基于ICESat/GLAS的山东省SRTM与ASTER GDEM高程精度评价与修正

doi:10.12082/dqxxkx.2020.190411

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (3): 351-360.doi: 10.12082/dqxxkx.2020.190411

• “数字地形分析”专栏 • 上一篇下一篇

基于ICESat/GLAS的山东省SRTM与ASTER GDEM高程精度评价与修正

秦臣臣¹, 陈传法^1,^*(), 杨娜², 高原¹, 王梦樱¹

^1. 山东科技大学测绘科学与工程学院,青岛 266590
^2. 济南市勘察测绘研究院,济南 250101

收稿日期:2019-07-30 修回日期:2019-12-19 出版日期:2020-03-25 发布日期:2020-05-18
通讯作者: 陈传法 E-mail:chencf@lreis.ac.cn
作者简介:秦臣臣（1993— ）,男,山东枣庄人,硕士生,研究方向为空间数据质量改善。E-mail:1157860213@qq.com
基金资助:
国家自然科学基金项目(41804001);国家自然科学基金项目(41371367);山东省自然科学基金项目(ZR2019MD007);山东省自然科学基金项目(ZR2019BD006);山东省高等学校青创科技支持计划(2019KJH007)

Elevation Accuracy Evaluation and Correction of SRTM and ASTER GDEM in Shandong Province based on ICESat/GLAS

QIN Chenchen¹, CHEN Chuanfa^1,^*(), YANG Na², GAO Yuan¹, WANG Mengying¹

^1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
^2. Jinan Geotechnical Investigation and Surveying Institute, Ji'nan 250101, China

Received:2019-07-30 Revised:2019-12-19 Online:2020-03-25 Published:2020-05-18
Contact: CHEN Chuanfa E-mail:chencf@lreis.ac.cn
Supported by:
National Natural Science Foundation of China(41804001);National Natural Science Foundation of China(41371367);Shandong Provincial Natural Science Foundation, China(ZR2019MD007);Shandong Provincial Natural Science Foundation, China(ZR2019BD006);A Project of Shandong Province Higher Educational Youth Innovation Science and Technology Program(2019KJH007)

摘要/Abstract

摘要：

SRTM3和ASTER GDEM V2数据具有较高的空间分辨率和广泛的覆盖范围,对于地学研究具有重要意义;但在不同地形复杂度和地面覆盖物区域,两类数据的误差分布并不均匀。SRTM3和ASTER GDEM V2 数据自公布以来,其精度修正一直是研究热点。然而大范围区域精度验证缺乏有效手段,传统方法可靠性差且数据获取成本较高。自ICESat-1数据公开以来,它们已成为SRTM3和ASTER GDEM V2精度评定的主要检核点。为此,本文以山东省为研究区域,借助ICESat-1评估了SRTM3和ASTER GDEM V2的高程精度,并根据插值误差曲面对两种DEM进行了修正。分析表明,原始SRTM和ASTER高程中误差分别为5.57 m和7.20 m,均高于标称精度;随着坡度的增大,高程精度呈降低的趋势。通过分析土地覆盖类型与误差分布关系表明：农田、灌丛土地类型精度较高;森林、湿地精度较低。分别采用反距离加权、普通克里金、地形转栅格和自然邻域插值方法构建误差曲面。结果表明：不同的插值方法构建的误差曲面的特征和精度也不同。其中,反距离加权修正的效果最佳,其次是地形转栅格和自然邻域,而普通克里金修正的效果最差。

关键词: SRTM, ASTER, ICESat/GLAS, 高程精度评价, 插值, 山东省, 误差分布, 修正方法

Abstract:

Shuttle Radar Topography Mission(SRTM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) GDEM have a high spatial resolution and wide spatial coverage, which play an important role in many Earth researches. However, their error distributions are heterogeneous on different terrain types. In order to assess the elevation accuracy of the two DEMs, data from Geoscience Laser Altimeter System (GLAS) carried on the Ice, Cloud, and land Elevation Satellite (ICESat) are often used as the checkpoints due to their high accuracy. Taking Shandong Province as the research area, the accuracy of SRTM and ASTER GDEM are first evaluated by ICESat/GLAS in the years of 2003-2010 in this paper. Results indicate that the root mean squared errors (RMSEs) of SRTM and ASTER are 5.57 m and 7.20 m, respectively, which are much lower than the nominated accuracy. We further analyzed the effect of terrain slope and landscape type on the accuracy of SRTM and ASTER GDEM. Specifically, the study area was first divided into different sub-regions according to slope ranges (0~5°, 5~10°, 10~15°, 15~20°, 20~25°, 30~35°, 35~40°, 40~45°) and landscape types (farmland, shrub, forest, grassland, wetlands, water body), respectively. Then, the RMSE of each sub-region was computed and analyzed. We found that with the increasing of terrain slope, the accuracy of the two DEMs decreases, and under different land cover types, they also have different accuracy. More specifically, the two DEMs have a higher accuracy on farmland and shrub; while have a lower accuracy on forest and wet lands. To improve the accuracy of SRTM and ASTER, their error surfaces were first produced by interpolating the elevation differences between the DEM and randomly selected ICESat/GLAS data with the proportion of 90%. The interpolation methods include Inverse Distance Weight (IDW), Ordinary Kriging (OK), terrain-to-grid method (T2G) and Natural Neighborhood (NN). Then, the interpolated error surfaces were added to the original DEMs. Accuracy assessment of the improved SRTM and ASTER using the remaining 10% ICESat/GLAS demonstrates that IDW with the RMSEs of 2.20 m and 5.31 m is more accurate than the other interpolation methods. IDW is closely followed by T2G and NN. It is surprised to see that OK produces the worst results. Hence, SRTM and ASTER GDEM are improved with the IDW-based error surfaces. The ICESat-2 satellite was launched on September 15, 2018. It can emit 10,000 laser pulses per second, monitoring the height of glaciers and land in unprecedented detail. ICESat-2 collects elevation data over all surfaces spanning the world's frozen regions, forests, lakes, urban areas, and more. Thus, further researches will focus on improving the accuracy of SRTM and ASTER with the ICESat2 data.

Key words: SRTM, ASTER GDEM, ICESat/GLAS, accuracy correction, interpolation, Shandong Province, error distribution, rectification method

秦臣臣, 陈传法, 杨娜, 高原, 王梦樱. 基于ICESat/GLAS的山东省SRTM与ASTER GDEM高程精度评价与修正[J]. 地球信息科学学报, 2020, 22(3): 351-360.DOI:10.12082/dqxxkx.2020.190411

QIN Chenchen, CHEN Chuanfa, YANG Na, GAO Yuan, WANG Mengying. Elevation Accuracy Evaluation and Correction of SRTM and ASTER GDEM in Shandong Province based on ICESat/GLAS[J]. Journal of Geo-information Science, 2020, 22(3): 351-360.DOI:10.12082/dqxxkx.2020.190411

