预测区域红树林植被碳库存,整合UAV-LiDAR和卫星数据
Zongyang Wang1, Yuan Zhang1, Feilong Li1
1Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China; Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Guangdong University of Technology, Guangzhou, 510006, China.
Journal of environmental management
|August 22, 2024
概括
整合无人机和卫星数据显著改善了红树林碳库存预测. 像树冠高度这样的结构特征是关键的,XGBOOST显示了高精度的精确红树林植被碳库存 (MVC) 估计.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 林业林业 林业 林业 林业
背景情况:
- 卫星遥感 (RS) 被广泛用于大规模预测红树林植被碳库存 (MVC).
- 卫星数据面临诸如和问题等局限性,阻碍了准确的MVC预测.
- 无人机LiDAR提供了详细的结构植被信息,克服了卫星数据的限制.
研究的目的:
- 通过整合UAV-LiDAR和卫星RS数据,开发一种轻量级但高效的MVC预测模型.
- 评估光谱,结构和纹理特征对MVC预测准确性的影响.
- 构建一个框架来估计中国最大的红树林地区的地面 (ACG) 和地下 (BCG) 碳储存.
主要方法:
- 集成的UAV-LiDAR,Sentinel-1和Sentinel-2数据用于特征提取.
- 在区域范围内提取了光谱,结构和纹理特征.
- 评估了多种机器学习方法,包括SVM,RF,GBDT和XGBOOST用于MVC预测.
主要成果:
- 与单独的卫星RS相比,结合无人机和卫星RS数据显著提高了MVC预测准确性.
- 结构特征,特别是树冠高度,对于精确的MVC预测至关重要.
- 在评估的机器学习方法中,XGBOOST表现出最高的精度.
结论:
- 整合无人机和卫星RS数据与融合特征对于准确的区域MVC预测至关重要.
- 建议在林业监测中优先考虑无人机LiDAR应用,并建立长期红树林数据库,以管理蓝色碳资源和交易.
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