN. The study area was categorized into five land cover classes: urban or built-up area, forest, mixed vegetation, bare soil and water. It was found that vegetation (-7.71%) and bare soil (-9.04%) decreased during the period while built-up (17.29%) area was expanded throughout the whole period (1996-2016), indicating gradual urbanization. The changes came along with the LST rise in the urban area of built-up and bare soil in Seoul. In addition, the frequency of TN has increased in 4.108% and 7.03% for July and August respectively between the two periods of the 10 year interval, 1996-2006 and 2006-2016. By comparing the increasing trends of land cover, LST, and TN, we found a high probability that the frequency of TN had a relationship with land cover changes by the urbanization process in the study area.

주제어

표/그림 (11)

그림 Fig. 1. Study area
그림 Fig. 2. Scheme of data processing
표 Table 1. Information of Landsat images
그림 Fig. 3. Classiﬁcation results of (a) 1996, (b) 2006, and (c) 2016
표 Table 2. Accuracies of land covers in the study area
그림 Fig. 4. Ratios of land covers in the study area for 1996, 2006, and 2016
그림 Fig. 5. Gains and losses of land cover during two decades (1996-2016) (Unit: km²)
그림 Fig. 6 . Contributions to other land covers from 1996 to 2006 (km²)
그림 Fig. 7. Variations of average LSTs over diﬀerent land covers
그림 Fig. 8. Trends of the frequency of TN for last 20 years
표 Table 3. Land surface temperature by land cover type

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제안 방법

, 2015). Researchers, therefore, attempted to understand possible causing factors of UHI by comparing various datasets collected from different sensors. As an alternative, urban heat island intensity has been often used as to measure the temperature difference between the urban and rural areas(Kim and Baik, 2004).
Among several classification schemes, we adopted the ISODATA classification scheme because it is simple and can partially implement a supervised classification procedure that can increase the classification accuracy. The study area was segmented into 100 classes and each class was merged into one of the five classes by identifying actual landcover types with reference maps such as photographs from National Geographic Information Institute. Some classified areas, however, were still unidentifiable and, in this case, they were belonged to a miscellany class which was not included in this study.
Therefore, we compared the relationship between urban heat distribution on the ground surface according to the change of land covers in Seoul and atmospheric temperature datasets. This study includes comprehensive analysis on land cover changes on Septembers in 1996, 2006 and 2016 to identify the urbanization process in Seoul and the relationship between the two factors of the urbanization of Seoul and the frequency of TN was compared to evaluate whether the urbanization trend has the same trend as the frequency of TNs during this period.

대상 데이터

The study area covers Seoul and its surrounding areas as shown in Fig. 1. Seoul is one of the largest cities in the world with packed high-rise buildings and large population and distinguished by cold and dry winter and hot humid summer with the annual average temperature of 12.

데이터처리

After classification, the classification accuracy was evaluated with producer’s and user’s accuracies and kappa coefficients (Campbell and Wynne, 2011) by selecting300 random pixels and subsequently compared against the reference.

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