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Acta Prataculturae Sinica ›› 2022, Vol. 31 ›› Issue (6): 23-34.DOI: 10.11686/cyxb2021180

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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

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