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

doi:10.11686/cyxb2021180

Abstract

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 （NDVI_STARFM） 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 NDVI_STARFM 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 NDVI_STARFM 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

Yu-zhuo ZHANG, Zhi-gui YANG, Hong-yan YU, Qiang ZHANG, Shu-xia YANG, Ting ZHAO, Hua-hua XU, Bao-ping MENG, Yan-yan LV. 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[J]. Acta Prataculturae Sinica, 2022, 31(6): 23-34.

Figures/Tables 12

References 35

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MODIS影像日期 Date of MODIS image	Landsat影像日期 Date of Landsat image	采样时间 Sampling time	样地数 Number of plots	样方数 Number of samples
2013-08-13	2013-08-08	2013-08-08	11	55
2014-07-28	2014-07-26	2014-07-28	13	65
2015-07-12	2015-07-13	2015-07-13	13	65
2016-07-27	2016-07-31	2016-07-26	11	55

MODIS影像日期 Date of MODIS image	Landsat影像日期 Date of Landsat image	采样时间 Sampling time	样地数 Number of plots	样方数 Number of samples
2013-08-13	2013-08-08	2013-08-08	11	55
2014-07-28	2014-07-26	2014-07-28	13	65
2015-07-12	2015-07-13	2015-07-13	13	65
2016-07-27	2016-07-31	2016-07-26	11	55

植被指数和生物量Vegetation index and biomass	最大值Max	最小值Min	平均值Mean	标准偏差STD	变异系数CV
NDVI_MODIS	0.84	0.58	0.74	0.08	0.15
NDVI_Landsat	0.91	0.55	0.77	0.10	0.13
NDVI_STARFM	0.95	0.47	0.72	0.11	0.17
生物量Biomass （kg·hm^-2）	3997.33	745.52	2299.30	972.60	0.42

植被指数和生物量Vegetation index and biomass	最大值Max	最小值Min	平均值Mean	标准偏差STD	变异系数CV
NDVI_MODIS	0.84	0.58	0.74	0.08	0.15
NDVI_Landsat	0.91	0.55	0.77	0.10	0.13
NDVI_STARFM	0.95	0.47	0.72	0.11	0.17
生物量Biomass （kg·hm^-2）	3997.33	745.52	2299.30	972.60	0.42

植被指数 Vegetation index	线性Linear		对数Logarithmic		乘幂Power		指数Exponential
植被指数 Vegetation index	R²	RMSE	R²	RMSE	R²	RMSE	R²	RMSE
NDVI_MODIS	0.17	917.21	0.17	916.80	0.24	937.79	0.24	940.72
NDVI_Landsat	0.65	586.72	0.66	576.76	0.76	634.83	0.75	665.32
NDVI_STARFM	0.49	714.93	0.52	690.97	0.58	795.62	0.54	847.99