|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Human activity has boosted carbon dioxide emissions, causing temperatures to rise. The average temperature on Earth is roughly 15��C, but it has been much higher and lower in the past. There are natural climatic changes, but experts say temperatures are already rising faster than at any other period in history. Unplanned urbanization can sometimes backfire, causing negative consequences that harm the economy and contribute to environmental damages, especially in developing countries like Bangladesh. Because of the strong association between land use/land cover and land surface temperature (LST), the study attempted to analyze and estimate LULC and seasonal (both summer and winter) LSTs using Landsat satellite images at 5-year intervals from 1995 to 2020. Later, the study forecasted both LULC and seasonal LSTs for 2030 and 2040 using cellular automata (CA) and artificial neural network (ANN) algorithms for Rajshahi district. As supporting parameters for determining the magnitude of climate change effects owing to urbanization and temperature rise, primary data collection procedures such as focus group discussions (FGDs) and key informant interviews (KIIs) with experts from diverse sectors were used. Results reveal that the built-up area was increased from 158.22 km2 (6.64%) to 386.74 km2 (16.23%) in this 25�years? timeframe, and it contributed the highest average temperature (41.68��C in 2020 in summer) comparing with other LULCs. The LSTs were increasing at an alarming rate with 1?2��C standard deviations per 5�years and maximum temperature was increased from 1995 to 2020 by 37.22 to 42.7��C) in summer and 22.18 to 28.94��C in winter. Prediction states that net increase of built-up area will be 2.51 and 5.29, respectively, in 2030 and 2050 from 2020. Maximum LST will likely to be increased to 43.23��C (2030) and 45.92��C (2040) in summer, and 30.94��C (2030) and 31.77��C (2040) in winter. FGDs and KIIs assessments indicate that frequent LULC change was the main reason for increasing LSTs (71%) and 76% experts agreed that heat waves are the most influencing factors for adverse climate change, among other parameters. The work introduces new methods for integrating remote sensing data with primary datasets, which will provide substantial insights to urban planners and policymakers in terms of participatory and sustainable planning. ? 2021, Societ� Italiana di Fotogrammetria e Topografia (SIFET). |
| References |
(2016). Climate Change & Sustainable Report, Retrieved from www.scopus.com Mitigation of climate change. (2007). Mitigation of Climate Change, Retrieved from www.scopus.com Abdullah-Al-Faisal, Abdulla - Al Kafy, Foyezur Rahman, A. N. M., Rakib, A. A., Akter, K. S., Raikwar, V., . . . Kona, M. A. (2021). Assessment and prediction of seasonal land surface temperature change using multi-temporal landsat images and their impacts on agricultural yields in rajshahi, bangladesh. Environmental Challenges, 4 doi:10.1016/j.envc.2021.100147 Ahmed, B., Kamruzzaman, M. D., Zhu, X., Shahinoor Rahman, M. D., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in dhaka, bangladesh. Remote Sensing, 5(11), 5969-5998. doi:10.3390/rs5115969 Ahmed, S. (2018). Assessment of urban heat islands and impact of climate change on socioeconomic over suez governorate using remote sensing and GIS techniques. Egyptian Journal of Remote Sensing and Space Science, 21(1), 15-25. doi:10.1016/j.ejrs.2017.08.001 Al Rakib, A., Akter, K. S., Rahman, M. N., Arpi, S., & Kafy, A. -. (2020). Analyzing the pattern of land use land cover change and its impact on land surface temperature: A remote sensing approach in mymensingh, bangladesh. 1st Int.Student Res.Conf., Retrieved from www.scopus.com Al Rakib, A., Ayan, S. M., Orthy, T. T., Sarker, O., Intisar, L., & Arnob, M. A. (2020). In depth-analysis of urban resident-satisfaction level of mirpur. A Participatory Approach.1St Int.Student Res.Conf.2020, Retrieved from www.scopus.com Al Rakib, A., Rahman, M. N., Arpi, S., Ratu, J. F., Afroz, F., Hossain, N., & Zubayer, M. S. (2020). An assessment on the housing satisfaction of padma residential area, rajshahi. 