Accurate mapping of wetland distribution is required for wetland conservation, management, and restoration, but remains a challenge due to the complexity of wetland landscapes. This research employed four seasons of multispectral images from Gaofen-1 satellite to map wetland land-cover distribution in Hangzhou bay coastal wetland (245 km²) in China. Maximum likelihood classifier (MLC), random forest (RF), and the expert-based approach were examined based on spectral, spatial, and phenological features. The results showed that land-cover classification accuracies of 83.9% using RF and 90.3% using the expert-based approach were obtained, and they had higher accuracy than MLC, which had an overall accuracy of only 63.3%. The high classification accuracy for nine land-cover classes using the expert-based approach indicated the important role of expert knowledge from the phenological features in improving wetland classification accuracy. As high spatial resolution satellite images become more easily obtainable, effective use of temporal information of different sensor data will be valuable for detailed land-cover classification with higher accuracy. The approach to establish expert rules from multitemporal images provides a new way to improve land-cover classification in different terrestrial ecosystems.

Background: Spartina alterniflora is an invasive plant on the coast of China that replaces native vegetation and has a serious negative impact on local ecosystems. Monitoring the spatial distribution of S. alterniflora and its changes over time can reveal its expansion mechanism, which is crucial for the management of coastal ecosystems. The purpose of this study was to map the distribution of S. alterniflora in Zhejiang Province from 1985 to 2015 using a time series of Landsat TM/OLI images and analyze the temporal and spatial patterns of expansion of this species. Results: After analyzing the distribution of coastal vegetation, the vegetation index was calculated based on Landsat images for 4 years (1985, 1995, 2005 and 2015). According to a threshold determined based on expert knowledge, the distribution of S. alterniflora in Zhejiang Province was extracted, and the temporal and spatial changes in the distribution of S. alterniflora were analyzed. The classification accuracy was 90.3%. S. alterniflora has expanded rapidly in recent decades after being introduced into southern Zhejiang. Between 1985 and 2015, S. alterniflora increased its area of distribution by 10,000 hm², and it replaced native vegetation to become the most abundant halophyte in tidal flats. Overall, S. alterniflora expanded from south to north over the decades of the study, and the fastest expansion rate was 463.64 hm²/year, which occurred between 1995 and 2005. S. alterniflora was widely distributed in the tidal flats of bays and estuaries and expanded outward as sediment accumulated. Conclusions: This study reveals the changes over time in S. alterniflora cover in Zhejiang and can contribute to the control and management of this invasive plant.

Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial distribution and area of tea plantations. In this study, we developed a simple method to accurately map tea plantations based on their unique phenological characteristics observed from VENμS high-spatiotemporal-resolution multispectral imagery. The normalized difference vegetation index (NDVI) and red—green ratio index (RGRI) of time series were calculated using 40 VENμS images taken in 2018 to evaluate the phenology of tea plantations. The unique phenological period of tea plantations in northern Zhejiang is from April to May, with obvious deep pruning, which is very different from the phenological period of other vegetation. During this period, the RGRI values of tea plantations were much higher than those of other vegetation such as broadleaf forest and bamboo forest. Therefore, it is possible to identify tea plantations from the vegetation in images acquired during their phenological period. This method was applied to tea plantation mapping in northern Zhejiang. The NDVI value of the winter image was used to extract a vegetation coverage map, and spatial intersection analysis combined with maps of tea plantation phenological information was performed to obtain a tea plantation distribution map. The resulting tea plantation map had a high accuracy, with a 94% producer accuracy and 95.9% user accuracy. The method was also applied to Sentinel-2 images at the regional scale, and the obtained tea plantation distribution map had an accuracy of 88.7%, indicating the good applicability of the method.