Effects of Climate Change and Spatial Characteristics on Urban Heat Island in Chiang Mai
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ผลกระทบการเปลี่ยนแปลงภูมิอากาศกับลักษณะเชิงพื้นที่ต่อปรากฎการณ์เกาะความร้อนเมืองในเขตเมืองเชียงใหม่ (Effects of Climate Change and Spatial Characteristics on Urban Heat Island in Chiang Mai Metropolitan Area, Thailand) มานัส ศรีวณิช The 1st Climate Thailand Conference 2010:National Risks and Opportunities in Global Climate Changes (Link: http://www.conference.tgo.or.th/) ABSTRACT: Chiang Mai Metropolitan Area (CMMA) is the largest city in northern of Thailand, experiencing rapid urbanization that has resulted in remarkable the urban heat island (UHI) effect which will be sure to influence the regional climate, environment, and socio-economic development. In this study, we review the use of thermal remote sensing in the study of urban climates, focusing primarily on the UHI effect and an integrated remote sensing-based approach to investigate the spatial characteristics of urban thermal environment. The LANDSAT ETM+ images from 2000 and 2006 were utilized to assess the surface urban heat island (SUHI) which will be further analyzed by investigating the relationships with several urban environment and development indices including; the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Density of Building (DenBldg), Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) in the urban area of CMMA. Results show that the SUHI effect has become more prominent in areas with rapid urbanization in CMMA. It was found that the average of SUHI (Mean±S.D.) in the center of CMMA was about 20.52±1.05˚C in 2000, but this difference jumped to 28.08±1.50˚C in 2006. This could lead to an intensified the UHI effect in the urban areas. In order to analyze the relationship between surface temperatures with the spatial characteristic indices, the results of the correlation can understand impacts of the configuration and composition of spatial characteristics on local thermal environment which was the basic information for finding the reduction methods of urban temperature and the establishment of environmentally friendly urban planning in the future. Overall, remote sensing technology was an effective approach for monitoring and analyzing urban growth patterns and evaluating their impacts on urban climates.
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Effects of Climate Change and Spatial Characteristics on Urban Heat Island in Chiang Mai
- 1. The 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes ผลกระทบการเปลียนแปลงภูมอากาศกับลักษณะเชิงพืนทีต่อปรากฏการณ์ ่ ิ ้ ่ เกาะความร้อนเมืองในเขตเมืองเชียงใหม่ Effects of Climate Change and Spatial Characteristics on Urban Heat Island in Chiang Mai Metropolitan Area, Thailand นำเสนอโดย อำจำรย์มำนัส ศรีวณิช คณะสถำปัตยกรรมศำสตร์และกำรผังเมือง มหำวิทยำลัยธรรมศำสตร์(ศูนย์รงสิต) ั E-mail: s.manat@gmail.com 19 – 21 August 2010 IMPACT Exhibition and Convention Center BANGKOK, THAILAND
- 2. ปรากฏการณ์ เกาะความร้อนเมือง (Urban Heat Island :UHI)? “การที่อุณหภูมิที่ชั้นอากาศใกล้ พืนดิน ในเขตชุมชนเมืองสูงกว่ าพืนที่โดยรอบที่เป็ นเขต ้ ้ กสิ กรรมและพืนที่ปกคลุมด้ วยป่ าไม้ ประมาณ 2-5 องศาเซลเซียส” ้ Figure 1: A stylized cross-section of a typical urban heat island (from Oke, 1978) Source: http://www.sc.edu/acmooregarden/UrbanHeatIslands.htm THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 3. กระบวนการเกิดปรากฏการณ์เกาะความร้อนเมือง Fig. Schematic depiction of radiation and energy fluxes over an urban and a rural area on a clear day. source: adapted from Oke, T. 1988, The urban energy balance, Progress in Physical Geography. THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 4. วัตถุประสงค์ของการวิจย (Objectives) ั 1. เพื่อติดตามสถานการณ์อุณหภูมิพ้ืนผิวเมือง (Surface Urban Heat Island: SUHI) ของเมืองเชียงใหม่ดวยวิธีการประยุกต์ใช้เทคโนโลยีดานการ ้ ้ สารวจระยะไกล (Remote Sensing) จากข้อมูลคลื่นอินฟราเรดความร้อน (Thermal Infrared Band) ดาวเทียม