TIẾP CẬN HỒI QUY KHÔNG GIAN ĐÁNH GIÁ BIẾN ĐỘNG BỀ MẶT KHÔNG THẤM TẠI THÀNH PHỐ CẦN THƠ GIAI ĐOẠN 2000-2020
Tóm tắt
Nghiên cứu nhằm đánh giá biến động không gian và thời gian bề mặt không thấm tại thành phố Cần Thơ sử dụng ảnh Landsat đa thời gian, được tải từ công nghệ điện toán Google Earth Engine và tiếp cận hồi quy không gian. Chỉ số chuẩn hóa khác biệt xây dựng và phương pháp bình phương tối thiểu đã được sử dụng để đánh giá đánh giá biến động của quá trình mở rộng bề mặt không thấm trong giai đoạn 2000-2020. Kết quả nghiên cứu chỉ ra rằng, mật độ xây dựng tập trung chủ yếu ở khu vực ven sông Hậu và mở rộng sang các địa phương khác theo hướng Tây Bắc. Xét về xu thế mở rộng của diện tích xây dựng trong suốt giai đoạn nghiên cứu, diện tích bề mặt không thấm có xu thế gia tăng 485ha, 399ha, và 376ha tại các quận Ninh Kiều, Bình Thủy và Thốt Nốt tương ứng. Kết quả nhận được từ nghiên cứu này có thể làm tài liệu tham khảo để chính quyền địa phương đề xuất chiến lược phát triển thành phố thông minh trong bối cảnh công nghệ số.
Từ khóa
Toàn văn:
PDFTrích dẫn
Acharya, T., Kumbhakar, S., Prasad, R., Mondal, S., & Biswas, A. (2017). Delineation of potential groundwater recharge zones in the coastal area of north-eastern India using geoinformatics. Sustainable Water Resources Management, 1-8.
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.
DOI: https://doi.org/10.54607/hcmue.js.18.3.2978(2021)
Tình trạng
- Danh sách trống