High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data and Preprocessing
2.2.1. The Gridded Geographic Data
2.2.2. Livestock Statistics
3. Methodology
3.1. Machine Learning Methods
3.1.1. Support Vector Machine
3.1.2. Random Forest
3.1.3. Deep Neural Network
3.2. Livestock Density Estimation Models
3.2.1. Livestock Density Estimation
3.2.2. Livestock Density Adjustment
3.2.3. Performance Evaluation
4. Results
4.1. Gridded Livestock Distribution Maps
4.2. Spatiotemporal Changes of Livestock
5. Discussion
5.1. Comparison with the Open Access Gridded Livestock Datasets
5.2. The Reasonableness of the Hypothesis
5.3. Selection and Contribution of Environmental Factors
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Variables | Time 1 | Source | Initial Data Declaration |
---|---|---|---|---|
Environmental factors | Grassland coverage | 2000–2015 | Chinese Academy of Sciences Resource and Environmental Science Data Center (http://www.resdc.cn, accessed on 10 March 2021) | 100 m |
Arable land coverage | 2000–2015 | 100 m | ||
Forest land coverage | 2000–2015 | 100 m | ||
Desert coverage | 2000–2015 | 100 m | ||
NDVI | 2000–2015 | Geospatial Data Cloud (http://www.gscloud.cn, accessed on 19 March 2021) | 500 m | |
Elevation | 2000 | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, accessed on 21 December 2020) | 1000 m | |
Slope | 2000 | 1000 m | ||
Daytime surface temperature | 2000–2015 | Geospatial Data Cloud (http://www.gscloud.cn, accessed on 19 March 2021) | 1000 m | |
Precipitation | 2000–2015 | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, accessed on 25 March 2021) | 1000 m | |
Distance to river | 2000–2015 | Open Street Map (https://www.openstreetmap.org, accessed on 7 April 2021) | shapefile | |
Travel time to major cities | 2000, 2015 | Nelson A. D. et al., D. J. Weiss et al. | 1000 m | |
Population grid data | 2000–2015 | Resource and Environment Science and Data Center (https://www.resdc.cn, accessed on 10 April 2021) | 1000 m | |
GDP grid data | 2000–2015 | 1000 m | ||
Unsuitable areas | Permanent water | 2000–2015 | Chinese Academy of Sciences Resource and Environmental Science Data Center (http://www.resdc.cn, accessed on 10 March 2021) | 100 m |
Urban cores | 2000–2015 | Resource and Environment Science and Data Center (https://www.resdc.cn, accessed on 10 April 2021) | 1000 m | |
Protected areas | 2000–2015 | World Database of Protected Areas (WDPA) (https://www.protectedplanet.net/country/CHN, accessed on 14 April 2021) | shapefile | |
Pasture suitability | 2005 | United Nations Food and Agriculture Organization (https://data.apps.fao.org/map/catalog, accessed on 15 April 2021) | 10,000 m | |
Census | Stock data of cattle | 2000–2015 | China Statistical Yearbooks (http://www.stats.gov.cn/tjsj/pcsj/, accessed on 27 November 2020) | County |
Stock data of sheep | 2000–2015 | County |
Species | Model | Training Set | Test Set | ||
---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||
Cattle | SVM | 0.50 | 14.86 | 0.54 | 13.21 |
RF | 0.92 | 5.82 | 0.74 | 9.57 | |
DNN | 0.95 | 4.73 | 0.75 | 8.98 | |
Sheep | SVM | 0.55 | 43.38 | 0.52 | 52.65 |
RF | 0.93 | 19.59 | 0.72 | 34.58 | |
DNN | 0.96 | 14.71 | 0.73 | 33.97 |
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Li, X.; Hou, J.; Huang, C. High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning. Remote Sens. 2021, 13, 5038. https://doi.org/10.3390/rs13245038
Li X, Hou J, Huang C. High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning. Remote Sensing. 2021; 13(24):5038. https://doi.org/10.3390/rs13245038
Chicago/Turabian StyleLi, Xianghua, Jinliang Hou, and Chunlin Huang. 2021. "High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning" Remote Sensing 13, no. 24: 5038. https://doi.org/10.3390/rs13245038