Application of Geographic Information System (GIS) for Urban Heat Island Analysis: A Case Study of Samut Prakan Province
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Abstract
This research aims to study the monthly average temperature using the Inverse Distance Weighting (IDW) spatial interpolation method. The classification of land use into five categories through supervised classification using Landsat-8 satellite imagery and analyzed the relationship between the average temperature and the areas of urban and built-up in Samut Prakan Province from 2020 to 2022 using linear regression statistical analysis. The study found that from 2020 to 2022, the highest average temperature was 31.4 °C in May, 30.8 °C in May, and 30.5 °C in June, respectively. The lowest average temperature was 27.8 °C in December, 26 °C in January, and 27 °C in December, respectively. The average temperatures were 29.8, 29.3, and 29.2 °C, respectively. As for land use classification, it was found that from 2020 to 2022, the land use for urban and built-up areas had an increasing trend each year, with values of 342.32, 342.65, and 367.62 km², respectively. When testing the relationship between average temperature and urban and built-up areas from 2020 to 2022, it was found that the coefficient of determination correlated with the size of urban and built-up areas, with values of 0.79, 0.73, and 0.84, respectively. This evidence indicates that an increase in the size of urban and built-up areas significantly raises the average temperature of the urban area.
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ผู้ส่งบทความ (และคณะผู้วิจัยทุกคน) ตระหนักและปฎิบัติตามจริยธรรมการวิจัยอย่างเคร่งครัด ทั้งนี้บทความ เนื้อหา ข้อมูล ข้อความ ภาพ ตาราง แผนภาพ แผนผัง หรือข้อคิดเห็นใดๆ ที่ปรากฎในบทความ เป็นความคิดเห็นและความรับผิดชอบของผู้ส่งบทความ กองบรรณาธิการไม่จำเป็นต้องเห็นตามเสมอไป และไม่มีส่วนรับผิดชอบใดๆ โดยถือเป็นความรับผิดของของเจ้าของบทความเพียงผู้เดียว
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