How Does Tourism Affect Urban Ecological Standards? A Geospatial Analysis of Wetland Transformations in the Coastal Resort Town of Digha, West Bengal, India

Roy, Asit Kumar; Mitra, Suman; Datta, Debajit

doi:10.1007/978-3-030-71945-6_20

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…However, this trend of highly intensive culture and unhygienic practice of culture has led to water pollution Direct disposal of this polluted water without treatment to the rivers, creeks, and canals has induced water pollution in natural waterbodies (Salunke et al, 2020). The same scenario has been observed in the present study region as well (Roy et al, 2021a).…”

Section: Consequences Of Cba Growthsupporting

confidence: 72%

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A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

Naskar,

Neogy,

Kumar Roy

et al. 2024

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The spatio-temporal growth trajectory of coastal brackishwater aquaculture (CBA) in the coastal tracts of Purba Medinipur, India and its impending repercussions on the immediate environs, was primarily assessed in this study through application of geostatistics, landscape metrics, and geospatial technologies. Three Community Development (CD) Blocks, namely, Contai-I, Deshapran, and Ramnagar-II were considered to analyze the growth pattern of the area under CBA. Landsat datasets of the assessment years (1991, 2001, 2011, 2021, and 2023) were used to prepare the land use/ land cover (LULC) maps and to derive pertinent landscape metrics of patch and class levels for this region. This brought forward a highly fragmented and dispersive spatial concentration of the CBA farms in the entire study area. Additionally, Census Village-wise growth pattern of CBA was analyzed by conducting a spatial autocorrelation analysis which depicted prominent clustering of villages with a higher concentration of CBA. Results showed that there has been an incessant growth of CBA in the last three decades, however, a sharp drop has been recorded owing to recurrent bouts of diseases, swelling production costs, and a sharp drop-in market rate. The nature of growth and/ or decay of the CBA was predicted for the year 2025 using Cellular Automata and Artificial Neural Network (CA-ANN) model. After careful calibration and validation, the model projected further lessening of the CBA area along with a continued expansion of abandoned aquaculture. Accordingly, ecologically viable livelihood alternatives and environmentally sustainable management measures were suggested for the efficient monitoring of these highly fragile tropical ecosystems.

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Section: Consequences Of Cba Growthsupporting

confidence: 72%

“…Alongside, overall accuracy and Kappa Coefficient were generated to validate the accuracy level. Following the image classification, LULC transformation matrices were generated to assess the inter-class LULC changes within the assessment period (Mazumder et al, 2021;Roy et al, 2021a).…”

Section: Image Processing and Classificationmentioning

confidence: 99%

A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

Naskar,

Neogy,

Kumar Roy

et al. 2024

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“…Deep-learning techniques in the field of environmental remote sensing undoubtedly proved one of the most important breakthroughs, offering solutions to several problems (Baniya et al 2018;Tzampoglou and Loupasakis 2017). This would be possible if the fusion of multisensory data and machine-learning technologies were established and arranged in a synchronized way for advanced decision analysis (Li et al 2021;Saggi and Jain 2018;Syed et al 2019). These techniques proved to be more accurate and efficient when compared to the traditional methods for the mentioned applications: The Simple linear iterative clustering (SLIC)based approach was used at first to segment remote-sensing imagery into super pixels.…”

Section: Significance Of Deep Learning and Applicationsmentioning

confidence: 99%

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Kumar,

Bassill,

Ghosh

2024

International Journal on Smart Sensing and Intelligent Systems

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Deep learning has changed the approach of urban environmental risk assessment and management. These methods enable solid models for large data sets, enabling early identification, prediction, and description of environmental risks. The current work analyses the advances in deep learning for urban environmental hazard assessments and disaster studies to provide monitoring, management, and mitigation measures. It reports the improvement in self-supervised learning, transformer architectures, persistent learning, attention mechanisms, adversarial robustness, associated learning, meta-learning, and multimodal learning within the domain of urban environmental hazard analysis. These approaches allow the creation of robust models for handling vast data volumes, facilitating early detection, prediction, and characterisation of diverse environmental threats. This trends analysis for urban applications will bring insights for connecting deep-learning models for effective and proactive approaches to tackle urban environmental hazards and disasters.

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“…Trees purify the atmosphere by removing nitrous and carbon dioxide, carbon monoxide sulphur dioxide, and ozone. The species with the largest leaf surface and the highest [20]- [24].…”

Section: Benefits Associated With Investment In Green Infrastructurementioning

confidence: 99%

Green Infrastructure- A Roadmap Towards Sustainable Development

Kumar

Mukul²,

Kaur³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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India is the fifth more vulnerable country to climate change consequences, with 2.54.5 per cent of Gross Domestic Product (GDP) at risk every year. In conclusion, India has committed to reducing the greenhouse emissions of its Gross Domestic Product by 33-35 per cent by 2030, compared to 2005 levels. However, India will need to raise $2.5 trillion between 2016 and 2030 to accomplish this goal (MOEFCC, 2015). However, climate-related investments — both public and private — in green infrastructure development remain restricted. While India has taken several measures to solve this issue, rising green investments would need a greater emphasis on India’s infrastructure development. Long-term infrastructure is essential for a better future in an era when India announced that it would begin its decarburization journey to reach specified green targets. Green infrastructure is innovation and practices that employ natural systems to get better the overall value of the environment and provide ecological, social, and economic rewards. The study describes the contribution and investment in green infrastructure to optimize the growth with sustainable development in India. The research reveals that effective planning for green investment helps to maintain the adequate trade-off between development and ecosystem.

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How Does Tourism Affect Urban Ecological Standards? A Geospatial Analysis of Wetland Transformations in the Coastal Resort Town of Digha, West Bengal, India

Cited by 5 publications

References 25 publications

A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

A landscape in turmoil: characterizing the multi-decadal growth trajectory of brackishwater aquaculture in Medinipur Coastal Plain, India

Analyzing recent trends in deep-learning approaches: a review on urban environmental hazards and disaster studies for monitoring, management, and mitigation toward sustainability

Green Infrastructure- A Roadmap Towards Sustainable Development

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