Fine-scale grassland land-cover classification using UAV RGB and multispectral imagery with staged feature selection

Accurate mapping of grassland vegetation and bare land is important for grassland degradation assessment and ecological management. This study investigated the effectiveness of integrating visible and multispectral imagery with staged feature selection for fine-scale grassland land-cover classification.

Four representative grassland land-cover classes, including Caragana microphylla, Artemisia scoparia, Agropyron cristatum, and bare land, were selected as classification categories. Spectral bands, vegetation indices, and texture metrics were combined to construct a multi-source feature pool, and different feature optimization strategies and classification models were systematically compared.

Multi-source feature fusion improved the discrimination of spectrally similar land-cover classes, while staged feature selection provided a workflow for reducing cross-source feature redundancy while retaining complementary information. The model with the highest observed validation accuracy selected only ten features from the original feature pool and achieved an overall accuracy of 91.54% with a Kappa coefficient of 88.72%. Compared with direct feature fusion, the optimized feature subset substantially reduced feature dimensionality while maintaining comparable classification accuracy. The resulting classification maps showed broadly consistent spatial patterns, with differences mainly reflected in local boundary and fragmentation patterns.

Under the conditions evaluated in this study, these findings indicate that staged feature selection provides a compact and interpretable feature optimization strategy for fine-scale UAV-based grassland land-cover classification. However, the data were acquired from a single study area during a single growing season, and the robustness of the framework across different regions, years, and phenological stages remains to be validated.