联合无人机光学与机载LiDAR在高位滑坡要素识别中的应用:以川西汶川龙溪沟滑坡为例

doi:10.19657/j.geoscience.1000-8527.2023.101

现代地质 ›› 2024, Vol. 38 ›› Issue (02): 464-476.DOI: 10.19657/j.geoscience.1000-8527.2023.101

• 水文地质、工程地质和环境地质 • 上一篇下一篇

联合无人机光学与机载LiDAR在高位滑坡要素识别中的应用:以川西汶川龙溪沟滑坡为例

王德富¹(), 李永鑫¹^,²(), 任娟³, 范亚军¹, 刘立¹^,², 罗超¹

1.自然资源部第三地理信息制图院,四川成都 610100
2.自然资源部数字制图与国土信息应用重点实验室,四川成都 610100
3.四川省国土空间生态修复与地质灾害防治研究院,四川成都 610036

收稿日期:2023-07-09 修回日期:2023-08-18 出版日期:2024-04-10 发布日期:2024-05-22
通讯作者: 李永鑫,男,高级工程师,1986年出生,地理信息系统专业,主要从事测绘遥感技术生产与管理研究。Email: 317307006@qq.com。
作者简介:王德富,男,工程师,1990年出生,地质学专业,主要从事水工环地质与遥感技术应用研究。Email: 497333919@qq.com。
基金资助:
四川省自然资源厅地质灾害隐患遥感识别监测及高分遥感应用服务项目(N5100012022001470);自然资源部科技发展司自然资源技术融合与应用示范项目(121204007000204101);自然资源部四川省滑坡灾害隐患遥感智能防控体系部省合作研究项目(SCDZRS2023)

Application of Joint UAV Optics and Airborne LiDAR in High Level Landslide Element Identification: A Case Study from the Longxigou Landslide in Wenchuan, Western Sichuan

WANG Defu¹(), LI Yongxin¹^,²(), REN Juan³, FAN Yajun¹, LIU Li¹^,², LUO Chao¹

1. The Third Geoinformation Mapping Institute of Ministry of Natural Resources, Chengdu, Sichuan 610100, China
2. Key Laboratory of Digital Mapping and Homeland Information, Ministry of Natural Resources, Chengdu, Sichuan 610100, China
3. Sichuan Academy of Territorial Space Ecorestoration and Geohazard Prevention, Chengdu, Sichuan 610036, China

Received:2023-07-09 Revised:2023-08-18 Online:2024-04-10 Published:2024-05-22

摘要/Abstract

摘要：

高位和高隐蔽性滑坡调查人力难至、效率低下且安全风险大,利用无人机光学和机载LiDAR等航空遥感技术进行调查识别能够有效克服该问题,已成为当前研究应用的热点。目前多数研究与应用以宏观调查和解译为主,针对滑坡拉裂缝、滑坡壁等发育要素的精细化识别和量测研究较少。为进一步细化无人机光学和机载LiDAR识别的滑坡要素解译特征,提高该技术的综合应用效果,本文选取川西地震活跃区的汶川龙溪沟高位滑坡为示范,采用无人机摄影测量、机载LiDAR、解译对比与空间测量以及实地调查复核等方法,提取了28条拉张裂缝、2条剪切裂缝、8处下挫陡坎、2条滑坡壁、5条前缘边界、5个次级变形体,基于滑坡要素排列组合划分了2个次级滑体、4个变形区。野外验证结果与室内解译高度吻合,证明了该方法的可靠性和准确性。研究成果总结对比滑坡要素在不同数据上的色调、纹理、光谱差异,探索联合无人机光学与机载LiDAR的滑坡要素“色调-纹理-图谱”协同对比提取方法,阐述了高位滑坡“先解译要素再分区组合”的精细识别过程。研究成果可为其他高危高位滑坡的精细化识别研究及防治应用提供技术参考,具有应用推广价值。

关键词: 无人机, 机载LiDAR, 高位滑坡, 要素识别, 汶川龙溪沟

Abstract:

Investigation of high-level and highly concealed landslides is challenging and difficult to be reached by manpower, with low efficiency and high risks. The use of aerial remote sensing technologies such as drone optics and airborne LiDAR can effectively overcome these problems, and has been extensively applied to today’s research. However, currently most of those research and applications focus on macroscopic investigations and interpretations, with only limited research on refined identifications and measurements of the development factors, such as landslide cracks and landslide walls. In order to further refine the interpretation features of the landslide elements based on drone optics and airborne LiDAR recognition, and improve the comprehensive application of this technology, this study selected the Wenchuan Longxigou high-level landslide in the active area of earthquake in western Sichuan as an example. The methods include drone digital photogrammetry, airborne LiDAR distance measurement, interpretation comparison and spatial measurement, as well as on-site investigation and verification. In total, twenty eight tension cracks, two shear cracks, and eight steep slopes were extracted. Two landslide walls, five leading edge boundaries, and five secondary deformation zones were divided based on the arrangement and combination of the landslide elements into two secondary sliding zones and four deformation zones. The validations are highly consistent with the indoor interpretations, proving the reliability and accuracy of this proposed method. The research results summarized and compared the differences in color tone, texture, and spectrum of landslide elements on different data, explored the collaborative extraction method of ‘color tone texture map’ of landslide elements using unmanned aerial vehicle optics and airborne LiDAR, and elaborated on the fine identification processes of high-level landslides by ‘interpreting elements first and then zoning and combining’. The results provide technical references for the fine identification and prevention of other high-risk high-level landslides, and have a significant value in the applications.

Key words: UVA, airborne LiDAR, high level landslide, element identification, Longxigou of Wenchuan

中图分类号:

P642.22
P694

王德富, 李永鑫, 任娟, 范亚军, 刘立, 罗超. 联合无人机光学与机载LiDAR在高位滑坡要素识别中的应用:以川西汶川龙溪沟滑坡为例[J]. 现代地质, 2024, 38(02): 464-476.

WANG Defu, LI Yongxin, REN Juan, FAN Yajun, LIU Li, LUO Chao. Application of Joint UAV Optics and Airborne LiDAR in High Level Landslide Element Identification: A Case Study from the Longxigou Landslide in Wenchuan, Western Sichuan[J]. Geoscience, 2024, 38(02): 464-476.

图/表 17

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调查比例尺	航飞影像质量要求			机载LiDAR点云密度要求
调查比例尺	地形类别	影像分辨率 (m)	平面位置中误差(m)	林分郁闭度	点云密度 (点/m²)
1：5000	平原、丘陵	0.2	2.5	[0.7,1]	[15,20)
	低山地	0.2	3.5	[0.2,0.69)	[10,15)
	中高山地	0.2	3.7	[0,0.2)	[2,4)

调查比例尺	航飞影像质量要求			机载LiDAR点云密度要求
调查比例尺	地形类别	影像分辨率 (m)	平面位置中误差(m)	林分郁闭度	点云密度 (点/m²)
1：5000	平原、丘陵	0.2	2.5	[0.7,1]	[15,20)
	低山地	0.2	3.5	[0.2,0.69)	[10,15)
	中高山地	0.2	3.7	[0,0.2)	[2,4)

