Remotely-Sensed Derived Built-up Area as an Alternative Indicator in the Study of Thailand’s Regional Development
Sirivilai Teerarojanarat(1*)
(1) Geography and Geoinformatics Research Unit, Faculty of Arts, Chulalongkorn University, Thailand
(*) Corresponding Author
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Adam H. E., Csaplovics E., & Elhaja, M. E. (2016). A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan. IOP Conference Series: Earth and Environmental Science, 37, 012061. doi:10.1088/1755-1315/37/1/012061
Al-Bilbisi, H. (2019). Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan. Sustainability, 11(8), 2260. doi:10.3390/su11082260
As-syakur, A. R., Adnyana, I. W. S., Arthana, I.W., & Nuarsa, I. W. (2012). Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing, 4(10), 2957-2970. https://doi.org/10.3390/rs4102957
Ayal, E. B. (1992). Thailand's Development: The Role of Bangkok. Pacific Affairs, 65(3), 353-367.
Bhatti, S. S., & Tripathi, N. K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 51(4), 445–467. doi:10.1080/15481603.2014.939539
Chen, C., He, X., Liu, Z., Sun, W., Dong, H., & Chu, Y. (2020). Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery. Sci Rep, 10, 12721. https://doi.org/10.1038/s41598-020-69716-2
Congalton, R. G., & Green, K. (2009). Assessing the Accuracy of Remotely Sensed Data : Principles and Practices. Boca Raton: CRC Press.
Encyclopædia Britannica. (2022).Thailand (Settlement patterns) Retrieved from https://www.britannica.com/place/Thailand/Settlement-patterns
Estoque, R. C., Murayama, Y., & Akiyama, C. M. (2015) Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes, Geocarto International, 30(10), 1113-1129. doi:10.1080/10106049.2015.1027291
Faisal, K., & Shaker, A. (2014). The use of remote sensing technique to predict Gross Domestic Product (GDP): An analysis of built-up index and GDP in nine major cities in Canada. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XL-7, 85-92. doi:10.5194/isprsarchives-XL-7-85-2014
Faisal, K., Shaker, A., & Habbani, S. (2016). Modelling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada. ISPRS Int. J. Geo-Inform, 5(23). doi:10.3390/ijgi5030023
Fedajev, A., & Nikolic, R. (2012). The Concepts, Methods and Measurement of EU Regional Development. Economics Management Information Technology, 1(1), 48-57.
Ghazaryan, G., Rienow, A., Oldenburg, C., Thonfeld, F., Trampnau, B., Sticksel, S., & Jürgens, C. (2021). Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach. Remote Sensing, 13(9), 1694. doi:10.3390/rs13091694
Goletsis, Y., & Chletsos, M. (2011). Measurement of development and regional disparities in Greek Periphery: a multivariate approach. Socioecon. Plann. Sci., 45(4), 174–183. https://doi.org/10.1016/j.seps.2011.06.002
Hidayati, I. N., Suharyadi, R., & Danoedor, P. (2018). Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index. Forum Geografi, 32(1), 1-9. doi:10.23917/forgeo.v32i1.5907
Hu, T., Yang, J., Li, X., & Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sensing, 8(2), 151. doi:10.3390/rs8020151
Huang, C., Yang, J., Clinton, N., Yu, L., Huang, H., Dronova, I., & Jin, J. (2021). Mapping the maximum extents of urban green spaces in 1039 cities using dense satellite images. Environ. Res. Lett., 16, 064072. https://doi.org/10.1088/1748-9326/ac03dc
Huang, W., Zeng, Y., & Li, S. (2015). An analysis of urban expansion and its associated thermal characteristics using Landsat imagery. Geocarto International, 30(1), 93-103. doi:10.1080/10106049.2014.965756
Imran, H. M., Hossain, A., Islam, A. K. M. S., Rahman, A., Bhuiyan, M. A., Paul, S., & Alam, A. (2021). Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka City of Bangladesh. Earth Systems and Environment, 5(3), 667-693. https://doi.org/10.1007/s41748-021-00243-4
Jovovic, R., Draskovic, M., Delibasic, M., & Jovovic, M. (2017). The concept of sustainable regional development – institutional aspects, policies and prospects. Journal of International Studies, 10(1), 255-266. doi:10.14254/2071-8330.2017/10-1/18
