在蒙古塞伦格河流域使用随机森林绘制牲畜密度分布图
Yaping Liu1,2, Juanle Wang3,4,5, Keming Yang1
1College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing, 100083, China.
Scientific reports
|May 15, 2024
概括
在广的草原中准确地绘制牲畜地图是具有挑战性的. 本研究使用随机森林 (RF) 来创建用于畜牧管理的高分辨率空间数据,揭示人类活动是关键驱动因素.
科学领域:
- 生态生态学 生态生态学
- 地理空间分析是什么?
- 畜牧业 动物养殖 动物养殖
背景情况:
- 传统的采用行政数据的畜牧业绘图受到数据质量和地理因素的限制.
- 空间畜牧数据对于有效的畜牧管理和与环境数据的整合至关重要.
研究的目的:
- 开发一种空间化方法,用于在塞伦格河流域的高分辨率牲畜分布数据.
- 量化分析影响牲畜空间分布的因素.
- 在草原地区为精确的畜牧业法规提供基础支持.
主要方法:
- 使用随机森林 (RF) 算法进行空间建模.
- 生成的高分辨率的网格分布数据,用于所有牲畜,绵羊和山羊,牛和马.
- 采用地质探测器来分析牲畜分布的驱动因素.
主要成果:
- 确定了西南地区的高畜牧密度和塞伦格河流域北部的低密度.
- 绵羊和山羊的密度集中在0-125/km2之间,高密度地区位于特定的省份.
- 牛和马的密度集中在0-25/km2,主要在西南部和中部盆地.
结论:
- 射电模型有效地描绘了塞伦格河流域的牲畜分布特征.
- 人类活动被确定为牲畜空间分布的主要驱动因素.
- 该研究为在大草原生态系统中的知情畜牧管理提供了必要的数据.
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