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

Abstract

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 km2, 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.

Superpixel-Based Regional-Scale Grassland Community Classification Using Genetic Programming with Sentinel-1 SAR and Sentinel-2 Multispectral Images

“…A classification method with high universality means that the method obtained in the current study area can be applied to other areas and still achieve promising results. Therefore, it has a higher application value than the methods with low universality [85,86]. Three key parts of the method that significantly affect the final classification results include segmenting the image at the optimal scale, selecting the optimal subset of features, and generating the optimal classification model.…”

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

“…Combined with relevant research progress at home and abroad, it can be seen that multirotor UAVs are the most commonly used types [8], mainly including quad-rotor, hexa-rotor and octo-rotor UAVs, among which octo-rotor UAVs are the most widely used. The overall payload of the UAV ranges from 2.5 kg to 6.0 kg, with a maximum payload of 50 kg [23,[27][28][29][30][31]. The flight altitude of UAVs varies from 10 to 100 meters, and some scholars have studied the impact of flight altitude on modelling accuracy [32,33].…”

“…The results showed that the images obtained by UAV remote sensing technology had high spatial resolution, and the overall accuracy of species classification was approximately 85%, which could provide a reference method for relevant studies [23]. Meng et al constructed a machine learning algorithm for grassland vegetation community classification by using UAV technology and combining high-resolution satellite images and topographic indices in alpine meadows of the Qinghai-Tibet Plateau and achieved spatial mapping of the Kobresia pygmaea community [27].…”

Herbaceous Dominant the Changes of Normalized Difference Vegetation Index in the Transition Zone Between Desert and Typical Steppe in Inner Mongolia, China

“…On the other hand, the FragMap system realized a repeated and long term monitoring (Yi, 2017), i.e., For each set of way points of GRID route, multiple flights with fixed height (20 m) could be executed at different times to conduct repeated monitoring of NDVI. Combined with BELT route (2 m), species monitoring is possible (Sun et al, 2018a;Wei et al, 2020;Meng et al, 2021). In our future studies, we will monitor dynamic changes in species and their NDVI over long-term period and over large regions, including the desert steppe, typical steppe, and the transition zone, to reveal the mechanism of the asymmetric response of ANPP to precipitation using a UAV.…”