编号	经度	纬度	解译长度(m)	数据源	编号	经度	纬度	解译长度 (m)	数据源
LZLF-01	103°31'28.301″	31°33'33.949″	79.27	UAV	LZLF-26	103°31'30.166″	31°33'32.218″	25.70	UAV
LZLF-02	103°31'26.699″	31°33'31.648″	39.34	UAV	LZLF-27	103°31'28.409″	31°33'32.072″	36.55	UAV
LZLF-03	103°31'25.592″	31°33'28.118″	227.31	UAV+LiDAR	LZLF-28	103°31'34.203″	31°33'28.988″	53.58	UAV+LiDAR
LZLF-04	103°31'11.317″	31°33'20.577″	27.49	UAV	JQLF-01	103°31'25.179″	31°33'27.065″	127.84	UAV+LiDAR
LZLF-05	103°31'8.443″	31°33'19.543″	92.64	UAV	JQLF-02	103°31'28.827″	31°33'31.336″	56.33	UAV
LZLF-06	103°31'5.144″	31°33'19.638″	35.76	UAV	XCDK-01	103°31'19.210″	31°33'28.953″	28.58	LiDAR
LZLF-07	103°31'32.275″	31°33'28.104″	44.73	UAV+LiDAR	XCDK-02	103°31'20.946″	31°33'25.098″	24.73	LiDAR
LZLF-08	103°31'28.615″	31°33'26.308″	196.80	UAV+LiDAR	XCDK-03	103°31'35.606″	31°33'21.720″	289.47	LiDAR
LZLF-09	103°31'24.484″	31°33'23.036″	124.34	UAV+LiDAR	XCDK-04	103°31'14.622″	31°33'16.148″	228.15	LiDAR
LZLF-10	103°31'32.964″	31°33'27.661″	35.58	UAV+LiDAR	XCDK-05	103°31'35.675″	31°33'35.322″	57.48	UAV+LiDAR
LZLF-11	103°31'29.147″	31°33'27.998″	76.07	UAV	XCDK-06	103°31'33.409″	31°33'36.425″	67.98	UAV+LiDAR
LZLF-12	103°31'31.702″	31°33'28.441″	21.60	UAV	XCDK-07	103°31'29.022″	31°33'36.919″	122.35	UAV+LiDAR
LZLF-13	103°31'32.860″	31°33'24.858″	15.61	UAV	XCDK-08	103°31'24.127″	31°33'31.968″	130.96	UAV+LiDAR
LZLF-14	103°31'32.701″	31°33'24.564″	19.57	UAV	HPB-01	103°31'33.636″	31°33'16.551″	1822.41	LiDAR
LZLF-15	103°31'32.643″	31°33'24.433″	15.08	UAV	HPB-02	103°31'25.368″	31°33'8.839″	1730.82	LiDAR
LZLF-16	103°31'32.864″	31°33'25.359″	15.53	UAV	QYDJ-01	103°31'30.643″	31°33'41.233″	328.10	UAV+LiDAR
LZLF-17	103°31'32.400″	31°33'25.121″	14.87	UAV	QYDJ-02	103°31'1.323″	31°33'24.602″	162.49	UAV+LiDAR
LZLF-18	103°31'31.671″	31°33'24.773″	12.85	UAV	QYDJ-03	103°31'8.743″	31°33'32.113″	339.34	UAV+LiDAR
LZLF-19	103°31'31.252″	31°33'24.641″	6.77	UAV	QYDJ-04	103°31'15.904″	31°33'35.455″	100.43	UAV+LiDAR
LZLF-20	103°31'30.880″	31°33'24.687″	10.20	UAV	QYDJ-05	103°31'19.188″	31°33'39.955″	292.20	UAV+LiDAR
LZLF-21	103°31'18.898″	31°33'14.474″	218.83	UAV+LiDAR	BXT-01	103°31'17.773″	31°33'26.923″	446.04	LiDAR
LZLF-22	103°31'17.895″	31°33'23.470″	77.46	LiDAR	BXT-02	103°31'11.288″	31°33'20.288″	661.71	LiDAR
LZLF-23	103°31'18.842″	31°33'23.375″	38.43	LiDAR	BXT-03	103°31'33.172″	31°33'34.402″	796.72	UAV+LiDAR
LZLF-24	103°31'31.153″	31°33'31.655″	26.14	UAV	BXT-04	103°31'24.471″	31°33'27.910″	830.85	UAV+LiDAR
LZLF-25	103°31'31.424″	31°33'31.355″	37.40	UAV	BXT-05	103°31'14.206″	31°33'21.988″	662.65	LiDAR

