Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India

Yatoo, Saleem Ahmad; Sahu, Paulami; Kalubarme, Manik H.; Kansara, Bhagirath B.

doi:10.1007/s10708-020-10274-5

Cited by 49 publications

(21 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Under rapid regional socioeconomic development and urban mechanisms, the GBA experienced a transformation that has had a tremendous impact on the spatial pattern of LULC changes [71,72]. In this study, we modeled the spatiotemporal transition potential and future scenario of LULC with the help of the Modules for Land-Use Change Simulation (MOLUSCE) plugin within QGIS software [73][74][75]. The MOLUSCE plugin incorporates some well-known algorithms, such as artificial neural networks (ANNs) and Monte Carlo cellular automata (CA) modeling approaches [76].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Abbas

Guang

Zhong

et al. 2021

Land

View full text Add to dashboard Cite

Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Abbas

Guang

Zhong

et al. 2021

Land

View full text Add to dashboard Cite

show abstract

“…The predicted map 2020 was compared with the observed classified map of 2020 to assess the degree of agre ment between the pixels of the two maps. The overall kappa coefficient was calculate the value of which ranges from 0 to 1 [15]. The high degree of agreement and satisfactor value of kappa coefficient indicated the validation of the simulation model.…”

Section: Artificial Neural Network-cellular Automata Modelingmentioning

confidence: 98%

“…In this study, a combination of ANN and CA was used to simulate and evaluate the LULC trends of the Perak River basin up to the year 2050 using open source QGIS software version 2.18.25 [60]. The CA feature in QGIS is based on the Markov chain algorithm; i.e., it relies on the present state of land use rather than the previous state [15]. This model generates the output data in the form of tables and maps by combining previous and current land use maps with spatial input parameters [61].…”

Section: Artificial Neural Network-cellular Automata Modelingmentioning

confidence: 99%

“…Confusion matrix was created between testing samples and classified images for the measurement of accuracy. It is a simple way to evaluate how frequently a pixel misidentifies class [15]. For the formation of confusion matrix table, 189, 202, and 177 random points were selected on the classified maps for years 2000, 2010, and 2020 respectively (Table 3).…”

Section: Land Use Change and Accuracy Assessmentmentioning

confidence: 99%

“…Geomorphic factors were examined for the establishment of a decision support system by [14] in order to avoid the occurrence of landslides using remote sensing. Furthermore, it has proved useful in identifying changes in agricultural patterns, land cover changes, and urban sprawls [15]. GIS are regarded as essential for identifying the changes in remotely sensed images [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Zeshan

Mustafa

Baig

2021

Water

View full text Add to dashboard Cite

Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries in Southeast Asia. It is essential for sustainable development, particularly efficient water management practices, to know about the impact of land use and land cover (LULC) changes. Geographic information systems (GIS) and remote sensing were used for monitoring land use changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic information systems (QGIS) was performed for prediction of LULC changes. This study investigated the changes in LULC in the Perak River basin for the years 2000, 2010, and 2020. The study also provides predictions of future changes for the years 2030, 2040, and 2050. Landsat satellite images were utilized to monitor the land use changes. For the classification of Landsat images, maximum-likelihood supervised classification was implemented. The broad classification defines four main classes in the study area, including (i) waterbodies, (ii) agricultural lands, (iii) barren and urban lands, and (iv) dense forests. The outcomes revealed a considerable reduction in dense forests from the year 2000 to 2020, whereas a substantial increase in barren lands (up to 547.39 km2) had occurred by the year 2020, while urban land use has seen a rapid rise. The kappa coefficient was used to assess the validity of classified images, with an overall kappa coefficient of 0.86, 0.88, and 0.91 for the years 2000, 2010, and 2020, respectively. In addition, ANN-CA simulation results predicted that barren and urban lands will expand in the future at the expense of other classes in the years 2030, 2040, and 2050. However, a considerable decrease will occur in the area of dense forests in the simulated years. The study successfully presents LULC changes and future predictions highlighting significant pattern of land use change in the Perak River basin. This information could be helpful for land use administration and future planning in the region.

show abstract

Geospatial Analysis of Urban Sprawl in Agartala Municipal Council, India, from 1991 to 2021

Chettry

2023

SDGs in the Asia and Pacific Region

View full text Add to dashboard Cite

Monitoring land use changes and its future prospects using cellular automata simulation and artificial neural network for Ahmedabad city, India

Cited by 49 publications

References 59 publications

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Geospatial Analysis of Urban Sprawl in Agartala Municipal Council, India, from 1991 to 2021

Contact Info

Product

Resources

About