Revolutionizing Forestry with AI GPS and Space Technologies for Sustainable Management

Targhoot Mahmood; Muhammad Asif; Zeshan Haider Raza

Targhoot Mahmood University of Essex, UK
Muhammad Asif University of Agriculture Faisalabad, Pakistan
Zeshan Haider Raza University of Veterinary and Animal Sciences, Lahore, Pakistan

Keywords: Forestry Management, Artificial Intelligence, GPS Technology, Space-Based Monitoring, Sustainable Forest Management

Abstract

Forests play a crucial role in maintaining ecological balance, yet they face growing threats from deforestation, climate change, and illegal logging. Traditional forestry management methods often lack the speed and accuracy required for effective conservation and resource utilization. The integration of artificial intelligence (AI), GPS technology, and space-based monitoring has revolutionized forestry, enabling more precise and efficient management strategies. AI-driven machine learning models analyze vast datasets from satellites, drones, and ground sensors to detect deforestation patterns, predict wildfires, and assess forest health. GPS technology enhances real-time tracking of tree cover, wildlife movement, and illegal logging activities, providing valuable geospatial insights for policymakers and conservationists. Meanwhile, space-based technologies, such as satellite remote sensing, offer high-resolution imagery and spectral analysis for monitoring carbon sequestration, biomass density, and ecosystem changes on a global scale. The synergy of AI, GPS, and space technologies enables data-driven decision-making, improving forest sustainability while mitigating environmental risks. This paper explores the transformative impact of these technologies in forestry, emphasizing their applications, challenges, and future potential in fostering sustainable forest management and climate resilience.

