Deep Learning for Terrestrial Ecosystem Sustainability

This book discusses how artificial intelligence can help monitor, understand, and protect terrestrial ecosystems. It presents applications of CNNs, RNNs, GANs, Vision Transformers, and Graph Neural Networks in vegetation classification, forest health, climate forecasting, deforestation detection, and biodiversity tracking. Combining case studies with responsible AI practices, the book shows how data-driven technologies can support conservation, informed decision-making, and sustainable development. This book is for academicians and professional readers seeking practical applications of deep learning, explainable AI, IoT, computer vision, and data-driven technologies for ecosystem monitoring, biodiversity protection, climate resilience, and responsible environmental decision-making. Key FeaturesExplore deep learning models including CNNs, RNNs, GANs, Vision Transformers, and Graph Neural Networks for ecosystem applications. Examine AI applications in vegetation classification, forest health, climate forecasting, deforestation, and biodiversity monitoring. Apply explainable AI and responsible-AI principles to environmental monitoring and conservation. Integrate IoT, multimodal data fusion, and intelligent systems for smart waste management and sustainable ecosystems. Investigate drone-assisted precision farming and AI-enabled approaches to sustainable resource management. Assess cybersecurity challenges and secure AI frameworks for smart cities and ecosystem management. Analyse real-world case studies and methodologies aligned with the United Nations Sustainable Development Goals.