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題名 使用衛星影像估算泰國東部經濟走廊的 LULC、光照強度和社會經濟因素之間的關係
Estimating the Relationship Between LULC, Light Intensity, and Socioeconomic Factors in Thailand’s Eastern Economic Corridor Using Satellite Images
作者 林玲玉
Rukhengkul, Thanatcha
貢獻者 范噶色
Stephan Van Gasselt
林玲玉
Thanatcha Rukhengkul
關鍵詞 城市擴張
東部經濟走廊
LULC 分析
NTL 製圖
多元迴歸
碳排放
Urban expansion
Eastern Economic Corridor (EEC)
LULC analysis
NTL mapping
Carbon emissions
日期 2024
上傳時間 5-Aug-2024 13:28:51 (UTC+8)
摘要 本研究使用 MODIS 和 Sentinel-2 進行土地利用土地覆蓋 (LULC) 分析，並使用 VIIRS 進行夜間燈光 (NTL) 測繪，分析泰國東部經濟走廊 (EEC) 的城市擴張。它使用 2017 年和 2022 年的 Sentinel-2 影像以及 2013 年至 2022 年的 MODIS 提供了土地利用變化的詳細空間分類。在春武里府，結果顯示 LCRPGR 值增加至 1.2745，顯示土地消耗的成長速度快於人口的成長速度。 然後使用統計分析（包括皮爾遜相關係數和多元迴歸）找出變數之間的關係。分析顯示，NTL 與EEC、省和地區級別的城市化之間存在高度顯著的係數，MODIS 得出的城市地區數據證明更適合省級分析，光照強度與碳排放之間的顯著係數( R² = 70.2 %)增加代表土地利用變化和城市擴張（例如城市和森林面積）影響的自變數。 然而，LULC 的準確分類涉及合併與回歸相互作用的各種自變量，以闡明 NTL 與城市化（以城市地區衡量）之間的關係。碳排放量與總光發射量之間的相關性根據所使用的碳排放量計算源的不同而不同，導致不同的方向關係。未來的分析可以考慮額外的自變數、不同的衛星來源和碳排放計算方法，以評估這些關係在多年間的變化。
This study analyzes urban expansion in Thailand's Eastern Economic Corridor (EEC) using MODIS and Sentinel-2 for Land Use Land Cover (LULC) analysis and VIIRS for Nighttime Light (NTL) mapping. It provides a detailed spatial classification of land use changes using Sentinel-2 images from 2017 and 2022 and MODIS from 2013 to 2022. The study also incorporates the SDG 11.3.1 indicator to enhance understanding urbanization dynamics. In Chonburi province, results highlight an increase to a 1.2745 LCRPGR value, indicating that land consumption is increasing faster than the population is growing. Then find the relationship between variables using statistical analysis, including Pearson correlation coefficients and Multiple Regression. This analysis shows a highly significant coefficient between NTL and urbanization at EEC, provincial, and district levels, with MODIS-derived urban area data proving more suitable in provincial analysis, a significant coefficient between light intensity and carbon emissions (R² = 70.2 %) after adding independent variables representing impacts of land use change and urban expansion, such as urban and forest areas. However, accurate classification of LULC involves incorporating various independent variables that interact with regression to elucidate the relationship between NTL and urbanization, as measured by urban areas. The correlation between carbon emissions and total light emissions varies depending on the carbon emissions calculation source, resulting in different directional relationships. Future analyses could consider additional independent variables, different satellite sources, and carbon emission calculation methods to assess how these relationships vary across multiple years.
