资源与生态学报 ›› 2017, Vol. 8 ›› Issue (1): 42-49.DOI: 10.5814/j.issn.1674-764x.2017.01.006

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利用MODIS影像和气候数据模拟青藏高原草地地上生物量

付刚1, 孙维1, 李少伟1, 张晶2, 余成群1, 沈振西1   

  1. 1. 中国科学院地理科学与资源研究所生态系统网络观测与模拟重点实验室拉萨站,北京 100101;
    2. 北京师范大学地理学与遥感科学学院,北京 100875
  • 收稿日期:2016-10-24 出版日期:2017-01-20 发布日期:2017-01-20

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

FU Gang1, SUN Wei1, LI Shaowei1, ZHANG Jing2, YU Chengqun1, SHEN Zhenxi1,*   

  1. 1. Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. School of geography, Beijing Normal University, Beijing 100875, China
  • Received:2016-10-24 Online:2017-01-20 Published:2017-01-20
  • Contact: SHEN Zhenxi, E-mail: shenzx@igsnrr.ac.cn.
  • Supported by:
    National Natural Science Foundation of China (31600432), National Key Research Projects of China (2016YFC0502005; 2016YFC0502006), Chinese Academy of Science Western Light Talents Program (Response of livestock carrying capability to climatic change and grazing in the alpine meadow of Northern Tibetan Plateau), Science and Technology Plan Projects of Tibet Autonomous Region (Forage Grass Industry) and National Science and Technology Plan Project of China (2013BAC04B01, 2011BAC09B03, 2007BAC06B01).

摘要: 精确量化高寒区域的草地地上生物量在精确量化全球碳循环方面起着非常重要的作用。本研究利用月尺度的归一化植被指数、增强型植被指数、平均空气温度、≥5℃积温、总降水、降水积温比模拟了青藏高原高寒草地地上生物量。本研究对比分析了三种多重逐步回归模型,即地上生物量与归一化植被指数和增强型植被指数的逐步回归模型,地上生物量与空气温度、积温、降水和降水积温比的逐步回归模型,地上生物量与归一化植被指数、增强型植被指数、空气温度、积温、降水和降水积温比的逐步回归模型。结果表明,在高寒草甸,归一化植被指数模拟的地上生物量与观测的地上生物量间的平均绝对误差和均方根误差分别为31.05 g m-2和44.12 g m-2;在高寒草原,归一化植被指数模拟的地上生物量与观测的地上生物量间的平均绝对误差和均方根误差分别为95.43 g m-2和131.58 g m-2。在高寒草原,积温模拟的地上生物量与观测的地上生物量间的平均绝对误差和均方根误差分别为33.61g m-2和48.04 g m-2。在高寒草甸,植被指数和气象数据模拟的地上生物量与观测的地上生物量间的平均绝对误差和均方根误差分别为28.09 g m-2和42.71 g m-2;在高寒草原,植被指数和气象数据模拟的地上生物量与观测的地上生物量间的平均绝对误差和均方根误差分别为35.86 g m-2和47.94 g m-2。因此,植被指数和气候数据同时参与的逐步回归模型比植被指数或气候数据单独参与的逐步回归模型的精度高;不同高寒草地类型的回归模型精度不同。

关键词: 高寒草地, 归一化植被指数, 降水, 空气温度, 增强型植被指数

Abstract: Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling. The monthly normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mean air temperature (Ta), ≥5℃ accumulated air temperature (AccT), total precipitation (TP), and the ratio of TP to AccT (TP/AccT) were used to model aboveground biomass (AGB) in grasslands on the Tibetan Plateau. Three stepwise multiple regression methods, including stepwise multiple regression of AGB with NDVI and EVI, stepwise multiple regression of AGB with Ta, AccT, TP and TP/AccT, and stepwise multiple regression of AGB with NDVI, EVI, Ta, AccT, TP and TP/AccT were compared. The mean absolute error (MAE) and root mean squared error (RMSE) values between estimated AGB by the NDVI and measured AGB were 31.05 g m-2 and 44.12 g m-2, and 95.43 g m-2 and 131.58 g m-2 in the meadow and steppe, respectively. The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61g m-2 and 48.04 g m-2 in the steppe, respectively. The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m-2 and 42.71 g m-2, and 35.86 g m-2 and 47.94 g m-2, in the meadow and steppe, respectively. The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data. The accuracy of estimates varied depending on the type of grassland.

Key words: air temperature, alpine grassland, normalized difference vegetation index, precipitation, enhanced vegetation index