LANDSAT 2. ศึกษาปั จจัยความสัมพันธ์ระหว่างอุณหภูมิพ้ืนผิวกับปั จจัยเชิงพื้นที่ได้แก่ ดัชนีผลต่างพืชพรรณ (NDVI), ดัชนีผลต่างน้ า (NDWI), ตัวบ่งชี้ค่าระดับ ความหนาแน่นของเมืองโดยใช้ F.A.R. และ B.C.R. 3. ใช้เป็ นข้อมูลพื้นฐานสาหรับการกาหนดมาตรการลดอุณหภูมิพ้ืนผิวและ การวางแผนเมืองที่เป็ นมิตรต่อสิ่ งแวดล้อมต่อไป THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 5. พื้นทีศึกษา: เมืองเชียงใหม่และอาเภอข้างเคียง ่ 2 0 ÍÓàÀÍáÁèÃÔÁ o 2 4 Kilometers ¢Íºà¢µ¾×é¹·ÕèÈÖ¡ÉÒàÁ×ͧàªÕ§ãËÁèáÅлÃÔÁ³±Å ࢵ¡Òû¡¤Ãͧ ÍÓàÀÍàÁ×ͧàªÕ§ãËÁè ÍÓàÀÍ´ÍÂÊÐà¡ç´ ÍÓàÀÍáÁèÃÔÁ ÍÓàÀÍÊѹ¡Óᾧ ÍÓàÀÍÊѹ·ÃÒ ÍÓàÀÍÊÒÃÀÕ ÍÓàÀÍËÒ§´§ ÃÇÁ·Ñé§ÊÔé¹ ÍÓàÀÍÊѹ·ÃÒ à¹×éÍ·Õè (µÃ.¡Á.)* 108.23 17.85 41.84 22.52 67.01 88.25 63.19 408.89 ÊÑ´Êèǹ (ÃéÍÂÅÐ) 26.47 4.37 10.23 5.51 16.39 21.58 15.45 100.00 ·ÕèÁÒ: ¨Ò¡¡ÒÃÇÔà¤ÃÒÐËì´éÇ ÂÃкºÊÒÃʹà·ÈÀÙÁÔÈÒʵÃì (GIS) ÍÓàÀÍ´ÍÂÊÐà¡ç´ ÍÓàÀÍàÁ×ͧàªÕ§ãËÁè ÍÓàÀÍÊѹ¡Óᾧ ¨Ñ§ËÇÑ´àªÕ§ãËÁè ÍÓàÀÍÊÒÃÀÕ ÍÓàÀÍËÒ§´§ ¡Ãا෾ÁËÒ¹¤Ã · # THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 6. พัฒนำกำรกำรขยำยตัวของเมืองเชียงใหม่ RTSD Aerial Photo 1954/2497 QuickBird Satellite 2008/2551 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 7. RTSD Aerial Photo 1954/2497
- 8. RTSD Aerial Photo 1975/2518
- 10. สี่ แยกกลางเวียง ภาพถ่ายทางอากาศ เมื่อ พ.ศ. 2512 , โดย บุญเสริ ม สาตราภัย
- 11. ภาพถ่ายทางอากาศ ทางซ้ายมือของภาพคือกาดหลวง, โดย บุญเสริ ม สาตราภัย
- 12. ภาพถ่ายทางอากาศ บริ เวณตลาดวโรรสและศาลเจ้าปุนเถ้ากง เมื่อ พ.ศ.2515, โดย บุญเสริ ม สาตราภัย
- 13. พ.ศ.2552 ที่มา:ชมรมร่ มบินเชียงใหม่ http://www.chiangmaiparamotor.com
- 14. พ.ศ. 2552 ที่มา:ชมรมร่ มบินเชียงใหม่ http://www.chiangmaiparamotor.com
- 15. การสู ญเสี ยต้นไม้ใหญ่ที่ถูกตัดโค่นเพื่อการก่อสร้างในเขตพื้นที่เมืองเชียงใหม่ RTSD Aerial Photo 1975/2518 QuickBird Satellite 2008/2551 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 16. ต้นฉาฉาสองฝั่งถนนสายเชียงใหม่-สันกาแพง เมื่อ พ.ศ.2515,โดย บุญเสริ ม สาตราภัย
- 20. ความแตกต่ างของอุณหภูมิอากาศในเขตเมืองเชียงใหม่ กับนอกเขตเมืองในปี พ.ศ. 2552 Air Temperature (Celsius) Dry Season Dry Season Wet Season Extend detail Urban Heat Island Intensity Urban Heat Island Intensity Urban สถานีท่าแพ ต.ช้ างคลาน อ.เมืองเชี ยงใหม่ Rural สถานีดอยสะเก็ด ต.เชิ งดอย อ.ดอยสะเก็ด Date/Time ที่มา: ข้ อมูลอากาศประเทศไทย http://www.thaiweather.net THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 21. ความแตกต่ างของอุณหภูมอากาศระหว่ างวันช่ วงที่เกิดปรากฎการณ์ เกาะความร้ อน ิ 45 Air Temperature (Celsius) Nighttime 40 Sunrise Sunset Sunrise Daytime Nighttime Sunset Daytime Nighttime 35 30 Urban 25 20 Rural Heat Island Intensity 15 10 5 0 Tu r CLUHI 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 I February25,2009 I February 26,2009 I Date/Time (hrs.) ที่มา: ข้ อมูลอากาศประเทศไทย http://www.thaiweather.net THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 22. Characteristics of Landsat ETM+ (Enhanced Thematic Mapper Plus) Imagery of Chiang Mai, Thailand. February18,2006. a. Band1 (0.450-0.515µm) Visible Blue e. Band5 (1.550-1.750µm) Middle Infrared b. Band2 (0.525-0.605µm) Visible Green c. Band3 (0.603-0.690µm) Visible Red f. Band6 (10.40-12.50µm) Thermal Infrared d. Band4 (0.750-0.900µm) Near Infrared g. Band7 (2.080-2.350µm) Middle Infrared THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 23. METHODOLOGY AND PROCESSING OF SATELLITE DATA Radiometric and Geometric correction LANDSAT ETM+ Satellite images March 5th, 2000 and February 18th, 2006 Thermal Infrared Band (10.4–12.5 m) or Band 6 Conversion of digital numbers to absolute radiance value surface temperature in Celsius (˚C) GIS Vector Data Scale 1:4,000 Building Layers Survey in 2006 (Source: Department of Public Works and Town & Country Planning) Spectral reflectance in ETM+ red (band3) and near-infrared (band4) NDVI RNIR Rred RNIR Rred Normalized difference vegetation index (NDVI) spectral reflectance of near-infrared band (band4) and mid-infrared band (band5) NDWI Spatial Pattern Analysis (Quadrat Count Methods) 500 x 500 meters RNIR RMIR RNIR RMIR Normalized difference water index (NDWI) Calculate floor area ratio (F.A.R.) and building coverage ratio (B.C.R.) Relationship between urban thermal environment and spatial characteristics (correlation analysis and scatter diagram)
