干旱区研究

干旱区研究 ›› 2016, Vol. 33 ›› Issue (5): 1028-1035.doi: 10.13866/j.azr.2016.05.16

• 生物资源 • 上一篇    下一篇

乌梁素海芦苇湿地遥感生物量估算研究

包菡1, 卓义1, 刘华民2, 刘东伟1, 清华1, 温璐1, 王立新1,3   

  1. 1.内蒙古大学环境与资源学院,内蒙古 呼和浩特 010021;
    2.内蒙古大学生命科学学院,内蒙古 呼和浩特 010021;
    3.内蒙古大学SUCCESS中心,内蒙古 呼和浩特 010021
  • 收稿日期:2015-01-14 修回日期:2015-04-16 出版日期:2016-09-15 发布日期:2025-12-01
  • 作者简介:包菡(1989-),女,蒙古族,硕士研究生,主要研究方向为湿地生态学和水环境学.E-mail:bao210han@126.com
  • 基金资助:
    国家自然科学基金项目(31560146,41562020,41571090);国家科技支撑计划项目(2011BAC02B03);内蒙古教育厅项目(NJYT-15-A02);内蒙古科技厅项目(20140707)资助

Estimation of RS-based Biomass of Phragmites communis in Wetland of the Ulansuhai Lake

BAO Han1, ZHUO Yi1, LIU Hua-min2, LIU Dong-wei1, QING Hua1, WEN Lu1, WANG Li-xin1,3   

  1. 1. School of Environment and Resources, Inner Mongolia University, Hohhot 010021, Inner Mongolia,China;
    2. School of Life Sciences, Inner Mongolia University, Hohhot 010021, Inner Mongolia,China;
    3. Sino-US Center for Conservation, Energy and Sustainability Science in Inner Mongolia, Hohhot 010021, Inner Mongolia,China
  • Received:2015-01-14 Revised:2015-04-16 Published:2016-09-15 Online:2025-12-01

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摘要： 以定量遥感的手段实现对内蒙古乌梁素海湿地生物量的估算。在定点监测芦苇生长周期内的生物量基础上,分析了各监测点生物量(鲜重和干重)实测数据与同期4种植被指数NDVI、DVI、PVI、RVI的相关关系。建立一元线性估算模型及多种非线性估算模型并进行对比分析。结果表明:对于乌梁素海而言,芦苇地上生物量(鲜重与干重)与所选4种植被指数均存在显著正相关关系,鲜重、干重的最优模型均是基于NDVI的三次多项式估算模型。精度检验结果显示:用NDVI三次多项式估算模型计算出的鲜重和干重的预测值与实测值较接近,鲜重的平均误差为19.90%,拟合精度达到80.10%;干重的平均误差为18.71%,拟合精度达到81.29%,可以满足乌梁素海地区芦苇生物量宏观估测的需要。通过分析2013年7月研究区芦苇总生物量干鲜重的空间分布图可得,乌梁素海地区芦苇干重在1 000~1 500 g·m-2,鲜重在3 000~4 500 g·m-2,且高生物量和低生物量相对较少。

关键词: TM影像, 芦苇, 生物量, 植被指数, 反演模型, 乌梁素海

Abstract: In this study, the biomass values of Phragmites communis in the Ulansuhai Lake in Inner Mongolia were estimated by the means of quantitative remote sensing. Based on monitoring the biomass in growing season of Phragmites communis on the fixed locations, the correlations between the measured data of fresh weight and dry weight at different monitoring points and the four vegetation indexes (NDVI, DVI, PVI and RVI) in the same period were analyzed. The linear estimation model and a variety of non-linear estimation models were developed. The results showed that there were the significant positive correlations between the aboveground biomass (including fresh weight and dry weight) of Phragmites communis in the Ulansuhai Lake and the selected four vegetation indices. The optimal models of fresh weight and dry weight were all based on the cubic polynomial estimation model of NDVI. The accuracy test results showed that the predicted values were close to the measured ones by using NDVI cubic polynomial model to calculate the fresh weight and dry weight. The average error of fresh weight was 19.90%, and the fitting accuracy was as high as 80.10%. The average error of dry weight was 18.71%, and the fitting accuracy reached 81.29%. These could meet the need of macro estimation of regional biomass of Phragmites communis in the Ulansuhai Lake. Through analyzing the spatial distribution of biomass of Phragmites communis in the study area in July 2013, it could be obtained that the dry and fresh weights of biomass of Phragmites communis varied in ranges of 1 000-1 500 g·m-2 and 3 000-4 500 g·m-2 respectively.

Key words: TM image, Phragmites communis, biomass, vegetation index, inversion model, Ulansuhai Lake