| Reference: | 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. 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. |
