Analysis and Future Projections of Land Use and Land Cover Changes in the Hindon River Basin, India Using the CA-Markov Model DOI Open Access
Ritu Singh, Suresh Chand, Prabuddh Kumar Mishra

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10722 - 10722

Published: Dec. 6, 2024

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, urbanization responsible for these developments. Monitoring mapping changes essential to the sustainable development management area. study attempts track LULC pattern years 2002, 2013, 2023 Hindon River Basin, major tributary Yamuna River, using remote sensing geographic information system techniques. Images obtained from Landsat data were employed extract historical maps. Additionally, CA-Markov model was implemented forecast future patterns. This examines predicted Field observations site-specific interviews used confirm determine ground realities. High-resolution images evaluate accuracy classified map. According results, agricultural decreased 60.98% 2002 54.70% 2050, while built-up areas increased 12.95% 21.25% during same period. By vegetation increase 2.58%, whereas surface water, fallow land, barren areas, dry water bodies are decrease 0.58%, 18.87%, 1.20%, 0.83%, respectively. rapid pace facilitating economic growth within country; however, this occurring at expense natural landscape, which subsequently diminishes overall quality human life. In order maintain proper urban planning essential. Important policy implications conservation basin highlighted by study’s research findings.

Language: Английский

Typical Crop Classification of Agricultural Multispectral Remote Sensing Images by Fusing Multi-Attention Mechanism ResNet Networks DOI Creative Commons
Zongpu Li, Zhiyun Xiao,

Yulong Zhou

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(7), P. 2237 - 2237

Published: April 2, 2025

Traditional crop classification methods have three critical limitations: (1) dependency on labor-intensive field surveys with limited spatial coverage, (2) susceptibility to human subjectivity during manual data collection, and (3) the inability capture fine-grained spectral variations due lack of multispectral analysis. This research introduces an enhanced identification model based a residual ResNet network. leverages remote sensing images from unmanned aerial vehicles (UAVs) accurately classify complex planting structures. The focuses four typical crops: sunflower, corn, beet, pepper. By acquiring preprocessing image data, improved ResNet50 integrating ACmix self-attention module coordinate attention mechanism is developed enhance recognition accuracy these crops. Experimental results demonstrate that achieves 97.8% images, outperforming both RGB traditional methods. highlights potential combining UAV technology deep learning for precise classification, offering valuable technical support precision agriculture management.

Language: Английский

Citations

0
Assessing the Impact of Erratic Governance on Local and International NGOs in Zambia: An Exploratory Study Using Machine Learning and Artificial Intelligence DOI
Petros Chavula, Fredrick Kayusi,

Timothy Mwewa

et al.

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 79 - 79

Published: Jan. 6, 2025

This study explores the impact of erratic governance on local and international NGOs in Zambia, using a mixed-methods approach that combines survey data, in-depth interviews, machine learning (ML) artificial intelligence (AI) techniques. The finds practices, including funding constraints, operational challenges, limited access to services, significantly affect operations effectiveness Zambia. Weak institutional frameworks, corruption, lack transparency accountability, political instability, civic engagement are identified as key factors contributing governance. demonstrates potential ML AI analyzing predicting NGOs, predictive modeling, risk analysis, data visualization, automated reporting, decision support systems. findings this have implications for policymakers, NGO managers, development practitioners seeking promote more effective sustainable outcomes

Language: Английский

Citations

0
Estimating Agricultural Vulnerability to Climate Change Using a Hybrid Machine Learning Model DOI
Usharani Bhimavarapu

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 156

Published: April 4, 2025

Agriculture is highly vulnerable to the impacts of climate change, which affects crop productivity and food security. This study proposes a hybrid model combining BiLSTM (Bidirectional Long Short-Term Memory), GRU (Gated Recurrent Unit), Random Forest estimate extent vulnerability agriculture change by considering both climatic socio-economic factors. The integrates time-series data key variables, including rainfall, temperature, humidity, solar radiation, wind speed, growing degree days, soil moisture, drought frequency, along with factors such as population density, farm size, irrigation coverage, yield, access credit, market access. captures long-term temporal dependencies in data, while accounts for short-term fluctuations variables.

Language: Английский

Citations

0
Insights into the linkages of forest structure dynamics with ecosystem services DOI Creative Commons

T. V. Ramachandra,

Paras Negi,

Tulika Mondal

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 4, 2025

Large-scale land cover changes leading to degradation and deforestation in fragile ecosystems such as the Western Ghats have impaired ecosystem services, evident from conversion of perennial water bodies seasonal, which necessitates an understanding forest structure dynamics with services evolve appropriate location-specific mitigation measures arrest degradation. The current study evaluates extent condition Goa Central Ghats, a biodiversity hotspot. Land use is assessed through supervised hierarchical classifier based on Random Forest Machine Learning Algorithm, revealing that total declined by 3.75% during post-1990s due market forces associated globalization. Likely uses predicated CA-Markov-based Analytic Hierarchy Process (AHP) highlight decline evergreen 10.98%. carbon sequestration potential forests InVEST model highlights storage 56,131.16 Gg carbon, accounts for 373.47 billion INR (4.49 USD). supply value (TESV) was computed aggregating provisioning, regulating, cultural 481.76 per year. TESV helps accounting cost towards development green GDP (Gross Domestic Product). Prioritization Ecologically Sensitive Regions (ESR) considering bio-geo-climatic, ecological, social characteristics at disaggregated levels reveals 54.41% region highly sensitive (ESR1 ESR2). outcome research offers invaluable insights formulation strategic natural resource management approaches.

Language: Английский

Citations

0
An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island DOI Creative Commons
Dilnu Chanuwan Wijesinghe, Neel Chaminda Withanage, Prabuddh Kumar Mishra

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 167, P. 112681 - 112681

Published: Oct. 1, 2024

Language: Английский

Citations

2
Evaluating Urban Heat Islands Dynamics and Environmental Criticality in a Growing City of a Tropical Country Using Remote-Sensing Indices: The Example of Matara City, Sri Lanka DOI Open Access
Chathurika Buddhini Jayasinghe, Neel Chaminda Withanage, Prabuddh Kumar Mishra

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10635 - 10635

Published: Dec. 4, 2024

Urbanization has undeniably improved human living conditions but also significantly altered the natural landscape, leading to increased Urban Heat Island (UHI) effects. While many studies have examined these impacts in other countries, research on this topic Sri Lanka remains limited. This study aimed evaluate effects of changes built-up areas (BAs) and Vegetation Cover (VC) UHI environmental criticality (EC) Matara cityCity, Lanka, utilizing Landsat data. employed commonly used remote-sensing (RS) indices such as land surface temperature (LST), Index, Environmental Criticality Index (ECI). Various techniques were utilized including supervised image classification, Urban–Rural Gradient Zone (URGZ) analysis, grid-based profiles, regression analysis. The results revealed that by 12.21 km2, while vegetation cover decreased 9.94 urban expansion led a 2.7 °C rise mean LST over 26 years. By 2023, newly developed BA showed highest criticality, with values ranging from 25 21 URGZs 1 15 near city center, lower 16 40 47 further core. correlation analysis highlighted strong positive relationship between NDBI LST, underscoring significant impact LST. Consequently, high-density are experiencing high criticality. To minimize effects, planning agencies should prioritize green strategies, particularly zones. approach can be applied cities assess phenomena, goal protecting environment promoting health dwellers.

Language: Английский

Citations

1
Analysis and Future Projections of Land Use and Land Cover Changes in the Hindon River Basin, India Using the CA-Markov Model DOI Open Access
Ritu Singh, Suresh Chand, Prabuddh Kumar Mishra

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10722 - 10722

Published: Dec. 6, 2024

Land use and land cover change is a significant issue in emerging countries. The enormous rate of population growth, industrialization, urbanization responsible for these developments. Monitoring mapping changes essential to the sustainable development management area. study attempts track LULC pattern years 2002, 2013, 2023 Hindon River Basin, major tributary Yamuna River, using remote sensing geographic information system techniques. Images obtained from Landsat data were employed extract historical maps. Additionally, CA-Markov model was implemented forecast future patterns. This examines predicted Field observations site-specific interviews used confirm determine ground realities. High-resolution images evaluate accuracy classified map. According results, agricultural decreased 60.98% 2002 54.70% 2050, while built-up areas increased 12.95% 21.25% during same period. By vegetation increase 2.58%, whereas surface water, fallow land, barren areas, dry water bodies are decrease 0.58%, 18.87%, 1.20%, 0.83%, respectively. rapid pace facilitating economic growth within country; however, this occurring at expense natural landscape, which subsequently diminishes overall quality human life. In order maintain proper urban planning essential. Important policy implications conservation basin highlighted by study’s research findings.

Language: Английский

Citations

0