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Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China

1
State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
2
Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture, Lanzhou University, Lanzhou 730020, China
3
Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
4
Laboratory for Remote Sensing and Geoinformatics, University of Texas at San Antonio, San Antonio, TX 78249, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 320; https://doi.org/10.3390/rs10020320
Received: 10 January 2018 / Revised: 7 February 2018 / Accepted: 16 February 2018 / Published: 21 February 2018
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
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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. View Full-Text
Keywords: grassland cover; unmanned aerial vehicle; multi-factor; inversion model; dynamic variation grassland cover; unmanned aerial vehicle; multi-factor; inversion model; dynamic variation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Meng, B.; Gao, J.; Liang, T.; Cui, X.; Ge, J.; Yin, J.; Feng, Q.; Xie, H. Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China. Remote Sens. 2018, 10, 320.

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