改善湿地地图:整合哨兵-1/2号,GEDI数据和谷歌地球引擎
Hamid Jafarzadeh1, Masoud Mahdianpari1,2, Eric W Gill1
1Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
先进的地球观测数据,包括LiDAR,显著提高了湿地绘图的准确性. 整合植被树冠高度 (VCH) 增强分类,帮助这些重要生态系统的保护工作.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
背景情况:
- 湿地是关键的生态系统,由于景观复杂性和光谱相似性,它们面临着绘制挑战.
- 准确的湿地绘制对于有效的保护和管理策略至关重要.
- 地球观测 (EO) 数据为增强湿地特征提供了潜力.
研究的目的:
- 为加拿大纽芬兰制作高分辨率 (10米) 湿地分类地图.
- 评估光学,SAR和LiDAR数据在湿地绘图中的协同使用.
- 评估在湿地分类中从GEDI LiDAR获得的植被树冠高度 (VCH) 的附加值.
主要方法:
- 使用了多个来源的EO数据,包括Sentinel-1/2,GEDI LiDAR,MERIT Hydro和ERA5.5等.
- 综合的海拔和地形导数 (斜率,方面).
- 采用谷歌地球引擎 (GEE) 和随机森林 (RF) 模型,生成一个VCH地图并将其用作预测器.
主要成果:
- 来自GEDI的VCH变量显著提高了湿地分类的准确性.
- VCH实现了高精度 (R2=0.69,RMSE=1.51m,MAE=1.26m) 的使用.
- 整合VCH提高了整体准确度,达到93.45% (卡帕=0.92,F1=0.88).
结论:
- 多源,多传感器的EO数据方法对于有效的湿地绘图至关重要.
- 来自GEDI的VCH的整合是湿地分类的宝贵进步.
- 这项研究为未来的湿地绘图计划提供了强大的方法.
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