Published on 10/10/202510/10/2025 by Environmental Scientists 50 / 100 Powered by Rank Math SEO SEO Score Dr. Fahad Shahzad | Forestry | Best Researcher Award Research Assistant | Beijing Forestry University | China Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500. Fahad Shahzad | Forestry | Best Researcher Award
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Dr. Fahad Shahzad is a dedicated researcher specializing in Environmental Remote Sensing and Forest Management at Beijing Forestry University. He has developed expertise in machine learning applications for environmental monitoring, vegetation dynamics, and forest fire prediction, contributing extensively to high-impact international journals. His work encompasses forest fire prediction models, spatio-temporal analysis of vegetation stress under climatic variability, biomass and carbon stock modeling, and urban heat island dynamics. Fahad has led and collaborated on numerous research projects across China, Pakistan, and Europe, integrating geospatial technologies, Google Earth Engine, and advanced GIS tools to generate actionable insights for sustainable forest management and climate resilience. Beyond research, he has contributed to consultancy projects with government and development organizations, supervised field data collection, and reviewed peer-reviewed articles for leading scientific journals. Through his collaborations with institutions such as Wuhan University, University of Évora, University of Swat, and University of Peshawar, he actively bridges multidisciplinary expertise in environmental science. His work advances predictive modeling for vegetation health, wildfire risk, and carbon dynamics, mentoring early-career researchers and providing practical solutions to environmental and climate challenges. Dr. Fahad Shahzad commitment lies in leveraging data-driven approaches to address global sustainability issues, combining rigorous scientific research with actionable environmental management strategies. His research has garnered 281 citations across 140 documents, with 18 published papers contributing to an h-index of 10. Profile: Scopus | Orcid | Google Scholar | Researchgate
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Featured Publications Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.
Mehmood, K., Anees, S. A., Muhammad, S., Hussain, K., Shahzad, F., Liu, Q., … 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports, 14(1), 11775. Anees, S. A., Mehmood, K., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Unveiling fractional vegetation cover dynamics: A spatiotemporal analysis using MODIS NDVI and machine learning. Environmental and Sustainability Indicators, 24, 100485. Mehmood, K., Anees, S. A., Rehman, A., Rehman, N. U., Muhammad, S., … 2024. Assessment of climatic influences on net primary productivity along elevation gradients in temperate ecoregions. Trees, Forests and People, 18, 100657. Shahzad, F., Mehmood, K., Hussain, K., Haidar, I., Anees, S. A., Muhammad, S., … 2024. Comparing machine learning algorithms to predict vegetation fire detections in Pakistan. Fire Ecology, 20(1), 57. Hussain, K., Mehmood, K., Yujun, S., Badshah, T., Anees, S. A., Shahzad, F., … 2025. Analysing LULC transformations using remote sensing data: Insights from a multilayer perceptron neural network approach. Annals of GIS, 31(3), 473-500.