自然资源学报 ›› 2013, Vol. 28 ›› Issue (12): 2044-2055.doi: 10.11849/zrzyxb.2013.12.003

• 资源生态 • 上一篇    下一篇

盐城海滨湿地植被地上生物量遥感估算研究

谭清梅, 刘红玉, 张华兵, 王聪, 侯明行   

  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
  • 收稿日期:2012-12-28 修回日期:2013-04-24 出版日期:2013-12-20 发布日期:2013-12-19
  • 作者简介:谭清梅(1988-),女,四川达州人,硕士,主要从事湿地遥感与GIS研究。E-mail:tanqingmei0109@163.com
  • 基金资助:
    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)。

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

TAN Qing-mei, LIU Hong-yu, ZHANG Hua-bing, WANG Cong, HOU Ming-hang   

  1. Jiangsu Key Laboratory of Environmental Change and Ecological Construction, College of Geography Science, Nanjing Normal University, Nanjing 210023, China
  • Received:2012-12-28 Revised:2013-04-24 Online:2013-12-20 Published:2013-12-19

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2.28×108 kg,湿重总重量为6.10×108 kg。

关键词: 遥感, 回归模型, 盐城保护区, 湿地生物量, Landsat/ETM+数据

Abstract: 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 2.28×108 kg and the biomass fresh weight weights 6.10×108 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 places of Spartina alterniflora. The weight of S. alterniflora biomass was more than the reed biomass, and that of the reed biomass was more than the Suaeda salsa biomass. The low weight was distributed in places of Suaeda salsa and around the culture pond and neighboring bare flat.

Key words: wetland vegetation biomass, Landsat ETM+, Yancheng Natural Reserve, remote sensing, regression model

中图分类号: 

  • TP79