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Bhandari, R., Xue, W., Virdis, S. G., Winijkul, E., Nguyen, T. P. L., & Joshi, S. (2023). Monitoring and Assessing Urbanization Progress in Thailand between 2000 and 2020 Using SDG Indicator 11.3. 1. Sustainability, 15(12), 9794. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. CRC Press. Breiman, L. (2001). "Random forests." Machine learning, 45(1), 5-32. Briassoulis, H. (2000). Analysis of land use change: theoretical and modeling approaches, the web book of regional Science. Regional research institute, West Virginia University, USA. Chaiwat, T. (2016). Night lights, economic growth, and spatial inequality of Thailand (No. 26). Puey Ungphakorn Institute for Economic Research. Chapin, F., S. Jr. and E.J. Kaiser. 1979. Urban Land Use Planning. Urbana: University of Illinois Press. Chen, Y., Liu, X., Li, X., 2017a. Analyzing parcel-level relationships between urban land expansion and activity changes by integrating landsat and nighttime light data. Remote Sens. (Basel) 9 (2), 164. Congalton, R. G., & Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC Press. Cortes, C., & Vapnik, V. (1995). "Support-vector networks." Machine learning, 20(3), 273-297. Eastern Economic Corridor Office of Thailand. (n.d.). Retrieved from https://www.eeco.or.th/en Elvidge, C. D., Baugh, K. E., Kihn, E. A., Koehl, H. W., Davis, E. R., & Davis, C. W. (1997). "Relation between satellite observed visible-near infrared emissions, population, and energy consumption." International Journal of Remote Sensing, 18(6), 1373-1379. Elvidge, C., Baugh, K., Zhizhin, M., & Hsu, F.-C. (2013). Why VIIRS data are superior to DMSP for mapping nighttime lights. Proc. Asia-Pacific Adv. Netw. 35(0), 62. doi: 10.7125/apan.35.7. Elvidge, C. D., Sutton, P. C., Ghosh, T., et al. (2014). "A global poverty map derived from satellite data." Computers & Geosciences, 64, 1-13. Elvidge, C. D., Baugh, K. E., Zhizhin, M., Hsu, F. C., & Ghosh, T. (2017). "VIIRS night-time lights." International Journal of Remote Sensing, 38(21), 5860-5879. Feng, Z., Huang, G., & Chi, D. (2020). Classification of the complex agricultural planting structure with a semi-supervised extreme learning machine framework. Remote Sensing, 12(22), 3708. Foody, G. M., & Arora, M. K. (2006). Uncertainty in Remote Sensing and GIS: Fundamentals. In Foody, G. M., & Arora, M. K. (Eds.), Uncertainty in Remote Sensing and GIS (pp. 1-12). Wiley. Foody, G. M. (2008). Harshness in image classification accuracy assessment. International Journal of Remote Sensing, 29(11), 3137-3158. Friedl, M. A., et al. (2010). "MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets." Remote Sensing of Environment, 114(1), 168-182. Gilbert, K. M., & Shi, Y. (2023). Nighttime Lights and Urban Expansion: Illuminating the Correlation between Built-Up Areas of Lagos City and Changes in Climate Parameters. Buildings, 13(12), 2999. Gislason PO, Benediktsson JA, Sveinsson JR. Random Forests for land cover classification. Pattern Recognition Letters 2006; 27(4):294-300. Hamad, R. (2020). An assessment of artificial neural networks, support vector machines and decision trees for land cover classification using sentinel-2A data. Sciences, 8(6), 459-464. Han, G., Zhou, T., Sun, Y., & Zhu, S. (2022). The relationship between night-time light and socioeconomic factors in China and India. PLoS One, 17(1), e0262503. Hung, L.W., Anderson, S.J., Pipkin, A., Fristrup, K., 2021. Changes in night sky brightness after a countywide LED retrofit. J Environ Manage 292, 112776. Jensen, J. R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective (4th ed.). Pearson. Jiang, W., He, G., Long, T., Liu, H., 2017. Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light. Remote Sens. (Basel) 9 (8), 798. Katz, Y., Levin, N., 2016. Quantifying urban light pollution — a comparison between field measurements and EROS-B imagery. Remote Sens. Environ. 177, 65–77. Kruasilp, J., Pattanakiat, S., Phutthai, T., Vardhanabindu, P., & Nakmuenwai, P. (2023). Evaluation of land use land cover changes in Nan Province, Thailand, using multi-sensor satellite data and Google Earth Engine. Environ. Nat. Resour. J, 21(2), 186-197. Kulpanich, N., Worachairungreung, M., Waiyasusri, K., Sae-Ngow, P., Chaysmithikul, P., & Thanakunwutthirot, K. (2023). Relationship Between Urbanization And Road Networks In The Lower Northeastern Region Of Thailand Using Nighttime Light Satellite Imagery. Geography, Environment, Sustainability, 15(4), 124-133. Lambin, E. F., Geist, H. J., & Lepers, E. (2003). "Dynamics of land-use and land-cover change in tropical regions." Annual Review of Environment and Resources, 28, 205-241. Lambin, E. F., & Geist, H. J. (Eds.). (2006). Land-use and land-cover change: Local processes and global impacts. Springer. Levin, N., Johansen, K., Hacker, J.M., Phinn, S., 2014. A new source for high spatial resolution night time images — the EROS-B commercial satellite. Remote Sens. Environ. 