Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh

Kafy, Abdulla Al; Naim, Md. Nazmul Huda; Subramanyam, G.; Faisal, Abdullah-Al-; Ahmed, Nessar Uddin; Rakib, Abdullah Al; Kona, Marium Akter; Sattar, Golam Sabbir

doi:10.1016/j.envc.2021.100084

Cited by 87 publications

(30 citation statements)

References 59 publications

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“…Recently, geographical information systems (GIS) and remote sensing have been extensively used for LULC mapping and change detection across the world [6,16,[33][34][35]. Moreover, advances in remote sensing such as the use of digital image processing algorithms have increased the use of satellite imageries such as Landsat data in studies concerned with LULC changes across multiple spatial and temporal scales [35,36].…”

Section: Introductionmentioning

confidence: 99%

Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

2021

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Mapping and quantifying the status of Land use/Land cover (LULC) changes and drivers of change are important for identifying vulnerable areas for change and designing sustainable ecosystem services. This study analyzed the status of LULC changes and key drivers of change for the last 30 years through a combination of remote sensing and GIS with the surveying of the local community understanding of LULC patterns and drivers in the Gubalafto district, Northeastern Ethiopia. Five major LULC types (cultivated and settlement, forest cover, grazing land, bush land and bare land) from Landsat images of 1986, 2000, and 2016 were mapped. The results demonstrated that cultivated and settlement constituted the most extensive type of LULC in the study area and increased by 9% extent. It also revealed that a substantial expansion of bush land and bare land areas during the past 30 years. On the other hand, LULC classes that has high environmental importance such as grazing land and forest cover have reduced drastically through time with expanding cultivated and settlement during the same period. The grazing land in 1986 was about 11.1% of the total study area, and it had decreased to 5.7% in 2016. In contrast, cultivated and settlement increased from 45.6% in 1986 to 49.5% in 2016. Bush land increased from 14.8 to 21% in the same period, while forest cover declined from 8.9 to 2% in the same period. The root causes for LULC changes in this particular area include population growth, land tenure insecurity, and common property rights, persistent poverty, climate change, and lack of public awareness. Therefore, the causes for LULC changes have to be controlled, and sustainable resources use is essential; else, these scarce natural resource bases will soon be lost and will no longer be able to play their contribution in sustainable ecosystem services. Article Highlights Forest cover and grazing lands declined rapidly. Fluctuating trends in cultivated and settlement, bush land and bare land. Population pressure and associated demand are the main causes behind LULC changes in the study area.

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Section: Introductionmentioning

confidence: 99%

Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

2021

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“…The use of ML algorithms in combination with geographic object-oriented analysis techniques is expanding rapidly for GEE, where it is an effective environment for creating multitemporal composite images and implementing complex image processing and classi cation processes in a simple way. Urban expansion is linked to ve major factors: rural-urban migration, new employment prospects, improving quality of life, natural development, and urban rede nition (Bank, 2007;Kafy et al, 2021). Obtained results were discussed as zone 1 and zone 2 for analysis.…”

Section: Discussion On Lulc Change Patternsmentioning

confidence: 99%

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India

Abijith

Saravanan

2021

Environ Sci Pollut Res

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Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classi ed in GEE using Random Forest (RF). LULC maps were then framed with the CA-Markov model to forecast future LULC change. The CA-Markov's Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coe cient was above 87% for all three years. This region is highly susceptible to inland oods, coastal oods, and other natural disasters; thus, this study's results support future development plans and decision-making.

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“…The urban CL in six concentrated rings was drawn from the city center to cover the boundary of the study area [65]. This study created a concentric buffer zone structure of Baghdad City in ArcGIS 10.8 platform to analyze the different directions of city expansion by drawing 16 directions to evaluate the expanding rays of the study area [66]. An equal interval of 22.5 • between the four main directions, such as north, south, east, and west with the distance of 20 km 2 , where each interval ring was considered.…”

Section: Spatial Directional Change Of Urban Sprawlmentioning

confidence: 99%

Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

et al. 2021

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The global and regional land use/cover changes (LUCCs) are experiencing widespread changes, particularly in Baghdad City, the oldest city of Iraq, where it lacks ecological restoration and environmental management actions at present. To date, multiple land uses are experiencing urban construction-related land expansion, population increase, and socioeconomic development. Comprehensive evaluation and understanding of the effect of urban sprawl and its rapid LUCC are of great importance to managing land surface resources for sustainable development. The present research applied remote sensing data, such as Landsat-5 Thematic Mapper and Landsat-8 Operation Land Imager, on selected images between July and August from 1985 to 2020 with the use of multiple types of software to explore, classify, and analyze the historical and future LUCCs in Baghdad City. Three historical LUCC maps from 1985, 2000, and 2020 were created and analyzed. The result shows that urban construction land expands quickly, and agricultural land and natural vegetation have had a large loss of coverage during the last 35 years. The change analysis derived from previous land use was used as a change direction for future simulation, where natural and anthropogenic factors were selected as the drivers’ variables in the process of multilayer perceptron neural network Markov chain model. The future land use/cover change (FLUCC) modeling results from 2030 to 2050 show that agriculture is the only land use type with a massive decreasing trend from 1985 to 2050 compared with other categories. The entire change in urban sprawl derived from historical and FLUCC in each period shows that urban construction land increases the fastest between 2020 and 2030. The rapid urbanization along with unplanned urban growth and rising population migration from rural to urban is the main driver of all transformation in land use. These findings facilitate sustainable ecological development in Baghdad City and theoretically support environmental decision making.

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Cellular Automata approach in dynamic modelling of land cover changes using RapidEye images in Dhaka, Bangladesh

Cited by 87 publications

References 59 publications

Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

Analysing land use/land cover changes and its dynamics using remote sensing and GIS in Gubalafito district, Northeastern Ethiopia

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India

Remote Sensing-Based Urban Sprawl Modeling Using Multilayer Perceptron Neural Network Markov Chain in Baghdad, Iraq

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