A novel method for monitoring Cladophora blooms in Qinghai Lake based on UAV imagery

Abstract

The outbreak of Cladophora in the nearshore region of Qinghai Lake has significantly affected the surrounding ecological landscape and tourism industry. However, existing satellite remote sensing methods for monitoring Cladophora blooms have several limitations owing to cloud cover in optical images and the small areas of Cladophora bloom patches, making it challenging to obtain detailed dynamic characteristics of these blooms. Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras serve as valuable complements to traditional satellite remote sensing techniques. In this study, we propose a new spectral index called the visible band spectral slope index (VSSI) and integrate it with a triangular threshold segmentation algorithm to automatically extract features of Cladophora blooms from very-high-resolution UAV imagery. Comparative analysis with commonly used visible band indices such as the normalized green-red difference index (NGRDI), color index vegetation (CIVE), vegetation (VEG) index, visible-band difference vegetation index (VDVI), and red-green-blue floating algae index (RGB-FAI) demonstrated that the VSSI was most effective for detecting Cladophora blooms. The validation results revealed that the VSSI had the highest accuracy, achieving an F1 score of 0.89 and improving overall accuracy by 11.25 % to 36.92 % compared to other indices' accuracy levels. When compared with UAV images, both Sentinel-2 MSI and Landsat OLI images significantly overestimated the areas of Cladophora blooms by values of 110.65 % and 517.99 %, respectively. Moreover, UAV images captured at different times within the same day confirmed that wind speed and direction are crucial factors influencing dynamic changes in Cladophora blooms over short periods. This work provides a valuable reference for accurately mapping spatiotemporal dynamics of Cladophora blooms and effectively managing lake water environments.