编号	经度	纬度	解译长度(m)	数据源	编号	经度	纬度	解译长度 (m)	数据源
LZLF-01	103°31'28.301″	31°33'33.949″	79.27	UAV	LZLF-26	103°31'30.166″	31°33'32.218″	25.70	UAV
LZLF-02	103°31'26.699″	31°33'31.648″	39.34	UAV	LZLF-27	103°31'28.409″	31°33'32.072″	36.55	UAV
LZLF-03	103°31'25.592″	31°33'28.118″	227.31	UAV+LiDAR	LZLF-28	103°31'34.203″	31°33'28.988″	53.58	UAV+LiDAR
LZLF-04	103°31'11.317″	31°33'20.577″	27.49	UAV	JQLF-01	103°31'25.179″	31°33'27.065″	127.84	UAV+LiDAR
LZLF-05	103°31'8.443″	31°33'19.543″	92.64	UAV	JQLF-02	103°31'28.827″	31°33'31.336″	56.33	UAV
LZLF-06	103°31'5.144″	31°33'19.638″	35.76	UAV	XCDK-01	103°31'19.210″	31°33'28.953″	28.58	LiDAR
LZLF-07	103°31'32.275″	31°33'28.104″	44.73	UAV+LiDAR	XCDK-02	103°31'20.946″	31°33'25.098″	24.73	LiDAR
LZLF-08	103°31'28.615″	31°33'26.308″	196.80	UAV+LiDAR	XCDK-03	103°31'35.606″	31°33'21.720″	289.47	LiDAR
LZLF-09	103°31'24.484″	31°33'23.036″	124.34	UAV+LiDAR	XCDK-04	103°31'14.622″	31°33'16.148″	228.15	LiDAR
LZLF-10	103°31'32.964″	31°33'27.661″	35.58	UAV+LiDAR	XCDK-05	103°31'35.675″	31°33'35.322″	57.48	UAV+LiDAR
LZLF-11	103°31'29.147″	31°33'27.998″	76.07	UAV	XCDK-06	103°31'33.409″	31°33'36.425″	67.98	UAV+LiDAR
LZLF-12	103°31'31.702″	31°33'28.441″	21.60	UAV	XCDK-07	103°31'29.022″	31°33'36.919″	122.35	UAV+LiDAR
LZLF-13	103°31'32.860″	31°33'24.858″	15.61	UAV	XCDK-08	103°31'24.127″	31°33'31.968″	130.96	UAV+LiDAR
LZLF-14	103°31'32.701″	31°33'24.564″	19.57	UAV	HPB-01	103°31'33.636″	31°33'16.551″	1822.41	LiDAR
LZLF-15	103°31'32.643″	31°33'24.433″	15.08	UAV	HPB-02	103°31'25.368″	31°33'8.839″	1730.82	LiDAR
LZLF-16	103°31'32.864″	31°33'25.359″	15.53	UAV	QYDJ-01	103°31'30.643″	31°33'41.233″	328.10	UAV+LiDAR
LZLF-17	103°31'32.400″	31°33'25.121″	14.87	UAV	QYDJ-02	103°31'1.323″	31°33'24.602″	162.49	UAV+LiDAR
LZLF-18	103°31'31.671″	31°33'24.773″	12.85	UAV	QYDJ-03	103°31'8.743″	31°33'32.113″	339.34	UAV+LiDAR
LZLF-19	103°31'31.252″	31°33'24.641″	6.77	UAV	QYDJ-04	103°31'15.904″	31°33'35.455″	100.43	UAV+LiDAR
LZLF-20	103°31'30.880″	31°33'24.687″	10.20	UAV	QYDJ-05	103°31'19.188″	31°33'39.955″	292.20	UAV+LiDAR
LZLF-21	103°31'18.898″	31°33'14.474″	218.83	UAV+LiDAR	BXT-01	103°31'17.773″	31°33'26.923″	446.04	LiDAR
LZLF-22	103°31'17.895″	31°33'23.470″	77.46	LiDAR	BXT-02	103°31'11.288″	31°33'20.288″	661.71	LiDAR
LZLF-23	103°31'18.842″	31°33'23.375″	38.43	LiDAR	BXT-03	103°31'33.172″	31°33'34.402″	796.72	UAV+LiDAR
LZLF-24	103°31'31.153″	31°33'31.655″	26.14	UAV	BXT-04	103°31'24.471″	31°33'27.910″	830.85	UAV+LiDAR
LZLF-25	103°31'31.424″	31°33'31.355″	37.40	UAV	BXT-05	103°31'14.206″	31°33'21.988″	662.65	LiDAR

一级滑体	二级变形体	经度	纬度	分区面积(m²)	滑体面积(10⁴ m²)	推测厚度(m)	估算体积(10⁴ m³)
HPT1	HP1-1	103°31'51.528″	31°33'40.624″	68336.04	29.08	4.55	132.31
HPT1	HP1-2	103°31'20.377″	31°33'53.573″	66054.13	29.08	4.55	132.31
HPT2	HP2-1	103°31'09.236″	31°33'38.424″	95738.30	13.77	4.96	68.32
HPT2	HP2-2	103°31'07.096″	31°33'34.374″	44967.48	13.77	4.96	68.32

