Scholarly record

HARNESSING A DIGITAL TRIAD: AI, REMOTE SENSING, AND PREDICTIVE MODELING TO REVOLUTIONIZE ENVIRONMENTAL MONITORING AND FOOD INPUTS OPTIMIZATION IN THE MOSCOW REGION

Emmanuel Igwe, Shaibu Ochoche, Onyinyechi Grace Opara, Laith Mohammad Jadee AIOudat, Victor Samuel Ibanichuka

Abstract

The convergence of remote sensing (RS), artificial intelligence (AI), and advanced computational modeling presents a paradigm shift in addressing interconnected environmental crises. This research demonstrates how this integrated framework delivers actionable insights for biodiversity conservation, pollution mitigation, and climate adaptation. RS provides high-resolution, temporal data on habitat extent, deforestation rates, pollutant dispersion, and climate variables. AI algorithms, particularly machine learning, analyze these vast datasets to predict species distribution shifts, identify illegal land-use activities, and forecast ecosystem vulnerabilities. Dynamic models then simulate complex scenarios, including the impacts of habitat fragmentation, the efficacy of proposed conservation corridors, pollution remediation outcomes, and long-term climate adaptation strategies. Empirical findings from our applied studies confirm the enhanced decision-support capacity of this synergy. For instance, RS-enabled monitoring detected a 15% reduction in protected area encroachment following targeted enforcement, while AI analysis of satellite and social media data improved public engagement strategies by 40% by aligning messaging with localized environmental perceptions. Participatory system modeling, informed by RS and AI outputs, fostered stakeholder consensus on resource allocation, leading to the development of optimized watershed management plans that reduced predicted nutrient loading by 25% under climate stress scenarios. Despite challenges such as data integration barriers, model validation needs, and regulatory fragmentation, the tripartite integration of RS, AI, and modeling establishes a critical framework for evidence-based policy. It transforms raw environmental data into compelling narratives for policymakers, enables real-time compliance tracking, and anticipates emerging risks. Ultimately, this technological convergence equips scientists, policymakers, and communities with robust tools to develop resilient, sustainable strategies and catalyze effective collaborative action for environmental protection.

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