Remotely-Sensed Derived Built-up Area as an Alternative Indicator in the Study of Thailand’s Regional Development

https://doi.org/10.22146/ijg.72921

Sirivilai Teerarojanarat(1*)

(1) Geography and Geoinformatics Research Unit, Faculty of Arts, Chulalongkorn University, Thailand
(*) Corresponding Author

Abstract


Nowadays measuring national and regional development primarily relies on demographic and socio-economic indicators. An indicator in physical dimension e.g., areas of human settlements and their economic uses of lands is usually ignored due to unavailability of data in countries like Thailand. Remotely-sensed derived built-up area was used, for the first time, as a physical indicator for studying Thailand’s regional development. Remote sensing - using the decision tree classifier with the combination indices of band ratios, NDVI, MNDWI, and NDBI - and GIS techniques were utilized to estimate the regional proportion of built-up area. The relationships between the percentage of the derived built-up area and the three development indicators - urbanization rate, Gross Regional Product, and Human Achievement Index - were analyzed. Resultantly, the estimate of the 2019 derived built-up area in Thailand was 2.46% with the average accuracy of 84.5%. Regional variation in development levels existed and relationships between the percentage of built-up area and the three development indicators for the regions were strong. However, there was no relationship after excluding the region having the effect of Bangkok. Therefore, remotely-sensed derived built-up area gives new information and is suggested for use for the analysis of Thailand’s regional development.

Keywords


Built-up area; Remote sensing, GIS; Regional study; Thailand

Full Text:

PDF


References

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

Article Metrics

Abstract views : 1770 | views : 958

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2022 Sirivilai Teerarojanarat

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

Web Analytics IJG STATISTIC