Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity

Accurate approaches to aboveground biomass (AGB) estimation are required to support appraisal of the effectiveness of land use measures, which seek to protect grazing-adapted grasslands atop the Qinghai-Tibet Plateau (QTP). This methodological study assesses the effectiveness of one commonly used visible band vegetation index, Red Green Blue Vegetation Index (RGBVI), obtained from unmanned aerial vehicle (UAV), in estimating AGB timely and accurately at the local scale, seeking to improve the estimation accuracy by taking into account in situ collected information on disturbed grassland. Particular emphasis is placed upon the mapping and quantification of areas disturbed by grazing (simulated via mowing) and plateau pika (Ochotona curzoniae) that have led to the emergence of bare ground. The initial model involving only RGBVI performed poorly in AGB estimation by underestimating high AGB by around 10% and overestimating low AGB by about 10%. The estimation model was modified by the mowing intensity ratio and bare ground metrics. The former almost doubled the estimation accuracy from R-2 = 0.44 to 0.81. However, this modification caused the bare ground AGB to be overestimated by about 38 and 19 g m(-2) for 2018 and 2019, respectively. Although further modification of the model by bare ground metrics improved the accuracy slightly to 0.88, it markedly reduced the overestimation of low AGB values. It is recommended that grazing intensity be incorporated into the micro-scale estimation of AGB, together with the bare ground modification metrics, especially for severely disturbed meadows with a sizable portion of bare ground.

Automated summary

This study evaluates the effectiveness of a commonly used visible band vegetation index in estimating aboveground biomass on the Qinghai-Tibet Plateau grasslands, and demonstrates that introducing mowing intensity ratio and bare ground metrics can improve estimation accuracy, especially for severely disturbed meadows.