Study on the Extraction of Land Cover Information From Multisource Remote Sensing Data for Refined Management of National Parks

As a key vehicle for ecological conservation, National Parks (NPs) require accurate and timely land cover (LC) information for refined management. However, complex terrain and frequent human activities pose challenges to efficient LC extraction. This study focuses on the Chengdu section of the Giant Panda National Park and proposes a framework of multisource data integration, dynamic feature selection, algorithm performance evaluation, temporal sample migration, and LC change analysis. Sentinel-1, Sentinel-2 A, and SRTM data are integrated to construct 67 multidimensional features. Recursive feature elimination combined with Bayesian optimization is used for feature selection, and the classification performance of random forest (RF), support vector machine, and classification and regression tree are compared. Spectral angle mapper and spectral Euclidean distance are introduced for temporal sample migration. Results show that the RF classifier with optimized features yields the best performance, achieving an overall accuracy of 0.9330 and a Kappa coefficient of 0.9196, significantly outperforming GLC_FCS30D, Esri Land Cover, and China Land Cover Dataset. Accuracy after sample migration remains above 0.8800 annually. The framework effectively identified bamboo forests critical to panda habitats. For example, using only water indexes, bamboo forest producer accuracy was 0.0555, but increased to 0.8571 with added spectral and vegetation features. From 2018 to 2023, woodland increased by 64.77 km<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> and bamboo forest by 22.26 km<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>, while barren land and construction land increased by 116.04km and 174.28 km<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>. The proposed framework effectively enhances LC monitoring in mountainous environments and provides technical support for conservation planning and ecological supervision in NPs.