149, 1–12. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation. John Wiley & Sons. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic Information Systems and Science (4th ed.). Wiley. Mard, J., Di Baldassarre, G., Mazzoleni, M., 2018. Nighttime light data reveal how flood protection shapes human proximity to rivers. Sci. Adv. 4 (8), eaar5779. Moniruzzam, M., Roy, A., Bhatt, C. M., Gupta, A., An, N. T. T., & Hassan, M. R. (2018). Impact analysis of urbanization on land use land cover change for Khulna City, Bangladesh using temporal landsat imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 757-760. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regression Analysis (5th ed.). Wiley. Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. Noosuwan, C. (2022). Night-time Lights as a Proxy for Urbanization in Thailand: ภาพถ่าย ดาวเทียม ของ แสง ไฟ ยาม ค่ำคืน และ การ วัด ความ เป็น เมือง ใน ประเทศไทย. Journal of Applied Economics and Management Strategy, 9(2), 252-266. Oruc, M., Marangoz, A. M., & Buyuksalih, G. (2004). Comparison of pixel-based and object- oriented classification approaches using Landsat-7 ETM spectral bands. Pearson, K. (1895). "Note on regression and inheritance in the case of two parents." Proceedings of the Royal Society of London, 58, 240-242. Royal Forest Department. (2022). Final report on the forest land status data project, 2022. Bureau of Forest Land Management, Ministry of Natural Resources and Environment. Sangkasem, K., & Puttanapong, N. (2018). Poverty and inequality assessment using DMSP/OLS nighttime light satellite imageries at provincial level in Thailand (Doctoral dissertation, Thesis: Thammasat University). Shah, Z., Klugman, N., Cadamuro, G., Hsu, F.-C., Elvidge, C.D., Taneja, J., 2022. The electricity scene from above: exploring power grid inconsistencies using satellite data in Accra, Ghana. Appl. Energy 319, 119237. Shi, K., Shen, J., Wu, Y., Liu, S., & Li, L. (2021). Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data. International Journal of Digital Earth, 14(11), 1514-1527. Shi, K., Wu, Y., Li, D., & Li, X. (2022). Population, GDP, and carbon emissions as revealed by SNPP-VIIRS nighttime light data in China with different scales. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. Silayot, W., & Chaiyakarm, T. (2023). Monitoring Trends in Light Pollution in Nakhon Ratchasima Province Based on Nighttime Satellite Imagery. Burapha Science Journal, 1250-1264. Stern, D. I. (2017). "The role of energy in economic growth." In Handbook of Energy Economics and Policy (pp. 67-104). Elsevier. Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y. A., & Rahman, A. (2020). Land-use land- cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135. Tsai YH, Stow D, Chen HL, Lewison R, An L, Shi L. Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine. Remote Sensing 2018;10(6):Article No. 927. Tu, Y., Zhou, H., Lang, W., Chen, T., Li, X., & Xu, B. (2020). A novel cross-sensor calibration method to generate a consistent night-time lights time series dataset. International Journal of Remote Sensing, 41(14), 5482-5502. Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666-20671. Vibhute, A. D., & Gawali, B. W. (2013). Analysis and modeling of agricultural land use using remote sensing and geographic information system: a review. International Journal of Engineering Research and Applications, 3(3), 081-091. Wang, L., Yan, J., Mu, L., & Huang, L. (2020). Knowledge discovery from remote sensing images: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5), e1371. Wolman, M.G. 1987. “Criteria for Land Use.” In Resources and World Development, eds. D.J. McLaren and B.J. Skinner, 643-657. New York: John Wiley. Xu, G., Xiu, T., Li, X., Liang, X., Jiao, L., 2021. Lockdown induced night-time light dynamics during the COVID-19 epidemic in global megacities. Int. J. Appl. Earth Obs. Geoinf. 102421 Zhou, Y., Smith, S.J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B., Elvidge, C. D., 2015. A global map of urban extent from nightlights. Environ. Res. Lett. 10 (5), 054011. Zheng, Q., Seto, K. C., Zhou, Y., You, S., & Weng, Q. (2023). Nighttime light remote sensing for urban applications: Progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 202, 125-141.