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参考文献 30

[1]	唐川, 齐信, 丁军 , 等. 汶川地震高烈度区暴雨滑坡活动的遥感动态分析[J]. 地球科学·中国地质大学学报, 2010,35(2):317-323.
	[ Tang C, Qi X, Ding J , et al. Dynamic analysis on rainfall-induced landslide activity in high seismic intensity areas of the Wenchuan earthquake using remote sensing image[J]. Earth Science — Journal of China University of Geosciences, 2010,35(2):317-323. ]
[2]	陈俊勇 . 对SRTM3和GTOPO30地形数据质量的评估[J]. 武汉大学学报·信息科学版, 2005,30(11):941-944.
	[ Chen J Y . Quality evaluation of topographic data from SRTM3 and GTOPO30[J]. Geomatics and Information Science of Wuhan University, 2005,30(11):941-944.]
[3]	Chaieb A, Rebair N, Bouazizb S . Vertical accuracy assessment of SRTM Ver 4.1 and ASTER GDEM Ver 2 using GPS measurements in central west of Tunisia[J]. Journal of Geographic Information System, 2016,8(1):57-64.
[4]	郭笑怡, 张洪岩, 张正祥 , 等. ASTER-GDEM与SRTM3数据质量精度对比分析[J]. 遥感技术与应用, 2011(3):75-80.
	[ Guo X Y, Zhang H Y, Zhang Z X , et al. Comparative analysis of ASTER-GDEM and SRTM3 data quality accuracy[J]. Remote Sensing Technology and Application, 2011(3):75-80. ]
[5]	袁乐先, 李斐, 张胜凯 , 等. 基于ICESat数据的南极冰盖DEM插值方法比较及精度分析[J]. 冰川冻土, 2015,37(4):946-953.
	[ Yuan L X, Li F, Zhang S K , et al. Comparing the interpolation algorithms and analyzing the accuracy of a digital elevation model of the Antarctic ice sheet based on ICESat data[J]. Journal of Glaciology, 2015,37(4):946-953. ]
[6]	张朝忙, 刘庆生, 刘高焕 , 等. 太湖地区SRTMDEM高程精度质量评价[J]. 测绘科学, 2013,39(3):139-142.
	[ Zhang C M, Liu Q S, Liu G H , et al. Quality evaluation of elevation precision of SRTM DEM in Taihu lake area[J]. Science of Surveying and Mapping, 2013,39(3):139-142. ]
[7]	卢丽君, 张继贤, 王腾 . 一种基于高分辨率雷达影像以及外部DEM辅助的复杂地形制图方法[J]. 测绘学报, 2011(4):61-65.
	[ Lu L J, Zhang J X, Wang T . A DEM mapping method assisted by External DEM with high resolution InSAR data in complex terrain area[J]. Acta Geodaetica et Cartographica Sinica, 2011(4):61-65. ]
[8]	云烨, 曾琪明, 焦健 , 等. 基于参考DEM的机载InSAR定标方法[J]. 测绘学报, 2014,43(1):74-82.
	[ Yun Y, Zeng Q M, Jiao J , et al. Calibration of Airborne Ihterferometric SAR data based on reference DEM[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(1):74-82. ]
[9]	杜小平, 郭华东, 范湘涛 , 等. 基于ICESat/GLAS数据的中国典型区域SRTM与ASTER GDEM高程精度评价[J]. 地球科学——中国地质大学学报, 2013(4):229-239.
	[ Du X P, Guo H D, Fan X T , et al. Elevation accuracy evaluation of SRTM and ASTER GDEM in typical regions of China based on ICESat/GLAS data[J]. Earth Science (Journal of China University of Geosciences), 2013(4):229-239. ]
[10]	吴宇鑫, 赵牡丹, 高志远 , 等. 中国3类典型区SRTMGL1和SRTMV4精度对比分析[J]. 水土保持研究, 2019(4):36-42.
	[ Wu Y X, Zhao M D, Gao Z Y , et al. Comparative analysis of the accuracy of SRTMGL1 and SRTM V4 in three typical regions of China[J]. Research of Soil and Water Conservation, 2019(4):36-42. ]
[11]	万杰, 廖静娟, 许涛 , 等. 基于ICESat/GLAS高度计数据的SRTM数据精度评估——以青藏高原地区为例[J]. 国土资源遥感, 2015,27(1):100-105.
	[ Wan J, Liao J J, Xu T , et al. Accuracy evaluation of SRTM data based on ICESat/GLAS altimeter data: A case study in the Tibetan Plateau[J]. Remote Sensing for land and Resources, 2015,27(1):100-105. ]
[12]	沈焕锋, 刘露, 岳林蔚 , 等. 多源DEM融合的高差拟合神经网络方法[J]. 测绘学报, 2018,47(6):854-863.
	[ Shen H F, Liu L, Yue L W , et al. A multi-source DEM fusion method based on elevation difference fitting neural network[J]. Acta Geodaetica et Cartographica Sinica, 2018,47(6):854-863. ]
[13]	Yue L, Shen H, Zhang L , et al. High-quality seamless DEM generation blending SRTM-1,ASTER GDEM v2 and ICESat/GLAS observations[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017,123:20-34.
