Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai-Tibetan Plateau

文献类型： 外文期刊

第一作者： Meng, Baoping

作者： Meng, Baoping;Yang, Zhigui;Sun, Yi;Zhang, Jianguo;Li, Meng;Lv, Yanyan;Yi, Shuhua;Meng, Baoping;Yang, Zhigui;Sun, Yi;Zhang, Jianguo;Li, Meng;Lv, Yanyan;Yi, Shuhua;Yu, Hongyan;Qin, Yu;Zhang, Wei;Chen, Jianjun;Wang, Zhiwei

作者机构：

关键词： Kobresia pygmaea community; unmanned aerial vehicle; Gaofen satellite; spatial distribution

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 13 期

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收录情况： SCI

摘要： The Kobresia pygmaea (KP) community is a key succession stage of alpine meadow degradation on the Qinghai-Tibet Plateau (QTP). However, most of the grassland classification and mapping studies have been performed at the grassland type level. The spatial distribution and impact factors of KP on the QTP are still unclear. In this study, field measurements of the grassland vegetation community in the eastern part of the QTP (Counties of Zeku, Henan and Maqu) from 2015 to 2019 were acquired using unmanned aerial vehicle (UAV) technology. The machine learning algorithms for grassland vegetation community classification were constructed by combining Gaofen satellite images and topographic indices. Then, the spatial distribution of KP community was mapped. The results showed that: (1) For all field observed sites, the alpine meadow vegetation communities demonstrated a considerable spatial heterogeneity. The traditional classification methods can hardly distinguish those communities due to the high similarity of their spectral characteristics. (2) The random forest method based on the combination of satellite vegetation indices, texture feature and topographic indices exhibited the best performance in three counties, with overall accuracy and Kappa coefficient ranged from 74.06% to 83.92% and 0.65 to 0.80, respectively. (3) As a whole, the area of KP community reached 1434.07 km(2), and accounted for 7.20% of the study area. We concluded that the combination of satellite remote sensing, UAV surveying and machine learning can be used for KP classification and mapping at community level.

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