- 24. ้ื ่ อุณหภูมิพนทีผิว (Surface Urban Heat Island: SUHI)? (1)Bird’s-eye or plan view (3)Roof-top (2)Ground-level (4)Surface UHI (as viewed by remote sensor) THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 25. การวิเคราะห์ สหสัมพันธ์ ร่วมกับระบบสารสนเทศภูมศาสตร์ (GIS) ิ 32.95900 # Y = 26.1594809 + 0.0881972X R-Squared = 0.3097 # Mean LST (Centigrade) # 32.95900 # # Mean LST (Centigrade) # # # # # # # 30.38200 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 25.22800 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 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# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 22.65100 # 0.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 24.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 48.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # BCR # # # # # # # # # # # # # Y = 26.1594809 + 0.0881972X R-Squared = 0.3097 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 25.22800 # # 30.38200 # # 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # 22.65100 The unit space for spatial analysis was built by 500m×500m GRID 0.00000 24.00000 BCR 48.000 1st Climate THE Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 26. แนวโน้ มและทิศการขยายตัวของเมืองเชียงใหม่ (a.) ปี พ.ศ. 2543 (b.) ปี พ.ศ. 2549 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 27. การกระจายตัวของอาคารบ้ านเรื อนในเขตเมืองเชียงใหม่ และพืนทีโดยรอบ ้ ่ 2 0 2 Kilo m eter s Buildin g D ensity (N umbe r of Build ing pe r Grid) Less th an 10 0 101 - 200 201 - 300 301 - 400 401 - 500 Mo re th an 50 0 Retriev ed from GIS data, Sourc e: DTC P,T hailand Grid Size: 500m.x500m. THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 28. สัดส่วนพื้นทีอาคารคลุมดินกับระดับความหนาแน่ นของเมืองในพื้นทีศึกษา ่ ่ 2 0 2 2 0 2 Kilo m eter s Bu ilding Cove rag e Ra tio (BC R): (Pe rcen ta ge) Kilo m eter s Floor Area Ratio (FAR) 0.0 0.2 0.4 0.6 0.8 1.0 Less th an 10 10 - 1 5 15 - 2 5 25 - 3 0 30 - 3 5 Over tha n 35 S tati sti cs; M a x im u m =4 8 .00 , M e a n =6 .8 9 G ri d S ize : 5 0 0 m .x5 0 0 m . (a.) สัดส่วนพื ้นที่อาคารคลุมดิน - 0.2 0.4 0.6 0.8 1.0 1.5 Retriev ed from GIS data, Sourc e: DTC P,T hailand Grid Size: 500m.x500m. (b.) ระดับความหนาแน่นของเมือง THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 29. สัดส่วนพื้นทีอาคารคลุมดินกับระดับความหนาแน่ นของเมืองในเขตเมืองเชียงใหม่ ่ (a.) สัดส่วนพื ้นที่อาคารคลุมดิน (b.) ระดับความหนาแน่นของเมือง
- 30. การประเมินความสมบูรณ์ของพืชพรรณจากการคานวณหาดัชนี ผลต่างพืชพรรณ (Normalized Difference Vegetation Index: NDVI) (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 31. สถานการณ์การเปลียนแปลงของความสมบูรณ์ พืชพรรณในเขตพื้นทีเ่ มืองเชียงใหม่ ่ 1 2 3 (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549
- 32. 1 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 33. 2 โรงเรี ยนปริ นส์ รอย์ ย่ านวัดเกตการาม รร.รถไฟ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 34. คลองแม่ ข่า 3 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 35. การประเมินความชุ่มชื้นจากการคานวณหาดัชนี ผลต่างน้ าในเขตพื้นทีศึกษา ่ (Normalized Difference Water Index: NDWI) (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 36. สถานการณ์การเปลียนแปลงความชุ่มชื้นของน้ าในเขตพื้นทีเ่ มืองเชียงใหม่ ่ (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549
- 37. การประเมินสถานการณ์การเปลียนแปลงอุณหภูมิพ้ ืนผิวในเขตพื้นทีศึกษา ่ ่ (Surface Urban Heat Island: SUHI) (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 38. (a.) มีนาคม พ.ศ.2543 (b.) กุมภาพันธ์ พ.ศ.2549
