Applications of Artificial Intelligence in Restoration of Degraded Ecosystems
DOI:
https://doi.org/10.36808/if/2026/v152i6A/171304Keywords:
Artificial intelligence, Restoration, Degraded ecosystem, Degraded landscapes.Abstract
Global forest ecosystems, which occupy about 4 billion hectares (or 30%) of the world's land mass, are the most visible indicators of the health of the Planet and act as critical carbon sinks, storing 66% of all terrestrial carbon. Yet, the world has lost 32% of forest cover due to industrialization and urban development, compounded by climate change effects such as severe weather events, changing drought patterns, and increasing wildfires. This paper presents the use of Artificial Intelligence (AI) and Machine Learning (ML) in rehabilitating these ecosystems. Through integration of the "scorpan" variables - soil, climate, organisms, relief, parent material, age, and space - AI models offer unprecedented predictive accuracy in predictions for species survival, growth and carbon storage. This paper explores key algorithms such as Random Forests (RF), Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, as well as use of Unmanned Aerial Vehicles (UAVs) for targeted reforestation and decision-making. The results suggest that AI-based restoration efforts not only improve ecosystem resilience but are crucial to reaching global carbon neutrality, provided regional data gaps and domain shift issues are overcome.
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