Synergy of Remote Sensing and Geospatial Technologies to Advance Sustainable Development Goals for Future Coastal Urbanization and Environmental Challenges in a Riverine Megacity

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(1), P. 30 - 30

Published: Jan. 14, 2025

Riverine coastal megacities, particularly in semi-arid South Asian regions, face escalating environmental challenges due to rapid urbanization and climate change. While previous studies have examined urban growth patterns or impacts independently, there remains a critical gap understanding the integrated of land use/land cover (LULC) changes on both ecosystem vulnerability sustainable development achievements. This study addresses this through an innovative integration multitemporal Landsat imagery (5, 7, 8), SRTM-DEM, historical use maps, population data using MOLUSCE plugin with cellular automata–artificial neural networks (CA-ANN) modelling monitor LULC over three decades (1990–2020) project future for 2025, 2030, 2035, supporting Sustainable Development Goals (SDGs) Karachi, southern Pakistan, one world’s most populous megacities. The framework integrates analysis SDG metrics, achieving overall accuracy greater than 97%, user producer accuracies above 77% Kappa coefficient approaching 1, demonstrating high level agreement. Results revealed significant expansion from 13.4% 23.7% total area between 1990 2020, concurrent reductions vegetation cover, water bodies, wetlands. Erosion along riverbank has caused Malir River’s decrease 17.19 5.07 km2 by highlighting key factor contributing flooding during monsoon season. Flood risk projections indicate that urbanized areas will be affected, 66.65% potentially inundated 2035. study’s contribution lies quantifying achievements, showing varied progress: 26% 9 (Industry, Innovation, Infrastructure), 18% 11 (Sustainable Cities Communities), 13% 13 (Climate Action), 16% 8 (Decent Work Economic Growth). However, declining bodies pose 15 (Life Land) 6 (Clean Water Sanitation), 11%, respectively. approach provides valuable insights planners, offering novel adaptive planning strategies advancing practices similar stressed megacity regions.

Language: Английский

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Spatiotemporal LULC change detection and future prediction for the Mand catchment using MOLUSCE tool

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(2)

Published: Jan. 1, 2024

Language: Английский

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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting

GIScience & Remote Sensing, Journal Year: 2024, Volume and Issue: 61(1)

Published: Jan. 16, 2024

Understanding grassland habitat dynamics in space and time is crucial for evaluating the effectiveness of protection measures developing sustainable management practices, specifically within Natura 2000 network light European Biodiversity Strategy. Land cover maps, derived from remote sensing data, are essential understanding long-term changes vegetation land use assessing impact on ecosystems. In this study, we conducted a 20-year analysis landscapes Umbria, Italy, using Random Forest classifications Landsat data Google Earth Engine. Our was based years 2000, 2010, 2020. We integrated harmonic modeling, Gray-Level Co-occurrence Matrix (GLCM) textural analysis, statistical image gradient other spectral Digital Terrain Model (DTM)-derived indices to enhance classification capabilities. The LandTrendr (LT) algorithm used GEE collect ground control points no-change areas automatically. method Multilayer Perceptron-Artificial Neural Networks (MLP-ANNs) forecast 2040 cover. scenario model validation achieved an overall accuracy over 90%. However, shrublands proved challenging due their mixed composition unique spatial patterns, resulting lower accuracies. Feature importance demonstrated value enhanced map composition, applying simplified diachronic (LULC) change by supporting automatic training collection. Results support interpretation Umbria past two decades identify affected encroachment shrubs, woody plants, or those with reduced green biomass. forecasting along selection drivers predict change, high efficiency compared studies. A specific developed where conservation related have been more less effective preserving grasslands. Overall, research provides scientific foundation methodology helpful informing policy decisions defining spatially explicit strategies inside outside areas.

