Browsing by Author "Ma Q"

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    Predicting the distribution of oxytropis ochrocephala bunge in the source region of the yellow river (China) based on uav sampling data and species distribution model
    (1/12/2021) Zhang X; Yuan Y; Zhu Z; Ma Q; Yu H; Li M; Ma J; Yi S; He XZ; Sun Y
    Oxytropis ochrocephala Bunge is an herbaceous perennial poisonous weed. It severely affects the production of local animal husbandry and ecosystem stability in the source region of Yellow River (SRYR), China. To date, however, the spatiotemporal distribution of O. ochrocephala is still unclear, mainly due to lack of high-precision observation data and effective methods at a regional scale. In this study, an efficient sampling method, based on unmanned aerial vehicle (UAV), was proposed to supply basic sampling data for species distribution models (SDMs, BIOMOD in this study). A total of 3232 aerial photographs were obtained, from 2018 to 2020, in SRYR, and the potential and future distribution of O. ochrocephala were predicted by an ensemble model, consisting of six basic models of BIOMOD. The results showed that: (1) O. ochrocephala mainly distributed in the southwest, middle, and northeast of the SRYR, and the high suitable habitat of O. ochrocephala accounted for 3.19%; (2) annual precipitation and annual mean temperature were the two most important factors that affect the distribution of O. ochrocephala, with a cumulative importance of 60.45%; and (3) the distribution probability of O. ochrocephala tends to increase from now to the 2070s, while spatial distribution ranges will remain in the southwest, middle, and northeast of the SRYR. This study shows that UAVs can potentially be used to obtain the basic data for species distribution modeling; the results are both beneficial to establishing reasonable management practices and animal husbandry in alpine grassland systems.
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    UAV Assisted Livestock Distribution Monitoring and Quantification: A Low-Cost and High-Precision Solution
    (MDPI AG, 29/09/2023) Ji W; Luo Y; Liao Y; Wu W; Wei X; Yang Y; Shen Y; Ma Q; He X; Yi S; Sun Y
    Grazing management is one of the most widely practiced land uses globally. Quantifying the spatiotemporal distribution of livestock is critical for effective management of livestock-grassland grazing ecosystem. However, to date, there are few convincing solutions for livestock dynamic monitor and key parameters quantification under actual grazing situations. In this study, we proposed a pragmatic method for quantifying the grazing density (GD) and herding proximities (HP) based on unmanned aerial vehicles (UAVs). We further tested its feasibility at three typical household pastures on the Qinghai-Tibetan Plateau, China. We found that: (1) yak herds grazing followed a rotational grazing pattern spontaneously within the pastures, (2) Dispersion Index of yak herds varied as an M-shaped curve within one day, and it was the lowest in July and August, and (3) the average distance between the yak herd and the campsites in the cold season was significantly shorter than that in the warm season. In this study, we developed a method to characterize the dynamic GD and HP of yak herds precisely and effectively. This method is ideal for studying animal behavior and determining the correlation between the distribution of pastoral livestock and resource usability, delivering critical information for the development of grassland ecosystem and the implementation of sustainable grassland management.