Multi-Temporal Mapping of Built-up Areas Using Daytime and Nighttime Satellite Images Based on Google Earth Engine Platform

Authors: S. Hutasavi, D. Chen

Abstract:

The built-up area is a significant proxy to measure regional economic growth and reflects the Gross Provincial Product (GPP). However, an up-to-date and reliable database of built-up areas is not always available, especially in developing countries. The cloud-based geospatial analysis platform such as Google Earth Engine (GEE) provides an opportunity with accessibility and computational power for those countries to generate the built-up data. Therefore, this study aims to extract the built-up areas in Eastern Economic Corridor (EEC), Thailand using day and nighttime satellite imagery based on GEE facilities. The normalized indices were generated from Landsat 8 surface reflectance dataset, including Normalized Difference Built-up Index (NDBI), Built-up Index (BUI), and Modified Built-up Index (MBUI). These indices were applied to identify built-up areas in EEC. The result shows that MBUI performs better than BUI and NDBI, with the highest accuracy of 0.85 and Kappa of 0.82. Moreover, the overall accuracy of classification was improved from 79% to 90%, and error of total built-up area was decreased from 29% to 0.7%, after night-time light data from the Visible and Infrared Imaging Suite (VIIRS) Day Night Band (DNB). The results suggest that MBUI with night-time light imagery is appropriate for built-up area extraction and be utilize for further study of socioeconomic impacts of regional development policy over the EEC region.

Keywords: Built-up area extraction, Google earth engine, adaptive thresholding method, rapid mapping.

References:

[1] T. Ferreira, “Using satellite data to track socio-economic outcomes: a case study of Namibia,” Work. Pap., 2018.
[2] W. Prasomsup, P. Piyatadsananon, W. Aunphoklang, and A. Boonrang, “Extraction technic for built-up area classification in Landsat 8 imagery,” Int. J. Environ. Sci. Dev., vol. 11, no. 1, pp. 15–20, 2020, doi: 10.18178/ijesd.2020.11.1.1219.
[3] I. N. Hidayati and R. Suharyadi, “A Comparative Study of various Indices for extraction urban impervious surface of Landsat 8 OLI,” Forum Geogr., vol. 33, no. 2, pp. 162–172, 2019, doi: 10.23917/forgeo.v33i2.9179.
[4] V. S. Bramhe, S. K. Ghosh, and P. K. Garg, “Extraction of Built-Up Area by Combining Textural Features and Spectral Indices from Landsat-8 Multispectral Image,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII–5, no. November, pp. 727–733, 2018, doi: 10.5194/isprs-archives-xlii-5-727-2018.
[5] M. K. Firozjaei, A. Sedighi, M. Kiavarz, S. Qureshi, D. Haase, and S. K. Alavipanah, “Automated built-up extraction index: A new technique for mapping surface built-up areas using LANDSAT 8 OLI imagery,” Remote Sens., vol. 11, no. 17, 2019, doi: 10.3390/rs11171966.
[6] A. Rasul et al., “Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates,” Land, vol. 7, no. 3, 2018, doi: 10.3390/land7030081.
[7] S. S. Bhatti and N. K. Tripathi, “Built-up area extraction using Landsat 8 OLI imagery,” GIScience Remote Sens., vol. 51, no. 4, pp. 445–467, 2014, doi: 10.1080/15481603.2014.939539.
[8] R. Goldblatt et al., “Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ., vol. 205, no. February 2017, pp. 253–275, 2018, doi: 10.1016/j.rse.2017.11.026.
[9] J. C. Duque, N. Lozano-Gracia, J. E. Patino, P. Restrepo, and W. A. Velasquez, “Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery,” Landsc. Urban Plan., vol. 191, no. January, 2019, doi: 10.1016/j.landurbplan.2019.103640.
[10] X. Liu et al., “High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform,” Remote Sens. Environ., vol. 209, no. January, pp. 227–239, 2018, doi: 10.1016/j.rse.2018.02.055.
[11] D. C. Pu, J. Y. Sun, Q. Ding, Q. Zheng, T. T. Li, and X. F. Niu, “Mapping Urban Areas Using Dense Time Series of Landsat Images and Google Earth Engine,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 3/W10, pp. 403–409, 2020, doi: 10.5194/isprs-archives-XLII-3-W10-403-2020.
[12] R. Goldblatt, W. You, G. Hanson, and A. K. Khandelwal, “Detecting the boundaries of urban areas in India: A dataset for pixel-based image classification in google earth engine,” Remote Sens., vol. 8, no. 8, 2016, doi: 10.3390/rs8080634.
[13] P. Guruprasad, “Overview of Different Thresholding Methods in Image Processing,” TEQIP Spons. 3rd Natl. Conf. ETACC, no. June, 2020.