使用机器学习和公民科学绘制城市水鸟息地的地图:比利时布鲁塞尔的多层次分析
Xiapeng Jiang1, Liancheng Zhang2, Kaidong Feng3
1Department of Geography, Ghent University, Ghent, 9000, Belgium.
Journal of environmental management
|November 17, 2025
概括
公民科学数据和遥感揭示了塑造城市水鸟息地的关键因素. 机器学习模型准确地预测息地适合性,指导布鲁塞尔的保护.
科学领域:
- 城市生态学 城市生态学
- 生物多样性信息学 生物多样性信息学
- 地理空间分析是什么?
背景情况:
- 了解水鸟息地要求对于城市生物多样性管理至关重要.
- 公民科学数据 (eBird) 为物种分布提供了宝贵的见解.
- 遥感为息地建模提供环境变量.
研究的目的:
- 探索布鲁塞尔城市水鸟息地的空间分布.
- 通过机器学习评估环境因素对息地适宜性的影响.
- 确定有效的城市保护战略的关键预测因素.
主要方法:
- 综合公民科学 (eBird) 和遥感数据.
- 在多个空间分辨率 (5-100m) 上应用机器学习模型 (XGBoost,随机森林,额外树木,LightGBM,CatBoost).
- 纳入的陆地覆盖面,不透密度,生物气候和人类活动变量 (噪声压力).
主要成果:
- 土地覆盖因素与水鸟息地的适宜性高度相关.
- 使用XGBoost和随机森林模型的10米分辨率数据显示出强大的预测准确度.
- 不透密度地图 (IDM) 通过减少城市化地区的过高估计,改善了预测.
- 生物气候 (降雨季节性/最潮湿/最温暖的季度) 和噪音压力显著影响了息地.
结论:
- 机器学习模型,特别是在10米分辨率下,对于城市水鸟息地的空间建模是有效的.
- 保护战略应侧重于保护关键息地,增强连通性和减轻人类影响.
- 这些发现为布鲁塞尔的城市生物多样性管理提供了直接指导.
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