This study mapped the areal extent, identified the species composition, and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019. With the aid of the cloud platform of the Google Earth Engine, we selected Landsat 5/8 and Sentinel-2 images and used the support vector machine classification method to extract salt marsh information for the years of 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2019. Seven major species of salt marshes: Phragmites australis , Suaeda spp., Spartina alterniflora , Scirpus mariqueter , Tamarix chinensis , Cyperus malaccensis , and Sesuvium portulacastrum were identified. Our results showed that salt marshes are mainly distributed in Liaoning, Shandong, Jiangsu, Shanghai, and Zhejiang, with varying patterns of shrinking, expansion, or wavering in different places. The distribution of salt marshes has declined considerably from 151,324 ha in 1985 to 115,397 ha in 2019, a drop of 23.7%. During the same period, the area of native species has dropped 95.4% from 77,741 ha to 3,563 ha for Suaeda spp. and 45.1% from 60,511 ha to 33,193 ha for P. australis ; on the contrary, the area of exotic species, S. alterniflora , has exhibited a sharp rise from just 99 ha to 67,527 ha. For the past 35 years, the driving factors causing salt marsh changes are mainly land reclamation, variations in water and sand fluxes, and interspecific competition and succession of salt marsh vegetation. These results provide fundamental reference information and could form the scientific basis for formulating policies for the conservation and utilization of salt marsh resources in China.

Land-use change simulation for large-scale regions (i.e. provincial regions or countries) is very useful for many global studies. Such simulation, however, is affected by computational capability of general computers. This paper proposes a method to implement cellular automata (CA) for land use change simulation based on graphics processing units (GPUs). This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA. We carried out the experiments by simulating land-use change processes at a provincial scale. This involves a lot of sophisticated techniques, such as model mapping, and computational procedure of GPU-CA model. This proposed model has been validated by land-use change simulation in Guangdong Province, China. The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times. Such improvement is crucial for land-use change simulations in provincial regions and countries. The outputs of the simulation can be further used to provide information to other global change models.