Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model DOI
Bowen Li, Li Liu, Zikang Xu

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: unknown, P. 120653 - 120653

Published: Dec. 1, 2024

Language: Английский

Spatiotemporal dynamics of vegetation cover: integrative machine learning analysis of multispectral imagery and environmental predictors DOI
Shoaib Ahmad Anees, Kaleem Mehmood, Waseem Razzaq Khan

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

Citations

8
Machine Learning and Spatio Temporal Analysis for Assessing Ecological Impacts of the Billion Tree Afforestation Project DOI Creative Commons
Kaleem Mehmood, Shoaib Ahmad Anees, Sultan Muhammad

et al.

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

ABSTRACT This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel‐2 imagery, we observed an increase tree cover from 25.02% 2015 29.99% 2023 a decrease barren land 20.64% 16.81%, with accuracy above 85%. Hotspot spatial clustering analyses revealed significant vegetation recovery, high‐confidence hotspots rising 36.76% 42.56%. A predictive model for Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture precipitation as primary drivers of growth, ANN achieving R 2 0.8556 RMSE 0.0607 on testing dataset. These results demonstrate effectiveness integrating learning framework support data‐driven afforestation efforts inform sustainable environmental management practices.

Language: Английский

Citations

3
Derivation of Allometric Equations and Carbon Content Estimation in Mangrove Forests of Malaysia DOI Creative Commons
Waseem Razzaq Khan, Michele Giani, Stanislao Bevilacqua

et al.

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100618 - 100618

Published: Jan. 1, 2025

Language: Английский

Citations

2
Comparative analysis of sensors and classification algorithms for land cover classification in Islamabad, Pakistan DOI
Khadim Hussain,

Tariq Badshah,

Kaleem Mehmood

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 29, 2025

Language: Английский

Citations

1
Advancing forest fire prediction: A multi-layer stacking ensemble model approach DOI

Fahad Shahzad,

Kaleem Mehmood, Shoaib Ahmad Anees

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 19, 2025

Language: Английский

Citations

1
Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities DOI Creative Commons
Shoaib Ahmad Anees, Kaleem Mehmood, S. K. Raza

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102986 - 102986

Published: Dec. 1, 2024

Language: Английский

Citations

6
Improving Total Carbon Storage Estimation Using Multi-Source Remote Sensing DOI Open Access
Huoyan Zhou, Wenjun Liu, Hans J. De Boeck

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 453 - 453

Published: March 3, 2025

Accurate estimations of forest total carbon storage are essential for understanding ecosystem functioning and improving management. This study investigates how multi-source remote sensing data can be used to provide accurate diameter at breast height (DBH) the plot level, enhancing biomass across 39.41 × 104 km2. The is focused on Yunnan Province, China, which characterized by complex terrain diverse vegetation. Using ground-based survey from hundreds plots model calibration validation, methodology combines data, machine learning algorithms, statistical analysis develop models estimating DBH distribution regional scales. Decision tree showed best overall performance. effectiveness improved when stratified climatic zones, highlighting importance environmental context. Traditional methods based kNDVI index had a mean squared error (MSE) 2575 t/ha an R2 value 0.69. In contrast, combining model-estimated values with resulted in substantially lower MSE 212 significantly 0.97. results demonstrate that incorporating not only reduced prediction errors but also model’s ability explain variability. addition, region classification further increased accuracy, suggesting future efforts should consider zoning. Our analyses indicate water availability during cool dry periods this monsoon-influenced was especially critical influencing different subtropical zones. summary, integrates high-resolution advanced algorithms estimation. findings suggest approach support management contribute research balance assessment.

Language: Английский

Citations

0
Spatial mismatch and drivers of carbon sequestration services supply-demand in China DOI
Qi Pang,

Jie Xu,

Ying Zhou

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113389 - 113389

Published: March 27, 2025

Language: Английский

Citations

0
Vegetation dynamics in Mainland Southeast Asia: Climate and anthropogenic influences DOI
Dafang Zhuang,

Chenxi Cui,

Zhanpeng Liu

et al.

Land Use Policy, Journal Year: 2025, Volume and Issue: 153, P. 107546 - 107546

Published: March 29, 2025

Language: Английский

Citations

0
Performance of multiscale landscape metrics as indicators of soil C, N, P, and physicochemical properties of NFV in ML reaches of Yellow River Basin, China DOI Creative Commons
Chenhui Wei,

Kaili Chen,

Yang Zhan

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113391 - 113391

Published: March 30, 2025

Language: Английский

Citations

0