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全文摘要次数: 118 全文下载次数: 52
引用本文:

DOI:

10.11834/jrs.20243390

收稿日期:

2023-09-10

修改日期:

2023-12-22

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方向自适应的OPTICS山地林区地表及冠层表面检测方法
谢俊峰1, 杨晓梦1, 徐超鹏1, 张丽斌2, 芦祎霖3, 莫凡1, 吕鑫1, 刘仁4, 曾俊泽1
1.自然资源部国土卫星遥感应用中心;2.云南省遥感中心;3.中国资源卫星应用中心;4.云南师范大学地理学部
摘要:

针对新一代冰、云、陆地高程卫星(Ice, Cloud, and Land Elevation Satellite-2, ICESat-2)/先进地形激光高度计系统(Advanced Topographic Laser Altimeter System, ATLAS)在山地林区信号提取精度不高导致地表及冠层表面检测困难等问题,本文提出了一种方向自适应的点排序识别聚类结构(Ordering points to identify the clustering structure,OPTICS)山地林区地表及冠层表面检测方法,首先利用随机抽样一致((Random Sample Consensus, RANSAC)分段曲线拟合初始地表,用于构建方向自适应的椭圆搜索域替换传统OPTICS的圆,形成方向自适应的OPTICS,并结合大津法(Nobuyuki Otsu method, OTSU)提取地面信号,以信号提取与地表拟合迭代获取精细地表;随后,参考精细地表消除地形对点云影响,采用垂直椭圆的OPTICS提取植被信号并检测冠层表面。以黑龙江孟家岗林场、辽宁抚顺林区ATLAS为研究对象开展实验,并利用人工标注样本及无人机产品验证精度。结果表明,本文方法提取地表与植被信号精度(F值)达到0.97,相较于基于椭圆的OPTICS,精度提升了约0.07;另外,本文方法检测的地表及冠层表面高程均方根误差(Root Mean Square Error, RMSE)分别为1.08m、2.33m,相较于ATL08的1.92m、3.29m,精度提升明显。故本文方法提取植被信号、检测地表及冠层表面的精度更高,更适用于山地林区等坡度变化较大的区域,可为后续森林空间结构反演提供可靠的数据基础。

Ground and canopy surface detection method based on direction adaptive OPTICS in mountainous forest area
Abstract:

Objective: The successful launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), carrying the Advanced Topographic Laser Altimeter System (ATLAS), has made it possible to accurately quantify global vegetation structure. However, due to limitations of the sensitive photon detection system, its data contains a large amount of background noise photons. Aiming at the problem that ICESat-2/ATLAS has low signal extraction accuracy in mountain forest area, which leads to the difficulty of ground and canopy surface detection, the method of ground and canopy surface detection based on direction adaptive ordering points to identify the clustering structure (OPTICS) is proposed. Method:Firstly, the initial ground surface is obtained through segmented curve fitting based on Random Sample Consensus (RANSAC), which is used to construct a direction adaptive elliptical searching area to replace the traditional circle in OPTICS, forming the direction adaptive OPTICS algorithm. Based on this algorithm, the reachability distance (RD) of all photon point clouds is obtained first, and then potential ground signals and potential ground surface are obtained successively by a "two-step method", that is, Nobuyuki Otsu method (OTSU) is first introduced to obtain potential ground signals, and then potential ground surface is obtained by fitting potential ground signals based on RANSAC. Iterate the "two-step method" several times until the similarity between the potential ground surface obtained before and after is greater than 90%, indicating that the potential ground surface is considered a fine ground surface. Secondly, the effect of terrain on photons is eliminated by reference to the fine ground surface, and then the vegetation signal is extracted by vertical elliptical OPTICS. Finally, based on the vegetation signal, the surface of canopy is detected by combining the elevation percentile and Piecewise Cubic Hermite Interpolation (PCHIP) curve fitting. Result:The ATLAS data of Mengjiagang Forest Farm in Heilongjiang Province and Fushun Forest Farm in Liaoning Province are used as the research objects to carry out experiments, and the accuracy is verified by manually labeled samples and Unmanned Aerial Vehicle (UAV) products. The results show that the extraction accuracy (F) of ground surface and vegetation signals in the mountainous forest area is 0.97, which is about 0.07 higher than that of the OPTICS based on elliptical searching area. In addition, the RMSE of ground and canopy surface detected by the proposed method are 1.08m and 2.33m, respectively, compared with 1.92m and 3.29m of ATL08, which significantly improves the accuracy. Conclusion:Therefore, compared with the OPTICS based on elliptical searching area and ATL08 results, the proposed method has higher precision in extracting vegetation signal photons and detecting the surface of ground and canopy. It is more suitable for areas with large gradient changes such as mountain forest area, and can provide a reliable data foundation for the subsequent inversion of forest spatial structure.

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