顾及DEM误差空间自相关的地形变化检测方法

doi:10.12082/dqxxkx.2022.220209

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (12): 2297-2308.doi: 10.12082/dqxxkx.2022.220209

• 地球信息科学理论与方法 • 上一篇下一篇

顾及DEM误差空间自相关的地形变化检测方法

代文¹(), 陈凯¹, 王春¹^,²^,³^,^*(), 李敏², 陶宇²

1.南京信息工程大学地理科学学院，南京 210044
2.滁州学院地理信息与旅游学院，滁州 239000
3.实景地理环境安徽省重点实验室，滁州 239000

收稿日期:2022-04-20 修回日期:2022-07-15 出版日期:2022-12-25 发布日期:2023-02-25
通讯作者: *王春（1975— ），男，宁夏固原人，教授，研究方向为实景三维建模。E-mail: wangchun93@126.com
作者简介:代文（1995— ），男，贵州毕节人，讲师，研究方向为实景三维建模与数字地形分析。E-mail: wen.dai@nuist.edu.cn
基金资助:
安徽高校省级自然科学研究重大项目(KJ2021ZD0130);实景地理环境智能科技滁州市“113”产业创新团队;江苏省高等学校自然科学研究项目(22KJB170016);国家自然科学基金项目(41930102);国家自然科学基金项目(42171402);南京信息工程大学科研启动经费(2022r019)

Topographic Change Detection that Considers the Spatial Autocorrelation of DEM Errors

DAI Wen¹(), CHEN Kai¹, WANG Chun¹^,²^,³^,^*(), LI Min², TAO Yu²

1. School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. School of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
3. Anhui Province Key Laboratory of Physical Geographical Environment, Chuzhou 239000, China

Received:2022-04-20 Revised:2022-07-15 Online:2022-12-25 Published:2023-02-25
Contact: WANG Chun
Supported by:
Major Project of Natural Science Research of Anhui Provincial Department of Education(KJ2021ZD0130);Chuzhou "113" Industrial Innovation Team;Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB170016);National Natural Science Foundation of China(41930102);National Natural Science Foundation of China(42171402);Nanjing University of Information Science & Technology Research start-up fund(2022r019)

摘要/Abstract

摘要：

传统的地形变化检测方法忽略了DEM误差的空间自相关性，针对此问题，本文提出了顾及DEM误差空间自相关的地形变化检测方法。首先，通过2期DEM相减得到差值DEM（DoD），并通过蒙特卡罗方法评估DEM的误差空间分布；其次，基于DEM误差空间分布图，通过误差传播公式计算DoD误差，并使用半变异函数分析其空间自相关程度；最后，在误差空间自相关分析和显著性检测的基础上计算地形变化量（体积）和对应的误差限。本文在4个黄土小流域进行了实验，结果表明：无人机摄影测量DEM的高程误差存在一定程度的空间自相关，通过光束平差蒙特卡罗方法可以模拟无人机摄影测量DEM的误差空间分布；在进行地形变化检测时，使用误差空间分布代替中误差进行地形变化检测有效降低了检测结果对显著性阈值的敏感性；显著性阈值从68%提高到95%时，使用误差空间分布的检测结果损失的观测值比使用中误差低5.39%~6.75%。顾及空间自相关的地形变化检测方法能够更加科学、精确地量化地形变化特征，也可有效地应用于地表变形监测、流域侵蚀监测、输沙量评估等领域。

关键词: 地形变化检测, 显著性检测, DEM误差空间分布, 蒙特卡罗方法, 土壤侵蚀监测, 差值DEM

Abstract:

Traditional topographic change detection methods often ignore the spatial autocorrelation of DEM errors. To solve this problem, a topographic change detection method that considers the spatial autocorrelation of DEM errors is proposed in this paper. Firstly, the DEM of Difference (DoD) is obtained from two original DEMs, and the spatial distribution of DEM errors is evaluated by the Monte Carlo method. Secondly, based on spatially distributed DEM errors, DoD errors are calculated by error propagation and their spatial autocorrelation degree is analyzed using the semi-variance function. Finally, topographic changes (erosion, deposition, and net changes) are calculated based on the spatial autocorrelation analysis and significance detection. The results in four small catchments show that the elevation errors of UAV-photogrammetry DEM are spatially autocorrelated, which can be simulated by the Monte Carlo method. The use of spatially distributed error instead of RMSE for topographic change detection effectively reduces the sensitivity of the detection results to the significance threshold. When the significance threshold is increased from 68% to 95%, the loss of observations using the spatially distributed error is 5.39%~6.75% lower than that using the RMSE. The proposed method can be effectively used in the fields of surface deformation monitoring, erosion monitoring, sediment transport assessment, and so on.

