Monitoring the Invasive Plant Spartina alterniflora in Jiangsu Coastal Wetland Using MRCNN and Long-Time Series Landsat Data

Jiangsu coastal wetland has the largest area of the invasive plant, Spartina alterniflora (S. alterniflora), in China. S. alterniflora has been present in the wetland for nearly 40 years and poses a substantial threat to the safety of coastal wetland ecosystems. There is an urgent need to control the distribution of S. alterniflora. The biological characteristics of the invasion process of S. alterniflora contribute to its multi-scale distribution. However, the current classification methods do not deal successfully with multi-scale problems, and it is also difficult to perform high-precision land cover classification on multi-temporal remote sensing images. In this study, based on Landsat data from 1990 to 2020, a new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify S. alterniflora. In this method, features at different scales are extracted and concatenated to obtain multi-scale information, and residual connections are introduced to ensure gradient propagation. A multi-year data unified training method was adopted to improve the temporal scalability of the MRCNN. The MRCNN model was able to identify the annual S. alterniflora distribution more accurately, overcame the disadvantage that traditional CNNs can only extract feature information at a single scale, and offered significant advantages in spatial characterization. A thematic map of S. alterniflora distribution was obtained. Since it was introduced in 1982, the distribution of S. alterniflora has expanded to approximately 17,400 ha. In Jiangsu, the expansion process of S. alterniflora over time was divided into three stages: the growth period (1982-1994), the outbreak period (1995-2004), and the plateau period (2005-2020). The spatial expansion direction was mainly parallel and perpendicular to the coastline. The hydrodynamic conditions and tidal flat environment on the coast of Jiangsu Province are suitable for the growth of S. alterniflora. Reclamation of tidal flats is the main factor affecting the expansion of S. alterniflora.

Automated summary

A new deep learning multi-scale residual convolutional neural network (MRCNN) model was developed to identify the distribution of Spartina alterniflora (S. alterniflora) in Jiangsu coastal wetland. This model improved the accuracy of identifying S. alterniflora distribution and showed advantages in spatial characterization. The study found that the distribution of S. alterniflora in Jiangsu has expanded over time, with three stages identified: the growth period, the outbreak period, and the plateau period. The expansion of S. alterniflora mainly occurred parallel and perpendicular to the coastline, with reclamation of tidal flats being the main factor.