Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data.
Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data.
复制标题
基于羊轨迹数据的放牧强度时空分布估计
DOI：
10.3390/s22041469
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发表时间：
2022-02-14
期刊：
影响因子：
--
通讯作者：
Meng Y
中科院分区：
文献类型：
--
作者：
Fan X;Xuan C;Zhang M;Ma Y;Meng Y
在我国北方干旱草原，不合理的放牧方式会降低草地植被的含水量和物种数。该项目使用太阳能驱动的GPS项圈来获取羊群放牧的跟踪数据。为了消除休息区和饮水区的轨迹数据，采用核密度分析方法对轨迹点数据进行聚类。同时，通过卫星遥感影像获取了试验区的植被指数，包括高程、坡度和坡向数据。因此，利用轨迹数据和遥感影像数据建立绵羊放牧强度的神经网络模型，模型精度较高。结果表明，植被指数、绵羊体重、持续时间、移动距离和环境温度的组合是模型的最佳输入参数，其决定系数和均方误差该模型得到的放牧强度误差最小，放牧强度的时空分布能够反映不同地点放牧强度的实际情况。对绵羊的放牧行为进行真实的实时监测，获得其放牧强度的时空分布，可以为科学放牧提供依据。
In the arid grasslands of northern China, unreasonable grazing methods can reduce the water content and species numbers of grassland vegetation. This project uses solar-powered GPS collars to obtain track data for sheep grazing. In order to eliminate the trajectory data of the rest area and the drinking area, the kernel density analysis method was used to cluster the trajectory point data. At the same time, the vegetation index of the experimental area, including elevation, slope and aspect data, was obtained through satellite remote sensing images. Therefore, using trajectory data and remote sensing image data to establish a neural network model of grazing intensity of sheep, the accuracy of the model could be high. The results showed that the best input parameters of the model were the combination of vegetation index, sheep weight, duration, moving distance and ambient temperature, where the coefficient of determination , and the mean square error MSE = 0.73. The error of grazing intensity obtained by the model is the smallest, and the spatial-temporal distribution of grazing intensity can reflect the actual situation of grazing intensity in different locations. Monitoring the grazing behavior of sheep in real time and obtaining the spatial-temporal distribution of their grazing intensity can provide a basis for scientific grazing.
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影响因子：
3.7
作者：
Triguero-Ocana, Roxana;Martinez-Lopez, Beatriz;Acevedo, Pelayo
通讯作者：
Acevedo, Pelayo
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影响因子：
2.7
作者：
Tong, C;Wu, J;Yong, W
通讯作者：
Yong, W
影响因子：
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作者：
Zhan Shu-Xia;Zheng Shu-Xia;Bai Yong-Fei
通讯作者：
Bai Yong-Fei
影响因子：
3.7
作者：
Parsons MB;Gillespie TR;Lonsdorf EV;Travis D;Lipende I;Gilagiza B;Kamenya S;Pintea L;Vazquez-Prokopec GM
通讯作者：
Vazquez-Prokopec GM
影响因子：
2.4
作者：
Laca, EA;WallisDeVries, MF
通讯作者：
WallisDeVries, MF