Currently, the degradation of global ecosystem services and the decline in biodiversity are pressing issues, and the conflict between ecological conservation and economic development in old industrial bases is intensifying. This study takes Liaoning Province, which exhibits a distinct ecological gradient, as its research area. By constructing a three-tier nested framework at the grid, county, and city levels, and combining spatial autocorrelation, GWR, SOM, and XGBoost-SHAP models, we assess the spatial differentiation, trade-offs and synergies, and multi-scale driving associations of five core ecosystem services. The results indicate that: ① ecosystem services exhibit a pattern characterized by “mountains in the east, water in the west, and a granary in the center, Wet mountains in the east, dry hills in the west, granary plains in the central region” with the intensity of spatial aggregation decreasing as the scale increases; ② food provisioning and regulating support services remain in a trade-off relationship, and service bundles shift from locally fine-grained differentiation to city-level functional aggregation; ③ riving associations exhibit significant scale-dependent differentiation, with factors exhibiting nonlinear thresholds and interactions. This study establishes a multi-scale analytical framework for ecosystem services in old industrial bases, providing a foundation for regional ecological restoration and coordinated development.

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage.