Spartina alterniflora is one of the most hazardous invasive plant species in China. Monitoring the changes in dominant plant species can help identify the invasion mechanisms of S. alterniflora, thereby providing scientific guidelines on managing or controlling the spreading of this invasive species at Jiuduansha Wetland in Shanghai, China. However, because of the complex terrain and the inaccessibility of tidal wetlands, it is very difficult to conduct field experiments on a large scale in this wetland. Hence, remote sensing plays an important role in monitoring the dynamics of plant species and its distribution on both spatial and temporal scales. In this study, based on multi-spectral and high resolution (<10 m) remote sensing images and field observational data, we analyzed spectral characteristics of four dominant plant species at different green-up phenophases. Based on the difference in spectral characteristics, a decision tree classification was built for identifying the distribution of these plant species. The results indicated that the overall classification accuracy for plant species was 87.17%, and the Kappa Coefficient was 0.81, implying that our classification method could effectively identify the four plant species. We found that the area of Phragmites australi showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 33.77% and 31.92%, respectively. The area of Scirpus mariqueter displayed an increasing trend from 1997 to 2004 (12.16% per year) and a decreasing trend from 2004 to 2012 (−7.05% per year). S. alterniflora has the biggest area (3302.20 ha) as compared to other species, accounting for 51% of total vegetated area at the study region in 2012. It showed an increasing trend from 1997 to 2004 and from 2004 to 2012, with an annual spreading rate of 130.63% and 28.11%, respectively. As a result, the native species P. australi was surrounded and the habitats of S. mariqueter were occupied by S. alterniflora. The high proliferation ability and competitive advantage for S. alterniflora inhibited the growth of other plant species and we anticipate a continuous expansion of this invasive species at Jiuduansha Wetland. Effective measures should be taken to control the invasion of S. alterniflora. Full article

The long-term Normalized Difference Vegetation Index (NDVI) time-series data set generated from the Advanced Very High Resolution Radiometers (AVHRR) has been widely used to monitor vegetation activity change. The third version of NDVI (NDVI3g) produced by the Global Inventory Modeling and Mapping Studies (GIMMS) group was released recently. The comparisons between the new and old versions should be conducted for linking existing studies with future applications of NDVI3g in monitoring vegetation activity change. Based on simple and piecewise linear regression methods, this study made a comparative analysis between NDVIg and NDVI3g for monitoring vegetation activity change and its responses to climate change in the middle and high latitudes of the Northern Hemisphere during 1982–2008. Our results indicated that there were large differences between NDVIg and NDVI3g in the spatial patterns for both the overall changing trends and the timing of Turning Points (TP) in NDVI time series, which spread over almost the entire study region. The average NDVI trend from NDVI3g was almost twice as great as that from NDVIg and the detected average timing of TP from NDVI3g was about one year later. Although the general spatial patterns were consistent between two data sets for detecting the responses of growing-season NDVI to temperature and precipitation changes, there were large differences in the response magnitude, with a higher response magnitude to temperature in NDVI3g and an opposite response to precipitation change for the two data sets. These results demonstrated that the NDVIg data set may underestimate the vegetation activity change trend and its response to climate change in the middle and high latitudes of the Northern Hemisphere during the past three decades. Full article