气候变化领域集成服务门户(CCPortal): High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning

CCPortal

DOI	10.3390/rs13245038
	High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning
	Li, Xianghua; Hou, Jinliang; Huang, Chunlin
通讯作者	Hou, JL (通讯作者)，Northwest Inst Ecoenvironm & Resources, Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China. ; Hou, JL (通讯作者)，Northwest Inst Ecoenvironm & Resources, Chinese Acad Sci, Heihe Remote Sensing Expt Res Stn, Zhangye 734000, Peoples R China.
发表日期	2021
EISSN	2072-4292
卷号	13 期号:24
英文摘要	Accurate high-resolution gridded livestock distribution data are of great significance for the rational utilization of grassland resources, environmental impact assessment, and the sustainable development of animal husbandry. Traditional livestock distribution data are collected at the administrative unit level, which does not provide a sufficiently detailed geographical description of livestock distribution. In this study, we proposed a scheme by integrating high-resolution gridded geographic data and livestock statistics through machine learning regression models to spatially disaggregate the livestock statistics data into 1 km x 1 km spatial resolution. Three machine learning models, including support vector machine (SVM), random forest (RF), and deep neural network (DNN), were constructed to represent the complex nonlinear relationship between various environmental factors (e.g., land use practice, topography, climate, and socioeconomic factors) and livestock density. By applying the proposed method, we generated a set of 1 km x 1 km spatial distribution maps of cattle and sheep for western China from 2000 to 2015 at five-year intervals. Our projected cattle and sheep distribution maps reveal the spatial heterogeneity structures and change trend of livestock distribution at the grid level from 2000 to 2015. Compared with the traditional census livestock density, the gridded livestock distribution based on DNN has the highest accuracy, with the determinant coefficient (R-2) of 0.75, root mean square error (RMSE) of 9.82 heads/km(2) for cattle, and the R-2 of 0.73, RMSE of 31.38 heads/km(2) for sheep. The accuracy of the RF is slightly lower than the DNN but higher than the SVM. The projection accuracy of the three machine learning models is superior to those of the published Gridded Livestock of the World (GLW) datasets. Consequently, deep learning has the potential to be an effective tool for high-resolution gridded livestock projection by combining geographic and census data.
关键词	PATTERNS DUCKS
英文关键词	machine learning; livestock; spatialization; western China
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000737233900001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/253620
作者单位	[Li, Xianghua; Hou, Jinliang; Huang, Chunlin] Northwest Inst Ecoenvironm & Resources, Chinese Acad Sci, Key Lab Remote Sensing Gansu Prov, Lanzhou 730000, Peoples R China; [Li, Xianghua; Hou, Jinliang; Huang, Chunlin] Northwest Inst Ecoenvironm & Resources, Chinese Acad Sci, Heihe Remote Sensing Expt Res Stn, Zhangye 734000, Peoples R China; [Li, Xianghua] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Li, Xianghua,Hou, Jinliang,Huang, Chunlin. High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning[J]. 中国科学院西北生态环境资源研究院,2021,13(24).
APA	Li, Xianghua,Hou, Jinliang,&Huang, Chunlin.(2021).High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning.REMOTE SENSING,13(24).
MLA	Li, Xianghua,et al."High-Resolution Gridded Livestock Projection for Western China Based on Machine Learning".REMOTE SENSING 13.24(2021).