Monitoring of above-ground biomass in alpine grassland based on agricultural digital camera and MODIS remote sensing data: A case study in the Yellow River Headwater Region

doi:10.11686/cyxb2017010

Abstract

Abstract: We collected grassland biomass and MODIS satellite remote sensing data, and calculated vegetation indices (VIs) from data obtained by an agricultural digital camera (ADC) in the Yellow River Headwater Region (YRHR) in August of 2015-2016. We explored the correlations between each of three ADC vegetation indices (NDVI_ADC, SAVI_ADC, and GNDVI_ADC) and field-measured grassland above-ground biomass (AGB), and selected the optimal ADC vegetation index to construct an AGB inversion model. Grassland AGB inversion models based on ADC vegetation indices and MODIS NDVI (denoted as NDVI_MOD) were constructed, and the accuracy of each model was evaluated by leave-one-out cross validation (LOOCV) to identify the optimal grassland AGB monitoring model. The NDVI_ADC was used to correct the NDVI_MOD to obtain the optimized grassland AGB model with high resolution and accuracy. The results showed that: 1) among the three VIs-based ADC indices, the NDVI_ADC was most closely related to the AGB of alpine grassland, followed by SAVI_ADC and GNDVI_ADC. 2) The NDVI_ADC-based AGB monitoring model (RMSEP: 383.55-393.18 kg DW/ha; r: 0.65-0.66) was more accurate than the NDVI_MOD model (RMSEP: 421.08-427.00 kg DW/ha; r: 0.55-0.58). Therefore, the grassland AGB inversion value from the NDVI_ADC-based model was much closer to the actual grassland AGB in YRHR, and the sampling values of NDVI_MOD were higher than those of NDVI_ADC as a whole. 3) Among the four models based on NDVI_ADC, the linear and power models showed better performance in grassland AGB simulations. The linear model (y=3248.93×NDVI_ADC-305.59, RMSEP=383.55 kg DW/ha, r=0.66) was more accurate than the power model, and the linear model provided a novel and simple method to estimate grassland biomass in the study area.4) There was a strong correlation between NDVI_ADC and NDVI_MOD; therefore, we could obtain an optimized grassland AGB model by using NDVI_ADC to correct NDVI_MOD. The optimized model was y=2455.54×NDVI_MOD-301.69. This model could be used to estimate the grassland biomass in YRHR on a large scale, and its precision was close to that of the field measurements.

GE Jing, MENG Bao-Ping, YANG Shu-Xia, GAO Jin-Long, YIN Jian-Peng, ZHANG Ren-Ping, FENG Qi-Sheng, LIANG Tian-Gang. Monitoring of above-ground biomass in alpine grassland based on agricultural digital camera and MODIS remote sensing data: A case study in the Yellow River Headwater Region[J]. Acta Prataculturae Sinica, 2017, 26(7): 23-34.

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