2006—2020年蒙古国放牧密度数据集

doi:10.19788/j.issn.2096-6369.100037

摘要/Abstract

摘要：

蒙古国草地系统的健康状况关系着其畜牧业效益和国内外生态安全。衡量牲畜放牧密度并合理控制放牧密度对于维护蒙古国草地生态系统健康以及实现畜牧业的可持续发展具有重要意义。空间放牧密度梯度信息的缺失阻碍了对草地承载力相关研究的推进。本研究基于2015年世界网格化牲畜数据集（gridded livestock of the world，GLW）、牧区人口密度、土壤水分、年降水、地表温度和净初级生产力（net primary productivity，NPP）等空间数据，利用谷歌地球引擎（Google Earth Engine，GEE）云平台运行随机森林回归算法，建立了蒙古国放牧密度估算模型；基于省域牲畜存栏量统计数据检验了模型的准确性，并结合不同年份预测因子数据，模拟了蒙古国2006—2020年放牧密度空间分布。为确保数据集的准确性，采用判定系数（R²）、平均绝对误差（MAE）和均方根误差（RMSE）三个误差测量指标对数据集进行校验。模拟结果显示，2006—2020年蒙古国放牧密度在空间上整体呈现北高南低的特点；2006—2010年蒙古国放牧密度扩张明显，放牧密度高于5 TLU/km²区域面积占比由0.223%增加到51.390%；2010—2020年，蒙古国大部分地区放牧密度无显著变化。检验结果表明，该数据集较好地实现了蒙古国放牧密度空间化的模拟，2006、2010、2015和2020年模拟数据与蒙古国省域牲畜存栏量拟合R²分别为0.844、0.734、0.914、和0.926，均通过显著性检验，MAE分别为5.195、3.513、2.336、3.461，RMSE分别为8.135、5.257、4.200、5.909。本研究提供的蒙古国放牧密度数据集对该地区草地生态系统的可持续发展以及牧民的生计安全提供了重要信息支撑。

数据摘要：

项目	描述
数据库（集）名称	2006—2020年蒙古国放牧密度数据集
所属学科	测绘科学与技术
研究主题	蒙古国放牧密度数据集估算
数据时间范围	2006年、2010年、2015年、2020年
时间分辨率	年
数据地理空间覆盖	蒙古国
空间分辨率	1 km
数据类型与技术格式	.tif
数据库（集）组成	2006年、2010年、2015年、2020年蒙古国放牧密度数据集
数据量	36.37 MB
主要数据指标	牧区人口密度、土壤水分、年降水、地表温度、NPP
数据可用性	https://doi.org/17058.11.sciencedb.agriculture.00047 https://cstr.cn/10.57760/sciencedb.agriculture.00047
经费支持	国家重点研发项目（2022YFE0119200），蒙古科学技术基金会（NSFC_2022/01, CHN2022/276）

关键词: 牲畜放牧密度, GEE云平台, 随机森林回归模型, 蒙古国

Abstract:

The health of Mongolia's grassland system is related to the efficiency of its livestock husbandry and ecological security at home and abroad. Measuring and controlling livestock grazing density is important for maintaining the health of Mongolia's grassland ecosystems and realizing the sustainable development of the livestock industry. The lack of information on spatial grazing density gradients has hindered the advancement of research related to grassland carrying capacity.This study is based on the 2015 gridded livestock of the world (GLW) dataset, population density, soil moisture, annual precipitation, surface temperature and net primary productivity (NPP). Using the Google Earth Engine (GEE) cloud platform to run the random forest regression algorithm, the Mongolian grazing density estimation model was established. The accuracy of the model was tested based on the statistical data of livestock stocks in the province, and combined with the predictor data of different years, the spatial distribution of the grazing density in Mongolia from 2006 to 2020 was simulated. In order to ensure the accuracy of the dataset, three error measurement indexes of decision coefficient (R²), mean absolute error (MAE) and root mean square error (RMSE) were used to verify the dataset. The simulation results showed that the grazing density in Mongolia from 2006 to 2020 was higher in the north and lower in the south. From 2006 to 2010, Mongolia grazing density expanded significantly, and the proportion of grazing density above 5 TLU/km² increased from 0.223% to 51.390%. There was no significant change in grazing density in most areas of Mongolia from 2010 to 2020. The test results showed that the dataset could well realize the spatial simulation of grazing density in Mongolia. The fitting R² of the simulation data in 2006, 2010, 2015 and 2020 with the livestock stocks in Mongolia province were 0.844, 0.734, 0.914 and 0.926, respectively, which passed the significance test. MAE were 5.195, 3.513, 2.336, 3.461, and RMSE were 8.135, 5.257, 4.200, 5.909, respectively. The grazing density dataset in Mongolia provided by this study provides important information support for the sustainable development of grassland ecosystem and the livelihood security of herders in this region.

Data summary:

Item	Description
Dataset name	Mongolia Grazing Density Dataset from 2006 to 2020
Specific subject area	Surveying and mapping science and technology
Research topic	Estimation of grazing density dataset in Mongolia
Time range	2006, 2010, 2015, 2020
Temporal resolution	Year
Geographical scope	Mongolia
Spatial resolution	1 km
Data types and technical formats	.tif
Dataset structure	Dataset on grazing intensity in Mongolia in 2006, 2010, 2015, 2020
Volume of dataset	36.37 MB
Key index in dataset	Pastoral population density, soil moisture, annual precipitation, surface temperature, NPP
Data accessibility	https://doi.org/17058.11.sciencedb.agriculture.00047 https://cstr.cn/10.57760/sciencedb.agriculture.00047
Financial support	National Key R&D Program of China (2022YFE0119200),Mongolian Foundation for Science and Technology (grant number NSFC_2022/01, CHN2022/276)

Key words: livestock grazing density, GEE cloud platform, Random forest regression model, Mongolia

黄静, 李婷, 李朋飞, Altansukh Ochir, 杨梅焕, 王涛, 李莎. 2006—2020年蒙古国放牧密度数据集[J]. 农业大数据学报, 2025, 7(1): 77-84.

HUANG Jing, LI Ting, LI PengFei, ALTANSUKH Ochir, YANG MeiHuan, WANG Tao, LI Sha. Mongolia Grazing Density Dataset from 2006 to 2020[J]. Journal of Agricultural Big Data, 2025, 7(1): 77-84.

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数据名称	空间分辨率	数据类型	数据来源
2006年GLW数据集	10 km	连续性	http://www.livestock.geo-wiki.org
2010年、2015年GLW数据集	1 km	连续性	https://dataverse.harvard.edu/
夜间灯光指数	500 m	连续性	https://www.geodata.cn/
土地覆盖	30 m	连续性	https://data.casearth.cn/
人口密度	1 km	连续性	https://landscan.ornl.gov/
降雨	0.1°	连续性	https://cds.climate.copernicus.eu/
地表温度	1 km	连续性	https://data.tpdc.ac.cn/
NPP	500 m	连续性	https://www.geodata.cn/
土壤水分	1 km	连续性	https://data.tpdc.ac.cn/
牲畜存栏量	-	-	https://www2.1212.mn/