ANALYZING THE PATTERN OF IMPERVIOUS SURFACE VARIATIONS IN CAN THO CITY DURING THE 2000-2020 PERIOD USING SPATIAL REGRESSION APPROACHES
Main Article Content
Abstract
The study aims at examining the spatiotemporal pattern of impervious surface changes using Landsat time-series imagery obtained through Google Earth Engine and spatial regression approaches in Can Tho city. The Normalised Difference Built-up Index and Ordinary Least Square method were applied to characterize the pattern of dynamics of urban expansion during the 2000-2020 period. The results showed that the built-up density mainly focused on the riparian area of the Hau River and expanded northern-west to other areas. Regarding the trend in built-up patterns, an increased area of 485ha, 399ha, and 376ha was found in Ninh Kieu, Binh Thuy, and Thot Not districts respectively during the period. The results from this study can be used as a reference for local government to propose appropriate strategies for developing a smart city in the context of digital technologies.
Keywords
impervious surface, Landsat, NDBI, urbanization, Ordinary Least Square, remote sensing
Article Details
References
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Suriya, S., & Mudgal, B. V. (2012). Impact of urbanization on flooding: The Thirusoolam sub watershed–A case study. Journal of Hydrology, (412), 210-219.
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Tran, T. V., Tran, D. X., Myint, S. W., Huang, C., Pham, H. V., Luu, T. H., & Vo, T. M. (2019a). Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach. Science of the Total Environment, 687, 1087-1097.
Wang, C., & Myint, S. W. (2016). Environmental concerns of deforestation in Myanmar 2001-2010. Remote Sensing, 8(9), 728.
Wang, C., Myint, S. W., Wang, Z., & Song, J. (2016). Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications. Remote Sensing, 8(3), 185. https://doi.org/10.3390/rs8030185
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Xu, Hanqiu (2007). Extraction of urban built-up land features from Landsat imagery using a thematicoriented index combination technique. Photogrammetric Engineering & Remote Sensing, 73(12), 1381-1391.
Xu, Hao, & Bai, Y. (2015). Evaluation of urban lake evolution using Google Earth Engine-A case study of Wuhan, China. Agro-Geoinformatics (Agro-Geoinformatics), 2015 Fourth International Conference On, 322-325.
Yang, X., Qin, Q., Grussenmeyer, P., & Koehl, M. (2018). Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing of Environment, 219, 259-270.
Yang, X., Zhao, S., Qin, X., Zhao, N., & Liang, L. (2017). Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sensing, 9(6), 596.
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.
Zhang, F., Zhu, X., & Liu, D. (2014). Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 35(9), 3237-3253. https://doi.org/10.1080/01431161.2014.903351
Zhang, Y., Ni, S. X., & Yang, S. (2003). An Effective Approach to Automatically Extract Urban Land-use from TM lmagery. Journal of Remote Sensing Beijing, 7(1), 37-40.
Zhao, H., & Chen, X. (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. International Geoscience and Remote Sensing Symposium, 3, 1666.
Zhou, Y., Luo, J., Shen, Z., Hu, X., & Yang, H. (2014). Multiscale water body extraction in urban environments from satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4301-4312.
Bhatti, S. S., & Tripathi, N. K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 51(4), 445-467. https://doi.org/10.1080/15481603.2014.939539
Castrence, M., Nong, D. H., Tran, C. C., Young, L., & Fox, J. (2014). Mapping urban transitions using multi-temporal Landsat and DMSP-OLS night-time lights imagery of the Red River Delta in Vietnam. Land, 3(1), 148-166.
Du, S., Shi, P., Van Rompaey, A., & Wen, J. (2015). Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Natural Hazards, 76(3), 1457-1471.
Ghosh, D. K., Mandal, A. C., Majumder, R., Patra, P., & Bhunia, G. S. (2018). Analysis for Mapping of Built-Up Area Using Remotely Sensed Indices–A Case Study of Rajarhat Block in Barasat Sadar Sub-Division in West Bengal (India). Journal of Landscape Ecology, 11(2), 67-76.
Ghosh, S., & Siddique, G. (2018). Change Detection of Built-Up Areas Applying Built-Up Index for Chandannagar City. International Journal of Technology Research and Management, 5(4), 1-7.
Huynh, H. T. L., & Pathirana, A. (2011). Urbanization and climate change impacts on future urban flood risk in Can Tho city, Vietnam. Hydrology & Earth System Sciences Discussions, 8(6).
Ishtiaque, A., Myint, S. W., & Wang, C. (2016a). Examining the ecosystem health and sustainability of the world’s largest mangrove forest using multi-temporal MODIS products. Science of the Total Environment, (569), 1241-1254.
Jha, A., Lamond, J., Bloch, R., Bhattacharya, N., Lopez, A., Papachristodoulou, N., Bird, A., Proverbs, D., Davies, J., & Barker, R. (2011). Five feet high and rising: Cities and flooding in the 21st century. The World Bank.
Kawamura, M. (1996). Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. Proc. 51st Annual Conference of the Japan Society of Civil Engineers, 190-191.
