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首页> 外文期刊>International journal of remote sensing >Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia's grassland between 2001 and 2011
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Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia's grassland between 2001 and 2011

机译：利用MODIS时间序列数据估算2001年至2011年内蒙古草原地上生物量及其时空变化

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摘要

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia's grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10~(12) g) over a total area of 66.01 × 10~4 km~2 between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R~2 = 0.05,p ＞ 0.05). (2) The mean AGB density was 28.9 g C m~(-2) over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R~2 = 0.41, p ＜ 0.001), while temperature seemed to be a minor factor (R~2 = 0.14, p ＜ 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R~2 = 0.45, p ＜ 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.
机译：了解草地生物量及其动力学对研究区域碳循环和草地资源的可持续利用至关重要。在这项研究中，我们使用田间样本，中分辨率成像光谱仪归一化差异植被指数（MODIS-NDVI）时间序列数据和基于统计模型，估算了2001年至2011年内蒙古草原的地上生物量（AGB）及其时空分布NDVI和AGB之间的关系。我们还探讨了AGB时空模式与气候因素之间的可能关系。得到以下结果：（1）2001年至2011年之间，AGB平均总面积为19.1 Tg C（1 Tg = 10〜（12）g），总面积为66.01×10〜4 km〜2，经历了总体波动（系数为差异= 9.43％），但没有随时间变化的显着趋势（R〜2 = 0.05，p＞ 0.05）。 （2）在11年的研究期内，整个研究区的平均AGB密度为28.9 g C m〜（-2），并且从草地的东北部向西南部减小，表现出较大的空间异质性。 （3）11年期间的AGB变化与1月至7月的降水模式密切相关，但是我们没有发现AGB与相应的温度变化之间存在显着的关系。降水也是研究区域AGB空间格局的重要因素（R〜2 = 0.41，p ＜0.001），而温度似乎是次要因素（R〜2 = 0.14，p ＜0.001）。结合了降水和温度影响的水分指数解释了AGB的变化要比单独的降水更大（R〜2 = 0.45，p ＜0.001）。我们的发现表明，针对不同植被状况建立单独的统计模型可以在较大的空间范围内降低AGB估算的不确定性。该研究为内蒙古畜牧业的草地管理和碳储量评估提供了支持。

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  • 来源
    《International journal of remote sensing》 |2013年第22期|7796-7810|共15页
  • 作者

    Tian Gao; Bin Xu; Xiuchun Yang; Yunxiang Jin; Hailong Ma; Jinya Li; Haida Yu;

  • 作者单位

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

    Key Laboratory of Agri-informatics of the Ministry of Agriculture, Institute of Agricultural Resources and Regional Planning, China Academy of Agriculture Sciences, Beijing, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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