盐城海滨湿地植被地上生物量遥感估算研究  

An Estimation of Aboveground Vegetation Biomass in Coastal Wetland of Yancheng Natural Reserve

作　　者：谭清梅[1] 刘红玉[1] 张华兵[1] 王聪[1] 侯明行[1] 

机构地区：[1]江苏省环境演变与生态建设重点实验室南京师范大学地理科学学院,南京210023

出　　处：《自然资源学报》2013年第12期2044-2055,共12页Journal of Natural Resources

基　　金：国家自然科学基金项目(41041119);江苏省高校自然科学研究重大项目(10KJA170029);江苏省省属高校自然科学研究项目(12KJB170006)

摘　　要：以盐城湿地自然保护区核心区的ETM+图像数据和同期野外实测的31个样方地上生物量干重、湿重数据为数据源,分析了15个遥感信息变量与湿地植被地上生物量干重、湿重的相关关系,并选择在0.01水平上显著相关的8个遥感变量建立一元线性回归模型、一元曲线回归模型以及多元逐步回归模型,并对比得出最优模型,进而计算出整个研究区的地上生物量。研究得出:①与研究区湿地植被地上生物量干重和湿重相关性最大的都是ETM+4波段,干重的相关系数为0.833,湿重的相关系数为0.796;②研究区植被地上生物量干重和湿重的遥感估算模型都是一元三次函数模型,且干重模型的拟合精度要优于湿重模型;③得到研究区地上生物量干重总重量为2.28×108kg,湿重总重量为6.10×108kg。With the development of remote sensing technology, it has become an important technical means used to investigate vegetation biomass. The biomass of wetland vegetation is an essential index to describe the wetland ecosystem of primary productivity. Therefore, the investigation of wetland vegetation biomass has important practical significance. In this paper, the core area of the Yancheng Natural Reserve was selected as the study area. The ETM＋ image on September 24, 2011 and 31 samples of biomass data in the same period were used as the data source to establish the estimation models. The correlation between the 15 remote sensing information variables and measured biomass were analyzed in this paper. The remote sensing information that showed significant correlation at level 0.01 was selected and the estimation models were established based on eight remote sensing information variables. The models included the simple regression models, the curve regression models and the stepwise regression models, the best estimation models were obtained. The total aboveground vegetation biomass of the study area could then be calculated by the best model in this paper. The conclusions of the study were as follows： 1） Both biomass dry weight and fresh weight of the study area has the best positive correlation to the ETM ＋ 4. The coefficient of biomass dry weight was 0.833 and the coefficient of biomass fresh weight was 0.796. 2） A one variable cubic function model was used by both the biomass dry weight models and biomass fresh weight models. The biomass dry weight models were better than the biomass fresh weight models. 3） The total biomass dry weight of the study area was 2.28108 kg and the biomass fresh weight weights 6.10108 kg based on the best estimation models. In this study area, dry biomass was mainly between 1000 g/m2 and 3000 g/m2 and the humid biomass was mainly between 3000 g/m2 and 6000 g/m2. There was little extreme high biomass for both dry weight and fresh weight, which was mainly distributed in the p

关 键 词：遥感 湿地生物量 回归模型 LANDSAT ETM+数据 盐城保护区 

分 类 号：S184[农业科学—农业基础科学] S127