The growing demand for sustainable and adaptable landscape designs underscores the importance of integrating environmental monitoring and optimization techniques to improve ecological stability and sustainability. Recent advancements in artificial intelligence, particularly in the utilization of environmental monitoring data, have demonstrated clear advantages in analyzing complex ecological environments. To address these challenges, we propose a novel framework that combines adaptive environmental monitoring, enhanced fuzzy control methods, and Particle Swarm Optimization (PSO) to improve the environmental adaptability of landscape designs. This framework dynamically adjusts monitoring frequencies and data collection strategies in response to environmental changes, ensuring the real-time optimization of design parameters. PSO effectively explores large, nonlinear design spaces, identifying optimal configurations for enhanced ecological and functional performance. Experimental results using real-world datasets from ESA CCI and NOAA reveal that the proposed approach significantly outperforms traditional methods in terms of environmental adaptability, resource utilization efficiency, and sustainability. For example, adaptability scores increased by an average of 10%, and resource efficiency improved by over 15% compared to baseline models. These findings validate the effectiveness of the proposed framework in achieving high-performing and environmentally responsive landscape designs. This study offers a robust solution to the limitations of static monitoring and conventional optimization techniques, contributing to the development of intelligent and sustainable landscape systems. By bridging the gap between real-time environmental data and design optimization, this research provides valuable insights for future advancements in adaptive landscape planning.
Li et al. (2026) studied this question.