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草业学报 ›› 2022, Vol. 31 ›› Issue (6): 23-34.DOI: 10.11686/cyxb2021180

• 研究论文 • 上一篇    下一篇

基于STARFM的草地地上生物量遥感估测研究——以甘肃省夏河县桑科草原为例

张玉琢1(), 杨志贵1, 于红妍2, 张强3, 杨淑霞3, 赵婷1, 许画画1, 孟宝平1(), 吕燕燕1   

  1. 1.南通大学脆弱生态研究所,地理科学学院,江苏 南通 226007
    2.祁连山国家公园青海服务保障中心,青海 西宁 810001
    3.甘肃省环境监测中心站,甘肃 兰州 730020
  • 收稿日期:2021-05-07 修回日期:2021-06-21 出版日期:2022-06-20 发布日期:2022-05-11
  • 通讯作者: 孟宝平
  • 作者简介:E-mail: mengbp09@lzu.edu.cn
    张玉琢(2001-),女,江苏南通人,在读本科。E-mail: 1822021005@stmail.ntu.edu.cn
  • 基金资助:
    国家重点研发(2017YFA0604801);国家自然科学基金项目(42071056)

Estimating grassland above ground biomass based on the STARFM algorithm and remote sensing data——A case study in the Sangke grassland in Xiahe County, Gansu Province

Yu-zhuo ZHANG1(), Zhi-gui YANG1, Hong-yan YU2, Qiang ZHANG3, Shu-xia YANG3, Ting ZHAO1, Hua-hua XU1, Bao-ping MENG1(), Yan-yan LV1   

  1. 1.Institute of Fragile Eco-environment,School of Geographic Science,Nantong University,Nantong 226007,China
    2.Qinghai Service and Guarantee Center of Qilian Mountain National Park,Xining 810001,China
    3.Gansu Environmental Monitoring Center Station,Lanzhou 730020,China
  • Received:2021-05-07 Revised:2021-06-21 Online:2022-06-20 Published:2022-05-11
  • Contact: Bao-ping MENG

摘要:

遥感数据具有实时、动态、大范围等特点,在草地资源监测与管理研究中获得了广泛应用。然而,单一的遥感植被指数无法同时满足草地地上生物量观测中时空分辨率的需求。因此,本研究基于时间序列Landsat NDVI和MODIS NDVI数据,结合时空融合算法(spatial and temporal adaptive reflectance fusion model, STARFM),生成了2000-2016年高时空分辨率的植被指数数据集(NDVISTARFM,时间分辨率为16 d,空间分辨率为30 m),并基于2013-2016年地面实测草地地上生物量数据,构建了夏河县桑科草原高寒草地地上生物量遥感反演模型,分析了2000-2016年研究区草地地上生物量生长状况和变化趋势。结果表明:1)基于NDVISTARFM的最优估测模型为乘幂模型,其R2为0.58,均方根误差(root mean square error, RMSE)为795.62 kg·hm-2,模型的表现能力次于Landsat NDVI最优估测模型(R2=0.76,RMSE=634.83 kg·hm-2),而优于MODIS NDVI最优估测模型(R2=0.24,RMSE=937.79 kg·hm-2);2)基于NDVISTARFM最优估测模型对各样区草地地上生物量总产的估测精度优于MODIS NDVI而次于Landsat NDVI,总体精度达84.05%;3)2000-2016年来,夏河县研究区草地地上生物量总体呈现增加趋势,其中90%左右的区域年增量大于30 kg·hm-2,草地地上生物量呈现减少趋势的区域仅占2.30%。

关键词: 高寒草甸, STARFM, 生物量估测模型, 时空动态变化, MODIS, Landsat

Abstract:

Characteristics of remote sensing data include that they are real-time, dynamic and large-scale, so such data have been widely used in grassland resource monitoring and management research. However, a single remote sensing vegetation index can not meet the needs of temporal and spatial resolution in grassland above ground biomass (AGB) monitoring. Therefore, this study generated a high spatial and temporal resolution vegetation index data set based on a time series of Landsat NDVI and MODIS NDVI data, combined with the spatial and temporal adaptive reflectance fusion model (STARFM). The data set so generated (NDVISTARFM) had a temporal resolution 16 d and a spatial resolution 30 m. The optimum grassland above ground biomass inversion model was constructed based on measured grassland above ground biomass and NDVISTARFM during the grass growth seasons of 2013-2016. Finally, the spatiotemporal dynamic variation trends of grassland above ground biomass in the study area were analyzed for the period from 2000-2016. It was found that: 1) the optimal estimation model based on NDVISTARFM was a power model, with an R2 of 0.58 and an RMSE 795.62 kg·ha-1. The performance of this model was lower than that of the Landsat NDVI optimal estimation model (R2 =0.76, RMSE=634.83 kg·ha-1), but better than that of the MODIS NDVI optimal estimation model (R2 =0.24, RMSE=937.79 kg·ha-1). 2) The overall accuracy of the optimal estimation model was 84.05%, it was higher than that of MODIS NDVI but lower than that of Landsat NDVI. 3) The grassland above ground biomass showed an increasing trend in most areas from 2000-2016. About 90% of the study area showed an increasing trend with annual increment more than 30 kg·ha-1, while only 2.3% of the study area showed a decreasing trend.

Key words: alpine meadow, STARFM algorithm, biomass estimation model, spatiotemporal dynamic change, MODIS, Landsat