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一个增强的国家规模的城市树冠覆盖数据集,用于美国
Lucila M Corro1, Kenneth J Bagstad2, Mehdi P Heris3
1U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver, CO, 80225, USA. lcorro@usgs.gov.
Scientific data
|March 25, 2025
概括
这项研究开发了一套针对美国城市地区的增强城市树冠覆盖 (TCC) 数据集,使用高分辨率的土地覆盖数据. 新的数据集显著改善了中等分辨率产品,提供了更准确的TCC估计.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 城市生态城市生态学
背景情况:
- 中等分辨率 (30米) 的国家地图产品不充分反映了微小的城市异质性.
- 使用更高分辨率数据对城市树冠覆盖 (TCC) 估计的改进是有限的.
- 准确的TCC数据对于理解城市生态系统服务和气候适应性至关重要.
研究的目的:
- 通过整合高分辨率的土地覆盖数据,为美国城市地区开发一个增强的城市TCC数据集.
- 与现有的中分辨率产品相比,提高TCC估计的准确性和空间细节性.
- 为全球应用提供稳定可扩展的建模方法.
主要方法:
- 采用71个美国城市地区的高分辨率土地覆盖数据,应用了随机森林模型.
- 适度分辨率的国家土地覆盖数据库 (NLCD) 树冠覆盖 (TCC) 和额外的气候/结构数据被纳入.
- 用各种交叉验证技术严格评估模型性能.
主要成果:
- 改进的模型实现了0.747的确定系数 (R 2),估计62个城市地区的TCC在3%以内.
- 与本地NLCD TCC产品相比,新数据集平均增加了13.4%的城市级TCC.
- 交叉验证结果显示了强大的模型稳定性 (中位数R2在0.675到0.752之间).
结论:
- 开发的方法显著提高了城市TCC绘图的准确性和细节性.
- 该方法是稳固的,适合创建国家范围的TCC数据集.
- 该模型代码为改善全球TCC估计提供了可转移的解决方案,即使高分辨率数据的可用性有限.
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