Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using markov chain model: a case study in Siliguri Metropolitan area, West Bengal, India

Bose, Arghadeep; Chowdhury, Indrajit Roy

doi:10.1007/s40808-020-00842-6

Cited by 71 publications

(29 citation statements)

References 35 publications

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“…The total population of Delhi exceeds 11 million inhabitants, and the annual growth rate was recorded as 3.9 percent during 1991-2001, which is twice the national average [38]. The rapid urban expansion in Delhi has led to the loss of large areas of arable land, which has had a negative impact on urban sustainability [39,40]. Therefore, the issue of urban expansion is considered one of the most urgent issues in Delhi.…”

Section: Study Areamentioning

confidence: 99%

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989–2020, which has led to habitat fragmentation, deforestation, and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989–2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.

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Section: Study Areamentioning

confidence: 99%

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

et al. 2021

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“…To summarise, it was found that GEE aids in the study of LULC shift in a cost-effective and timeconsuming manner, and it is commonly used in the literature (Agarwal and Nagendra 2019;Gomes et al 2020;Noi Phan et al 2020;Sidhu et al 2018;Tamiminia et al 2020;Tassi and Vizzari 2020;Xing et al 2021). CA -Markov chain model helps in determining the future land cover change and their land-use patterns for decision-makers to provide sustainable development (Aburas et al 2018;Ansari and Golabi 2019;Bose and Chowdhury 2020;Faichia et al 2020;Fu et al 2018;116 Ghosh et al 2017;Gidey et al 2017;Halmy et al 2015;Hamad et al 2018;Ozturk 2015). 117 In this work, LULC was classified in the GEE platform using two standard machine learning 118 approaches, the Random Forest (RF) classifier and the Support Vector Machines (SVM).…”

Section: Ca-markov Chain Analysismentioning

confidence: 99%

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change for the Vembanad Lake System, Kerala – a Machine Learning Approach

Parthasarathy

Deka

2021

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Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake System (VLS), Kerala in the short term, i.e., within a decade, utilizing two standard machine learning approaches, Random Forest (RF) and Support Vector Machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the two techniques, SVM is classified at an average accuracy of around 84.5%, while RF is classified at 89%. The RF outperformed the SVM in almost identical spectral classes such as barren land and built-up areas. As a result, RF classified LULC is considered to predict the Spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the Cellular Automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

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“…Berg, Alexander C and Grabler, Floraine et.al [14] proposed a methods of parsing images of architectural scenes, where the parsing contents including roof, vegetation, Windows, building boundaries and doors etc part by training feature information of color and texture. Bose, Arghadeep and Chowdhury et.al [15] take the Siliguri metropolitan area in West Bengal, India as the research object, proposes a novelty study methods of Markov Chain model and analyzing the spatial distribution of urban land. Liu, Xudong and Tian, Yongzhong et.al [16] to scientifically plan the urbanization layout and improve the utilization rate of land space, the urban functional areas are identified and analyzed from the perspective of data mining, and taxi trajectory data is used as the research basis for urban functional areas.…”

Section: The Traditional Image Perception Of City and Villagesmentioning

confidence: 99%

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

Zhao

Tian

2021

Preprint

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With the rapid development of social economy, the urban and rural environment, form and infrastructure have also under- gone earth-shaking changes. As a gathering place for human activities, urban and rural areas play a vital role in the interaction between humans and society. If traditional machine learning methods are used to perceive changes in the intentional connotation of urban and rural areas, it is easy to ignore the detailed information of the intentional target. At the same time, the perception accuracy needs to be improved. Therefore, the deep neural network in this paper proposes a way to perceive the temporal and spatial changes of urban and rural intentional connotations from the perspective of remote sensing. The framework first uses the multi-branch DenseNet to capture the multi-scale spatiotemporal information of the intended target, and embeds it with high-level semantics and low-level physical appearance information. Secondly, a multi-branch cross-channel attention module is designed to perform reﬁned and integrated processing of multi-level spatiotemporal information to realize the aggregation of multi-branch information. Finally, it was tested and verified on two different time data sets, and good performance was achieved.

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Monitoring and modeling of spatio-temporal urban expansion and land-use/land-cover change using markov chain model: a case study in Siliguri Metropolitan area, West Bengal, India

Cited by 71 publications

References 35 publications

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study

Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change for the Vembanad Lake System, Kerala – a Machine Learning Approach

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

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