Assessing the Impact of the 2004 Indian Ocean Tsunami on South Andaman’s Coastal Shoreline: A Geospatial Analysis of Erosion and Accretion Patterns

Singh, Saurabh; Singh, Suraj Kumar; Prajapat, Deepak; Pandey, Vikas; Kanga, Shruti; Kumar, Pankaj; Meraj, Gowhar

doi:10.3390/jmse11061134

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…Several contemporary real-life case studies of sustainability and uncertainty use the CA-Markov model and other machine-learning models (Table 1). A study on land use and land cover (LULC) changes in the northern coastal districts of Tamil Nadu, India, was conducted to analyze the change using the CA-Markov chain model [34]. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools.…”

Section: Introductionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

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Section: Introductionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)

Choudhury

Singh

Kumar

et al. 2023

Earth

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Amid global concerns regarding climate change and urbanization, understanding the interplay between land use/land cover (LULC) changes, the urban heat island (UHI) effect, and land surface temperatures (LST) is paramount. This study provides an in-depth exploration of these relationships in the context of the Kamrup Metropolitan District, Northeast India, over a period of 22 years (2000–2022) and forecasts the potential implications up to 2032. Employing a high-accuracy supervised machine learning algorithm for LULC analysis, significant transformations are revealed, including the considerable growth in urban built-up areas and the corresponding decline in cultivated land. Concurrently, a progressive rise in LST is observed, underlining the escalating UHI effect. This association is further substantiated through correlation studies involving the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI). The study further leverages the cellular automata–artificial neural network (CA-ANN) model to project the potential scenario in 2032, indicating a predicted intensification in LST, especially in regions undergoing rapid urban expansion. The findings underscore the environmental implications of unchecked urban growth, such as rising temperatures and the intensification of UHI effects. Consequently, this research stresses the critical need for sustainable land management and urban planning strategies, as well as proactive measures to mitigate adverse environmental changes. The results serve as a vital resource for policymakers, urban planners, and environmental scientists working towards harmonizing urban growth with environmental sustainability in the face of escalating global climate change.

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Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

Cavalli

2024

Remote Sensing

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Since 1971, remote sensing techniques have been used to map and monitor phenomena and parameters of the coastal zone. However, updated reviews have only considered one phenomenon, parameter, remote data source, platform, or geographic region. No review has offered an updated overview of coastal phenomena and parameters that can be accurately mapped and monitored with remote data. This systematic review was performed to achieve this purpose. A total of 15,141 papers published from January 2021 to June 2023 were identified. The 1475 most cited papers were screened, and 502 eligible papers were included. The Web of Science and Scopus databases were searched using all possible combinations between two groups of keywords: all geographical names in coastal areas and all remote data and platforms. The systematic review demonstrated that, to date, many coastal phenomena (103) and parameters (39) can be mapped and monitored using remote data (e.g., coastline and land use and land cover changes, climate change, and coastal urban sprawl). Moreover, the authors validated 91% of the retrieved parameters, retrieved from remote data 39 parameters that were mapped or monitored 1158 times (88% of the parameters were combined together with other parameters), monitored 75% of the parameters over time, and retrieved 69% of the parameters from several remote data and compared the results with each other and with available products. They obtained 48% of the parameters using different methods, and their results were compared with each other and with available products. They combined 17% of the parameters that were retrieved with GIS and model techniques. In conclusion, the authors addressed the requirements needed to more effectively analyze coastal phenomena and parameters employing integrated approaches: they retrieved the parameters from different remote data, merged different data and parameters, compared different methods, and combined different techniques.

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Assessing the Impact of the 2004 Indian Ocean Tsunami on South Andaman’s Coastal Shoreline: A Geospatial Analysis of Erosion and Accretion Patterns

Cited by 5 publications

References 44 publications

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)

Remote Data for Mapping and Monitoring Coastal Phenomena and Parameters: A Systematic Review

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