全球城市树叶面积指数数据集用于城市气候建模
Wenzong Dong1, Hua Yuan2, Wanyi Lin1
1Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Scientific data
|March 12, 2025
概括
这项研究引入了一个新的高分辨率城市树叶面积指数 (LAI) 数据集. 这一数据集通过克服城市的叶片面积指数 (LAI) 的遥感限制,改善了城市气候建模.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 城市气候学 城市气候学
背景情况:
- 城市树木减轻热应激,但它们的叶面积指数 (LAI) 往往被隐藏在遥感数据中.
- 这种局限性阻碍了城市天花板模型 (UCM) 在了解城市气候方面的应用.
- 准确的城市LAI数据对于有效的城市规划和气候减缓战略至关重要.
研究的目的:
- 开发一个高分辨率的,长时间的城市树LAI数据集.
- 克服城市地区现有的遥感LAI数据的局限性.
- 加强城市天花板模型 (UCM) 在城市气候研究中的应用.
主要方法:
- 开发了一个随机森林模型,使用MODIS LAI数据进行训练.
- 综合气象变量和树高数据集,以提高准确性.
- 创建了一个高分辨率 (500m) 的城市树LAI数据集,涵盖2000-2022.
主要成果:
- 开发的数据集显示了高准确度,与地点参考地图 (R=0.85,RMSE=1.03 m2/m2) 相比验证.
- 与重新处理的MODIS LAI相比,模拟的LAI在全球范围内显示了更好的精度 (RMSE 0.36-0.64 m2/m2,R 0.89-0.97).
- 该数据集准确地反映了城市树木LAI的规模和季节性变化.
结论:
- 新的城市树LAI数据集有效地解决了遥感数据的局限性.
- 这一数据集为增强城市天花板模型 (UCM) 提供了有价值的工具.
- 这些发现有助于更准确的城市气候研究和规划.
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