An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: A GIS-based analysis

Dinda, Santanu; Chatterjee, Nilanjana Das; Ghosh, Subrata

doi:10.1016/j.ecolind.2020.107178

Cited by 83 publications

(36 citation statements)

References 109 publications

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“…On an annual basis, this equates to 22.7 square kilometers per year (standard scenario) or 7.19 square kilometers per year (conservative scenario). These rates are similar to those reported for the cities of La Serena, Santiago and Concepcion in Chile, over a similar 20-year time period [69], but were lower than those reported for Kolkata, India [70].…”

Section: The Overall Change In Urban Greenspace In Greater Melbournesupporting

confidence: 87%

Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016

Timalsina

Mavoa

Hahs

2021

Land

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Understanding changes in urban vegetation is essential for ensuring sustainable and healthy cities, mitigating disturbances due to climate change, sustaining urban biodiversity, and supporting human health and wellbeing. This study investigates and describes the distribution and dynamic changes in urban vegetation over a 15-year period in Greater Melbourne, Australia. The study investigates how vegetation cover across Melbourne has changed at five-yearly intervals from 2001 to 2016 using the newly proposed dynamic change approach that extends the net change approach to quantify the amount of vegetation gain as well as loss. We examine this question at two spatial resolutions: (1) at the municipal landscape scale to capture broadscale change regardless of land tenure; and (2) at the scale of designated public open spaces within the municipalities to investigate the extent to which the loss of vegetation has occurred on lands that are intended to provide public access to vegetated areas in the city. Vegetation was quantified at four different times (2001, 2006, 2011, 2016), using the normalized difference vegetation index (NDVI). Dynamic changes of gain and loss in urban vegetation between the three periods were quantified for six local government areas (LGAs) and their associated public open spaces using a change matrix. The results showed an overall net loss of 64.5 square kilometres of urban vegetation from 2001 to 2016 in six LGAs. When extrapolated to the Greater Melbourne Area, this is approximately equivalent to 109 times the size of Central Park in New York City.

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Section: The Overall Change In Urban Greenspace In Greater Melbournesupporting

confidence: 87%

Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016

Timalsina

Mavoa

Hahs

2021

Land

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“…Li et al [30] construct land-use and land-cover maps including green spaces using Landsat Operational Land Imager (OLI) and Enhanced ematic Mapper Plus (ETM+) imagery; convolutional neural network, random forest, and SVM are the employed machine learning models used for image data classification; this study reports a classification accuracy of 84.40% on the testing dataset. Dinda et al [31] construct an integrated model for studying urban growth and associated green space loss; the model relies on maximum likelihood classifier, artificial neural network, and SVM for performing pattern recognition task; the SVM model has attained the most desired classification accuracy and the area under the receiver operating characteristic curve (0.906).…”

Section: Research Background and Motivationmentioning

confidence: 99%

“…Hybrid machine learning models that harness advantages of various computational intelligence techniques are rarely investigated to construct urban green space detection models. Specifically, previous studies have mainly relied on the individual machine learning approach [3,13,29,31], and the employment of metaheuristic algorithms used for optimizing machine learning based remote sensing data classification has rarely been proposed and investigated.…”

Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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“…In this respect, several studies have shown that many Indian cities are increasingly facing challenges pertaining to poverty, inadequate housing and unemployment, water and sanitation, solid waste management, transport infrastructure and environmental vulnerabilities (Nandi & Gamkhar, 2013;Abhishek et al, 2017;Ebeke et al, 2017). Accordingly, sustainable management of urbanisation has become a growing concern for urban planners to manage various socioeconomic and environmental issues triggered by the process of urbanisation (Dinda et al, 2021). Therefore, understanding perception of urban sprawl among urban and peri-urban dwellers can contribute to a more sustainable urban planning that provides opportunities to influence the motives and attitudes of the public and effectively engage them in the design and implementation of urban planning policies.…”

Section: Introductionmentioning

confidence: 99%

Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis

Hatab

Padmaja

Nedumaran

et al. 2021

Environ Dev Sustain

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Like many other developing countries, urban sprawl is a growing phenomenon in India, which poses socio-economic and environmental challenges that worryingly affect urban sustainability. In this study, a latent class clustering approach was used to investigate perceptions of urban sprawl among 622 urban and peri-urban dwellers in Hyderabad. The empirical results clustered the respondents into three distinct classes based on their perceptions of urban sprawl impacts: ‘undecided respondents’, ‘negative perceivers’, and ‘opportunity perceivers’. The majority of respondents were undecided with no strong views towards the impacts of urban sprawl, which may increase their vulnerability and hinder effective adaptation to the adverse economic, social and environmental effects of urban sprawl. This also provokes concerns about the effectiveness of government interventions to build public awareness of urban development and its impacts on the city. With regard to the role of demographic and socio-economic characteristics in shaping the perception of the respondents, the results revealed that social caste plays a determining role in forming dwellers’ perception. In particular, members of marginalised social castes were more likely to form positive perceptions of the impacts of urban sprawl as urban expansion generates better and stable income that improve their social status. In addition, individuals with higher levels of education were more likely to form negative or positive perceptions, implying that efforts to raise social capital could be a useful means for mitigating the impacts of urban sprawl. Finally, membership in community development organisations was a key factor in dictating membership of the negative perceivers’ class. Overall, our findings suggest that an appropriate policy framework and specific programmes are needed for enhancing dwellers’ perception towards the impacts of urban sprawl, which can enhance the design, acceptance, and implementation of a more sustainable governance of urbanisation and contribute to achieving urban sustainability in developing countries.

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An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: A GIS-based analysis

Cited by 83 publications

References 109 publications

Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016

Dynamic Changes in Melbourne’s Urban Vegetation Cover—2001 to 2016

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Perceptions of the impacts of urban sprawl among urban and peri-urban dwellers of Hyderabad, India: a Latent class clustering analysis

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