Smart Reforestation Using AI and Geospatial Analysis
DOI:
https://doi.org/10.15662/IJEETR.2026.0802163Keywords:
Smart reforestation, Generative AI, Geospatial analysis, Google Earth Engine, Computer vision, Species suitability prediction, Spatial optimizationAbstract
Effective reforestation requires aligning tree species with site-specific environmental conditions, traditionally dependent on expert knowledge and labor-intensive geographic analysis. This paper presents Smart Reforestation using AI and Geospatial Analysis, an AI-driven framework for automating species suitability assessment and generating optimized planting strategies. The system integrates Google Earth Engine to extract environmental attributes such as elevation, temperature, soil pH, and climate variables for any geographic location. These features are processed using a supervised neural network trained to evaluate compatibility between environmental conditions and tree species requirements. Satellite imagery is analyzed using computer vision techniques to identify plantable zones by excluding roads, buildings, and water bodies. The framework enables scalable, data-driven, and reproducible reforestation planning. Experimental results demonstrate reliable suitability predictions aligned with ecological criteria. This approach enhances accuracy, efficiency, and sustainability, supporting large-scale restoration efforts and improving transparent decision-making
References
[1] A. Faisal, R. Mumtaz, M. D. M. Qureshi, M. A. Tahir,
N. Z. Jhanjhi, M. Masud, and M. Shorfuzzaman, “Data-driven spatial analysis of reforestation in Pakistan: Identifying optimal locations for sustainable forest growth and climate change mitigation,” Sustainability, 2024.
[2] R. Verma and S. Das, “Automated environmental planning using generative AI and GIS integration,” Sustainability, 2022.
[3] A. K. Singh et al., “AI-based land-use classification for forest monitoring using remote sensing,” Journal of Environmental Management, Elsevier, 2021.
[4] L. Chen and Y. Zhao, “Deep learning for forest mapping and tree species classification using satellite imagery,” Ecological Informatics, Elsevier, 2020.
[5] R. Verma and S. Das, “Predictive modeling for reforestation site suitability using machine learning and GIS,” Sustainability, 2022.
[6] C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
[7] C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
[8] C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
[9] S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
[10] S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
[11] S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
[12] C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
[13] C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
[14] C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
[15] C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
[16] Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
[17] M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
[18] R. Thakre et al., “Data-driven environmental management systems for sustainable reforestation planning,” International Journal of Environmental Science and Technology, 2023.
[19] M. Fadil, “Forest monitoring and land-use analysis using GIS and remote sensing technologies,” International Journal of Geospatial Science, 2024.
[20] S. Rekha, “A systematic review of reforestation and afforestation projects across ecological regions,” Environmental Research Communications, 2022.
[21] J. Sanchez et al., “Environmental decision-support systems: A comparative analysis,” Environmental Modelling
A. Faisal, R. Mumtaz, M. D. M. Qureshi, M. A. Tahir,.
[22] P. Laroiva, “Challenges in large-scale reforestation projects in degraded regions,” Journal of Sustainable Forestry, 2020.
[23] A. Woolf, “Intelligent systems for environmental planning and decision making,” AI Applications in Environmental Science, Springer, 2019.
[24] R. Nkambou et al., “AI-based environmental decision systems using integrated geospatial datasets,” Artificial Intelligence Review, Springer, 2022.
[25] M. Islam, A. Rahman, and S. Hossain, “AI-based adaptive environmental management model for dynamic decision support,” Environmental Modelling & Software, Elsevier, 2023.
[26] M. A. Shah, S. Khan, and A. Hussain, “AI-based image analysis for land-use classification using computer vision,” Journal of Geospatial Information Science, 2022.
[27] Y. Li, X. Wang, and H. Zhang, “Automated environmental assessment using machine learning techniques,” International Journal of Environmental Data Science, 2020.
[28] Gopinathan, V. R. (2024). Cyber-Resilient Digital Banking Analytics Using AI-Driven Federated Machine Learning on AWS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8419-8426.
[29] Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
[30] Anbazhagan, K. (2025). Next-Generation Enterprise Cloud AI for Healthcare: Secure CNN Pipelines and Privacy Controls. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15980.
