Multi-source remote sensing technology finds extensive application in natural resource management, ecological and environmental conservation, disaster response, and other fields. However, current challenges include shortcomings in multi-modal data governance, insufficient generalization capabilities in intelligent remote sensing interpretation, and inadequate coordination in monitoring applications. This paper focuses on the Integration and Intelligent Monitoring Technology System of Space-Air-Ground Remote Sensing, centering on three core technologies: multi-source data governance and collaborative association, component-based AI interpretation model development, and application agent construction based on multi-modal large models. It establishes a multi-modal data foundation through standardized data processing and cross-source retrieval, achieves precise multi-task interpretation via a flexible component-based architecture, and leverages application agents for natural language instruction parsing, autonomous task planning, and automated execution of multi-algorithm task chains. This forms a closed-loop"perception-cognition-decision-execution" closed-loop system. Through an applied case study of an ecological restoration project at an abandoned mine in Hunan Province, China, this research validated the feasibility and effectiveness of the system in comprehensively coordinating multi-source data, achieving intelligent identification of surface cover changes, and enabling full-cycle monitoring and evaluation. It provides efficient intelligent technical support for natural resource management.