Early Monitoring of Exotic Mangrove Sonneratia in Hong Kong Using Deep Convolutional Network at Half-Meter Resolution

Abstract

Sonneratia have posed a threat to native mangrove species in Hong Kong. Early detection of individual Sonneratia when they are introduced and naturalized before invasion is essential for native mangrove species protection, especially for Sonneratia with a strong ability of propagation. This letter aims to provide an effective way to the accurate detection of individual Sonneratia. Specifically, using very high spatial resolution remotely sensed data, we adapt the RetinaNet, incorporating multiscale features for sapling detection and convolutional neural networks for detecting the Sonneratia distributed scatteredly among native species. The Sonneratia were detected with a higher mean average precision (mAP) 0.50 of 0.3891 with a precision of 0.5465 than that from the deformable part model. In addition, 3678 Sonneratia were detected at early stage. This letter can support the government for mangrove forest management and offer a scientific guidance for adequate response to the species invasion, like annual removal of Sonneratia, and then reduce the consumption of labor and time over a large scale. In addition, it can provide a quantitative survey for Sonneratia management.