Spatiotemporal Dynamics of the Liaohe Delta (1987–2017) Using an Integrated Classification and Feature Selection Approach

Abstract

Wetland landscape classification is fundamental to monitoring changes in ecosystem patterns. This study proposes an ensemble classification approach that integrates Maximum Likelihood Classification (MLC) and Decision Tree (DT) methods with optimized feature selection for long-term wetland monitoring in the Liaohe Delta, China. Based on four periods of Landsat remote sensing images from 1987, 1997, 2007, and 2017, multi-dimensional features including PCA1, TCT, NDVI, and MNDWI were extracted to construct a hierarchical classification system comprising 13 landscape types. The results show that the integrated method achieved an overall classification accuracy of 87.71% and a Kappa coefficient of 0.85, improving by 16.50% and 19.72%, respectively, compared with the single MLC approach. The classification results reveal significant spatiotemporal variations in landscape patterns. Reed wetlands decreased from 1284.44 km2 in 1987 to 1006.70 km2 in 1997, followed by a recovery to 1275.53 km2 in 2017. In contrast, Suaeda communities experienced severe degradation, declining sharply from 227.48 km2 in 1987 to 30.52 km2 in 2017. Meanwhile, the coastline advanced landward by 263.24 km2, with the proportion of artificial shoreline increasing from 12.4% to 38.7%. The changes in wetland landscape types in the Liaohe Delta from 1987 to 2017 were mainly influenced by urbanization processes and ecological restoration policies. These findings indicate that the proposed method can effectively support long-term wetland landscape dynamics analysis and provide a useful reference for coastal wetland management.

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