Abstract
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.
Highlights
Remote sensing has been widely applied to monitor spatial distribution patterns of plant populations owing to the advantages of macroscopic view, speediness, dynamic, and comprehensiveness [1,2,3].remote sensing data contain a certain amount of uncertainty, such as different objects may have the same spectral features or the same object may have different spectral features [4,5]
Through the analysis of the differences in spectral characteristics and green-up timing of the four dominant plants, we found that P. australis and Z. latifolia had similar spectral characteristics in visible and near infrared bands in May, so it was hard to distinguish them using a single phase multispectral image
We identified the areas with NDVI5-high where NDVI3 > −0.06 was P. australis and the areas with NDVI3 ≤ −0.06 was Z
Summary
Remote sensing has been widely applied to monitor spatial distribution patterns of plant populations owing to the advantages of macroscopic view, speediness, dynamic, and comprehensiveness [1,2,3]. Agrawal et al [8] classified vegetation distribution through seasonal variation, reflected by multi-temporal NDVI, and gained a high degree of classification accuracy These studies suggest that the benchmarking direction for monitoring vegetation dynamics is to interpret multi-temporal remote sensing images at different phenological stages with the help of other supplemental information [9]. Most of the existing data productions of land cover on regional and global scales (such as the DISCover, provided by International Geosphere-Biosphere Programme (IGBP), the GLC2000, released by University of Maryland, or the MODIS land cover quarterly products [10,11,12,13,14]) are obtained from the classification results of low- and medium-resolution remote sensing data (spatial resolution is equal to or more than 30 m) and the classification system generally involves only several types of dominant plants Those productions are widely used in ecological environmental monitoring on large and mesoscales, but they are not suitable for eco-environmental surveys of community succession and plant. Based on previous research results, we further analyzed the dynamic changes in the dominant plants during 1997–2012 at Jiuduansha Wetland
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