摘要
湿地是具有丰富的生物多样性和较高生产力的生态系统,在全球碳循环中占有重要地位。湿地植被净初级生产力(NPP)是衡量湿地生态系统健康状况的重要指标。以上海崇明东滩湿地3种典型植被———芦苇、海三棱藨草、互花米草为研究对象,结合实地调查、实验室测定和遥感技术进行湿地植被净初级生产力估算研究。首先,设置湿地3种典型植被样方,测量植被鲜重、高度、密度、盖度和叶面积指数(LAI)等反映植被生物学特性的特征参数,按不同植被类型分别建立基于LAI的样方NPP回归模型;其次,利用一景相近时相的SPOT5影像,经过几何纠正和辐射定标后,采用面向对象分类方法对影像分类,同时,计算出能较好地反映植被特征和消除土壤背景影响的修正土壤调节植被指数(MSAVI),建立了基于MSAVI的3种典型植被LAI遥感估测模型;最后,分别根据样方3种典型植被的NPP估测模型以及LAI遥感估测模型,进行尺度化转换,估算出崇明东滩湿地典型植被净初级生产力。模型简单可行,精度较高,可为快速定量评估湿地植被碳贡献及碳储量提供依据。
Wetlands are rich in biological diversity and high productivity of the ecosystem,they play an important role in ecosystem carbon cycle.Wetland vegetation net primary productivity(NPP)is an important indicator of the health of wetland ecosystems.In this paper,combined with field surveys,laboratory measurement and remote sensing technology,the three kinds of typical wetland vegetation——Spartina alterniflora,Phragmites australis and Scirpus mariqueter NPP were studied in Shanghai Chongming Dongtan.Firstly,selected three kinds of typical wetland vegetation type,the biological characteristics parameters of the vegetation was surveyed,including fresh weight of vegetation,height,density,coverage and leaf area index(LAI),etc..The regression model between NPP and LAI was set up in the sample of different vegetation type.Secondly,using SPOT5 images,after geometric correction and radiometric calibration,the image was classified by object-oriented,and modified adjust soil vegetation index(MSAVI)was calculated,which can reflect the elimination of soil and vegetation characteristics of the background factors.Then estimation LAI model was set up with SPOT5 MSAVI.Finally,according the NPP regression model and LAI estimation model,different wetland vegetation NPP model was set up at regional scale in Chongming Dongtan.It provides a good method and theoretical base for typical wetland vegetation structure and function.
出处
《长江流域资源与环境》
CAS
CSSCI
CSCD
北大核心
2011年第11期1355-1360,共6页
Resources and Environment in the Yangtze Basin
基金
国家自然科学基金项目(40801168)
上海师范大学重点培育学科项目(DZL801)
上海市教委科研创新项目(10YZ72)
上海市重点学科建设项目(S30406)