1st. Student Res.Conf., Retrieved from www.scopus.com Alamgir, M., Khan, N., Shahid, S., Yaseen, Z. M., Dewan, A., Hassan, Q., & Rasheed, B. (2020). Evaluating severity–area–frequency (SAF) of seasonal droughts in bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment, 34(2), 447-464. doi:10.1007/s00477-020-01768-2 Al-Hamdan, M. Z., Oduor, P., Flores, A. I., Kotikot, S. M., Mugo, R., Ababu, J., & Farah, H. (2017). Evaluating land cover changes in eastern and southern africa from 2000 to 2010 using validated landsat and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 62, 8-26. doi:10.1016/j.jag.2017.04.007 Al-sharif, A. A. A., & Pradhan, B. (2015). A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, markov chain and cellular automata models in GIS. Geocarto International, 30(8), 858-881. doi:10.1080/10106049.2014.997308 Al-sharif, A. A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in tripoli metropolitan city using an integrated markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291-4301. doi:10.1007/s12517-013-1119-7 Amzad Hossain, M. (2017). Financing Small Scale Industries of Bangladesh with Special Refrence to Selected Small Industries in Rajshahi District, Retrieved from www.scopus.com Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2013). Integration of logistic regression, markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21(1), 265-275. doi:10.1016/j.jag.2011.12.014 Azari, M., Tayyebi, A., Helbich, M., & Reveshty, M. A. (2016). Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: Application to maragheh, iran. GIScience and Remote Sensing, 53(2), 183-205. doi:10.1080/15481603.2015.1137111 Balogun, I., & Ishola, K. (2017). Projection of future changes in landuse/landcover using cellular automata/markov model over akure city, nigeria. J.Remote Sens.Technol., 5(1), 22-31. Retrieved from www.scopus.com Bangladesh Bureau of Statistics (BBS). (2013). District statistics 2011 dhaka. dhaka. District Statistics 2011, Retrieved from www.scopus.com Bonafoni, S., Baldinelli, G., & Verducci, P. (2017). Sustainable strategies for smart cities: Analysis of the town development effect on surface urban heat island through remote sensing methodologies. Sustainable Cities and Society, 29, 211-218. doi:10.1016/j.scs.2016.11.005 Celik, B., Kaya, S., Alganci, U., & Seker, D. Z. (2019). Assessment of the relationship between land use/cover changes and land surface temperatures: A case study of thermal remote sensing. Fresenius Environmental Bulletin, 28(2), 541-547. Retrieved from www.scopus.com Chakroborty, S., Rakib, A., Kafy, A., & Al, A. (2020). Monitoring water quality based on community perception in the northwest region of bangladesh. 1st International Student Research Conference - 2020, Dhaka, Bangladesh, Retrieved from www.scopus.com Chen, X. -., Zhao, H. -., Li, P. -., & Yin, Z. -. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133-146. doi:10.1016/j.rse.2005.11.016 Clemett, A., Amin, M. M., Ara, S., & Akan, M. M. R. (2006). Background information for rajshahi city, bangladesh. WASPA Asia Project Report, 2, 1-29. Retrieved from www.scopus.com Connors, J. P., Galletti, C. S., & Chow, W. T. L. (2013). Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in phoenix, arizona. Landscape Ecology, 28(2), 271-283. doi:10.1007/s10980-012-9833-1 Dekić, J. P., Mitković, P. B., Dinic Branković, M. M., Igić, M. Z., Dekić, P. S., & Mitković, M. P. (2018). The study of effects of greenery on temperature reduction in urban areas. Thermal Science, 2018, 988-1000. doi:10.2298/TSCI170530122D Development Design Consultants Limited, M. C. A. (2008). Working paper on existning landuse, demographic and transport (revised). Government of the People's Republic of Bangladesh Ministry of Housing and Public Works, 47 Retrieved from www.scopus.com Dey, N. N., Al Rakib, A., Kafy, A. -., & Raikwar, V. (2021). Geospatial modelling of changes in land use/land cover dynamics using multi-layer perception markov chain model in rajshahi city, bangladesh. Environmental Challenges, 4 