Remote-Sensed Monitoring of Dominant Plant Species Distribution and Dynamics at Jiuduansha Wetland in Shanghai, China

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.

[1]  Li Xin,et al.  China Land Cover Classification at 1 km Spatial Resolution Based on a Multi-source Data Fusion Approach , 2009 .

[2]  Shi Pei-jun,et al.  Research on Regulation of NDVI Change of Chinese Primary Vegetation Types Based on NOAA/AVHRR Data , 1999 .

[3]  Huang Hua The spatio-temporal dynamics of salt marsh vegetation for Chongming Dongtan National Nature Reserve,Shanghai , 2007 .

[4]  赵斌,et al.  Invasive Spartina alterniflora: biology, ecology and management , 2006 .

[5]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[6]  Li Xiao,et al.  Research on Regulation of NDVI Change of Chinese Primary Vegetation Types Based on NOAA/AVHRR Data , 1999 .

[7]  J. Townshend,et al.  African Land-Cover Classification Using Satellite Data , 1985, Science.

[8]  [Classification and dynamic changes of grasslands in northern Tibet based on recent 20 years satellite data]. , 2007, Ying yong sheng tai xue bao = The journal of applied ecology.

[9]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[10]  Zhong Yan,et al.  [Biomass and carbon storage of Phragmites australis and Spartina alterniflora in Jiuduan Shoal Wetland of Yangtze Estuary, East China]. , 2013, Ying yong sheng tai xue bao = The journal of applied ecology.

[11]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[12]  Chen Ren-xi Review on Greenland Recognition from Urban High-resolution Satellite Imagery , 2013 .

[13]  Effects of the invasive plant Spartina alterniflora on insect diversity in Jiuduansha wetlands in the Yangtze River Estuary. , 2006 .

[14]  S. Running,et al.  What does Remote Sensing Do for Ecology , 1991 .

[15]  Pawan Kumar Joshi,et al.  SPOT vegetation multi temporal data for classifying vegetation in south central Asia , 2003 .

[16]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[17]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[18]  Tian Qing-jiu Vegetation Classification Based on High-resolution Satellite Image , 2007 .

[19]  Wenjiang Huang,et al.  [Accuracy of winter wheat identification based on multi-temporal CBERS-02 images]. , 2008, Ying yong sheng tai xue bao = The journal of applied ecology.