Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 489 - 509
Published: Jan. 1, 2025
Language: Английский
Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 423, P. 138890 - 138890
Published: Sept. 14, 2023
Language: Английский
Citations
83
Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102555 - 102555
Published: March 18, 2024
Vegetation plays a crucial role in terrestrial ecosystems, and there has been substantial shift global vegetation cover recent decades. China is recognized for its impact on changes, which are influenced by both climate change human activities. Therefore, this research aims to assess the respective influences of modification activities variations China. First, changes explored between 1982 2020 using satellite-image derived index, known as Normalized Difference Index (NDVI). Second, multiple regression model based time-lag analysis used simulate NDVI. In addition common climatic factors such temperature, precipitation, solar radiation intensity relative humidity, atmospheric CO2 concentration directly reflect considered model. Finally, influence variation alteration determined reconstructed Results: (1) Precipitation most important influences, while carbon dioxide humidity have least influence. (2) The simulation error before 2000 was 0.875%, considerably lower than after 2000. (3) After 2000, favorably affected recovery study area, with an average degree >30%.
Language: Английский
Citations
39
Urban Climate, Journal Year: 2024, Volume and Issue: 55, P. 101962 - 101962
Published: May 1, 2024
Language: Английский
Citations
39
Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141035 - 141035
Published: Feb. 8, 2024
Language: Английский
Citations
35
Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)
Published: April 24, 2024
Abstract Land use and land cover (LULC) analysis is crucial for understanding societal development assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing under cloud limited ground truth data. To enhance accuracy comprehensiveness of descriptions changes, this investigation employed a combination advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, addition to computing capabilities Google Earth Engine (GEE) platform. Additionally, study incorporated random forest (RF) algorithm. This aimed generate continuous maps 2014 2020 Shrirampur area Maharashtra, India. A novel multiple composite RF approach based on classification utilized final utilizing RF-50 RF-100 tree models. Both models seven input bands (B1 B7) as dataset classification. By incorporating these bands, were able influence spectral information captured by each band classify categories accurately. The inclusion enhanced discrimination classifiers, increasing assessment classes. indicated that exhibited higher training validation/testing (0.99 0.79/0.80, respectively). further revealed agricultural land, built-up water bodies have changed adequately undergone substantial variation among classes area. Overall, research provides insights into application machine learning (ML) emphasizes importance selecting optimal enhancing reliability GEE different present enabled interpretation pixel-level interactions while improving image suggested best through identification
Language: Английский
Citations
16
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 24, 2025
The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for planners decision-makers. current research was carried out to study use cover (LULC) changes associated LST patterns planned city (Kabul) unplanned (Jalalabad), Afghanistan, using Support Vector Machine (SVM) Landsat data from 1998 2018. Future LULC were predicted 2028 2038 Cellular Automata-Markov (CA-Markov) Artificial Neural Network (ANN) models. results clearly emphasize different between Kabul Jalalabad. Between 2018, built-up areas Jalalabad increased by 16% 30%, respectively, while bare soil vegetation decreased 15% 1% 4% 30% showed highest seasonal annual LST, followed vegetation. maximum occurred during summer both cities predictions that (48% 55% 2018) will increase approximately 59% 68% 79% Jalalabad, respectively. Similarly, simulations percentage with higher (> 35°C) would (0% 5% 22% 43% 2038, Kabul's shows lower than Jalalabad's city, primarily due urbanization greater center. Urban should limit development reduce potential impacts high temperatures.
Language: Английский
Citations
13
Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)
Published: Oct. 17, 2023
Evaluation of a site suitability for sustainable urban settlement growth is crucial. A system evaluating an interaction between the factors must be established to assure that critical selection interrelationships are not neglected. To solve this problem, proposed methodology was evaluated identify suitable residential development in Nashik, India. The Geographic information (GIS) -based multi-influence factor (MIF) approach used study find ideal locations future settlement. assessment based on 11 including vegetation, elevation, land use, industry, drainage network, slope, water bodies, road, health services, railway station, and population density. delineated by aggregating all considered their associated MIF weights using interrelationship factors. results showed 27.26% research area development, 16.82% low suitable, 30.65% moderately 16.48% highly 8.77% very suitable. Most sites located near existing habitant area, major roads. were validated Receiver-Operating Characteristic (ROC), Area Under Curve (AUC) value 0.895 indicated model effective. Sensitivity analysis revealed distance from services dominant selecting optimal location area. findings areas intensive will helpful planners policy makers future.
Language: Английский
Citations
37
Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242
Published: June 1, 2024
The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF
Language: Английский
Citations
16
Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 358, P. 120925 - 120925
Published: April 19, 2024
Language: Английский
Citations
14
Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)
Published: Jan. 1, 2024
The rapid urban growth and anthropogenic activities have posed a threat to the local environment ecosystem around world. This situation has become hindrance planners policy makers for sustainable development. Therefore, this study mainly focuses on assessment of patterns in relation sprawl Jalpaiguri agglomeration. Multi-temporal Landsat data been used land use change detection quantification. maximum likelihood classifier technique performed create cover maps each year (2001, 2011 2021). Urban expansion intensity index applied determine magnitude expansion. Landscape metrics Shannon’s entropy employed assess spatial extent. Spatiotemporal changes reveal that non-urban class (vegetation, agriculture, water bodies, fallow) decreasing consistently with an increase built-up areas over time. Built-up area increased by almost seven times span last 20 years (2001–2021). In first decade, rate was 145.42% medium speed next it 180.83% very high speed. show fragmentation entire landscape into small patches happened from 2001 higher indicating occurrence sprawling characteristics. But recent times, is aggregating large single which indicate clumpy would affect ecological environment. model also verifies compact different directions distances city centre. understanding dynamics essential addressing urbanization within region. There immediate need appropriate strategy effective utilization monitoring uncontrolled haphazard growth. research help planner take specific scope action future
Language: Английский
Citations
13