Theory, Method and Application for Ecological Security Evaluation in the Era of Digital Intelligence

The digital intelligence era has fundamentally transformed ecological security evaluation through the emergence of advanced technological tools and methodologies. Remote sensing technologies, artificial intelligence algorithms, big data analytics, and machine learning approaches have created unprecedented opportunities for comprehensive ecological monitoring and assessment. Ecological security evaluation serves as a cornerstone for ensuring sustainable environmental protection and green development, encompassing the complex interactions between natural ecosystems, human activities, and socio-economic systems. Traditional evaluation methods often face limitations in processing large-scale spatial-temporal data and capturing dynamic ecological processes. The integration of digital intelligence technologies—including satellite imagery analysis, IoT sensors, cloud computing, and intelligent decision support systems—enables real-time monitoring, predictive modeling, and precision assessment of ecological security status. These technological advances provide new pathways for developing more accurate, efficient, and comprehensive evaluation frameworks that can support evidence-based environmental management and policy-making in the context of rapid global environmental change.

This Research Topic aims to address the critical challenges in ecological security evaluation within the digital intelligence framework by advancing theoretical foundations, methodological innovations, and practical applications across multiple sectors. The primary objective is to harness cutting-edge digital technologies to develop robust, scalable, and adaptive evaluation systems that can effectively monitor and assess ecological security in agriculture, fisheries, and related environmental domains. Recent advances in remote sensing, artificial intelligence, and environmental informatics have created opportunities to overcome traditional limitations such as spatial-temporal data gaps, computational constraints, and integration challenges across different ecological scales. This Topic issue seeks to promote interdisciplinary collaboration by publishing high-quality research that demonstrates innovative approaches to ecological security assessment, including machine learning-based predictive models, multi-source data fusion techniques, and intelligent decision support frameworks. By fostering knowledge exchange between researchers, practitioners, and policymakers, this Research Topic will contribute to sustainable development goals by providing scientific foundations for evidence-based environmental management, risk assessment, and policy formulation in the era of digital transformation.

This Research Topic welcomes original research articles, reviews, and case studies addressing the following specific themes:
• Theoretical foundations of ecological security evaluation
• AI-based ecological security assessment methods
• Remote sensing applications in ecological monitoring
• Agricultural ecological security evaluation
• Plateau characteristic agricultural ecological security
• Fisheries ecological security assessment
• Marine ranching ecological security
• Multi-criteria decision analysis for ecological evaluation
• Big data analytics in environmental assessment
• Ecological security policy and management frameworks
We particularly encourage manuscripts that demonstrate practical applications, provide comprehensive case studies, offer cross-sectoral comparisons, or present novel technological solutions integrating digital intelligence approaches. Both empirical research and theoretical contributions that advance ecological security evaluation methodologies are welcome. Authors should ensure their work contributes to sustainable development practices and evidence-based environmental management strategies in the digital intelligence era.