Environmental Damage Assessment using Image Processing

Manish M. Goswami*, Yash O. Agrawal**, Priya M. Mankar***, Ankit N. Yadav****, Sejal M. Nandanwar*****, Prapti P. Porkut******
*-****** Department of Computer Science and Engineering, S. B. Jain Institute of Technology, Management and Research, Nagpur, Maharashtra, India.
Periodicity:July - September'2025

Abstract

Deforestation remains one of the most pressing environmental challenges, significantly impacting ecosystems, biodiversity, and climate stability. This paper proposes the development of an Environmental Damage Assessment System (EDAS) that utilizes advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms to process satellite imagery for deforestation monitoring. The system identifies trends, forecasts potential risks, and provides actionable insights to support sustainable forest management. EDAS serves as a valuable tool for policymakers, researchers, and conservationists aiming to mitigate environmental degradation. One of EDAS's key strengths is its ability to forecast areas at high risk of deforestation by analyzing historical imagery data. This enables timely interventions and effective conservation planning by policymakers and environmental organizations. Overall, EDAS serves as a valuable tool for understanding the broader environmental impacts of deforestation, supporting data-driven decisions for sustainable forest management.

Keywords

Enviromental Damage Assessment (EDA), Deforestation, Environment, Satellite Imagery, Sustainable Forest Management.

How to Cite this Article?

Goswami, M. M., Agrawal, Y. O., Mankar, P. M., Yadav, A. N., Nandanwar, S. M., and Porkut, P. P. (2025). Environmental Damage Assessment using Image Processing. i-manager’s Journal on Image Processing, 12(3), 35-44.

References

[1]. Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice. Indiana University.
[5]. Indarto, J. (2016). An overview of theoretical and empirical studies on deforestation. Journal of International Development and Cooperation (pp. 1-16).
[12]. Thombre, S., Prasad, M. S. V. K. V., Ballari, S. O., Deshpande, C. S., Solanki, U. J., & Killol, A. J. (2024). Prediction of environmental impacts through artificial intelligence techniques. Material Science and Technology, 23 (1), 406-412.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 15 15 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.