(1) Studying the dynamic correlation between land use and the eco-environment in the Dianchi Basin is important for improving the basin's spatial layout and enhancing ecological development and conservation; (2) Through dynamic analysis and comprehensive evaluation of land use, the introduction of ecological and environmental quality index, and the use of FLUS models, the impacts on eco-environments in the Dianchi Basin for the recent 20 years were analyzed; (3) The past two decades witnessed a constant increase in the construction land in the Dianchi Basin and a decline in the farmland at an average annual rate of 0.93 %; The utilization level of land in the Dianchi Basin presented a negative correlation with the quality of the area's eco-environment, which reduces first and then increases; When natural production becomes a priority, both the construction land and farmland have witnessed growth. However, when ecological protection becomes a priority, it is projected that by 2035, the Dianchi Basin will achieve its highest eco-environmental quality index; (4) Studying how the change of land use types affects eco-environment is crucial for optimizing the current allocation of land resources and promoting sustainable development in the basin.

Long-term series monitoring of ground surface settlement by remote sensing has become an effective method. Based on TS-InSAR (time series Interferometric Synthetic Aperture Radar) interferometry, this paper proposes a new model based on the Grey Wolf Optimizer (GWO) and Long Short-Term Memory (LSTM) to monitor and predict the ground surface settlement in Kunming City. The results show that the MAPE (mean absolute percentage error), RMSE (root mean square error), and MAE (mean absolute error) of GWO-LSTM are significantly reduced. R² (goodness of fit) in the six sub-study areas of Kunming City is improved in comparison to the LSTM model. The problem of manual parameter selection in the LSTM model is solved by the GWO algorithm to select parameters automatically. This approach not only significantly reduces the model’s training time but also identifies the most suitable network parameters. This can bring the best performance. Based on TS-InSAR data, the prediction of urban ground surface settlement by the GWO-LSTM model has good accuracy and robustness, which offers a scientific foundation for monitoring and issuing early warnings about urban land disasters.

Watershed area is an important indicator reflecting the status of water resources and changes in the water environment, and its changes have a significant impact on ecosystem function, hydrological cycle and climate change. This paper analyzes the characteristics and causes of the long time series changes of the watershed area in Kunming City by using PIE-engine remote sensing computing cloud service platform, based on Landsat TM/ETM+ remote sensing images, and adopting NDWI (Normalized Difference Water Index) index. The results show that the water area of Kunming City has shown a W-shaped trend in the past 30 years, with a dynamic fluctuation of decreasing and then increasing, then decreasing and then increasing. The change of water area in Kunming is mainly influenced by both natural and anthropogenic factors. Natural factors mainly include climate change, and anthropogenic factors mainly include urban expansion, development and utilization of urban water resources, land use changes, water conservancy project construction and environmental pollution. This paper provides an effective methodology and data support for the monitoring and protection of the watershed area in Kunming by analyzing the characteristics of the change of the watershed area in the past 30 years.