Khan, S. D. (2005). Urban development and flooding in Houston Texas, inferences from remote sensing data using neural network technique. Environmental Geology, 47(8), 1120-1127.
Memon, A. A., Muhammad, S., Rahman, S., & Haq, M. (2015). Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 99-106.
Ogden, F. L., Raj Pradhan, N., Downer, C. W., & Zahner, J. A. (2011). Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment. Water Resources Research, 47(12).
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418.
Pham, D. V. T., Nguyen, H. Q. G., & Huynh, P. D. P. (2012). Identifying the Urbanization Pattern based on Demographic and Socio-Economic Aspects. A Case Study in Can Tho City. Proceedings of the fourth International Conference on Vietnamese Studies, V, 746-759.
Pham, T. M. T., Raghavan, V., & Pawar, N. J. (2010). Urban expansion of Can Tho City, Vietnam: A study based on multi-temporal satellite images. Geoinformatics, 21(3), 147-160.
Pham, V. T., Nguyen, V. T., Nguyen, H. L., & Nguyen, D. H. (2018). Detection of the impervious surfaces expansion using SPOT-5 and Sentinel-2 data: A case study in Ho Chi Minh city. Journal of Mining and Earth Sciences, 59(2), 69-76.
Prime Minister, P. (2013). Decision No: 1515/QD-TTg the project on adjustment of the master plan of Can Tho city till 2030 and with a vision to 2050, dated in August 28th, 2013 [Goverment]. Decision No: 1515/QD-TTg the Project on Adjustment of the Master Plan of Can Tho City till 2030 and with a Vision to 2050, Dated in August 28th, 2013. https://vanbanphapluat.co/decision-no-1515-qd-ttg-the-project-on-adjustment-of-the-master-plan-of-can-tho-city-till-2030
Rikimaru, A. (1997). Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow. Presented Paper for the 18th ACRS.
Shanableh, A., Al-Ruzouq, R., Yilmaz, A., Siddique, M., Merabtene, T., & Imteaz, M. (2018). Effects of Land Cover Change on Urban Floods and Rainwater Harvesting: A Case Study in Sharjah, UAE. Water, 10(5), 631.
Son, N.-T., Chen, C.-F., & Chen, C.-R. (2019). Flood assessment using multi-temporal remotely sensed data in Cambodia. Geocarto International, just-accepted, 1-14.
Suriya, S., & Mudgal, B. V. (2012). Impact of urbanization on flooding: The Thirusoolam sub watershed–A case study. Journal of Hydrology, (412), 210-219.
Thenkabail, P. S. (2015). Land resources monitoring, modeling, and mapping with remote sensing. CRC Press.
Tran, T. V., Tran, D. X., Myint, S. W., Huang, C., Pham, H. V., Luu, T. H., & Vo, T. M. (2019a). Examining spatiotemporal salinity dynamics in the Mekong River Delta using Landsat time series imagery and a spatial regression approach. Science of the Total Environment, 687, 1087-1097.
Wang, C., & Myint, S. W. (2016). Environmental concerns of deforestation in Myanmar 2001-2010. Remote Sensing, 8(9), 728.
Wang, C., Myint, S. W., Wang, Z., & Song, J. (2016). Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications. Remote Sensing, 8(3), 185. https://doi.org/10.3390/rs8030185
Wang, G., Yu, M., & Xue, Y. (2016). Modeling the potential contribution of land cover changes to the late twentieth century Sahel drought using a regional climate model: Impact of lateral boundary conditions. Climate Dynamics, 47(11), 3457-3477. https://doi.org/10.1007/s00382-015-2812-x
Xu, Hanqiu (2007). Extraction of urban built-up land features from Landsat imagery using a thematicoriented index combination technique. Photogrammetric Engineering & Remote Sensing, 73(12), 1381-1391.
Xu, Hao, & Bai, Y. (2015). Evaluation of urban lake evolution using Google Earth Engine-A case study of Wuhan, China. Agro-Geoinformatics (Agro-Geoinformatics), 2015 Fourth International Conference On, 322-325.
Yang, X., Qin, Q., Grussenmeyer, P., & Koehl, M. (2018). Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sensing of Environment, 219, 259-270.
Yang, X., Zhao, S., Qin, X., Zhao, N., & Liang, L. (2017). Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sensing, 9(6), 596.
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.
Zhang, F., Zhu, X., & Liu, D. (2014). Blending MODIS and Landsat images for urban flood mapping. International Journal of Remote Sensing, 35(9), 3237-3253. https://doi.org/10.1080/01431161.2014.903351
Zhang, Y., Ni, S. X., & Yang, S. (2003). An Effective Approach to Automatically Extract Urban Land-use from TM lmagery. Journal of Remote Sensing Beijing, 7(1), 37-40.
Zhao, H., & Chen, X. (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. International Geoscience and Remote Sensing Symposium, 3, 1666.
Zhou, Y., Luo, J., Shen, Z., Hu, X., & Yang, H. (2014). Multiscale water body extraction in urban environments from satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4301-4312.