The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVIII-1-2024
https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-343-2024
https://doi.org/10.5194/isprs-archives-XLVIII-1-2024-343-2024
10 May 2024
 | 10 May 2024

Research on Satellite and Ground Multi-Sensor Collaborative Sensing Method for Floating Plants

Naiyi Li, Liang Xin, and Chen Meng

Keywords: Floating plants, collaborative sensing, satellite remote sensing, spectral computing, Semantic segmentation network, symmetric function neural network

Abstract. Dynamic monitoring of water environment is the basis of maintaining urban security and promoting urban sustainable development. To effectively solve the problems caused by the large-scale flooding of floating aquatic invasive plants, such as water environment destruction, clogging river, water quality pollution, water ecological balance destruction, etc., a satellite and ground multi-sensor collaborative sensing method for floating plants is proposed in this paper. For the monitoring of floating plants in macro watershed, the vegetation index and chlorophyll concentration were used to extract floating plants, analyzing, and calculating the distribution and coverage area of floating plants based on 4-band high-resolution satellite images. For the monitoring of floating plants in small watershed, combined with visible light video, multispectral images, and LiDAR data, optimized spectral feature variable processing, semantic segmentation network, and symmetric function neural network are utilized to obtain real-time information on the invasion location, distribution pattern, and coverage area of floating plants. In this paper, the upstream basin of the Huangpu River in Shanghai in 2023 was selected as the experimental area. The distribution of floating plants in different watercourse and different periods was successfully obtained by this method, and the coverage area was accurately calculated with an identification rate of more than 90%, which provided technical support for cross-regional management and efficient cleaning of floating plants.