高寒草地生物量变化对归一化物候指数的响应
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甘肃省委组织部2022年度陇原青年创新创业人才项目(甘组通字[2022]77号);国家自然科学基金资助项目(41801191)


Dose the normalized difference phenology index have the potential to reflect the changes of aboveground biomass of alpine grassland?
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    摘要:

    准确评价草地地上生物量(Above-ground biomass,AGB)对草地资源的可持续利用和保护具有重要意义。以甘南为典型研究区,利用2019-2021年Sentinel-2地表反射率和野外实测地上生物量数据,借助GEE (Google Earth Engine)平台和数理统计方法评价了9种植被指数对高寒草地AGB的估算精度,构建了高寒草地地上生物量反演模型,在此基础上分析了2019-2021年甘南州草地产量的时空动态变化。结果表明:在所有植被指数中,归一化物候指数(Normalized difference phenology index,NDPI)与草地AGB的R2值最高(0.72),其次为归一化植被指数(normalized difference vegetation index,NDVI)(R2=0.68),拟合效果最差的为增强型植被指数(enhanced vegetation index,EVI)(R2=0.37)和差值植被指数(different vegetation index,DVI)(R2=0.40),NDPI对高寒草地AGB更为敏感;在NDPI构建的4类估算模型中,乘幂模型的预测精度最高,基于NDPI的回归模型明显提高了高寒草地AGB预测精度;2019-2021年甘南高寒草地地上生物量空间分布差异明显,表现为西南部较高,东部和北部较低;对不同草地类型而言,山地草甸AGB多年平均值略高于高寒草甸和沼泽;过去3年间,高寒草甸和山地草甸地上生物量呈先增加后减小的趋势,沼泽草地则呈持续降低的趋势。基于本研究的结果,NDPI能够反映大范围的高寒草地生物量的时空变化特征。

    Abstract:

    The accurate assessment of grassland above-ground biomass (AGB) is crucial for the sustainable utilization and protection of grassland resources. In this study, the Gannan prefecture was selected as a representative case study area. The AGB estimation accuracies of 9 vegetation indices were evaluated and the final estimation model were established based on the Sentinel-2 surface reflectance products and field measurements of AGB from 2019 to 2021. The Google Earth Engine (GEE) platform and mathematical statistics method were employed for analysis. Then, the spatio-temporal dynamics of grassland AGB were analyzed in Gannan Prefecture from 2019 to 2021. The results indicated that the R2 value between the normalized difference phenology index (NDPI) and AGB was the highest (0.72), followed by normalized difference vegetation index (NDVI) (0.68), and the R2 values of the enhanced vegetation index (EVI) (0.37) and different vegetation index (DVI) (0.40) were the lowest, among all vegetation indices. The NDPI was more sensitive to grassland AGB than other vegetation indices. Among the four NDPI based estimation models, the power model showed the best performance in grassland AGB prediction, and the prediction accuracy of AGB in alpine grassland was obviously improved. From 2019 to 2021, the spatial distributions of AGB in alpine grassland were significantly different, and with higher values in the southwestern and lower values in the eastern and northern. The average AGB of mountain meadow was slightly larger than that of alpine meadow and swamp wetland. The AGB of alpine meadow and mountain meadow increased and then decreased, while the swamp wetland continued a decreasing trend over the past three years. Our findings suggest that NDPI can reflect the spatio-temporal dynamics of alpine grassland AGB on a large scale.

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于惠,杨世君,李晶,蔡海珍,李丽.高寒草地生物量变化对归一化物候指数的响应.生态学报,2023,43(19):8057~8065

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