Grassland health assessment based on indicators monitored by UAVs: a case study at a household scale

Grassland health assessment (GHA) is a bridge of study and management of grassland ecosystem. However, there is no standardized quantitative indicators and long-term monitor methods for GHA at a large scale, which may hinder theoretical study and practical application of GHA. In this study, along with previous concept and practices (i.e., CVOR, the integrated indexes of condition, vigor, organization and resilience), we proposed an assessment system based on the indicators monitored by unmanned aerial vehicles (UAVs)-UAVCVOR, and tested the feasibility of UAVCVOR at typical household pastures on the Qinghai-Tibetan Plateau, China. Our findings show that: (1) the key indicators of GHA could be measured directly or represented by the relative counterpart indicators that monitored by UAVs, (2) there was a significantly linear relationship between CVOR estimated by field- and UAV-based data, and (3) the CVOR decreased along with the increasing grazing intensity nonlinearly, and there are similar tendencies of CVOR that estimated by the two methods. These findings suggest that UAVs is suitable for GHA efficiently and correctly, which will be useful for the protection and sustainable management of grasslands.

An Improved Method for Monitoring Multiscale Plant Species Diversity of Alpine Grassland Using UAV: A Case Study in the Source Region of the Yellow River, China

Plant species diversity (PSD) is essential in evaluating the function and developing the management and conservation strategies of grassland. However, over a large region, an efficient and high precision method to monitor multiscale PSD (α-, β-, and γ-diversity) is lacking. In this study, we proposed and improved an unmanned aerial vehicle (UAV)-based PSD monitoring method (UAV_B) and tested the feasibility, and meanwhile, explored the potential relationship between multiscale PSD and precipitation on the alpine grassland of the source region of the Yellow River (SRYR), China. Our findings showed that: (1) UAV_B was more representative (larger monitoring areas and more species identified with higher α- and γ-diversity) than the traditional ground-based monitoring method, though a few specific species (small in size) were difficult to identify; (2) UAV_B is suitable for monitoring the multiscale PSD over a large region (the SRYR in this study), and the improvement by weighing the dominance of species improved the precision of α-diversity (higher R ² and lower P values of the linear regressions); and (3) the species diversity indices (α- and β-diversity) increased first and then they tended to be stable with the increase of precipitation in SRYR. These findings conclude that UAV_B is suitable for monitoring multiscale PSD of an alpine grassland community over a large region, which will be useful for revealing the relationship of diversity-function, and helpful for conservation and sustainable management of the alpine grassland.