- 39. Surface Urban Heat Island (SUHI) ย่ านวิทยาลัยครู ย่านช้ างเผือก ย่ านวัดเกตุ รพ.สวนดอก ย่านกาดหลวง ย่านสันป่ าข่ อย สนามบินเชียงใหม่ ย่านวัวลาย การเคหะฯหนองหอย THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 40. A สวนบวกหาด ในเขตคูเมืองเชียงใหม่ 34.59 29.96 34.59 24.96 29.96 ตลาดต้นลาไย ย่านวัวลาย คาร์ฟร์ ู ย่านสันป่ าข่อย LST2006 0 500 1000 1500 LST2000 2000 2500 3000 A 24.96 Surface Surface Temperature Temperature Centigrade) Centigrade) ย่านประตูเชียงใหม่ สนามบินเชียงใหม่ B ย่านวัดเกตุ 0 3500 4000 4500 5000 5500 6000 6500 7000 Distance (meters) 500 1000 1500 2000 2500 3000 A 3500 4000 7660 B 4500 5000 5500 6000 6500 7000 Distance (meters) 7660 B 0.223 34.59 LST NDVI NDWI 0 A 500 1000 1500 2000 2500 3000 3500 4000 Distance (meters) 4500 5000 5500 6000 6500 7000 7660 B -0.426 24.96 29.96 NDVI / NDW I Surface Temperature Centigrade) LST 2000 LST 2006
- 41. Correlation Analysis between land surface temperature (LST), NDVI and NDWI 32.95900 32.95900 30.38200 # # # Y = 26.4673345 - 8.6954943X R-Squared = 0.3230 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Y = 23.8166632 - 16.6466089X R-Squared = 0.7565 # # # # # 25.22800 # # # # # # Mean LST (Centigrade) Mean LST (Centigrade) Normalized Difference Water Index (NDWI) # # # 30.38200 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 25.22800 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 22.65100 -0.30000 0.03500 Normalized Difference Vegetation Index (NDVI) 22.65100 -0.40000 0.37000 (a) (b) 32.95900 32.95900 Y = 26.3296847 + 3.9614258X R-Squared = 0.2525 Mean LST (Centigrade) # # ## # 30.38200 # # # # # # # # # # # # Land Surface Temperature (LST) # # # # # # # ## # ## # # ## # ## # # # # ## # # # # ## # ## # ## # # # ## # ## ## # ## # # # # ## # ## # # # ## # # ## # # ## # ## # ## # # # ## # ## # ## # # # # # ## # ## # # # ## # ## # # # # # # ## # ## ## # # # # ## # ## # # ## # # # ## ## # ## # ## # ## # ## # # # # # ## # ## # ## # ## # ## # ## # # ## # ## ## # ## # ## # ## # ## # ## # ## # ## # ## # ## # ## # # # ## # ## # ## # ## ## # # ## # ## # ## # ## # ## # ## ## # # # ## # ## ## # # ## # ## # ## # ## # # ## # # # # ## # ## # ## # ## # ## # # # ## # ## # ## # ## # ## # # # ## # # # ## # ## # ## # ## # ## # ## ## ## # ## # ## # ## # ## # # ## # # ## # # # # ## # # # # ## # # ## ## # ## # ## # # ## ## # ## ## ## # ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # ## ## ## # ## # # ## # # ## # # # # # ## # ## ## # # # # ## # # # # ## # ## # # ## # # # # ## # ## # ## # # # ## # # ## # ## # ## # ## # # ## # ## # ## # ## # ## # # ## # # ## # # # ## # ## # # ## # ## # ## # ## # # ## ## # # # ## ## ## # # # ## ## ## # # ## ## ## ## ## ## ## ## ## ## ## # ## ## ## ## ## # ## # ## # ## # # # # ## # # # ## # # # ## # # # # # ## # # # # # # # # # # ## # ## # # # ## ## ## # ## # # ## ## ## # ## ## # # ## # ## # ## ## ## # # # # ## ## # ## ## ## # ## # ## ## # ## ## ## ## ## ## ## ## # ## ## ## ## ## ## ## ## # # # ## ## ## ## ## ## ## # ## ## ## ## # ## ## ## # ## ## ## ## ## ## ## ## ## # ## ## ## ## ## ## ## ## ## # # ## ## # ## # # # # # # ## # # # # ## # # # # # # # # ## # # # # ## # # # # # # ## ## # # # ## # # ## # # # ## # # ## # ## # # # # # # ## # ## ## # # # ## # ## # ## ## # # ### # # # ## # ## # ## # ## ## # # ## # # # # # ## # # ## # ## # ## # # ## ## # # ## ## # ## # # # ## # # ## ## # ## # # # # ## # ## # # ## # ## # # ## # # ## # # ## # # # # # # # ## # # # ## # # # # # ## # ## # # # # # # # # ## # ## # # # ## # # # # ## # # ## # ### # ## # ## # # # # ## # # # ## # # # ## # # # # # # # ## # ## # # # ## # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # ## # # # # # # # # # # # ## # # # # # # # # # 30.38200 30.38200 # # # # # # 25.22800 25.22800 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 1.45000 # 22.65100 22.65100 -0.30000 0.