Quantifying forest land-use changes using remote-sensing and CA-ANN model of Madhupur Sal Forests, Bangladesh

Heliyon, Journal Year: 2023, Volume and Issue: 9(5), P. e15617 - e15617

Published: April 26, 2023

The conversion of forest cover due to anthropogenic activities is great concern in the Madhupur Sal Forest Bangladesh. This study explored land use changes area from 1991 2020, with prediction 2030 and 2040. examined analyzed five classes viz., waterbodies, settlement, Forest, other vegetation, bare land, predict those using Cellular Automated Artificial Neural Network (CA-ANN) model. Sankey diagram was employed represent change percentage Land Use Cover (LULC). LULC for 1991, 2000, 2010, 2020 derived Landsat TM OLI images, were used periods During last 30 years, decreased by 23.35%, whereas settlement increased 107.19% 160.89%. greatest loss observed 2000 46.20%. At same period time settlements 92.68% indicating encroachment area. revealed a major found between vegetation There vis-à-vis 2010. Interestingly, there no conversation 2010 showed that will be 52.02% preservation increment suggested strong governmental policy implementation preserve forest.

Language: Английский

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Change Monitoring and Assessment of Land Use and Land Cover for the Municipality of Ouessè in Benin by the Modules for Land Use Change Evaluation Plugin

International Journal of Agricultural and Environmental Information Systems, Journal Year: 2025, Volume and Issue: 16(1), P. 1 - 18

Published: Feb. 15, 2025

Assessing land use and cover (LULC) changes is crucial in sub-Saharan Africa due to population growth degradation, emphasizing the significance of effective planning management. In this study, authors used modules for change simulations plugin within quantum geographic information system software analyze predict LULC Ouessè municipality. The Landsat images a cellular automata-artificial neural network model future scenarios. Major categories included woodlands, agricultural land, plantations, rock domes. Changes these were observed between 1986 2019, with predictions showing further shifts until 2049. This approach allowed detailed accurate assessment over time, providing valuable insights into dynamics study area. Smart agriculture livestock technologies, along business models, are needed achieve sustainable balance human activities natural resources.

Language: Английский

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Leveraging CA-ANN Modelling for SDGs Alignment: Previse Future Land Use Patterns and their Influence on Mirik Lake of sub-Himalayan region

World Development Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100218 - 100218

Published: April 1, 2025

Language: Английский

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Spatıotemporal analysıs of urban development and land USE in sakarya provınce, Türkiye: ımplıcatıons for future urban growth modelıng

GeoJournal, Journal Year: 2025, Volume and Issue: 90(3)

Published: May 9, 2025

Language: Английский

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The Susceptibility of Wetland Areas in the Yangtze River Basin to Temperature and Vegetation Changes

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(18), P. 4534 - 4534

Published: Sept. 14, 2023

Wetlands serve a critical function in water storage and ecological diversity maintenance. However, human activities have resulted wetland loss the middle lower reaches of Yangtze River Basin (MLYRB), while distribution this area shows great discrepancy previous estimates. It is, therefore, imperative to estimate potential wetlands at present project their variation under future climate change scenarios. In study, we simulate MLYRB 15″ resolution using 5 machine learning methods with 19 predicting factors topographic index, vegetation data, hydrological soil type data. A 5-fold cross-validation observed permanent that reconstructions from Adaptive Boosting tree (AdaBoost) algorithm highest accuracy 97.5%. The is approximately ~1.25 × 105 km2, accounting for 15.66% study region. Direct led nearly half wetlands. Furthermore, sensitivity experiments well-trained models are performed quantify response total each influencing factor. Results indicate vulnerability areas increases leaf index (LAI), coldest season temperature, warmest solar radiation. By 2100s, expected decrease by 40.5% 50.6% intermediate very high emissions scenarios, respectively. changes LAI temperature will contribute 50% 40% wetlands, Wetland may further undermine biodiversity, such as waterfowl, fail provide functions flood protection, supply. This work reveals spatial pattern changes, stressing need effective strategies mitigate specific regions MLYRB.

Language: Английский

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Energy potential assessment and techno–economic analysis of micro hydro–photovoltaic hybrid system in Goda Warke village, Ethiopia

