As a critical transition zone between land and sea, tidal flat areas hold immense ecological research value. However, environmental characteristics such as high humidity, strong reflectivity, and complex topography make it difficult for traditional remote sensing methods to achieve high-frequency, high-precision, and non-invasive detailed monitoring. The rapid development of unmanned aerial vehicle (UAV) multispectral remote sensing technology has provided a new technical approach for real-time monitoring of tidal flat areas. Nonetheless, existing methods still have shortcomings in terms of radiometric calibration accuracy, channel consistency, and spectral feature stability, which limit the accuracy and efficiency of tidal flat land cover classification. To address these issues, this study developed an integrated UAV multispectral detection system tailored to the complex tidal flat environment. A combined data processing method was designed, incorporating indoor spectral calibration, outdoor graycard correction, and global affine registration. This scheme effectively mitigates radiometric distortions caused by high humidity and strong reflectivity, eliminates geometric and spectral discrepancies across channels, and significantly enhances the stability and distinguishability of spectral features. Based on this, rapid classification rules were established using a combination of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference water index normalized red-edge near-infrared index (NRNI). Precise identification of intertidal land features was achieved through multi-index collaborative threshold decision-making. Field validation was conducted in a typical tidal flat area. The experimental results indicate that this method can effectively highlight spectral differences among intertidal land features. By using the thresholds NDVI>0.4, NDVI∈［0, 0.2］∩NDWI<-0.1, NDWI>-0.1∩NRNI> -0.1, enabling precise classification of three types of coastal features: the water′s edge, beach forests, and foreshore gravel. The overall accuracy reached 96.7%, with a Kappa coefficient of 94.5%. With a processing time of 0.001 seconds per sample, the method enables real-time monitoring of intertidal zones at sub-meter resolution, meeting the detailed requirements of ecological research.