Mapping the Fraction of Vegetation Coverage of Potamogeton crispus L. in a Shallow Lake of Northern China Based on UAV and Satellite Data

Abstract

Under the background of global change, the lake water environment is facing a huge threat from eutrophication. The rapid increase in curly-leaf pondweed (Potamogeton crispus L.) in recent years has seriously threatened the ecological balance and the water diversion safety of the eastern route of China’s South-to-North Water Diversion Project. The monitoring and control of curly-leaf pondweed is imperative in shallow lakes of northern China. Unmanned Aerial Vehicles (UAVs) have great potential for monitoring aquatic vegetation. However, merely using satellite remote sensing to detect submerged vegetation is not sufficient, and the monitoring of UAVs on aquatic vegetation is rarely systematically evaluated. In this study, taking Nansi Lake as a case, we employed Red–Green–Blue (RGB) UAV and satellite datasets to evaluate the monitoring of RGB Vegetation Indices (VIs) in pondweed and mapped the dynamic patterns of the pondweed Fractional Vegetation Coverage (FVC) in Nansi Lake. The pondweed FVC values were extracted using the RGB VIs and the machine learning method. The extraction of the UAV RGB images was evaluated by correlations, accuracy assessments and separability. The correlation between VIs and FVC was used to invert the pondweed FVC in Nansi Lake. The RGB VIs were also calculated using Gaofen-2 (GF-2) and were compared with UAV and Sentinel-2 data. Our results showed the following: (1) The RGB UAV could effectively monitor the FVC of pondweed, especially when using Support Vector Machine that (SVM) has a high ability to recognize pondweed in UAV RGB images. Two RGB VIs, RCC and RGRI, appeared best suited for monitoring aquatic plants. The correlations between four RGB VIs based on GF-2, i.e., GCC, BRI, VDVI, and RGBVI and FVCSVM calculated by the UAV (p < 0.01) were better than those obtained with other RGB VIs. Thus, the RGB VIs of GF-2 were not as effective as those of the UAV in pondweed monitoring. (2) The binomial estimation model constructed by the Normalized Difference Water Index (NDWI) of Sentinel-2 showed a high accuracy (R2 = 0.7505, RMSE = 0.169) for pondweed FVC and can be used for mapping the FVC of pondweed in Nansi Lake. (3) Combined with the Sentinel-2 time-series data, we mapped the dynamic patterns of pondweed FVC in Nansi Lake. It was determined that the flooding of pondweed in Nansi Lake has been alleviated in recent years, but the rapid increase in pondweed in part of Nansi Lake remains a challenging management issue. This study provides practical tools and methodology for the innovative remote sensing monitoring of submerged vegetation.