描述 碩士
國立政治大學
應用經濟與社會發展英語碩士學位學程(IMES)
111266001
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111266001
資料類型 thesis
dc.contributor.advisor 范噶色zh_TW
dc.contributor.advisor Stephan Van Gasselten_US
dc.contributor.author (Authors) 林玲玉zh_TW
dc.contributor.author (Authors) Thanatcha Rukhengkulen_US
dc.creator (作者) 林玲玉zh_TW
dc.creator (作者) Rukhengkul, Thanatchaen_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-Aug-2024 13:28:51 (UTC+8)-
dc.date.available 5-Aug-2024 13:28:51 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 13:28:51 (UTC+8)-
dc.identifier (Other Identifiers) G0111266001en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152672-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用經濟與社會發展英語碩士學位學程(IMES)zh_TW
dc.description (描述) 111266001zh_TW
dc.description.abstract (摘要) 本研究使用 MODIS 和 Sentinel-2 進行土地利用土地覆蓋 (LULC) 分析，並使用 VIIRS 進行夜間燈光 (NTL) 測繪，分析泰國東部經濟走廊 (EEC) 的城市擴張。它使用 2017 年和 2022 年的 Sentinel-2 影像以及 2013 年至 2022 年的 MODIS 提供了土地利用變化的詳細空間分類。在春武里府，結果顯示 LCRPGR 值增加至 1.2745，顯示土地消耗的成長速度快於人口的成長速度。 然後使用統計分析（包括皮爾遜相關係數和多元迴歸）找出變數之間的關係。分析顯示，NTL 與EEC、省和地區級別的城市化之間存在高度顯著的係數，MODIS 得出的城市地區數據證明更適合省級分析，光照強度與碳排放之間的顯著係數( R² = 70.2 %)增加代表土地利用變化和城市擴張（例如城市和森林面積）影響的自變數。 然而，LULC 的準確分類涉及合併與回歸相互作用的各種自變量，以闡明 NTL 與城市化（以城市地區衡量）之間的關係。碳排放量與總光發射量之間的相關性根據所使用的碳排放量計算源的不同而不同，導致不同的方向關係。未來的分析可以考慮額外的自變數、不同的衛星來源和碳排放計算方法，以評估這些關係在多年間的變化。zh_TW
dc.description.abstract (摘要) This study analyzes urban expansion in Thailand's Eastern Economic Corridor (EEC) using MODIS and Sentinel-2 for Land Use Land Cover (LULC) analysis and VIIRS for Nighttime Light (NTL) mapping. It provides a detailed spatial classification of land use changes using Sentinel-2 images from 2017 and 2022 and MODIS from 2013 to 2022. The study also incorporates the SDG 11.3.1 indicator to enhance understanding urbanization dynamics. In Chonburi province, results highlight an increase to a 1.2745 LCRPGR value, indicating that land consumption is increasing faster than the population is growing. Then find the relationship between variables using statistical analysis, including Pearson correlation coefficients and Multiple Regression. This analysis shows a highly significant coefficient between NTL and urbanization at EEC, provincial, and district levels, with MODIS-derived urban area data proving more suitable in provincial analysis, a significant coefficient between light intensity and carbon emissions (R² = 70.2 %) after adding independent variables representing impacts of land use change and urban expansion, such as urban and forest areas. However, accurate classification of LULC involves incorporating various independent variables that interact with regression to elucidate the relationship between NTL and urbanization, as measured by urban areas. The correlation between carbon emissions and total light emissions varies depending on the carbon emissions calculation source, resulting in different directional relationships. Future analyses could consider additional independent variables, different satellite sources, and carbon emission calculation methods to assess how these relationships vary across multiple years.en_US