联合无人机光学与机载LiDAR在高位滑坡要素识别中的应用:以川西汶川龙溪沟滑坡为例

Application of Joint UAV Optics and Airborne LiDAR in High Level Landslide Element Identification: A Case Study from the Longxigou Landslide in Wenchuan, Western Sichuan

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解译要素	基于光学标志解译数量	基于机载LiDAR 标志解译数量	两者共同解译数量	总计
拉张裂缝	19	2	7	28
剪切裂缝	1	0	1	2
下挫陡坎	0	4	4	8
滑坡壁	0	2	0	2
前缘堆积	0	0	5	5
次级变形	0	3	2	5

识别要素	无人机光学影像			LiDAR-DEM
识别要素	影像色调	纹理结构	图谱特征	影像色调	纹理结构	图谱特征
拉张裂缝	裂缝呈土黄色,周边地物呈浅绿至绿色	裂缝边缘(后壁边缘)纹理粗糙,周边地物纹理单一或渐变,两者界线清晰	呈弧形、不规则弯曲状	裂缝呈深灰至黑色,周边地物呈浅灰或灰白色	裂缝(陡坎)边缘纹理粗糙度不均一,与周边纹理相比表现为明显的阴影状	呈短弧状,与主滑方向呈垂直或锐角
剪切裂缝			呈半连续间断性长条状	裂缝呈深灰至黑色,周边地物呈浅灰或灰白色		呈短直线型,数条并列与主滑方向近似平行
滑坡后壁			呈半圆弧形、弓形、伞状	后壁边缘(陡坎处、滑塌变形边界)呈灰黑色,周边呈浅灰色		呈半圆弧形、扇形
下挫台坎	不明显	不明显	不明显			呈具有明显高差特征的不规则条带状
新鲜滑塌 (次级变形)	新鲜滑动处呈淡紫色,周边地物呈绿色	滑动边缘纹理粗糙,有颗粒感	滑动边缘呈半椭圆形,具圈闭状		变形边界纹理特征不明显	与周边地物高程相比,呈明显低洼负地形

编号	解译长度 (m)	实测长度 (m)	偏差 (m)	编号	解译长度 (m)	实测长度 (m)	偏差 (m)	编号	解译长度 (m)	实测长度 (m)	偏差 (m)
LZLF-01	79.27	80.67	1.4	LZLF-14	19.57	20.57	1.0	LZLF-27	36.55	36.15	-0.4
LZLF-02	39.34	40.14	0.8	LZLF-15	15.08	14.38	-0.7	LZLF-28	53.58	55.48	1.9
LZLF-03	227.31	228.01	0.7	LZLF-16	15.53	14.53	-1.0	JQLF-01	127.84	129.34	1.5
LZLF-04	27.49	29.09	1.6	LZLF-17	14.87	14.07	-0.8	JQLF-02	56.33	58.63	2.3
LZLF-05	92.64	93.74	1.1	LZLF-18	12.85	12.65	-0.2	XCDK-01	28.58	30.98	2.4
LZLF-06	35.76	35.06	-0.7	LZLF-19	6.77	5.87	-0.9	XCDK-02	24.73	25.43	0.7
LZLF-07	44.73	46.63	1.9	LZLF-20	10.20	9.40	-0.8	XCDK-03	289.47	291.67	2.2
LZLF-08	196.8	195.80	-1.0	LZLF-21	218.83	220.23	1.4	XCDK-04	228.15	229.15	1.0
LZLF-09	124.34	126.14	1.8	LZLF-22	77.46	77.36	-0.1	XCDK-05	57.48	59.68	2.2
LZLF-10	35.58	37.78	2.2	LZLF-23	38.43	39.93	1.5	XCDK-06	67.98	70.78	2.8
LZLF-11	76.07	78.97	2.9	LZLF-24	26.14	25.54	-0.6	XCDK-07	122.35	123.15	0.8
LZLF-12	21.60	23.10	1.5	LZLF-25	37.40	38.60	1.2	XCDK-08	130.96	130.66	-0.3
LZLF-13	15.61	17.71	2.1	LZLF-26	25.70	26.30	0.6