Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data
Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for model...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taiwan Association of Engineering and Technology Innovation
2023-01-01
|
| Series: | Proceedings of Engineering and Technology Innovation |
| Subjects: | |
| Online Access: | https://ojs.imeti.org/index.php/PETI/article/view/9617 |
| _version_ | 1797808572029992960 |
|---|---|
| author | Md Aminul Islam Tanzina Ahmed Rickty Pramit Kumar Das Md Bashirul Haque |
| author_facet | Md Aminul Islam Tanzina Ahmed Rickty Pramit Kumar Das Md Bashirul Haque |
| author_sort | Md Aminul Islam |
| collection | DOAJ |
| description |
Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to analyze, model, and simulate the land-use and land-cover (LULC) changes by incorporating the algorithm of logistic regression (LR). The calibrated model is used to predict the 2020 map, and the result shows that the predicted map and the actual map of 2020 are well agreed. By using the calibrated model, the simulated prediction map of 2035 shows an urban cell expansion of 220% between 2020 and 2035.
|
| first_indexed | 2024-03-13T06:39:32Z |
| format | Article |
| id | doaj.art-d4aab146cf6446a7b4417175e9161b12 |
| institution | Directory Open Access Journal |
| issn | 2413-7146 2518-833X |
| language | English |
| last_indexed | 2024-03-13T06:39:32Z |
| publishDate | 2023-01-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | Proceedings of Engineering and Technology Innovation |
| spelling | doaj.art-d4aab146cf6446a7b4417175e9161b122023-06-08T18:28:45ZengTaiwan Association of Engineering and Technology InnovationProceedings of Engineering and Technology Innovation2413-71462518-833X2023-01-012310.46604/peti.2023.9617Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data Md Aminul Islam0Tanzina Ahmed Rickty1Pramit Kumar Das2Md Bashirul Haque3Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, BangladeshDepartment of Civil and Environmental Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh Forecasting urban sprawl is important for land-use and transport planning. The aim of this study is to model and predict the future urban sprawl in Sylhet Sadar using remote sensing data. The ordinary least square (OLS) regression model and the geographic information system (GIS) are used for modeling urban expansion. The model is calibrated for the years 2014 to 2017 using eight explanatory variables extracted from the regression model. The regression coefficients of the variables are found statistically significant at a 99% confidence level. The cellular automata (CA) model is then used to analyze, model, and simulate the land-use and land-cover (LULC) changes by incorporating the algorithm of logistic regression (LR). The calibrated model is used to predict the 2020 map, and the result shows that the predicted map and the actual map of 2020 are well agreed. By using the calibrated model, the simulated prediction map of 2035 shows an urban cell expansion of 220% between 2020 and 2035. https://ojs.imeti.org/index.php/PETI/article/view/9617geographic information systemremote sensingland use and land coverurban sprawlordinary least square regressioncellular automata |
| spellingShingle | Md Aminul Islam Tanzina Ahmed Rickty Pramit Kumar Das Md Bashirul Haque Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data Proceedings of Engineering and Technology Innovation geographic information system remote sensing land use and land cover urban sprawl ordinary least square regression cellular automata |
| title | Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data |
| title_full | Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data |
| title_fullStr | Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data |
| title_full_unstemmed | Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data |
| title_short | Modeling and Forecasting Urban Sprawl in Sylhet Sadar Using Remote Sensing Data |
| title_sort | modeling and forecasting urban sprawl in sylhet sadar using remote sensing data |
| topic | geographic information system remote sensing land use and land cover urban sprawl ordinary least square regression cellular automata |
| url | https://ojs.imeti.org/index.php/PETI/article/view/9617 |
| work_keys_str_mv | AT mdaminulislam modelingandforecastingurbansprawlinsylhetsadarusingremotesensingdata AT tanzinaahmedrickty modelingandforecastingurbansprawlinsylhetsadarusingremotesensingdata AT pramitkumardas modelingandforecastingurbansprawlinsylhetsadarusingremotesensingdata AT mdbashirulhaque modelingandforecastingurbansprawlinsylhetsadarusingremotesensingdata |