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # ## # # # # # # ## # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # ## ## # # # # # ## # ## ## ## # # # ## # # # ## # # ## # # # # # # # # # # ## # # ## # ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## ## # # # ## ## ## ## # ## # # # # # # # ## # # # # # # # # # # # # # # # # # # ## # # # # # # # # # ## ## # # # # # # # # # # ## # # # # ## ## ## # ## # # ## ## ## # # # ## # # # # # # # # # ## # ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # ## # # # # # # # ## ## # # # # # # ## # # # # # ## # # ## # # # # ## ## # # ## # # # # # ## # # # ## # # # ## # # # # # # ## # # # # ## # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## ## ## # # # # # # # # ## ## # # # ## # ## ## # # # ## ## ## ## # ## # # ## ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## ## # ## ## ## # ## # ## # # # # # ## ## ## ## ## # # # # ## # # # # ## ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # ## # ## ## # ## # ## # ## # # # ## ## ## ## ## ## # # ## # ## # # # # ## ## ## # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # ## # ## # # ## ## # # ## # # # ## ## # ## # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ### # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 0.03500 24.00000 # 0.37000 48.00000 # Mean NDVI BCR FAR (c) 32.959 # # # # # # # # # 0.72500 # # # # # # # # # # # # # # # # # 27.80500 27.80500 # # # # # 22.65100 0.00000 Y = 26.1594809 +-0.0881972X Y = 26.4673345 8.6954943X R-Squared ==0.3097 R-Squared 0.3230 # # # # # Mean LST (Centigrade) Mean LST (Centigrade) ## 25.22800 0.14000 Mean NDWI 32.95900 27.80500 -0.13000 Mean NDVI (a) (d) 32.95900 THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 42. 0.00000 0.72500 0.00000 1.45000 24.00000 BCR FAR Correlation Analysis between land surface temperature (c) 32.959 32.95900 (LST), F.A.R. and B.C.R. (d) # Y = 26.4673345 - 8.6954943X R-Squared = 0.3230 Mean LST (Centigrade) Mean LST (Centigrade) # Density of Building # 30.38200 # # # # # # # # # # # # # # # # # # # # # # # # # Y = 26.1868939 + 0.0052722X R-Squared = 0.2250 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # ## # ### # ## # # # # # # # # # # # # # ## # # # # # # # # # # # ## # # # # # ## # # # # # ## # # # ## # # # # # # # # # # # # # # # ## # # # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # ## # # # # # # ### # # # # ## # # # # # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # ## # # # # ## ### ## # # # # # # # # # # # # # # # ## # # # ## # # ## # # ## # # ### # # # # # # ## # # # # # # # ## # # # ## # # # # # # # # # # # # # # ## # ## ## ### # # # # # # # # # ## # ## # ## ## # ## # ## # # ## # # # # # # # # # # # # # # # # # # ## # # # # # ## # # # # ## # # # ## # # # # # # # # # ## ### # # ## # # # ## # # # # # # ## # # # # ## # ## # # ## # ## # # ### # # # # # # # # # # # # # ## ## ### # # ## # # # # # # # # # # # # # # # # # ## # # # # # # # # ## # ##### # ## # # # # # # ## # # # ## # # # ## # # # # # # ### # # # # # # # # # # # ## # ## # # # # # # # ## # # # # # # # # # # ## ## # # # # # ## # # # ## # # # # # # # # # ## # # # # ## # # # # # # ## # ## # # # # # # # ### # ## ## ## # ## # # ## # # # ## # # # # # # # # # # # # # # # # # # # # # # ## # # # #### # # ## # ### ## # ## ## # # # # ### # # # ## # # ## # # ## # # # ## # # # ## # # ### # ## ## # # # # # # # ## ## # # ## ##### #### ##### # # # ### ## # ## ### # # ## # # # # # # ## # # # # # # # # ## # ## # ## # # # # # # # # # ## ## ## # # # # # # # # # # # # # # ### # # # # # ## # # # ## # # # # # # # ## # # # # # # # # # # # # # # ## # # ## # # # # # # # # # # ## # # # # # ## # #### # ## # ###### # # # ### # # ## # # # ## # ## # # # # # # # # # # # # # # # # # # ## # # # # # # # # ###### # # # # # ## # # # # # # # # # # ## # # # # # # ## # # # # # # # # ## # # #### # ### # # ## # # # # ## # # # ## # # # # # # # #### # # ## # # ## # # # # # # # # ###### #### # # # ## # # # # # # # ### # # # # # ## ## # # ## ## # # # ### # # # # # # # # # ## # # ## # # #### # #### ## # #### # # # # #### ## # # # # # # # # # # # # # ## # # # ## # # ## # ## # # ## # # # # ##### # ## # # # # # # ## # # #### # ### # # # ## # ## # # ## ## # # # # # ## # # # ## ## # #### ## # ## ### ### # # # # # ## # # # # # # # # ## # # #### # # # # # ### # # ## # ## # # ## ## ## ### #### ## ## # ## # # # # # # # ## ## # # ### # # # # # ## # # # ## ## # # # ##### # # ## # # ## ## ## # #### # # # # ## # # # ### # # ### # # # # ### ## # ### ## ##### # ## # # # ## # ## ## ## # # ## # ## # ## # # #### ## # # ## # # # # # # # ### # # # # ### # # #### # # # ## # ## # # ### ## # # # # ## ## # # # # # # # # # # ## # # # ## # # # # # # ## ## # # # # ## # # # # # # ### # # # # # # ### # # # # ## # # # # # ## ## # # ## # ### #### ### # # ## ## # # # # # # # # ### # ## # # ### # # # # ## ## # ## # # ## # # ## # # # ## #### # # # ######### # # ####### # # # ## ## # # # # ## # # # ### # ## # # # # # # # ### # # # # # # # # # ## ## # # # # # # ## # # # # # # ####### ### ### # # # # ## ## # # # # ### # # # # # ## # # # # # # # ####### # # # # # # # ## # # # # # # # # ## # ## # # ## # ## # # # # # # # # ## # # ## # # # ## # # # # ## ### # # # # # # # ## # # # # ### # # # # ## ### # # # # # ##### # # # # # ## # # # # # # # #### # # # # # # ### # ## # # # # ### # ### # # # ## # ### ## # # # # # # 30.382 27.80500 # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Mean LST (Centigrade) # # 32.959 27.805 25.22800 25.228 22.65100 -0.30000 # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # 30.382 27.805 25.228 # # 22.651 0.03500 0.37000 # # 30.38200 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 25.22800 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 22.65100 -0.30000 0.03500 0.37000 # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # ## ## # # ## # # # # # # # ## # # # # # ## # # # # # ## # # ## # # ## # # ## # ## # ## # # # ## # ## # # # ## # # ## # # # # # # # # ## # # # ## # # ## # # # ## # # # ## # # ## # # # ## # ## # ## # ## # # ## # ## # # # ## # # ## # # ## # # ## # # ## # # ## # # ## # ## # # ## # # ## # # ## # ## # # ## # # # ## # ## # # ## # # ## # # ## # # ## # # # ## # # # # ## # # ## # # ## # # # # # ## # ## # # # ## # # ## # ## ## ## # # ## # # ## # # ## # # ## # # # # ## ## # ## ## # ## # ## # ## # ## ## ## # # # # ## # 25.22800 22.65100 -0.40000 Mean NDVI # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # ## ## ## # ## # # ## # # ## ## ## ## # ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## # ## # ## # # # # ## # # # ## # ## # # # # ## # # ## # # ## # # # ## # # # ## # # ## # ## # # # ## # ## # # # # # # # ## # # ## # ## ## # ## # ## # ## # ## # ## # ## # ## # ## # ## # # ## ## # ## # ## # # ## # # # # # ## # # # ## # ## ## ## # ## # # ## # ## # ## # ## ## ## # ## # ## # ## # ## # ## ## # ## ## # # # # ## # # ## ## # # ## ## # ## # # # ## # ## # # # # ## # # ## # # ## ## ## # # # # ## # # # # # # # # # # # # # # ## # # # # # # # # # # # # # ## ## # # ## # ## # # ## # # # # # # # # # ## # ## # # ## # ## # # # # ## # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # ## # # # # # # # ## # # # # # ## # # # ## # # # # # # ## # # # ## # ## # # ## ## ### # # # ## # # # # ## ## # # # # # # # # # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # ## # # # # # # # # # # # ## # # # # # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # ## # # # ## # # # # # ## # # # # # # # ### # ## # # # # # # # # # # # # # # # # # # ## # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # -0.13000 0.14000 30.38 27.80 25.22 # Mean NDWI # 22.65100 0.00000 (a) # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## 32.95 R-Squared = 0.2525 # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # ## # ## ## # ## # ## ## ## # # ## ## # # ## # # ## ## ## ## ## # # ## ## ## ## ## ## ## ## ## ## ## # ## # ## ## ## ## ## # ## # ## # ## ## ## ## # ## # # # # # # ## # # # ## ## # ## ## # # ## ## ## # ## ## # # ## # ## # ## ## ## # # # # ## ## # ## ## ## # ## ## ## ## ## ## ## ## ## # ## ## ## ## ## ## ## ## ## # ## ## # # ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## # ## ## ## ## ## ## ## ## ## ## ## ## ## # ## ## ## ## # ## # ## ## # # ## # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # ## # (e)Y = 26.3296847 + 3.9614258X # # 27.80500 25.22800 # # R-Squared = 0.7565 # # # # 30.38200 27.80500 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Building Coverage Ratio (BCR) # 