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
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
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基于无人机技术和多因子方法的高寒草地覆盖建模——以青藏高原东部为例
DOI：
10.3390/rs10020320
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发表时间：
2018
期刊：
影响因子：
5
通讯作者：
Xie Hongjie
中科院分区：
文献类型：
--
作者：
Meng Baoping;Gao Jinlong;Liang Tiangang;Cui Xia;Ge Jing;Yin Jianpeng;Feng Qisheng;Xie Hongjie
草地覆盖及其随时间的变化是估算和监测生态系统及其功能的关键参数，特别是通过遥感进行估算和监测。然而，最适合于高寒草甸草地植被覆盖度估算的模型以及模型之间的差异却很少被研究。利用无人机（UAV）技术，对甘南州2014 - 2016年草地覆盖进行了实地测量。基于14个影响草地覆盖的因子，构建了单因子参数和多因子参数/非参数覆盖反演模型，分析了年最大覆盖度的动态变化。结果表明：（1）经度、纬度、海拔、土壤粘粒和砂粒含量、温度、降水、增强植被指数（EVI）和归一化植被指数（NDVI）等14个因子中，有9个因子对研究区草地覆盖度有显著影响。基于EVI的对数模型表现最好，R2和RMSE分别为0.52%和16.96%。单因子草地覆盖反演模型仅占生长季节覆盖变化的1-49%。(2)人工神经网络（BP-ANN）是草地覆盖率反演的最佳模型，其R2和RMSE分别为0.72%和13.38%，SDs分别为0.062%和1.615%。BP神经网络模型的准确性和稳定性均高于单因素参数模型和多因素参数/非参数模型。(3)甘南州的年最大覆盖度在整个研究区的60.60%以上呈增加趋势，36.54%目前保持稳定，2.86%呈减少趋势。
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.