doi:10.1016/j.envc.2021.100148 Durand, C. P., Andalib, M., Dunton, G. F., Wolch, J., & Pentz, M. A. (2011). A systematic review of built environment factors related to physical activity and obesity risk: Implications for smart growth urban planning. Obesity Reviews, 12(5), e173-e182. doi:10.1111/j.1467-789X.2010.00826.x Eastman, J. R. (2012). IDRISI selva manual. IDRISI Selva Manual, Retrieved from www.scopus.com Ettehadi Osgouei, P., & Kaya, S. (2017). Analysis of land cover/use changes using landsat 5 TM data and indices. Environmental Monitoring and Assessment, 189(4) doi:10.1007/s10661-017-5818-5 Fahad, M. G. R., Saiful Islam, A. K. M., Nazari, R., Alfi Hasan, M., Tarekul Islam, G. M., & Bala, S. K. (2018). Regional changes of precipitation and temperature over bangladesh using bias-corrected multi-model ensemble projections considering high-emission pathways. International Journal of Climatology, 38(4), 1634-1648. doi:10.1002/joc.5284 Fortin, M. -., Boots, B., Csillag, F., & Remmel, T. K. (2003). On the role of spatial stochastic models in understanding landscape indices in ecology. Oikos, 102(1), 203-212. doi:10.1034/j.1600-0706.2003.12447.x Fu, P., & Weng, Q. (2018). Responses of urban heat island in atlanta to different land-use scenarios. Theoretical and Applied Climatology, 133(1-2), 123-135. doi:10.1007/s00704-017-2160-3 Gaur, A., Eichenbaum, M. K., & Simonovic, S. P. (2018). Analysis and modelling of surface urban heat island in 20 canadian cities under climate and land-cover change. Journal of Environmental Management, 206, 145-157. doi:10.1016/j.jenvman.2017.10.002 Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., . . . Hazra, S. (2017). Application of cellular automata and markov-chain model in geospatial environmental modeling- A review. Remote Sensing Applications: Society and Environment, 5, 64-77. doi:10.1016/j.rsase.2017.01.005 Gopal, S., & Woodcock, C. (1996). Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing, 34(2), 398-404. doi:10.1109/36.485117 Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and markov model. Ecological Modelling, 222(20-22), 3761-3772. doi:10.1016/j.ecolmodel.2011.09.009 Habitat, U. (2016). Urbanization and development: Emerging futures. World Cities Report, 3(4), 4-51. Retrieved from www.scopus.com Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of egypt using markov-CA. Applied Geography, 63, 101-112. doi:10.1016/j.apgeog.2015.06.015 Handayanto, R. T., Kim, S. M., Tripathi, N. K., & Herlawati. (2018). Land use growth simulation and optimization in the urban area. Paper presented at the Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017, , 2018-January 1-6. doi:10.1109/IAC.2017.8280532 Retrieved from www.scopus.com Hart, M. A., & Sailor, D. J. (2009). Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island. Theoretical and Applied Climatology, 95(3-4), 397-406. doi:10.1007/s00704-008-0017-5 Hu, Z., & Lo, C. P. (2007). Modeling urban growth in atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667-688. doi:10.1016/j.compenvurbsys.2006.11.001 Islam, K., Rahman, M. F., & Jashimuddin, M. (2018). Modeling land use change using cellular automata and artificial neural network: The case of chunati wildlife sanctuary, bangladesh. Ecological Indicators, 88, 439-453. doi:10.1016/j.ecolind.2018.01.047 Islam, R. M., & Hasan, M. Z. (2011). Land use changing pattern and challenges for agricultural land: A study on rajshahi district. J.Life Earth Sci, 6, 69-74. Retrieved from www.scopus.com Kafy, A. -., Abdullah-Al-Faisal, Raikwar, V., Rakib, A. A., Kona, M. A., & Ferdousi, J. (2021). Geospatial approach for developing an integrated water resource management plan in rajshahi, bangladesh. Environmental Challenges, 4 doi:10.1016/j.envc.2021.100139 Kafy, A. -., Al Rakib, A., Akter, K. S., Rahaman, Z. A., Faisal, A. -., Mallik, S., . . . Ali, M. Y. (2021). Monitoring the effects of vegetation cover losses on land surface temperature dynamics using geospatial approach in rajshahi city, bangladesh. Environmental Challenges, 4 doi:10.1016/j.envc.2021.100187 Kafy, A. -., Faisal, A. -., Rahman, M. S., Islam, M., Al