32.95900 30.38200 # # # # # # 811.000 BuildingY = 23.8166632 - 16.6466089X Density # Mean LST (Centigrade) Y = 26.4673345 - 8.6954943X R-Squared = 0.3230 (a) 406.000 # # # Mean LST (Centigrade) Mean LST (Centigrade) # # Mean LST (Centigrade) Floor Area Ratio (FAR) Mean NDVI 22.651 32.95900 1.000 32.95900 22.65 0.72500 (b) 1.45000 FAR 32.95900 Y = 26.3296847 + 3.9614258X R-Squared = 0.2525 ## # 30.38200 # # # # # # # # # # # # # 25.22800 # # # # # # # # # # ## ## # # # ## # ## # # # # ## # # # ## # # ## # ## # # # ## # ## # ## # # ## # ## # # # ## # # ## # # ### # ## # ## # # ## # # ## # ## # ## # # # ## # ## ## # # ## # ## # # # # # # # ## # ## # # # # ## ## # # # ## # ## # # ## ## # # # ## # ## # ## ## # ## # # # # # ## # ## # ## # ## # ## # ## # ## ## # # # ## # ## # ## # ## # ## # ## # # ## # ## # ## # ## # ## # ## # ## # ## # ## # ## # ## # # # ## # # ## # ## # # # ## # ## # ## # # ## # ## ## # ## # # # ## # ## # ## # ## # ## # ## # # # ## # # # ## # ## # ## # # # # ## # ## # # # ## # ## # ## # ## # ## # ## ## # ## # ## # # ## # # # ## # # # ## # # ## # # # ## # # # ## ## # ## ## # ## # ## # ## # ## ## ## ## # ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # ## ## # # ## ## # ## # # ## ## ## ## # ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## # ## # ## # # # # ## # # # ## # # # # # # # ## # # ## # # ## # # # ## ## # # # ## ## ## # # # # ## # # # # # ## # # # ## # # # # # # # # # # ## # # # # # # # # # # ## # # ## ## # # # ## # # ## # # # ## # # ## # ## # # # # # # # # # # ## # ## # # # ## # # ## # # ## ### # # # # ## ## # ## # ## ## # # ## # # # # # # ## ## # # ## ## # ## ## ## # ## ## # ## # # # # # # # # # ## ## # # # # ## # # # # # # # ## # # # # # # ## # ## # # ## ## # # # ## # # ## # # # ## # # # # ## # ## # ## # # # ## # # ## # ## # ## # ## # # # ## # ## # # ## # # ## # # ## ## # # ## # # ## # # ## # ## # ## # ## # ## # # ## # # ## # # # ## # ## # # ## # ## ## # # # # # ## # ## # # # ## ## # ## ## ## # # ## ## # ## ## # # ## # ## # ## ## ## # ## # # # ## # ## ## ## # ## ## ## ## ## ## ## ## ## # ## ## ## ## ## # ## ## ## ## # ## # # # ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## # ## ## ## ## ## ## ## ## ## ## ## ## ## # ## ## ## ## # ## # ## ## # # # # ## # # # # # # # # ## # ## # # # # ## # ## # # # # ## ### # ## # # ## # # # # ## # # # ## # # ## # # # # # # # # ## # ## # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # ## # # # # # ## # # # # # # # # # # # # # ## ## ## # # # # # # # R-Squared = 0.3097 # # # 30.38200 FAR (c) 1.45000 32.959 # # # # # # # # # # # 27.80500 25.22800 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 22.65100 0.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 0.72500 # # # # 22.65100 0.00000 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # 25.228 24.00000 BCR # # # Y = 26.18 R-Squared # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # ## # # # # # ## # ## # # # # ## # # # ## # # # ## # ## ## # # # # # # # # # # # # # ### # # # # ## # # # # # # # # # # # ## # # # # # # # ## # # # # # ## ## # ## # ## # # # ### # ## # # # ## # ## # # # # # # ## ## ### # # ## # ## # # # # ## # ## # ## ## # ## # ## # # ## # # # # ## # # # ## # # # ## # ## ### # # ## # # # ## # # # ## # ## # ## # # # # # # # # # # # # # ## ## ### # # ## ## ## # # ## # # # #### # # # ## # # # # # # # # ## ## # # # ## # # ### ## # # # # ## # ## # # # # # # # ## # # # ## # # # # # # # # # # # # ## # # # # # ## # # # # # # ## # ## # ## # # ## # # # # ## ## ## # ## # # # # # # # # # ## # # # ## ## ## # # ## # ## # ### ## # ## ## # # # # ### # # # # ## # # # ## # # ### # # # # # # ## ## # # ## # ### # # ## ##### #### ##### # # # ### ## # ## ### # # ## # # # # # # ## # # # # # # # # ## # # # ## # # ## # ## # # # # # ### # # # # ## # # # # # # # ### # # # # # # # # # # ## ## # # # # # # # ## # #### # ## # ###### # # # ### # # ## # # # ## # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # ## ## # # # # ## # ##### # # ## ### # # ### ## #### # # ## # # #### # # # # # # ## # ## # ## # # # # # # # # ### # # # # ###### #### # # # ## # # # # ### # # # # ## # # ## ##### # # # # # ## # ## # # # # ## ## #### # #### ## # #### # # # # #### ## # # # ## # # # # ## # # # # # # # ## # # ##### ### ### ### #### ## # #### # ### # # ## ## # ## # ## # # ## ### ## # # # #### ## # # # ## # # # # # # ### # # # # # #### # # # # # # ### # # ## # ## # # ## ## ## ### #### ## ## # ## # # # # # # # ## # ### ## # ### # # # # # # # # # # ## # # ## # # ## # # ## ## ## # #### # # # # ## # # # # ### # ### # ## # ###### # ### ## ##### # ## # # # ## # ## ## ## ## # ### # ## ## # # ### ## # # ## # # # # # # # ### # # # # ### # # #### # # # ## # ## # # ### ## # # # # ## ## # # # # # # # # # # # # # # ## # # # # # # ## ## # # # # ## # # # # # # # ### # # # # # ### # # # # # # ## ## # # ## # ### #### ### # # # # ## # ## ## # # # # # # # # ### # ## # # ### # # # # ## ## # ## # # ## # # ## # # # ## #### # # # ######### # # ####### # # # ## ## # # # # ## # # # ### # ## # # # # # # ### # # # # # # # ## # ### ## # # ## ## ### # # # # # ## # ## # # # ## ## ## ## # ## # # # # # # # # # # ## # # # # # # # ####### # # # # # # # ## # # # # # ## # # ## # ## # # # # # # # ### ## ## # # ## # # # # # ## # # ## # # ## # ## # # # # # # # # ## # # # ### # # # # ## ### # # # # # ##### # # # # # ## # # # # # # # #### # # # # # # ### # ## # # # # ### # ### # # # ## # ### ## # # # # # # 27.805 # # # # # ## ## # # # # # # # # # # # # # # # # # # # # # # # # # 30.382 # # # # # # # # # # # # # Mean LST (Centigrade) Mean LST (Centigrade) Land Surface Temperature (LST) # Mean LST (Centigrade) ## # 27.80500 (c)26.1594809 + 0.0881972X Y= 32.95900 # # # # ## # # # # # # # # # # # # # # ## # # ## # # ## # # # # # # # ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # 48.00000 # THE 1st Climate Thailand Conference 2010: 22.651 1.000 406.000 National Risks and Opportunities in Global Climate Changes (d) Building Density #
- 46. Chiang mai Business Park Chiang mai Business Park
- 47. มาตรการลดอณหภูมิพนผิวและการวางแผนเมืองที่ ื้ ุ เป็ นมิตรต่ อสิ่งแวดล้ อม THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 49. Cool Roofing Parking Lot Shade
- 51. หลังคาเขียว (Green roof) ศ. เดชา บุญคา, ้ แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้
- 52. Fukuoka, Japan ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 53. กาแพงเขียว (Green walls) การปกคลุมผนังอาคารด้านถูก แดดด้วยพืชพรรณ ต้นไม้จะลดการแผ่รังสี ดวยการดูด ้ ซับพลังงานแสงแดดและคายน้ า เพื่อทาความเย็น ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 54. แนวคิดเชิงการผังเมือง (ความสั มพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 55. แนวคิดเชิงการผังเมือง (ความสัมพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 56. แนวคิดเชิงการผังเมือง (ความสัมพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 57. แนวคิดเชิงการผังเมือง (ความสัมพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 58. แนวคิดเชิงการผังเมือง (ความสัมพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 59. แนวคิดเชิงการผังเมือง (ความสัมพันธ์ ระหว่ างความหนาแน่ นกับทีเ่ ว้ นว่ าง) ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 60. การแบ่ งขันตอนการพัฒนาทางกายภาพ ้ การพัฒนาระยะแรก การพัฒนาระยะกลาง การพัฒนาระยะสุดท้ าย ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 61. ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 62. ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 63. ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้
- 64. หมู่บ้าน Ballwin Hills ในนครลอสแองเจลลิส พ.ศ. 2472 ศ. เดชา บุญคา , แนวคิดการเพิ่มพืนที่สีเขียวในภาคธุรกิจเอกชน: 2552 ้ ้ THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 66. THE 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes
- 67. The 1st Climate Thailand Conference 2010: National Risks and Opportunities in Global Climate Changes 19 – 21 August 2010 IMPACT Exhibition and Convention Center จบการนาเสนอ, ขอบคุณทุกท่านครับ. เสนอโดย อาจารย์มานัส ศรี วณิ ช คณะสถาปัตยกรรมศาสตร์และการผังเมือง มหาวิทยาลัยธรรมศาสตร์