Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Monitoring and Prediction of Spatiotemporal Land-Use/Land-Cover Change Using Markov Chain Cellular Automata Model in Barisal, Bangladesh
Land use and land cover (LULC) is the dominant approach to evaluating urban expansion and estimating proper urban planning and management. LULC changes reflect the economic and structural development of specific areas. These types of drivers of land use and land cover (LULC) are accountable for a slew of issues, including road congestion, flooding, sanitation facilities, and agricultural land depletion. This chapter utilized an integrated Markov chain cellular automata method to simulate LULC and prediction in the Barisal district to analyze these issues. Firstly, visualization of spatiotemporal changes was extracted for two different periods of 2002 and 2021 from satellite images. Then the combined technique of GIS, RS (Remote Sensing), and the Markov chain cellular automata method was employed to predict LULC in the 2031 period. The results of this study, the maximum area of water bodies increased from 4.47 to 9.93% between 2002 and 2021, while bare land decreased from 20.93 to 16.63%. The increase in water bodies may be the consequence of sea level rise and flooding as well. As a coastal location, the expected changes show that agricultural land might decline from 57.45 to 49.23% between 2021 and 2031 due to the water body’s dominance (9.94–10.03%). So, the future model could help with planning and building cities in a way that protects the environment and is sustainable.
Monitoring and Prediction of Spatiotemporal Land-Use/Land-Cover Change Using Markov Chain Cellular Automata Model in Barisal, Bangladesh
Land use and land cover (LULC) is the dominant approach to evaluating urban expansion and estimating proper urban planning and management. LULC changes reflect the economic and structural development of specific areas. These types of drivers of land use and land cover (LULC) are accountable for a slew of issues, including road congestion, flooding, sanitation facilities, and agricultural land depletion. This chapter utilized an integrated Markov chain cellular automata method to simulate LULC and prediction in the Barisal district to analyze these issues. Firstly, visualization of spatiotemporal changes was extracted for two different periods of 2002 and 2021 from satellite images. Then the combined technique of GIS, RS (Remote Sensing), and the Markov chain cellular automata method was employed to predict LULC in the 2031 period. The results of this study, the maximum area of water bodies increased from 4.47 to 9.93% between 2002 and 2021, while bare land decreased from 20.93 to 16.63%. The increase in water bodies may be the consequence of sea level rise and flooding as well. As a coastal location, the expected changes show that agricultural land might decline from 57.45 to 49.23% between 2021 and 2031 due to the water body’s dominance (9.94–10.03%). So, the future model could help with planning and building cities in a way that protects the environment and is sustainable.
Monitoring and Prediction of Spatiotemporal Land-Use/Land-Cover Change Using Markov Chain Cellular Automata Model in Barisal, Bangladesh
GIScience & Geo-environmental Modelling
Rahman, Atiqur (Herausgeber:in) / Sen Roy, Shouraseni (Herausgeber:in) / Talukdar, Swapan (Herausgeber:in) / Shahfahad (Herausgeber:in) / Naimur Rahman, Md. (Autor:in) / Mushfiqus Saleheen, Md. (Autor:in) / Shozib, Sajjad Hossain (Autor:in) / Towfiqul Islam, Abu Reza Md. (Autor:in)
04.03.2023
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
| DOAJ | 2018