基于ICESat-2数据的高程控制点提取和精度验证

doi:10.12082/dqxxkx.2022.210667

摘要/Abstract

摘要：

ICESat-2(Ice, Cloud and land Elevation Satellite-2)数据的平面定位精度达到米级,高程定位精度达到亚米级,但受各种外界因素的影响,部分激光足印点的高程精度较差,不能用作高程控制点。针对上述问题,本文提出一种适用于ICESat-2数据的多参数联合的高程控制点提取方法。该方法首先利用内置参数辅助检查激光足印点数据质量,滤除异常激光足印点,然后参考内置DEM（Digital Elevation Model）数据进行高程粗差剔除,最后结合云量标记、坡度参数以及时间标记属性参数设置阈值精细筛选,保留满足质量检查、坡度小、云量少的激光足印点作为最终高程控制点,并利用高精度参考高程数据进行精度验证。为验证本文方法的有效性,选取郑州西部、北科达他州西南部、印第安纳州北部地区的ICESat-2激光数据（高程平均绝对误差分别为3.711、0.582、0.333 m）进行高程控制点提取实验,实验结果表明,筛选后的激光足印点平均绝对误差分别达到0.827、0.393、0.131 m,该提取方法在多种地形场景下均能够提取出一定数量且精度较高的高程控制点,不仅能为1：50 000以及1：10 000立体测图提供数据支撑,还能为全国甚至是全球高程控制点提取和高程控制点库建设提供参考。

关键词: ICESat-2, ATLAS, ATL08, 激光测高, 激光足印点, 高程控制点, 提取, 精度验证

Abstract:

The horizontal positioning accuracy of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) data reaches the meter level, and the plane positioning accuracy reaches the sub-meter level. Nevertheless, it is inevitable that poorly accurate laser footprint cannot be used as elevation control point due to various external factors. Therefore, this paper proposes a technique that employs multiple parameters to extract high-precision elevation control points from ICESat-2 data. At first, this method utilizes the built-in parameters to check the quality of the laser footprint point data, eliminating abnormal laser footprint points. The second step is to remove the elevation error in reference to the built-in Digital Elevation Model (DEM) data. The final step aims to set thresholds for fine screening to reserve the elevation points that meet the criteria of quality inspection, small slope, and low cloud cover based on attributes parameters such as cloud cover marker, slope parameter, and a time marker. Moreover, high-precision reference elevation data are also used to verify the selected elevation control points. To verify the effectiveness of the proposed technique, we employed the ICESat-2 laser data from western Zhengzhou, southwestern part of North Kodata, and northern Indiana (mean absolute height elevation is 3.711 m, 0.582 m, and 0.333 m, respectively) to extract elevation control points. Experimental results show that the mean absolute errors of laser footprints were 0.827 m, 0.393 m, and 0.131 m after screening, respectively. The extraction method can extract a certain number of high-precision elevation control points in multiple terrain scenarios. It also provides data support for 1:50 000 and 1:10 000 stereo mapping and offers references to the elevation control points extraction and elevation control point database construction throughout China or around the globe.

Key words: ICESat-2, ATLAS, ATL08, laser altimeter, laser footprint, elevation control point, extraction, accuracy verification

郑迎辉, 张艳, 王涛, 赵祥, 张昆, 王龙辉. 基于ICESat-2数据的高程控制点提取和精度验证[J]. 地球信息科学学报, 2022, 24(7): 1234-1244.DOI:10.12082/dqxxkx.2022.210667

ZHENG Yinghui, ZHANG Yan, WANG Tao, ZHAO Xiang, ZHANG Kun, WANG Longhui. Elevation Control Points Extraction and Accuracy Validation based on ICESat-2 Data[J]. Journal of Geo-information Science, 2022, 24(7): 1234-1244.DOI:10.12082/dqxxkx.2022.210667

