Application of drones and artificial intelligence to monitor and protect natural ecosystems

¹ Kadyrov Chechen State University, Grozny, Russia
² Kazan State Energy University, Kazan, Russia
³ North Ossetian State University named after K.L. Khetagurova, Vladikavkaz, Russia

^* Corresponding author: raya.bek@yandex.ru

Abstract

In recent years, the integration of drones and artificial intelligence (AI) technologies has emerged as a promising approach for the conservation and management of natural ecosystems. This paper explores the application of drones and AI in monitoring and safeguarding these invaluable environments. Drones, equipped with various sensors such as cameras, LiDAR, and thermal imaging, offer unprecedented capabilities for data collection over large and inaccessible areas. Coupled with AI algorithms, these platforms can analyze vast amounts of data quickly and accurately, providing valuable insights into ecosystem health, biodiversity, and environmental changes. The utilization of AI enables the automation of tasks such as species identification, habitat mapping, and anomaly detection, significantly enhancing the efficiency and effectiveness of monitoring efforts. Machine learning algorithms can be trained on large datasets to recognize patterns and anomalies, enabling real-time detection of threats such as illegal logging, poaching, and habitat destruction. Furthermore, drones equipped with AI-powered systems can facilitate adaptive management strategies by providing timely and precise information to decision-makers. By monitoring changes in vegetation, water quality, and wildlife populations, conservation practitioners can implement targeted interventions to mitigate threats and promote ecosystem resilience.This paper highlights several case studies where drones and AI have been successfully employed in ecosystem monitoring and protection, ranging from tropical rainforests to coral reefs and savannas. These examples demonstrate the versatility and scalability of the approach across diverse habitats and geographical regions.