Rakib, A., Islam, M. A., . . . Sattar, G. S. (2021). Prediction of seasonal urban thermal field variance index using machine learning algorithms in cumilla, bangladesh. Sustainable Cities and Society, 64 doi:10.1016/j.scs.2020.102542 Kafy, A. -., Faisal, A. -., Sikdar, S., Hasan, M., Rahman, M., Khan, M. H., & Islam, R. (2020). Impact of LULC changes on LST in rajshahi district of bangladesh: A remote sensing approach. J.Geogr.Stud., 3(1), 11-23. Retrieved from www.scopus.com Kafy, A. A., Islam, M., Sikdar, M. S., Ashrafi, T. J., Faisal, A. A., Islam, M. A., . . . Ali, M. Y. (2021). Remote sensing-based approach to identify the influence of land use/land cover change on the urban thermal environment: A case study in chattogram city, bangladesh. Re-Envisioning Remote Sensing Applications: Perspective from Developing Countries, , 216-237. Retrieved from www.scopus.com Kafy, A. -., Naim, M. N. H., Khan, M. H. H., Islam, M. A., Al Rakib, A., Faisal, A. -., & Sarker, M. H. S. (2021). Prediction of urban expansion and identifying its impacts on the degradation of agricultural land: A machine learning-based remote-sensing approach in rajshahi, bangladesh. Re-Envisioning Remote Sensing Applications: Perspective from Developing Countries, , 85-106. Retrieved from www.scopus.com Kafy, A. -., Naim, M. N. H., Subramanyam, G., Faisal, A. -., Ahmed, N. U., Al Rakib, A., . . . Sattar, G. S. (2021). Cellular Automata Approach in Dynamic Modeling of Land Cover Changes using Rapideye Images in Dhaka, Retrieved from www.scopus.com Kafy, A. -., Rahman, M. N., Al Rakib, A., Arpi, S., & Faisal, A. -. (2019). Assessing satisfaction level of urban residential area: A comparative study based on resident's perception in rajshahi city, bangladesh. 1st International Conference on Urban and Regional Planning, Bangladesh, Bangladesh Institute of Planners, Dhaka, Bangladesh, , 225-235. Retrieved from www.scopus.com Kafy, A. -., Rahman, M. S., Faisal, A. -., Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in rajshahi, bangladesh. Remote Sensing Applications: Society and Environment, 18 doi:10.1016/j.rsase.2020.100314 Lai, L. -., & Cheng, W. -. (2010). Urban heat island and air pollution-an emerging role for hospital respiratory admissions in an urban area. Journal of Environmental Health, 72(6), 32-35. Retrieved from www.scopus.com Lilly Rose, A., & Devadas, M. D. (2009). Analysis of land surface temperature and land use/land cover types using remote sensing imagery—A case in chennai city, india. The Seventh International Conference on Urban Climate, , 1-4. Retrieved from www.scopus.com Liu, G., Jin, Q., Li, J., Li, L., He, C., Huang, Y., & Yao, Y. (2017). Policy factors impact analysis based on remote sensing data and the CLUE-S model in the lijiang river basin, china. Catena, 158, 286-297. doi:10.1016/j.catena.2017.07.003 Losiri, C., Nagai, M., Ninsawat, S., & Shrestha, R. P. (2016). Modeling urban expansion in bangkok metropolitan region using demographic-economic data through cellular automata-markov chain and multi-layer perceptron-markov chain models. Sustainability (Switzerland), 8(7) doi:10.3390/su8070686 Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the cellular Automata–Markov model. Environmental Monitoring and Assessment, 191(2) doi:10.1007/s10661-019-7200-2 Maduako, I., Ebinne, E., Zhang, Y., & Bassey, P. (2016). Prediction of land surface temperature (LST) changes within ikom city in nigeria using artificial neural network (ANN). Int J Remote Sens Appl, 6, 96. Retrieved from www.scopus.com Maduako, I. D., Yun, Z., & Patrick, B. (2016). Simulation and prediction of land surface temperature (LST) dynamics within ikom city in nigeria using artificial neural network (ANN). J.Remote Sens.GIS, 5(1), 1-7. Retrieved from www.scopus.com Maimaitiyiming, M., Ghulam, A., Tiyip, T., Pla, F., Latorre-Carmona, P., Halik, T., . . . Caetano, M. (2014). Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 89, 59-66. doi:10.1016/j.isprsjprs.2013.12.010 Maithani, S. (2015). Neural networks-based simulation of land cover scenarios in doon valley, india. Geocarto International, 30(2), 163-185. doi:10.1080/10106049.2014.927535 Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over delhi using landsat-7 ETM+. J.Ind.Geophys.Union, 12(3), 131-140. Retrieved from www.scopus.com Mansour, S., Al-Belushi, M., & Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of oman using GIS and CA-markov modelling techniques. Land use Policy, 91 doi:10.1016/j.landusepol.2019.104414 Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617-663. doi:10.1080/01431160701352154 Maulik, U., & Chakraborty, D. (2017). Remote sensing image classification: A survey of support-vector-machine-based advanced techniques. IEEE Geoscience and Remote Sensing Magazine, 5(1), 33-52. doi:10.1109/MGRS.2016.2641240 McCarthy, M. J., Radabaugh, K. R., Moyer, R. P., & Muller-Karger, F. E. (2018). Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites. Remote Sensing of Environment, 208, 189-201. doi:10.1016/j.rse.2018.02.021 Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-markov chain analysis for land use and land cover change prediction in patna district (bihar), india. Arabian Journal of Geosciences, 9(4) doi:10.1007/s12517-015-2138-3 Mishra, V. N., Rai, P. K., Prasad, R., Punia, M., & Nistor, M. -. (2018). Prediction of spatio-temporal land use/land cover dynamics in rapidly developing varanasi district of uttar pradesh, india, using geospatial approach: A comparison of hybrid models. Applied Geomatics, 10(3), 257-276. doi:10.1007/s12518-018-0223-5 Mozumder, C., & Tripathi, N. K. (2014). Geospatial scenario based modelling of urban and agricultural intrusions in ramsar wetland deepor beel in northeast india using a multi-layer perceptron neural network. International Journal of Applied Earth Observation and Geoinformation, 32(1), 92-104. doi:10.1016/j.jag.2014.03.002 Naim, M. N. H., & Kafy, A. -. (2021). Assessment of urban thermal field variance index and defining the relationship between land cover and surface temperature in chattogram city: A remote sensing and statistical approach. Environmental Challenges, 4 doi:10.1016/j.envc.2021.100107 Niyogi, D. (2019). Land surface processes. Current Trends in the Representation of Physical Processes in Weather and Climate Models, , 349-370. Retrieved from www.scopus.com Nurwanda, A., & Honjo, T. (2020). The prediction of city expansion and land surface temperature in bogor city, indonesia. Sustainable Cities and Society, 52 doi:10.1016/j.scs.2019.101772 Ogashawara, I., & da Silva Brum, V. (2012). A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sensing, 4(11), 3596-3618. doi:10.3390/rs4113544 Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in english bazar urban centre. Egyptian Journal of Remote Sensing and Space Science, 20(1), 125-145. doi:10.1016/j.ejrs.2016.11.003 Pontius Jr., R. G., & Millones, M. (2011). Death to kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429. doi:10.1080/01431161.2011.552923 Rahman, K. M., Melville, L., Fulford, D., & Huq, S. M. I. (2017). Green-house gas mitigation capacity of a small scale rural biogas plant calculations for bangladesh through a general life cycle assessment. Waste Management and Research, 35(10), 1023-1033. doi:10.1177/0734242X17721341 Rahman, M. S., Mohiuddin, H., Kafy, A. -., Sheel, P. K., & Di, L. (2018). Classification of cities in bangladesh based on remote sensing derived spatial characteristics. J.Urban Manag., Retrieved from www.scopus.com Rahman, M. T. (2016). Detection of land use/land cover changes and urban sprawl in al-khobar, saudi arabia: An analysis of multi-temporal remote sensing data. ISPRS International Journal of Geo-Information, 5(2) doi:10.3390/ijgi5020015 Rahman, M. T., Aldosary, A. S., & Mortoja, M. G. (2017). Modeling future land cover changes and their effects on the land surface temperatures in the saudi arabian eastern coastal city of dammam. Land, 6(2) doi:10.3390/land6020036 Roy, D. P., Wulder, M. A., Loveland, T. R., C.E., W., Allen, R. G., Anderson, M. C., . . . Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172. doi:10.1016/j.rse.2014.02.001 |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |