Quantifying spatiotemporal changes of the urban impervious surface of Dhaka District using Remote sensing Technology

https://doi.org/10.22146/ijg.72506

Mahzabin Abbasi(1), Samsunnahar Popy(2*), Tan Yumin(3)

(1) International School, Beihang University, Beijing
(2) Bangabondhu Sheikh Mujibur Rahman Science and Technology University
(3) International School, Beihang University, Beijing
(*) Corresponding Author

Abstract


Dhaka, the capital of Bangladesh, is one of the world's fastest-growing cities where imperviousness expanding in tandem. Therefore, accurate estimation of impervious surfaces is essential for urban planning and management. This paper attempts to quantify the changes of urban impervious surfaces in Dhaka district from 1990 to 2020 using remote sensing technology. Satellite images of 1990, 1995, 2000, 2005, 2010, 2015, and 2020 have been taken from the Landsat TM, ETM+, OLI sensor. Unsupervised classification with k-means clustering and three different RS indices NDVI, NDBI, and BUI was used to delineate the actual impervious area of Dhaka city. This study reveals that due to urbanization a net increase of 67.30 sq. miles impervious area is added to the existing amount over the study period. In 2020 total 300.749 sq. miles which contain 51.02% of the total land were occupied by impervious surfaces compared to the 233.446 sq. miles in 1990. Instantaneously taking appropriate strategies is crucial for sustainable urban growth.   


Keywords


BUI Index; Dhaka; Impervious Area; Remote Sensing; Unsupervised classification.

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References

Adams, J. B., Sabol, D. E., Kapos, V., Almeida Filho, R., Roberts, D. A., Smith, M. O., & Gillespie, A. R. (1995). Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon. Remote sensing of Environment, 52(2), 137-154.

Ahmed, B., Raj, M. R. H., & Maniruzzaman, K. M. (2009). Morphological change of Dhaka City over a period of 55 years: A case study of two wards. Journal of Bangladesh Institute of Planners, 2, 30-38.

Arnold, C. L., & Gibbons, C. J. (1996). Impervious surface coverage: The emergence of a key environmental indicator. Journal of the American Planning Association, 62, 243–258.

Atlas of Urban Expansion. (2016). Dhaka: Bangladesh: South and Central Asia. http://www.atlasofurbanexpansion.org/cities/view/Dhaka.

Bahadur Kshetri, T. (2018). NDVI, NDBI & NDWI Calculation Using Landsat 7, 8. Publicado en.

Bangladesh Bureau of Statistics (BBS) (2011). Population Census 2001. Dhaka: Ministry of Planning.

Bangladesh Bureau of Statistics (BBS) (2021). Population Census 2001. Dhaka: Ministry of Planning.

Bauer, M. E., Heinert, N. J., Doyle, J. K., & Yuan, F. (2004, May). Impervious surface mapping and change monitoring using Landsat remote sensing. In ASPRS annual conference proceedings (Vol. 10). Bethesda, MD: American Society for Photogrammetry and Remote Sensing.

Brabec, E., Schulte, S., & Richards, P. L. (2002). Impervious surfaces and water quality: a review of current literature and its implications for watershed planning. Journal of planning literature, 16(4), 499-514.

Bramhe, V. S., Ghosh, S. K., & Garg, P. K. (2018). EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.

Carter, R. W. (1961). Magnitude and frequency of floods in suburban areas. US Geological Survey Professional Paper, 424, 9-11.

Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), 390–401.

Dougherty, M., Dymond, R. L., Goetz, S. J., Jantz, C. A., & Goulet, N. (2004). Evaluation of impervious surface estimates in a rapidly urbanizing watershed. Photogrammetric Engineering & Remote Sensing, 70(11), 1275-1284.

Im, J., Lu, Z., Rhee, J., & Quackenbush, L. J. (2012). Impervious surface quantification using a synthesis of artificial immune networks and decision/regression trees from multi-sensor data. Remote Sensing of Environment, 117, 102-113.

Lefebvre, A., Sannier, C., & Corpetti, T. (2016). Monitoring urban areas with Sentinel-2A data: Application to the update of the Copernicus high resolution layer imperviousness degree. Remote Sensing, 8(7), 606.

Leopold, L. B. (1968). Hydrology for urban land planning: A guidebook on the hydrologic effects of urban land use (Vol. 554). US Geolgoical Survey.

Liu, C., Zhang, Q., Luo, H., Qi, S., Tao, S., Xu, H., & Yao, Y. (2019). An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks. Remote Sensing of Environment, 229, 114-132.