References

Arikhad, M., Khan, A. H., Tariq, M., & Al Abrar, A. AI-Powered Solutions for Precision Healthcare: Focusing on Heart and Brain Disorders. Revista de Inteligencia Artificial en Medicina, 15, 1278-1296.
Arikhad, M., Waqar, M., Khan, A. H., & Sultana, A. (2024). AI-driven innovations in cardiac and neurological healthcare: Redefining diagnosis and treatment. Revista Espanola de Documentacion Cientifica, 19(2), 124-136.
Arikhad, M., Waqar, M., Khan, A. H., & Sultana, A. (2024). The role of artificial intelligence in advancing heart and brain disease management. Revista Espanola de Documentacion Cientifica, 19(2), 137-148.
Bhatti, I., Rafi, H. & Rasool, S. (2024). Use of ICT Technologies for the Assistance of Disabled Migrants in USA. Revista Espanola de Documentacion Cientifica, 18(01), 66–99.
Cell, Q. E. Self-Assessment Report (Doctoral dissertation). Department of Biochemistry, Univ. Karachi, Pakistan.
Chowdhury, A. A. A., Rafi, A. H., Sultana, A., & Noman, A. A. (2024). Enhancing green economy with artificial intelligence: Role of energy use and FDI in the United States. arXiv preprint arXiv:2501.14747.
Chowdhury, A. A. A., Sultana, A., Rafi, A. H., & Tariq, M. (2024). AI-driven predictive analytics in orthopedic surgery outcomes. Revista Espanola de Documentacion Cientifica, 19(2), 104-124.
Dandamudi, S. R. P., Sajja, J., & Khanna, A. (2025). Advancing Cybersecurity and Data Networking Through Machine Learning-Driven Prediction Models. International Journal of Innovative Research in Computer Science and Technology, 13(1), 26-33.
Dandamudi, S. R. P., Sajja, J., & Khanna, A. (2025). AI Transforming Data Networking and Cybersecurity through Advanced Innovations. International Journal of Innovative Research in Computer Science and Technology, 13(1), 42-49.
Dandamudi, S. R. P., Sajja, J., & Khanna, A. (2025). Leveraging Artificial Intelligence for Data Networking and Cybersecurity in the United States. International Journal of Innovative Research in Computer Science and Technology, 13(1), 34-41.
Farhan, M., Rafi, H., & Rafiq, H. (2015). Dapoxetine treatment leads to attenuation of chronic unpredictable stress induced behavioral deficits in rats model of depression. Journal of Pharmacy and Nutrition Sciences, 5(4), 222-228.
Farhan, M., Rafi, H., & Rafiq, H. (2018). Behavioral evidence of neuropsychopharmacological effect of imipramine in animal model of unpredictable stress induced depression. International Journal of Biology and Biotechnology, 15(22), 213-221.
Farhan, M., Rafi, H., Rafiq, H., Siddiqui, F., Khan, R., & Anis, J. (2019). Study of mental illness in rat model of sodium azide induced oxidative stress. Journal of Pharmacy and Nutrition Sciences, 9(4), 213-221.
Farhan, M., Rafiq, H., & Rafi, H. (2015). Prevalence of depression in animal model of high fat diet induced obesity. Journal of Pharmacy and Nutrition Sciences, 5(3), 208-215.
Farhan, M., Rafiq, H., Rafi, H., Ali, R., & Jahan, S. (2019). Neuroprotective role of quercetin against neurotoxicity induced by lead acetate in male rats. Int. J. Biol. Biotech, 16(2), 291-29.
Farhan, M., Rafiq, H., Rafi, H., Rehman, S., & Arshad, M. (2022). Quercetin impact against psychological disturbances induced by fat rich diet. Pakistan Journal of Pharmaceutical Sciences, 35(5).
Ghulam, T., Rafi, H., Khan, A., Gul, K., & Yusuf, M. Z. (2021). Impact of SARS-CoV-2 Treatment on Development of Sensorineural Hearing Loss: Impact of SARS-CoV-2 treatment on SNHL. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 58(S), 45-54.
Husnain, A., Ali, M., Hussain, H. K., Shahroz, H. M., & Hayat, Y. (2024). Exploring physical therapists' perspectives on AI and NLP applications in COVID-19 rehabilitation: A cross-sectional study. Int. J. Adv. Eng. Technol. Innov., 1(4).
Husnain, A., Hussain, H. K., Shahroz, H. M., Ali, M., & Hayat, Y. (2024). Advancements in health through artificial intelligence and machine learning: A focus on brain health. Revista Espanola de Documentacion Cientifica, 18(01), 100-123.
Khan, A. H., Arikhad, M., & Tariq, M. Revolutionizing Heart and Brain Healthcare with Artificial Intelligence: Challenges and Opportunities. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 15, 997-1017.
Li, Z., Rasool, S., Cavus, M. F., & Shahid, W. (2024). Sustaining the future: How green capabilities and digitalization drive sustainability in modern business. Heliyon, 10(1).
Rafi, H. (2024). Peer Review of “Establishment of a Novel Fetal Ovine Heart Cell Line by Spontaneous Cell Fusion: Experimental Study”. JMIRx Bio, 2(1), e63336.
Rafi, H., & Farhan, M. (2015). Dapoxetine: An innovative approach in therapeutic management in animal model of depression. Pakistan Journal of Pharmaceutical Sciences, 2(1), 15-22.
Rafi, H., Ahmad, F., Anis, J., Khan, R., Rafiq, H., & Farhan, M. (2020). Comparative effectiveness of agmatine and choline treatment in rats with cognitive impairment induced by AlCl3 and forced swim stress. Current Clinical Pharmacology, 15(3), 251-264.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Antagonization of monoamine reuptake transporters by agmatine improves anxiolytic and locomotive behaviors commensurate with fluoxetine and methylphenidate. Beni-Suef University Journal of Basic and Applied Sciences, 10, 1-14.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Inhibition of NMDA receptors by agmatine is followed by GABA/glutamate balance in benzodiazepine withdrawal syndrome. Beni-Suef University Journal of Basic and Applied Sciences, 10, 1-13.
Rafi, H., Rafiq, H., & Farhan, M. (2023). Agmatine alleviates brain oxidative stress induced by sodium azide. Unpublished.
Rafi, H., Rafiq, H., & Farhan, M. (2024). Pharmacological profile of agmatine: An in-depth overview. Neuropeptides, 105, 102429.
Rafi, H., Rafiq, H., Hanif, I., Rizwan, R., & Farhan, M. (2018). Chronic agmatine treatment modulates behavioral deficits induced by chronic unpredictable stress in wistar rats. Journal of Pharmaceutical and Biological Sciences, 6(3), 80.
Rafi, H., Rafiq, H., Khan, R., Ahmad, F., Anis, J., & Farhan, M. (2019). Neuroethological study of ALCL3 and chronic forced swim stress induced memory and cognitive deficits in albino rats. The Journal of Neurobehavioral Sciences, 6(2), 149-158.
Rafiq, H., Farhan, M., Rafi, H., Rehman, S., Arshad, M., & Shakeel, S. (2022). Inhibition of drug induced Parkinsonism by chronic supplementation of quercetin in haloperidol-treated wistars. Pak J Pharm Sci, 35, 1655-1662.
Rasool, D., Ghafoor, A., & Fareed, D. (2021). Forecasting the Trends and Patterns of Crime in San Francisco using Machine Learning Model. International Journal of Science and Engineering Research, 12(6), 1262-1267. https://doi.org/10.13140/RG.2.2.25209.75367
Saeed, A., Husnain, A., Rasool, S., & Gill, A. Y. (2023). Healthcare Revolution: How AI and Machine Learning Are Changing Medicine. Journal Research of Social Science, Economics & Management, 3(3).
Sultana, A. (2024, September). Enhancing Breast Cancer Image Analysis through Attention Mechanisms: A Comparative Study of U-Net and Attention U-Net Models. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS) (pp. 1-8). IEEE.
Sultana, A., Rafi, A. H., Chowdhury, A. A. A., & Tariq, M. (2023). Leveraging artificial intelligence in neuroimaging for enhanced brain health diagnosis. Revista de Inteligencia Artificial en Medicina, 14(1), 1217-1235.
Sultana, A., Rafi, A. H., Chowdhury, A. A. A., & Tariq, M. (2023). AI in neurology: Predictive models for early detection of cognitive decline. Revista Espanola de Documentacion Cientifica, 17(2), 335-349.
Xiang, S., Rasool, S., Hang, Y., Javid, K., Javed, T., & Artene, A. E. (2021). The effect of COVID-19 pandemic on service sector sustainability and growth. Frontiers in Psychology, 12, 633597.
Zuberi, S., Rafi, H., Hussain, A., & Hashmi, S. (2023). Role of Nrf2 in myocardial infarction and ischemia-reperfusion injury. Physiology, 38(S1), 5734743.