Integrating AI technologies for enhanced ecosystem surveillance and biodiversity protection
DOI:
https://doi.org/10.64171/JAES.6.3.122-125Keywords:
Artificial intelligence, Biodiversity conservation, Ecosystem monitoring, Machine learning, Remote sensing, EDNA, Climate change, Species detection, Environmental sustainability, Predictive modeling, Deep neural networks, Biodiversity monitoringAbstract
The accelerating loss of biodiversity and increasing degradation of ecosystems are critical challenges to global environmental sustainability. Traditional approaches to biodiversity conservation and ecosystem monitoring are useful but often limited in scope, time and precision. In this scenario, Artificial Intelligence (AI) has emerged as a game-changer, offering novel approaches to enhance the efficiency, accuracy and scope of conservation efforts. This paper explores the role of AI-based ecosystem monitoring in facilitating the conservation of biodiversity through the integration of technologies such as machine learning, remote sensing, computer vision and big data analytics.AI-driven approaches can collect and analyze data from a variety of sources, such as satellite imagery, camera traps, acoustic sensors and environmental DNA (eDNA) in real time. Such tools make possible accurate species identification, population estimation, habitat mapping and detection of ecological change at spatial and temporal scales heretofore unimaginable. Furthermore, AI-driven predictive modeling helps forecast biodiversity patterns, assess the impact of climate change, and pinpoint priority areas for conservation. AI application also improves early warning systems for threats like deforestation, poaching, invasive species and habitat fragmentation. AI can automate data processing and reduce human biases, leading to more informed decision-making and policy formulation. In addition, the convergence of AI with citizen science platforms and IoT-based environmental sensors enables participatory conservation and continuous monitoring of ecosystems. Nevertheless, the application of AI in the conservation of biodiversity is not without its challenges and promises, including data scarcity, algorithmic biases, ethical considerations, and the requirement for interdisciplinary collaboration. It is imperative that these issues be addressed to ensure that AI technologies are utilized in ways that are equitable and sustainable. In conclusion, AI-powered ecosystem monitoring represents a paradigm shift in biodiversity conservation, providing proactive, data-driven, and scalable solutions for the protection and restoration of ecological systems. Its continued development and integration into conservation strategies is critical to meet global biodiversity targets and for long-term environmental sustainability.
References
Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Science Advances. 2015;1(5). doi:10.1126/sciadv.1400253.
Christin S, Hervet É, Lecomte N. Applications for deep learning in ecology. Methods in Ecology and Evolution. 2019;10(10):1632-1644. doi:10.1111/2041-210X.13256.
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science. 2013;342(6160):850-853. doi:10.1126/science.1244693.
IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo. 2019. doi:10.5281/zenodo.3553579.
Maxwell AE, Warner TA, Guillén LA. Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review. Remote Sensing. 2021;13(13):2450. doi:10.3390/rs13132450.
McCarthy MJ, Herrero HV, Insalaco SA, Hinten MT, Anyamba A. Satellite Remote Sensing for Environmental Sustainable Development Goals: A review of applications for terrestrial and marine Protected areas. Remote Sensing Applications Society and Environment. 2025:101450. doi:10.1016/j.rsase.2025.101450.
Pez-Osuna F. The environmental impact of shrimp aquaculture: Causes, effects, and mitigating alternatives. Environmental Management. 2001;28(1):131-140. doi:10.1007/s002670010212.
Reddy CS, Manaswini G, Jha CS, Diwakar PG, Dadhwal VK. Development of national database on long-term deforestation in Sri Lanka. Journal of the Indian Society of Remote Sensing. 2017;45(5):825-836.
doi:10.1007/s12524-016-0636-8.
Reddy CS, Saranya K, Pasha SV, Satish K, Jha C, Diwakar P, Dadhwal V, Rao P, Murthy YK. Assessment and monitoring of deforestation and forest fragmentation in South Asia since the 1930s. Global and Planetary Change. 2018;161:132-148. doi:10.1016/j.gloplacha.2017.10.007.
Sui Y. Analyzing the impact of industrial growth and agricultural development on environmental degradation in South and East Asia. Environmental Science and Pollution Research. 2023;30(57):121090-121106.
doi:10.1007/s11356-023-30766-4.
Voumik LC, Mimi MB, Raihan A. Nexus between urbanization, industrialization, natural resources rent, and anthropogenic carbon emissions in South Asia: CS-ARDL approach. Anthropocene Science. 2023;2(1):48-61. doi:10.1007/s44177-023-00047-3.
Watts J. Land degradation expanding by 1m sq km a year, study shows. The Guardian. 2024 Dec 1. Available from: https://www.theguardian.com/environment/2024/dec/01/land-degradation-expanding
Global Forest Resources Assessments. Food and Agriculture Organization of the United Nations. Available from:
https://www.fao.org/forest-resources-assessment/en/
Transforming our world: the 2030 Agenda for Sustainable Development. Department of Economic and Social Affairs. Available from: https://sdgs.un.org/2030agenda.
