Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta, China

Abstract

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m2). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.