[14]	田明璐, 常庆瑞, 冯冰凛 . 利用ASTER数据融合的SRTM空值填补方法[J]. 测绘科学, 2012(2):90-91,106.
	[ Tian M L, Chang Q R, Feng B L . SRTM null value filling method using ASTER data fusion[J]. Science of Surveying and Mapping, 2012(2):90-91,106. ]
[15]	孙亮, 严薇, 刘平芝 , 等. 采用小波分析的SRTM DEM与ASTER DEM数据融合[J]. 测绘科学技术学报, 2014(4):388-392.
	[ Sun L, Yan W, Liu P Z , et al. Data fusion of SRTM DEM and ASTER DEM based on wavelet analysis[J]. Journal of Geomatics Science and Technology, 2014(4):388-392. ]
[16]	Zhao X, Su Y, Hu T , et al. A global corrected SRTM DEM product for vegetated areas[J]. Remote Sensing Letters, 2018,9(4):393-402.
[17]	Su Y, Guo Q, Ma Q , et al. SRTM DEM Correction in vegetated mountain areas through the integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery[J]. Remote Sensing, 2015,7(9):11202-11225.
[18]	闫业超, 张树文, 岳书平 . 东北川岗地形区SRTM数据质量评价[J]. 中国科学院大学学报, 2008,25(1):41-46.
	[ Yan Y C, Zhang S W, Yue S P . Evaluation of SRTM data quality in area of undulating hills of northeast China[J]. Journal of the Graduate School of the Chinese Academy of Sciences, 2008,25(1):41-46. ]
[19]	马龙, 李颖 . 从GTOPO30到SRTM DEM精度研究——以西藏为例[J]. 水土保持通报, 2006,26(5):71-74.
	[ Ma L, Li Y . Study on accuracy of GTOPO30 and SRTM DEM:A case study of Tibet[J]. Bulletin of Soil and Water Conservation, 2006,26(5):71-74. ]
[20]	孙茜 . SRTM数据精度检测[D]. 西安:长安大学, 2010: 1-51.
	[ Sun Q . SRTM data accuracy detection[D]. Xi'an: Chang'an University, 2010: 1-51. ]
[21]	詹蕾, 汤国安, 杨昕 . SRTM DEM高程精度评价[J]. 地理与地理信息科学, 2010,26(1):34-36.
	[ Zhan L, Tang G A, Yang X . Evaluation of SRTM DEMs′ elevation accuracy[J]. Geography and Geo-information Science, 2010,26(1):34-36. ]
[22]	张锦明, 游雄, 万刚 . DEM插值参数优选的试验研究[J]. 测绘学报, 2014,43(2):178-185.
	[ Zhang J M, You X, Wan G . Experimental research on optimization of DEM interpolation parameters[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(2):178-185. ]
[23]	赵国松, 杜耘, 凌峰 , 等. ASTER GDEM与SRTM3高程差异影响因素分析[J]. 测绘科学, 2012,37(4):167-170.
	[ Zhao G S, Du Y, Ling F , et al. Analysis of influencing factors on height differences between ASTER GDEM and SRTM3[J]. Science of Surveying and Mapping, 2012,37(4):167-170. ]
[24]	Zwally H J, Schutz B, Abdalati W , et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land[J]. Journal of Geodynamics, 2002,34(3):405-445.
[25]	张泉, 杨勤科, 程洁 , 等. 中国地区3″ SRTM高程误差特征[J]. 武汉大学学报·信息科学版, 2018,43(5):685-689.
	[ Zhang Q, Yang Q K, Cheng J , et al. Characteristics of 3″ SRTM errors in China[J]. Geomatics and Information Science of Wuhan University, 2018,43(5):685-689. ]
[26]	Zong J B, Ye Q H, Tian L D . Recent naimona'Nyi glacier surface elevation changes on the Tibetan plateau based on ICESat/GLAS, SRTM DEM and GPS measurements[J]. Chinese Science Bulletin, 2014,59(21):2108-2118.
[27]	Zwally H J, Schutz B, Abdalati W , et al. ICESat's laser measurements of polar ice, atmosphere, ocean, and land[J]. Journal of Geodynamics, 2002,34(3):405-445.
[28]	Yue T X, Wang S H . Adjustment computation of HASM: A high-accuracy and high-speed method[J]. International Journal of Geographical Information Science, 2010,24(11):1725-1743.
[29]	Basaran M, Erpul G, Ozcan A U , et al. Spatial information of soil hydraulic conductivity and performance of cokriging over kriging in a semi-arid basin scale[J]. Environmental Earth Sciences, 2011,63(4):827-838.
[30]	Chatterjee S, Krishna A P, Sharma A P . Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India[J]. Environmental Earth Sciences, 2013,71(1):357-374.