Liu, Y., Yu, Y., Tian, F., Shen, Y., Liu, C., Liu, H., & Zhao, Z. (2017). The effects of arid climate on PAE accumulation in organic films on an impervious surface. Environmental Earth Sciences, 76(12), 1–9.

Lu, D., Li, G., Kuang, W., & Moran, E. (2014). Methods to extract impervious surface areas from satellite images. International Journal of Digital Earth, 7(2), 93-112.

Lu, D., Song, K., Zeng, L., Liu, D., Khan, S., Zhang, B., ... & Jin, C. (2008). Estimating impervious surface for the urban area expansion: Examples from changchun, northeast China. The international archives of the photogrammetry, remote sensing and spatial information sciences, 36, 385-391.

Luo, L., & Mountrakis, G. (2012). A multiprocess model of adaptable complexity for impervious surface detection. International journal of remote sensing, 33(2), 365-381.

Mathew, A., Khandelwal, S., & Kaul, N. (2016). Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: Study of Chandigarh city, India. Sustainable Cities and Society, 26, 264-277.

Mellino, S., & Ulgiati, S. (2015). Mapping the evolution of impervious surfaces to investigate landscape metabolism: An Emergy–GIS monitoring application. Ecological informatics, 26, 50-59.

Prasomsup, W., Piyatadsananon, P., Aunphoklang, W., & Boonrang, A. (2020). Extraction Technic for Built-up Area Classification in Landsat 8 Imagery. International Journal of Environmental Science and Development, 11(1).

Qiu, B., Li, H., Chen, C., Tang, Z., Zhang, K., & Berry, J. (2019). Tracking spatial–temporal landscape changes of impervious surface areas, bare lands, and inundation areas in China during 2001–2017. Land Degradation & Development, 30(15), 1802-1812.

Schueler, T. (1994). The importance of imperviousness. Watershed protection techniques, 1(3), 100-101.

Sexton, J. O., Song, X. P., Huang, C., Channan, S., Baker, M. E., & Townshend, J. R. (2013). Urban growth of the Washington, DC–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover. Remote Sensing of Environment, 129, 42-53.

U.S. Environmental Protecting Agency. (2003). Draft report on the environment, URL: http://www.epa.gov/indicators/, U.S. EPA (last date accessed: 07 May 2008).

United Nations (UN), 2018. World Urbanization Prospects: The 2018 Version-Highlights. Department of Economic and Social Affairs, New York.

Weng, Q. (2001). Modeling urban growth effects on surface runoff with the integration of remote sensing and GIS. Environmental management, 28(6), 737-748.

Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467-483.

Wu, C. (2004). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment, 93(4), 480-492.

Wu, C., & Yuan, F. (2007). Seasonal sensitivity analysis of impervious surface estimation with satellite imagery. Photogrammetric Engineering & Remote Sensing, 73(12), 1393-1401.

Xu, R., Liu, J., & Xu, J. (2018). Extraction of high-precision urban impervious surfaces from sentinel-2 multispectral imagery via modified linear spectral mixture analysis. Sensors, 18(9), 2873.

Yang, L., Huang, C., Homer, C. G., Wylie, B. K., & Coan, M. J. (2003). An approach for mapping large-area impervious surfaces: synergistic use of Landsat-7 ETM+ and high spatial resolution imagery. Canadian journal of remote sensing, 29(2), 230-240.

Yang, L., Xian, G., Klaver, J. M., & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69(9), 1003–1010.

Yuan, F. (2006). Mapping impervious surface area using high resolution imagery: a comparison of object-oriented classification to per-pixel classification. In Proceeding of American Society of Photogrammetry and Remote Sensing Annual Conference. May 1-5, Reno, NV, 2006.

Yuan, F., Bauer, M. E., Heinert, N. J., & Holden, G. R. (2005). Multi‐level land cover mapping of the Twin Cities (Minnesota) metropolitan area with multi‐seasonal Landsat TM/ETM+ data. Geocarto International, 20(2), 5-13.

Yuan, F., Wu, C., & Bauer, M. E. (2008). Comparison of spectral analysis techniques for impervious surface estimation using Landsat imagery. Photogrammetric Engineering & Remote Sensing, 74(8), 1045-1055.

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.

Zhou, Y., & Wang, Y. Q. (2008). Extraction of impervious surface areas from high spatial resolution imagery by multiple agent segmentation and classification. Photogrammetric Engineering & Remote Sensing, 74(7), 857-868.




DOI: https://doi.org/10.22146/ijg.72506

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