Khan, A., Chatterjee, S., Akbari, H., Bhatti, S. S., Dinda, A., Mitra, C., Hong, H., & Doan, Q. Y. (2017). Step-wise Landclass Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity. Geocarto International, 34(5), 504-527. doi:10.1080/10106049.2017.1408704
Lang, W., Pan, M., Wu, J., Chen, T., & Li, X. (2021). The patterns and driving forces of uneven regional growth in ASEAN countries: A tale of two Thailands' path toward regional coordinated development. Growth and Change, 52(1), 130-149. doi:10.1111/grow.12459
Li, X., Xu, H., Chen, X., & Li, C. (2013). Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional, Economy of China. Remote Sensing, 5(6), 3057-3081. doi:10.3390/rs5063057
Liu, T. and Yang, X. (2013). Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis. Remote Sens. Environ., 133, 251–264. https://doi.org/10.1016/j.rse.2013.02.020
Lu, D., & Weng, Q. (2006). Use of Impervious Surface in Urban Land Use Classification. Remote Sens. Environ., 102, 146–160. doi:10.1016/j.rse.2006.02.010
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2012). Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China’s cities. Remote Sens. Environ., 124, 99–107.
Ma, Y., & Xu, R. (2010). Remote sensing monitoring and driving force analysis of urban expansion in Guangzhou City, China. Habitat Int., 34, 228–235. doi:10.1016/j.habitatint.2009.09.007
Macarof, P., & Statescu, F. (2017). Comparison of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi. PESD, 11(2). 141-150. doi:10.1515/pesd-2017-0032
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.
Meyer, D. F., Jongh, J. De., & Meyer, N. (2016). The formulation of a composite regional development index. International Journal of Business and Management, 8(1), 100-116.
Mu, H., Xuecao, L., Yanan, W., Huang, J., Du, P., Su, W., Miao, S., & Geng, M. (2022). A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. ISPRS Int. J. Geo-Inf., 7(4), 135. https://doi.org/10.3390/ijgi7040135
Murayama, Y., Kamusoko, C., Yamashita, A., & Estoque, R. C. (2017). Urban Development in Asia and Africa : Geospatial Analysis of Metropolises. Singapore: Springer.
Mwakapuja, F., Liwa, E., & Kashaigili, J. (2013). Usage of Indices for Extraction of Built-up Areas and Vegetation Features from Landsat TM Image: A Case of Dar Es Salaam and Kisarawe Peri-Urban Areas, Tanzania. International Journal of Agriculture and Forestry, 3(7), 273-283. doi:10.5923/j.ijaf.20130307.04
NESDC (Office of the National Economic and Social Development Council). (2019a). NATIONAL STRATEGY 2018 – 2037 (Unofficial translation). Retrieved from http://nscr.nesdb.go.th/wp-content/uploads/2019/10/National-Strategy-Eng-Final-25-OCT-2019.pdf
NESDC (Office of the National Economic and Social Development Council). (2019b). Human Achievement Index 2019. Bangkok: NESDC.
NESDC (Office of the National Economic and Social Development Council). (2021). Gross Regional and Provincial Product Chain Volume Measures 2019 Edition. Bangkok: NESDC.
NSO (National Statistical Office), Thailand. (2020). Population Census 2019, Thailand. Retrieved from http://web.nso.go.th/en/stat.htm
Pooja, A. P., Jayanth, J., & Koliwad, S. (2011). Classification of RS data using Decision Tree Approach. International Journal of Computer Applications (0975 – 8887), 23(3), 7-11. doi:10.5120/2872-3729
Propastin, P., & Kappas, M. (2012). Assessing Satellite-observed nighttime lights for monitoring socioeconomic parameters in the Republic of Kazakhstan. GIScience & Remote Sensing, 49(4), 538–557. doi:10.2747/1548-1603.49.4.538
Qu, L., Chen, Z., Li, M., Zhi, J., & Wang, H. (2021). Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine. Remote Sensing, 13(3), 453. https://doi.org/10.3390/rs13030453
Rouse, J. W., Haas, R. W., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation NASA/GSFC Type III. (Final Report). Greenbelt, MD: NASA/ GSFC.
