Assessment of Vegetation Cover of Bengaluru City, India, Using Geospatial Techniques

Raj, Krishna; Trivedi, Shivam; Ramesh, K. S.; Sudha, R.; Subramoniam, Suresh; Ravishankar, H. M.; Vidya, A.

doi:10.1007/s12524-020-01259-5

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…The urban vegetation cover of Bengaluru city corresponds to 30.1% in 2006 and 26.1% in 2019. Rapid urbanization has led to a 4% decline in the urban vegetation cover [35]. In the present study, Landsat satellite images for the chosen four lakes were downloaded for the years 2001 and 2020.…”

Section: Description Of Lakesmentioning

confidence: 99%

New Classification Method to Evaluate Pollution Levels of Sewage Contaminated Lakes

Rao

Mogili

2021

Sustainability

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Monitoring water quality to minimize deterioration of a lake’s functionality is important, as several Indian lakes are exposed to sewage contamination. Public health laboratories, citizen scientists, and volunteers in developing nations often find it difficult to perform elaborate tests to monitor the water quality of freshwater systems. Developing a classification method to evaluate the pollution status of sewage-contaminated lakes using limited tests will expand environmental monitoring of freshwater systems and contribute valuable data to the regional and global repository. Four classes of lake pollution ranging from unpolluted (class 1) to mixed wastewater (class 4) were identified based on the distribution of data points in the K+ (potassium) versus COD (chemical oxygen demand) scatter chart. As pH, EC (electrical conductivity), turbidity, and DO (dissolved oxygen) are deteriorated by sewage contamination, these parameters were also incorporated in the proposed pollution classification table. Data of unpolluted and sewage polluted Indian lakes were employed to compile the limiting range of parameters in the proposed lake pollution classification. The five parameters (K+, pH, EC, DO, turbidity) required to categorize lake pollution (class 1 to 4) can be measured with equipment costing 800–1000 USD, while COD can be measured at 5 USD/sample in laboratories.

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Section: Description Of Lakesmentioning

confidence: 99%

New Classification Method to Evaluate Pollution Levels of Sewage Contaminated Lakes

Rao

Mogili

2021

Sustainability

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“…By comparing LULC maps from different time periods, researchers can detect patterns of change, such as deforestation, urban expansion, or agricultural intensi cation. These insights are crucial for environmental monitoring, urban planning, and resource management, providing a clearer understanding of how human activities and natural processes alter the landscape [13]. Thus, accurate LULC classi cation is essential for reliable change detection, enabling informed decision-making for sustainable development and conservation efforts [14,15].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Land Use and Land Cover Classification for Change Detection Studies

Pushpalatha,

Mallikarjuna,

Mahendra

et al. 2024

Preprint

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Accurate land use land cover (LULC) classification is crucial for monitoring environmental changes, urban planning, and natural resource management. Traditional methods and conventional machine learning algorithms can be time-consuming and less effective in handling large datasets. Hence, this paper presents the deep learning-based classification method, to classify Linear Imaging Self-Scanning Sensor-III ((LISS-III) satellite imagery into various land cover categories with improved accuracy and efficiency. We developed a Convolutional Neural Networks (CNN)-based deep learning model and trained on a diverse dataset of LISS-III images, covering the geographical region of Nanjangud taluk, Myusuru district, India. The developed model is applied to LISS-III images of the two years 2010 and 2020 respectively for LULC classification. Further, classified maps are used for change detection studies to identify and quantify land cover changes over time. The results indicate that the CNN approach achieved an overall accuracy of 94.08% for the 2010 data and 95.30% for the 2020 data, demonstrating the model's robustness and precision in LULC classification. The results highlight significant improvements in detecting small and large-scale changes, thereby providing valuable insights for environmental monitoring and policy-making.

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Assessment of Urban Dynamics Using Geospatial Techniques

Ravindranath

Trivedi

Subramoniam

et al. 2022

Water Science and Technology Library

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Assessment of Vegetation Cover of Bengaluru City, India, Using Geospatial Techniques

Cited by 10 publications

References 4 publications

New Classification Method to Evaluate Pollution Levels of Sewage Contaminated Lakes

New Classification Method to Evaluate Pollution Levels of Sewage Contaminated Lakes

Deep Learning-Based Land Use and Land Cover Classification for Change Detection Studies

Assessment of Urban Dynamics Using Geospatial Techniques

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