Key words: topographic change detection, level of detection, spatial distribution of DEM errors, Monte Carlo method, soil erosion monitoring, DEM of difference

代文, 陈凯, 王春, 李敏, 陶宇. 顾及DEM误差空间自相关的地形变化检测方法[J]. 地球信息科学学报, 2022, 24(12): 2297-2308.DOI:10.12082/dqxxkx.2022.220209

DAI Wen, CHEN Kai, WANG Chun, LI Min, TAO Yu. Topographic Change Detection that Considers the Spatial Autocorrelation of DEM Errors[J]. Journal of Geo-information Science, 2022, 24(12): 2297-2308.DOI:10.12082/dqxxkx.2022.220209

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参考文献 29

[1]	贺礼家, 冯光财, 冯志雄, 等. 哨兵-2号光学影像地表形变监测:以2016年MW7.8新西兰凯库拉地震为例[J]. 测绘学报, 2019, 48(3):339-351.
	[ He L J, Feng G C, Feng Z X, et al. Coseismic displacements of 2016 MW7.8 Kaikoura, New Zealand earthquake, using Sentinel-2 optical images[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(3):339-351. ]
[2]	代文, 汤国安, 胡光辉, 等. 基于无人机摄影测量的地形变化检测方法与小流域输沙模型研究[J]. 地理科学进展, 2021, 40(9):1570-1580. doi: 10.18306/dlkxjz.2021.09.011
	[ Dai W, Tang G A, Hu G H, et al. Modelling sediment transport in space in a watershed based on topographic change detection by UAV survey[J]. Progress in Geography, 2021, 40(9):1570-1580. ] doi: 10.18306/dlkxjz.2021.09.011
[3]	李德仁, 夏松, 江万寿, 等. 一种地形变化检测与DEM更新的方法研究[J]. 武汉大学学报·信息科学版, 2006, 31(7):565-568.
	[ Li D R, Xia S, Jiang W S, et al. Approach for terrain change detection and DEM updating[J]. Geomatics and Information Science of Wuhan University, 2006, 31(7):565-568. ] DOI:10.3321/j.issn:1671-8860.2006.07.001 doi: 10.3321/j.issn:1671-8860.2006.07.001
[4]	Meinen B U, Robinson D T. Mapping erosion and deposition in an agricultural landscape: Optimization of UAV image acquisition schemes for SfM-MVS[J]. Remote Sensing of Environment, 2020, 239:111666. DOI:10.1016/j.rse.2020.111666 doi: 10.1016/j.rse.2020.111666
[5]	张绍成, 殷飞, 明祖涛, 等. 先验形变约束的实时动态载波相位差分变形监测[J]. 测绘科学, 2020, 45(11):8-12.
	[ Zhang S C, Yin F, Ming Z T, et al. Real-time kinematic deformation monitoring with prior deformation constraint[J]. Science of Surveying and Mapping, 2020, 45(11):8-12. ] DOI:10.16251/j.cnki.1009-2307.2020.11.002 doi: 10.16251/j.cnki.1009-2307.2020.11.002
[6]	李治洪. GNSS系统在黄金峡坝肩边坡变形监测中的应用[J]. 人民黄河, 2021, 43(1):125-128.
	[ Li Z H. Application of the automatic GNSS deformation monitoring system in Huangjinxia hydro junction project[J]. Yellow River, 2021, 43(1):125-128. ]
[7]	李瑞峰, 常乐, 秦海. InSAR监测技术与水准测量技术对比研究[J]. 工程质量, 2021, 39(3):72-76.
	[ Li R F, Chang L, Qin H. Comparative study on InSAR monitoring technology and leveling technology[J]. Construction Quality, 2021, 39(3):72-76. ]
[8]	郭保. GPS技术在水库大坝变形监测中的应用[J]. 测绘与空间地理信息, 2020, 43(12):103-106.
	[ Guo B. Application of GPS technology in reservoir dam deformation monitoring[J]. Geomatics ＆ Spatial Information Technology, 2020, 43(12):103-106. ]
[9]	殷硕文, 邵茜. 基于地形匹配的InSAR地形变化检测方法研究[J]. 武汉大学学报·信息科学版, 2010, 35(1):118-121.
	[ Yin S W, Shao Q. A method of topographic change detection base InSAR terrain matching technology[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1):118-121. ] DOI:10.13203/j.whugis2010.01.015 doi: 10.13203/j.whugis2010.01.015
[10]	Tomiyama N, Koike K, Omura M. Detection of topographic changes associated with volcanic activities of Mt. Hossho using D-InSAR[J]. Advances in Space Research, 2004, 33(3):279-283. DOI:10.1016/S0273-1177(03)00483-6 doi: 10.1016/S0273-1177(03)00483-6
[11]	董雅竹, 谭颖, 张朋辉, 等. 利用PS-InSAR研究合肥地区活动构造变形[J]. 地球物理学进展, 2021, 36(5):1822-1833.
	[ Dong Y Z, Tan Y, Zhang P H, et al. Using PS-InSAR to study active tectonic deformation in Hefei area[J]. Progress in Geophysics, 2021, 36(5):1822-1833. ]
[12]	Lyu M Y, Ke Y H, Li X J, et al. Detection of seasonal deformation of highway overpasses using the PS-InSAR technique: A case study in Beijing urban area[J]. Remote Sensing, 2020, 12(18):3071. DOI:10.3390/rs12183071 doi: 10.3390/rs12183071
[13]	许强, 蒲川豪, 赵宽耀, 等. 延安新区地面沉降时空演化特征时序InSAR监测与分析[J]. 武汉大学学报·信息科学版, 2021, 46(7):957-969.
	[ Xu Q, Pu C H, Zhao K Y, et al. Time series InSAR monitoring and analysis of spatiotemporal evolution characteristics of land subsidence in Yan'an new district[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7):957-969. ] DOI:10.13203/j.whugis20200146 doi: 10.13203/j.whugis20200146