Sabo, F., Corbane, C., Florczyk, A. J., Ferri, S., Pesaresi, M., & Kemper, T. (2018). Comparison of built‐up area maps produced within the global human settlement framework. Transactions in GIS, 22(6), 1406-1436
Sharma, R., Ghosh, A., & Joshi, P. K. (2013). Decision tree approach for classification of remotely sensed satellite data using open source support. Indian Academy of Sciences, J. Earth Syst. Sci., 122(5), 1237–1247. doi:10.1007/s12040-013-0339-2
Spiezia, V. (2003). Measuring regional economies. OECD Statistics brief October 2003 No. 6, 1-8. [Leaflet]. Paris, France: OECD. Retrieved from https://www.oecd.org/sdd/15918996.pdf
Suarez-rubio, M., Lookingbill, T. R., & Elmore, A. J. (2012). Exurban development derived from Landsat from 1986 to 2009 surrounding the District of Columbia, USA. Remote Sensing of Environment, 124, 360-370. doi:10.1016/j.rse.2012.03.029
Tariq, A., Shu, H., Siddiqui, S., Imran, M., & Farhan, M. (2021). Monitoring Land Use and Land Cover Changes Using Geospatial Techniques, A Case Study of Fateh Jang, Attock, Pakistan. Geography, Environment, Sustainability, 14(1), 41-52. doi:10.24057/2071-9388-2020-117
Tso, B., & Mather, P. (2016). Classification Methods for Remotely Sensed Data. New York: CRC Press.
UNDP (United Nations Development Programme). (2020). Human Development Report 2020. The next frontier Human development and the Anthropocene. New York, NY: UNDP. Retrieved from https://hdr.undp.org/sites/default/files/hdr2020.pdf
Wang, W., Cheng, H., & Zhang, L. (2012). Poverty assessment using DMSP/OLS night-time light satellite imagery at a provincial scale in China. Advances in Space Research, 49(8), 1253–1264. https://doi.org/10.1016/j.asr.2012.01.025
Weih, N. D., & Riggan, R. C. (2010). Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XXXVIII-4/C7.
Wishlade, F., & Yuill, D. (1997). Measuring Disparities for Area Designation Purposes: Issues for the European Union. Regional and Industrial Policy (Research Paper Number 24). Retrieved from http://www.eprc.strath.ac.uk/eprc/Documents/PDF_files/R24MeasDispforAreaDesigPurposes.pdf.
World Bank. (2022). Thailand. Retrieved from https://data.worldbank.org/country/thailand
World Bank Group. (2015). East Asia’s Changing Urban Landscape : Measuring a Decade of Spatial Growth. Washington, DC: World Bank. Retrieved from https://openknowledge.worldbank.org/handle/10986/21159
Xu, H. (2006). Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27(14), 3025-3033. doi:10.1080/01431160600589179
Xu, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1353691. doi:10.1155/2017/1353691
Yin, C. L., Meng, F., Guo, L., Zhang, Y. X., Zhao, Z., Xing, H. Q., & Yao, G. B. (2021). Extraction and Evolution Analysis of Urban Built-up Areas in Beijing, 1984-2018. Applied Spatial Analysis and Policy, 14, 731–753. https://doi.org/10.1007/s12061-021-09374-7
Yue, W., Gao, J., & Yang, X. (2014). Estimation of gross domestic product using multi-sensor remote sensing data: A case study in Zhejiang province, East China. Remote Sensing, 6(8), 7260-7275, doi:10.3390/rs6087260
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583-594. doi:10.1080/01431160304987
Zhang, J., Li P., & Wang, J. (2014). Urban built-up area extraction from Landsat TM/ETM+ images using spectral information and multivariate texture. Remote Sensing, 6(8), 7339-7359. doi:10.3390/rs6087339
Zhou, Y., Tu, M., Wang, S., & Liu, W. (2018). A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect. ISPRS Int. J. Geo-Inf., 7(4), 135. https://doi.org/10.3390/ijgi7040135
DOI: https://doi.org/10.22146/ijg.72921
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