DEM	点数/个	最大值/m	最小值/m	平均值/m	标准差/m	均方差/m
SRTM	335 716	47.92	-47.83	-0.25	5.57	5.57
ASTER	328 366	47.94	-47.65	0.08	7.20	7.20

土地覆盖类型	SRTM 均方差	ASTER GDEM 均方差
农田	4.13	5.15
灌丛	5.02	6.32
森林	8.52	11.35
草地	5.72	6.87
湿地	7.21	9.54
水体	7.05	9.07

坡度/°	点数/个	最大值/m	最小值/m	平均值/m	标准差/m	均方差/m
0~5	296 699	99.91	-95.60	0.32	4.40	4.40
5~10	18 631	99.44	-60.26	0.12	12.48	12.48
10~15	8 365	98.03	-91.48	0.60	19.00	19.01
15~20	3 752	99.74	-85.74	0.96	24.08	24.08
20~25	1 107	78.73	-79.00	1.21	29.16	29.17
25~30	193	97.94	-82.80	5.12	33.08	33.17
30~35	30	74.47	-72.86	-8.70	39.10	39.77

坡度/°	点数/个	最大值/m	最小值/m	平均值/m	标准差/m	均方差/m
0~5	204 513	99.42	-86.30	-0.10	6.61	6.61
5~10	90 474	99.95	-97.83	0.37	8.40	8.40
10~15	21 270	98.96	-69.38	0.72	10.93	10.93
15~20	7 260	97.74	-91.60	0.54	12.83	12.83
20~25	3 222	99.56	-81.74	-0.47	13.85	13.85
25~30	1 362	78.97	-86.13	-1.45	15.93	15.94
30~35	445	59.58	-56.38	-1.09	17.64	17.66
35~40	135	84.85	-59.74	-3.64	23.59	23.68
40~45	23	62.67	-55.10	-9.58	21.72	22.21

插值方法	SRTM 均方差	ASTER GDEM 均方差
反距离加权	2.20	5.31
地形转栅格	2.57	5.47
自然邻域法	2.70	5.56
普通克里金	4.74	5.93

基于ICESat/GLAS的山东省SRTM与ASTER GDEM高程精度评价与修正

Elevation Accuracy Evaluation and Correction of SRTM and ASTER GDEM in Shandong Province based on ICESat/GLAS

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[15]	王金传, 谭喜成, 王召海, 钟燕飞, 董华萍, 周松涛, 成布怡. 基于Faster R-CNN深度网络的遥感影像目标识别方法研究[J]. 地球信息科学学报, 2018, 20(10): 1500-1508.