[14]	Shi M Y, Yang H L, Wang B C, et al. Improving boundary constraint of probability integral method in SBAS- InSAR for deformation monitoring in mining areas[J]. Remote Sensing, 2021, 13(8):1497. DOI:10.3390/rs13081497 doi: 10.3390/rs13081497
[15]	郭延辉, 杨溢, 杨志全, 等. 国产GB-InSAR在特大型水库滑坡变形监测中的应用[J]. 中国地质灾害与防治学报, 2021, 32(2):66-72.
	[ Guo Y H, Yang Y, Yang Z Q, et al. Application of GB-InSAR in deformation monitoring of huge landslide in reservoir area[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(2):66-72. ] DOI:10.16031/j.cnki.issn.1003-8035.2021.02.09 doi: 10.16031/j.cnki.issn.1003-8035.2021.02.09
[16]	孙建宝, 梁芳, 沈正康, 等. 汶川MS8.0地震InSAR形变观测及初步分析[J]. 地震地质, 2008, 30(3):789-795.
	[ Sun J B, Liang F, Shen Z K, et al. Insar deformation observation and preliminary analysis of the ms 8 Wenchuan earthquake[J]. Seismology and Geology, 2008, 30(3):789-795. ] DOI:10.3969/j.issn.0253-4967.2008.03.017 doi: 10.3969/j.issn.0253-4967.2008.03.017
[17]	Liu X J, Tang G A, Yang J Y, et al. Simulating evolution of a loess gully head with cellular automata[J]. Chinese Geographical Science, 2015, 25(6):765-774. DOI:10.1007/s11769-014-0716-z doi: 10.1007/s11769-014-0716-z
[18]	曹敏, 汤国安, 张芳, 等. 基于元胞自动机的黄土小流域地形演变模拟[J]. 农业工程学报, 2012, 28(22):149-155,294.
	[ Cao M, Tang G A, Zhang F, et al. Simulation of terrain evolution in small loess watershed based on cellular automata[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(22):149-155,294. ] DOI:10.3969/j.issn.1002-6819.2012.22.022 doi: 10.3969/j.issn.1002-6819.2012.22.022
[19]	王清. 数字地形分析中表面积与体积计算的不确定性建模与分析[D]. 武汉: 华中师范大学, 2016.
	[ Wang Q. Uncertainty modelling and analysis of surface area and earthwork calculation based on regular grid digital elevation model(DEM)[D]. Wuhan: Central China Normal University, 2016. ]
[20]	Lane S N, Westaway R M, Murray Hicks D. Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing[J]. Earth Surface Processes and Landforms, 2003, 28(3):249-271. DOI:10.1002/esp.483 doi: 10.1002/esp.483
[21]	Heckmann T, Vericat D. Computing spatially distributed sediment delivery ratios: Inferring functional sediment connectivity from repeat high-resolution digital elevation models[J]. Earth Surface Processes and Landforms, 2018, 43(7):1547-1554. DOI:10.1002/esp.4334 doi: 10.1002/esp.4334
[22]	Antoniazza G, Bakker M, Lane S N. Revisiting the morphological method in two‐dimensions to quantify bed‐material transport in braided rivers[J]. Earth Surface Processes and Landforms, 2019, 44(11):2251-2267. DOI:10.1002/esp.4633 doi: 10.1002/esp.4633
[23]	Anderson S W. Uncertainty in quantitative analyses of topographic change: Error propagation and the role of thresholding[J]. Earth Surface Processes and Landforms, 2019, 44(5):1015-1033. DOI:10.1002/esp.4551 doi: 10.1002/esp.4551
[24]	James M R, Robson S, Smith M W. 3-D uncertainty-based topographic change detection with structure-from-motion photogrammetry: Precision maps for ground control and directly georeferenced surveys[J]. Earth Surface Processes and Landforms, 2017, 42(12):1769-1788. DOI:10.1002/esp.4125 doi: 10.1002/esp.4125
[25]	Curran P J. The semivariogram in remote sensing: An introduction[J]. Remote Sensing of Environment, 1988,24(3):493-507. DOI:10.1016/0034-4257(88)90021-1 doi: 10.1016/0034-4257(88)90021-1
[26]	舒彦军, 张立亭. 求解半变异函数的常用方法与新方法研究[J]. 测绘与空间地理信息, 2012, 35(5):24-27.
	[ Shu Y J, Zhang L T. Study of the commonly used methods and new methods of solving semi-variogram[J]. Geomatics ＆ Spatial Information Technology, 2012, 35(5):24-27. ] DOI:10.3969/j.issn.1672-5867.2012.05.008 doi: 10.3969/j.issn.1672-5867.2012.05.008
[27]	Taylor J R. An introduction to error analysis: the study of uncertainties in physical measurements[M]. 2nd ed. Sau salito, Calif.: University Science Books, 1997
[28]	Rolstad C, Haug T, Denby B. Spatially integra ted geodetic glacier mass balance and its uncertainty based on geostatistical analysis: Application to the western Svartisen ice cap, Norway[J]. Journal of Glaciology, 2009, 55(192):666-680. DOI:10.3189/002214309789470950 doi: 10.3189/002214309789470950
[29]	高海东. 黄土高原丘陵沟壑区沟道治理工程的生态水文效应研究[D]. 北京: 中国科学院研究生院(教育部水土保持与生态环境研究中心), 2013.
	[ Gao H D. Hydro-ecological impact of the gully erosion control works in loess hilly-gully region[D]. Beijing: Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences, 2013. ]