Clean Energy, Journal Year: 2024, Volume and Issue: 8(1), P. 237 - 260

Published: Jan. 22, 2024

Abstract Rural Ethiopia has significant untapped potential for hydro and solar energy generation systems. However, challenges arise from seasonal variations unfavourable topographic positions of flowing rivers, hindering the efficient exploitation these resources. Despite country’s abundance in resources, >75% population still lack access to electricity national grid. This work deals with resource assessment techno–economic analysis micro hydro–photovoltaic (PV) hybrid systems, considered case study Goda Warke village, located Yaya Gulele district. A novel framework is proposed that utilizes Natural Resource Soil Conservation Service curve number method assess micro-hydro ungauged basins, specifically at exit point Girar River basin catchment. The average monthly flow rate 0.975 m3/s, while area exhibits a radiation 5.39 kWh/m2/day. Energy policy promotes expanding modern sources utilization indigenous Simulation results indicate hydro/PV/diesel generator (DG)/battery hydro/PV/battery systems are most optimal choices based on net present cost, inclusion DG economic comparison. Micro-hydro covers electric load area, achieving capacity factor 47.5%. cost were found be sensitive variables such as price diesel fuel, pipe head loss, growth village load. optimized system demonstrated 1405.37 MWh/year PV output 274.04 MWh/year, resulting levelized 0.0057 0.049 $/kWh components, respectively.

Language: Английский

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The Kerch Peninsula in Transition: A Comprehensive Analysis of Land Use and Land Cover Changes Over Thirty Years

Published: May 24, 2024

This study presents an in-depth analysis of land use and cover change on the Kerch Peninsula over a period spanning three decades. Utilizing convolutional neural networks alongside satellite imagery analysis, we have mapped quantified changes in cover, revealing significant trends transformations within peninsula's landscape. The research aims to elucidate interplay between anthropogenic activities, climatic variations, policy interventions shaping dynamics, thereby providing insights into environmental socio-economic impacts these changes. Our findings indicate marked increase urban expansion at expense natural ecosystems, including forests wetlands, underscoring urgent need for sustainable management strategies. highlights role agricultural intensification altering ecological balance emphasizes critical importance integrating prediction. By leveraging advanced remote sensing GIS technologies, our not only enhances understanding complex dynamics driving but also showcases potential predictive modeling forecasting future scenarios. implications this extend beyond Peninsula, offering valuable lessons managing conserving landscapes similar regions globally.

Language: Английский

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Analyzing Land Use/Land Cover Dynamics in Mountain Tourism Areas: A Case Study of the Core and Buffer Zones of Sagarmatha and Khaptad National Parks, Nepal

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10670 - 10670

Published: Dec. 5, 2024

Monitoring land use/land cover (LULC) dynamics facilitates effective management and mitigation measures by providing timely accurate information on the landscape. This study investigates LULC in Sagarmatha National Park (SNP), one of most popular destinations for mountain tourism, Khaptad (KNP), which are emerging destinations, though among domestic tourists. A random forest classification algorithm was employed to generate using Landsat data. High-resolution Planet Scope images Google Earth were used accuracy assessment. Archived tourist climatic data analyzed explore impacts change. Cellular automata–artificial neural network (CA-ANN)-based predictions predict future LULC. SNP revealed an increase bare land, grassland, shrubland, glacial lakes, agriculture, water bodies; however, snow/glacier experienced substantial decreases 140.25 km2 15.36 km2, respectively, from 1989 2021. In KNP, showed increasing trend bodies, land; shrubland a decrease 18.63 10.48 km2. The loss (19.33 km2) buffer zone KNP greater compared (13.45 km2). increment built-up area 0.80 1.11 indicating escalating activities population growth. For SNP, mean annual precipitation temperature 1994 2023 decreasing patterns, respectively. However, trends demonstrated pattern. Under business-as-usual scenario, estimated will be 1.61 2032 23.8 2030. significant decline snow/glaciers is projected core with 22.84 expected 2032. provides baseline changes KNP. Further, it showcases necessity diversified national park policies as per requirement.

Language: Английский

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Determination of future land use changes using remote sensing imagery and artificial neural network algorithm: A case study of Davao City, Philippines

Artificial Intelligence in Geosciences, Journal Year: 2023, Volume and Issue: 4, P. 111 - 118

Published: Aug. 26, 2023

Land use and land cover (LULC) changes refer to alterations in or physical characteristics. These can be caused by human activities, such as urbanization, agriculture, resource extraction, well natural phenomena, for example, erosion climate change. LULC significantly impact ecosystem services, biodiversity, welfare. In this study, Davao City, Philippines, were simulated, predicted, projected using a multilayer perception artificial neural network (MLP-ANN) model. The MLP-ANN model was employed analyze the of elevation proximity road networks (i.e., exploratory maps) on from 2017 2021. predicted 2021 map shows high correlation actual 2021, with kappa index 0.91 96.68% accuracy. applied project future 2030 2050). results suggest that 2030, built-up area trees are increasing 4.50% 2.31%, respectively. Unfortunately, water will decrease up 0.34%, crops is about approximately 3.25%. year 2050, continue increase 6.89%, while 0.53% 3.32%, Overall, show anthropogenic activities influence land's alterations. Moreover, study illustrates how machine learning models generate reliable scenario usage changes.

Language: Английский

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LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(11)

Published: Oct. 17, 2023

Language: Английский

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