Abstract
Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.
Highlights
Vegetation cover is a direct quantitative index, reflecting vegetation growth conditions, and is a key parameter in the estimation and monitoring of ecosystems and their functions, especially when remote-sensing methods are used, and on the expansive grasslands of the TibetanPlateau [1]
In Gannan Prefecture, the average cover values ranged from 58.37% to 83.83% over the three-year period, and the coefficient of variation ranged from 0.17 to 0.43
The three highest average values of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and cover were found in Maqu, Lintan and Luqu, respectively
Summary
Vegetation cover is a direct quantitative index, reflecting vegetation growth conditions, and is a key parameter in the estimation and monitoring of ecosystems and their functions, especially when remote-sensing methods are used, and on the expansive grasslands of the TibetanPlateau [1]. (2) Sampling methods, including quadrat sampling, belt transect sampling, point count, and shadow methods, are based on a quadrat (50 cm × 50 cm or 1 m × 1 m in grassland), and their accuracy is based on the number of quadrats examined and on a logical sampling strategy. These methods require considerable time and energy to obtain high accuracy [4]. SQS and TQS can increase accuracy in the measurement of grassland cover, these methods are not suitable for field observations because of the inconvenience associated with the equipment involved. UAVs have presented great potential for use by ecologists and have been evaluated as a valuable tool to replace traditional methods [16,17,18,19,20]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