	真实变化量	误差特征	观测数
无地形变化组	0	正态分布 u=0 σ=0.4	40×40
净侵蚀组	1
净沉积组	-1
混合变化组	-1 ~ 1		300

	真实值	观测值（无分割）	观测值（显著性阈值分割）
无变化组	0	2.65±16	1.75±16（68%）
净侵蚀组	1600	1602.65±16	1498.28±16（95%）
净沉积组	-1600	-1597.34±16	-1460.70±16（95%）

	真实值	观测值（无分割）	观测值（95%显著性分割）
毛侵蚀	50	76.18±4.89	46.9±4.89
毛沉积	-50	-76.03±4.89	-46.17±4.89
净变化	0	0.15±6.92	-0.72±6.92

样区	中误差检测			误差空间分布图检测
样区	68%显著性	95%显著性	下降程度	68%显著性	95%显著性	下降程度
A1	82.46	61.26	21.20	87.12	74.35	12.77
A2	84.74	67.53	17.21	88.26	76.45	11.82
B1	57.41	20.69	36.72	66.27	36.30	29.97
B2	57.72	27.14	30.58	63.95	35.95	28.00

样区	毛侵蚀量/t(误差)	毛沉积量/t(误差)	净变化量/t(误差)	输沙率/(t/yr)(误差)	输沙率相对误差/%
A1	210 709.34	-41 395.90	169 313.44	12 093.82	6.62
A1	(±10 173.24)	(±4703.33)	(±11 207.86)	(±800.56)	6.62
A2	215 870.70	-43088.44	172 782.26	12 341.59	6.24
A2	(±9685.18)	(±4733.91)	(±10 780.20)	(±770.01)	6.24
B1	15 523.22	-964.96	14 558.26	2911.65	26.50
B1	(±3589.14)	(±1415.46)	(±3858.16)	(±771.63)	26.50
B2	14 642.17	-3547.07	11 095.11	2219.02	29.96
B2	(±3196.08)	(±912.49)	(±3323.79)	(±664.76)	29.96

顾及DEM误差空间自相关的地形变化检测方法

Topographic Change Detection that Considers the Spatial Autocorrelation of DEM Errors

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