dc.description.tableofcontents CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Motivation 3 1.3 Research Questions and Hypothesis 5 Research Question: 5 Hypothesis: 5 CHAPTER 2 LITERATURE REVIEWS 6 2.1 Land Use and Land Cover 6 2.1.1 Defining LULC 6 2.1.2 Theories of land use change 7 2.1.3 Land Use and Land Cover Mapping 9 2.2 Nighttime Light Remote Sensing for Land Use Change and Urban Applications 18 2.2.1 Trends of Uban Applications with NTL data 19 CHAPTER 3 METHODOLOGY 27 3.1 Overview and Study Area Definition 27 3.1.1 Study Area 27 3.1.2 Study Flowchart 29 3.2 Spatial Data 30 3.2.1 Image Processing 30 3.3 Statistical Data 35 3.4 Data Collection 36 3.5 Quantitative Analysis 38 3.6 Statistical Analysis 40 CHAPTER 4 RESULTS AND DISCUSSIONS 44 4.1 Spatial Classification Mapping 44 4.1.1 Trends of Urban and Built-up area 45 4.1.2 Urban area, Road Network and NTL 49 4.2 Quantitative Analysis 51 4.2.1 Trends of Population and Population Mapping 51 4.2.2 Gross Regional and Provincial Product (GPP) 53 4.2.4 Spatial- Temporal Variations in PGR and LCR 54 4.2.5 Spatial- Temporal Variation in LCRPGR 55 4.2.6 Build-Up Area Expansion Rate 55 4.3 Statistical Results 56 4.3.1 Pearson Correlation Coefficient Results 56 4.3.2 Multiple Regression Results 61 CHAPTER 5 CONCLUSION 67 References 69 References (Figures) 76zh_TW
dc.format.extent 4296922 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111266001en_US
dc.subject (關鍵詞) 城市擴張zh_TW
dc.subject (關鍵詞) 東部經濟走廊zh_TW
dc.subject (關鍵詞) LULC 分析zh_TW
dc.subject (關鍵詞) NTL 製圖zh_TW
dc.subject (關鍵詞) 多元迴歸zh_TW
dc.subject (關鍵詞) 碳排放zh_TW
dc.subject (關鍵詞) Urban expansionen_US
dc.subject (關鍵詞) Eastern Economic Corridor (EEC)en_US
dc.subject (關鍵詞) LULC analysisen_US
dc.subject (關鍵詞) NTL mappingen_US
dc.subject (關鍵詞) Carbon emissionsen_US
dc.title (題名) 使用衛星影像估算泰國東部經濟走廊的 LULC、光照強度和社會經濟因素之間的關係zh_TW
dc.title (題名) Estimating the Relationship Between LULC, Light Intensity, and Socioeconomic Factors in Thailand’s Eastern Economic Corridor Using Satellite Imagesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8-17. Amani M, Ghorbanian A, Ahmadi SA, Kakooei M, Moghimi A, Mirmazloumi SM, et al. Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020;13: 5326-50. Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office. Ahmed, S. A., & N, H. (2023). Land use and land cover classification using machine learning algorithms in Google Earth Engine. Earth Science Informatics, 16(4), 3057-3073. Bennett, M.M., Smith, L.C., 2017. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sens. Environ. 192, 176–197. Bhandari, R., Xue, W., Virdis, S. G., Winijkul, E., Nguyen, T. P. L., & Joshi, S. (2023). Monitoring and Assessing Urbanization Progress in Thailand between 2000 and 2020 Using SDG Indicator 11.3. 1. Sustainability, 15(12), 9794. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. CRC Press. Breiman, L. (2001). "Random forests." Machine learning, 45(1), 5-32. Briassoulis, H. (2000). Analysis of land use change: theoretical and modeling approaches, the web book of regional Science. Regional research institute, West Virginia University, USA. Chaiwat, T. (2016). Night lights, economic growth, and spatial inequality of Thailand (No. 26). Puey Ungphakorn Institute for Economic Research. Chapin, F., S. Jr. and E.J. Kaiser. 1979. Urban Land Use Planning. Urbana: University of Illinois Press. Chen, Y., Liu, X., Li, X., 2017a. 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