Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data
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 drin...
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1469 |
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| author | Xiantao Fan Chuanzhong Xuan Mengqin Zhang Yanhua Ma Yunqi Meng |
| author_facet | Xiantao Fan Chuanzhong Xuan Mengqin Zhang Yanhua Ma Yunqi Meng |
| author_sort | Xiantao Fan |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.97</mn></mrow></semantics></math></inline-formula>, and the mean square error <i>MSE</i> = 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. |
| first_indexed | 2024-03-09T21:06:03Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:06:03Z |
| publishDate | 2022-02-01 |
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| series | Sensors |
| spelling | doaj.art-4f718a5c4662489aba6cf719730419f92023-11-23T21:59:59ZengMDPI AGSensors1424-82202022-02-01224146910.3390/s22041469Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory DataXiantao Fan0Chuanzhong Xuan1Mengqin Zhang2Yanhua Ma3Yunqi Meng4College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot 010018, ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot 010018, ChinaIn 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.97</mn></mrow></semantics></math></inline-formula>, and the mean square error <i>MSE</i> = 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.https://www.mdpi.com/1424-8220/22/4/1469trajectory datagrazing sheepneural networkgrazing intensityspatial-temporal distribution |
| spellingShingle | Xiantao Fan Chuanzhong Xuan Mengqin Zhang Yanhua Ma Yunqi Meng Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data Sensors trajectory data grazing sheep neural network grazing intensity spatial-temporal distribution |
| title | Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data |
| title_full | Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data |
| title_fullStr | Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data |
| title_full_unstemmed | Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data |
| title_short | Estimation of Spatial-Temporal Distribution of Grazing Intensity Based on Sheep Trajectory Data |
| title_sort | estimation of spatial temporal distribution of grazing intensity based on sheep trajectory data |
| topic | trajectory data grazing sheep neural network grazing intensity spatial-temporal distribution |
| url | https://www.mdpi.com/1424-8220/22/4/1469 |
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