Urban Mapping and Growth Prediction using Remote Sensing and GIS Techniques, Pune, India

Sivakumar, V.

doi:10.5194/isprsarchives-xl-8-967-2014

Cited by 12 publications

(3 citation statements)

References 4 publications

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“…It is optional to carry out the calibration process for the factors, as the criteria can maintain their original values of distances and heights without being standardzed [26]. The most important of these criteria are:…”

Section: Transition Potentialmentioning

confidence: 99%

Multilayer Perceptron for the Future Urban Growth of the Kharj Region in 2040

Alshahrane

Altuwaijri

2023

Sustainability

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Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, modern technology and mathematical models have been used to determine future urban growth on a large scale and develop sustainable urban policies in the long term. The cities of the Kingdom of Saudi Arabia have witnessed economic growth in recent decades, which has resulted in urban expansion, as is evident in this case study of the Kharj region. Since most of the previous studies have not applied mathematical models to predict the urban growth of the Kharj region, this study aims at simulating urban growth over the next two decades, between 2020 and 2040, by monitoring the growth during the past thirty years, which is the period between 1990 and 2020. This study relies on the satellite visualizations of the Landsat satellites 5, 7, and 8 for classifying the land cover by applying the land change model (LCM) and comparing the land-use maps for the years 2000 and 2020. Then, the factors affecting urban growth, such as distance from the city center, the road network, valleys, and land slopes, are determined to monitor the prediction of urban growth. The results showed that the urban areas extended significantly toward the south, southeast, southwest, and northwest, with an area of 269 km². The results further revealed a significant decline in agricultural and vacant lands due to their transformation into residential areas, educational establishments, and industrial facilities. The model’s accuracy was tested to confirm the mathematical model’s validity. The Kappa index findings indicated a high percentage, ranging from 89% in 2010 to 90% in 2020.

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Section: Transition Potentialmentioning

confidence: 99%

Multilayer Perceptron for the Future Urban Growth of the Kharj Region in 2040

Alshahrane

Altuwaijri

2023

Sustainability

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“…These methods statistically describe the relationships between variables [33]. Markov chain analysis (MCA) or Markov models [37], cellular automata (CA) [38], cellular automata-Markov models (CA-Markov) [39], artificial neural networks (ANNs), binary logistic regression, and fractal models [40,41] are among the most common models used for simulation and prediction of LULC changes [42]. For LULC simulation, CA constitute an appropriate and commonly used model [32].…”

Section: Introductionmentioning

confidence: 99%

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

Zeshan

Mustafa

Baig

2021

Water

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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.

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“…The availability of data sources and technologies in RS and GIS can predict future land-use change. The commonly used model for prediction and simulation of future land cover change are Markov Chain Analysis [6], Cellular Automata [10], Cellular Automata-Markov model (CA-Markov) [11,12], Artificial Neural Network (ANN) [8], and Binary Logistic Regression [13] or the mix between those models [14].…”

Section: Introductionmentioning

confidence: 99%