上海九段沙互花米草种群动态遥感研究

 该研究应用遥感技术(RS)，结合地理信息系统(GIS)、全球定位系统(GPS)和野外实地调查，对长江口新生沙洲九段沙互花米草(Spartina alterniflora)引种7年来的种群扩散过程进行了调查分析。 通过不同时相遥感卫星影像的解译，结合现场调查，分析了外来种互花米草种群自 1997年引种后7年期间的种群扩散动态过程。至2004年，互花米草群落已从种植的100 hm²扩展到1014 hm²。互花米草种群扩散过程可分为3个阶 段：1997年的成活定居期；1998至2000年的滞缓期；2000年以后的快速扩散期。互花米草种群的增长速度远高于土著种芦苇(Phragmites australis)，显示了互花米草极强的扩散能力、竞争优势和更广的生态幅。研究结果表明，3S技术可应用于植被分类、 群落动态和生物入侵动 态监测。3S技术具有宏观、快速、动态和综合的优势，可为外来植物的定量调查、分布格局和扩散动态研究提供有效的手段，为滩涂资源生物 多样性保护和可持续开发利用提供科学依据。

REMOTE SENSING ANALYSIS OF RANGE EXPANSION OF SPARTINA ALTERNIFLORA AT JIUDUANSHA SHOALS IN SHANGHAI, CHINA

Aims This study investigated the pattern of expansion of the exotic Spartina alterniflora for seven years after its introduction into the neonatal Jiuduansha shoals in the Yangtze River estuary, Shanghai. These time-based data are an important first-step in providing a scientific basis for wetland management and biodiversity conservation.  Methods Remote sensing (RS), combined with geographical information systems (GIS) and global positioning systems (GPS), was used to map the distribution of S .alterniflora salt marshes on the Jiuduansha shoals. Findings were checked using field surveys of selected areas. Important findings S. alterniflora populations expanded from 100 hm² in 1997 to 1014 hm² in 2004. The pattern of expansion was typical of invasions, i.e., initial colonization, a lag time and rapid population growth and range expansion. In the first year (1997) of planting,S. alterniflorapopulations colonized the shoals, and about 35 hm² S. alterniflora survived. The period between 1998 and 2000 was a lag time with a mean annual expansion rate <1%. The onset of rapid population growth and range expansion began in 2000, and the annual expansion rate in creased to 25%-116%, which exceeded the indigenous species and indicated the strong competitive capability, rapid range expansion and wide ecological niche of S. alterniflora. RS, combined with GIS and GPS, provides a potential tool for monitoring population dynamics and range expansion of invasive species on a large scale. Results of this study indicate this approach can provide timely data for biodiversity conservation of intertidal zones, resource management and sustainable development on coastal regions.