基于ICESat-2和Sentinel-2的河道管理范围内阻水植被提取研究
Study on Water-blocking Vegetation Extraction within River Management Areas Based on ICESat-2 and Sentinel-2
吴迪 1袁晓宏 2李冰 1杨光3
作者信息
- 1. 自然资源部黑龙江基础地理信息中心,黑龙江 哈尔滨 150081
- 2. 黑龙江省测绘地理信息学会,黑龙江 哈尔滨 150081
- 3. 黑龙江省河湖长制保障中心,黑龙江 哈尔滨 150001
- 折叠
摘要
针对河道管理范围内阻水植被实地调查工作量大、耗时长、难度大等问题,本文利用星载雷达ICESat-2的ATL03 和ATL08 产品,通过关联分析的方法筛选和计算激光点的植被高度,结合Sentinel-2 波谱变量,分别使用BP神经网络和随机森林回归算法,构建空间连续的植被高度信息反演模型,快速提取河道管理范围内的阻水植被.实验结果表明,随机森林回归模型在稳定性和准确性方面优于BP神经网络模型,能够更有效地提取河道管理范围内的阻水植被.本方法可提高河道管理中阻水植被调查的效率和准确性,为河道治理和管理工作提供科学的决策参考.
Abstract
This study addresses the issues of heavy workload,time consumption,and difficulty in field survey of water-blocking vege-tation within river management areas.Utilizing the ATL03 and ATL08 products from the ICESat-2 satellite ATLAS,vegetation heights were screened and calculated through correlation analysis of laser points.Combined with spectral variables of Sentinel-2,spatially continuous vegetation height inversion models were constructed using BP neural network and random forest regression algorithm to rap-idly extract water-blocking vegetation within river management areas.Experimental results indicate that the random forest regression model is superior to the BP neural network model in terms of stability and accuracy,effectively extracting water-blocking vegetation within river management areas.This method can improve the efficiency and accuracy of surveying water-blocking vegetation in river management,providing scientific decision-making support for river governance and management.
关键词
ICESat-2/BP神经网络/随机森林回归算法/阻水植被Key words
ICESat-2/BP neural network/random forest regression algorithm/water-blocking vegetation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据