Language: Английский

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Evaluation and mapping of predicted future land use changes using hybrid models in a coastal area

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102324 - 102324

Published: Oct. 2, 2023

Language: Английский

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Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh

Sustainability, Journal Year: 2023, Volume and Issue: 15(16), P. 12329 - 12329

Published: Aug. 13, 2023

Anthropogenic activities have a significant influence on land use and cover (LULC) changes, especially in rapidly growing areas. Among several models, the combination of cellular automata–artificial neural network (CA-ANN) model is being widely used for assessing future LULC changes using satellite images. This study aimed to investigate Gazipur City Corporation (GCC), Bangladesh, patterns over last two decades (2002 2022). In this study, maximum likelihood supervised classification technique was processing available The results show that urban area vegetation coverage increased by 150% 22.78%, whereas bare waterbody decreased 7.02% 78.9%, respectively, from 2002 2022 inside GCC area. For predictions, CA-ANN developed, accuracy percentage which 86.49%, kappa value 0.83. prediction will increase 47.61%, are supposed decrease 24.17% 67.23%, 2042. findings could be useful sustainable planning management, as well enabling decision making authorities improvements environmental ecological conditions

Language: Английский

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Multispectral remote sensing approach of predicting the potential distribution and evaluating the current spread of water hyacinth (Eichhornia crassipes)

Sustainable Water Resources Management, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 25, 2024

Abstract The water hyacinth is categorized among the world’s top ten worst invasive plant species of aquatic ecosystems. This study assessed changes in spatiotemporal distributions Lake Koka and Ziway Upper Awash River basin during peak growth season plant. Household questionnaires key informant interviews along with Landsat images for 2013, 2017, 2021 were collected to identify past, present, future potential two lakes. surveys prepared using Kobo Toolbox which monitors data collection online. A total number 413 households sampled analyzed through descriptive statistics. For images, a supervised classification technique was applied classify land use classes maximum likelihood algorithm. survey results showed increased expansion area since year 2011. affected 285 households’ livelihoods by invading 69.0% their farmlands caused 97.6% food scarcity districts. image indicated that invasion occupied 1.48% this 7.13% 2021, while body decreased from 75.94 69.90%, respectively. However, other vegetation nearly identical between years 2013–2021. Likewise, covered 4.66% raised 8.42% 2021. At coverage as it 16.19 10.67% but remained almost same years. Between 2013 2025, amount hyacinths both Ziway. According LULC data, hyacinth's rate spread 0.56% 0.95% revealed signals change due Basin considered an important aspect resources planning management.

Language: Английский

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Assessing the status and spatial-temporal dynamics of the Bamenda Mountains (BM), North West region of Cameroon

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(9)

Published: Aug. 17, 2023

Language: Английский

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Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Journal of Earth System Science, Journal Year: 2024, Volume and Issue: 133(2)

Published: June 5, 2024

Language: Английский

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A Methodological Benchmark in Determining the Urban Growth: Spatiotemporal Projections for Eskişehir, Türkiye

Applied Spatial Analysis and Policy, Journal Year: 2024, Volume and Issue: 17(4), P. 1485 - 1495

Published: Aug. 10, 2024

Abstract Urban growth changes spatial uses over time due to different dynamics. These processes cause many physical, environmental, and socioeconomic problems, such as climate change, pollution, population-related events. Therefore, it is essential predict future urban expansion produce effective policies in sustainable planning make long-term plans. Many models, dynamic, statistical, Cellular Automata Markov Chain (CA-MC) are used geographic information system (GIS) environments meet the high-performance requirements of land use modeling. This study estimated settled areas Eskişehir city center using models developed two methods. In this context, were examined within scope 1990–2018, 2046 predicted CA-Markov method Model 1: Quantum GIS (QGIS) MOLUSCE plugin 2: IDRISI Selva. While continuously increasing, other decreasing. 1 predicts an increase 1195 ha by 2046, while 2 45,022 ha. At same time, concluded that will grow a central location 1, they spread east-west extension 2. The results show QGIS-based modeling more limited than research interprets terms staging services, population size neighboring cities, distances, income levels based on internal external dynamics city.

Language: Английский

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Integration of multi-layer perceptron neural network and cellular Automata-Markov chain approach for the prediction of land use land cover in land change modeler

Ecological Modelling, Journal Year: 2025, Volume and Issue: 506, P. 111162 - 111162

Published: May 1, 2025

Language: Английский

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