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参数	描述
latitude	每个统计单元内中心光子的纬度
longitude	每个统计单元内中心光子的经度
dem_h	该地理位置的最佳可用(参考来源为Arctic, GMTED, MSS, Antarctis)DEM高程,参考椭球为WGS84椭球
h_dif_ref	h_te_median和ref_dem之间的差值
h_te_best_fit	每个统计单元内中心位置的最佳拟合地形高程,由3种拟合（线性、三阶和四阶多项式）中的最佳确定
h_te_interp	标记为地面光子的插值地表高程
h_te_max	标记为地面光子的地表高程的最大值
h_te_min	标记为地面光子的地表高程的最小值
h_te_mean	标记为地面光子的地表高程的平均值
h_te_median	标记为地面光子的地表高程的中位值
n_te_photons	每个统计单元内标记为地面光子的数量
terrain_slope	每个统计单元内地面的沿轨方向坡度,通过线性拟合计算
night_flag	夜晚标记,0=day,1=night
cloud_flag_atm	云量标记,取值范围为[0,10],0以上表示有云或气溶胶存在,[0,1]则表示云量小于或等于10%
urban_flag	城市标记,0=not_urban,1=urban

实验区	DEM数据名称	获取时间	数据源	坐标系统	高程精度
郑州西部实验区	Dengfeng_DEM_1_meter_2017	2017-06—2017-07	LiDAR	平面坐标：WGS84 高程基准：WGS84大地高	优于0.8 m
北科达他州西南部实验区	ND_KidderCO_LiDAR_2014	2015-04—2015-05	LiDAR	平面坐标：NAD83 高程基准： NAVD88	优于0.5 m
印第安纳州北部实验区	IN_Indiana_Statewide_LiDAR_2017	2017-03—2020-04	LiDAR	平面坐标：NAD83 高程基准： NAVD88	优于0.5 m

实验区	筛选条件	保留激光点数/个	MAE/m	RMSE/m	数据剔除率/%
郑州西部实验区	原始数据	22 853	3.711	5.989	0
	质量检查	8347	1.261	2.138	63.48
	参考DEM筛选	8260	1.232	2.083	0.38
	坡度参数	2826	1.166	1.904	23.78
	云量参数	2724	1.054	1.671	0.45
	时间标志	785	0.827	1.190	8.48
北科达他州西南部实验区	原始数据	39 741	0.582	1.759	0
	质量检查	37 193	0.480	1.215	6.41
	参考DEM筛选	37 084	0.476	1.145	1.12
	坡度参数	28 217	0.462	1.048	22.59
	云量参数	28 045	0.441	0.664	0.43
	时间标志	11 170	0.393	0.590	42.46
印尼安纳州北部实验区	原始数据	46 593	0.333	2.887	0
	质量检查	41 144	0.153	0.428	11.70
	参考DEM筛选	41 055	0.150	0.412	0.19
	坡度参数	35 756	0.142	0.392	11.30
	云量参数	35 511	0.141	0.297	0.53
	时间标志	11 103	0.131	0.263	52.38

实验区	高程控制点提取方法	保留激光点数/个	MAE/m	RMSE/m	数据剔除率/%
郑州西部实验区	基于参考高程数据和属性参数	4845	1.485	3.849	78.80
郑州西部实验区	本文方法	785	0.821	1.190	96.57
北科达他州西南部实验区	基于参考高程数据和属性参数	15 408	0.642	1.185	61.23
北科达他州西南部实验区	本文方法	11 170	0.393	0.590	73.01
印尼安纳州北部实验区	基于参考高程数据和属性参数	22 232	0.351	0.827	52.29
印尼安纳州北部实验区	本文方法	11 103	0.131	0.263	76.10

实验区	不同属性参数筛选条件	保留激光点数/个	MAE/m	RMSE/m	数据剔除率/%
郑州西部实验区	时间标志参与筛选	785	0.821	1.190	96.57
郑州西部实验区	时间标志不参与筛选	2724	1.054	1.671	88.09
北科达他州西南部实验区	时间标志参与筛选	11 170	0.393	0.590	73.01
北科达他州西南部实验区	时间标志不参与筛选	18 045	0.441	0.664	30.55
印尼安纳州北部实验区	时间标志参与筛选	11 103	0.131	0.263	76.10
印尼安纳州北部实验区	时间标志不参与筛选	25 511	0.141	0.297	23.72