时序InSAR形变梯度估计与城市建筑物风险评估: 以北京平原为例

左世诚; 董杰; 廖明生

doi:10.19509/j.cnki.dzkq.tb20240117

时序InSAR形变梯度估计与城市建筑物风险评估: 以北京平原为例

doi: 10.19509/j.cnki.dzkq.tb20240117

左世诚^a,,
董杰^{a, b},
廖明生^a, ,

a.
武汉大学测绘遥感信息工程国家重点实验室, 武汉 430079
b.
武汉大学遥感信息工程学院, 武汉 430079

基金项目:

国家自然科学基金项目 42374013

国家重点研发计划课题 2023YFC3009404

详细信息

作者简介:
左世诚, E-mail: a2409637129@163.com

通讯作者:
廖明生, E-mail: liao@whu.edu.cn

中图分类号: P642.26
计量
- 文章访问数: 74
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-26
- 录用日期: 2024-07-01
- 修回日期: 2024-05-21

Time-series InSAR deformation gradient estimation and urban buildings risk assessment: A case study in the Beijing Plain

ZUO Shicheng^a
,,
DONG Jie^{a, b},
LIAO Mingsheng^{a
, ,}

a.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
b.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

More Information

Author Bio:
ZUO Shicheng, E-mail: a2409637129@163.com

Corresponding author: LIAO Mingsheng, E-mail: liao@whu.edu.cn

摘要

摘要:
城市地表的差异性形变易对其表面基础设施造成断裂、扭曲等威胁或损害, 监测差异性形变并评估建筑物风险等级对人民生命财产安全至关重要。采用时序InSAR方法对Sentinel-1卫星数据进行时间序列分析, 获取了研究区地表形变及空间形变梯度; 结合夜光遥感数据、土地利用类型数据集、建筑物高程数据等多源外部数据, 采用网络层次分析法计算了研究区的危险性和易损性指标, 并由此开展了宏观风险评估; 在微观层面上评估建筑物的风险性, 识别潜在风险区域, 对宏观风险评估结果进行补充, 并进行对比实验验证了本研究的有效性。研究结果显示, 朝阳区东部、通州区西北部具有较大的差异性形变; 首都国际机场区域、安定南街附近等地区存在较高的风险性。因此, 利用多源数据进行差异性形变监测和风险评估对城市安全运行具有重要意义。
- 时序InSAR /
- 北京平原 /
- 空间形变梯度 /
- 夜光遥感 /
- 风险评估 /
- 城市建筑物
Abstract: Objective
Differential deformation of urban land surfaces can threaten or damage surface infrastructure, leading to fractures and distortions. Monitoring spatial differential deformation and assessing associated risk levels are crucial for urban safety management.
Methods
This study employs Sentinel-1 satellite data and the time series InSAR techniques to analyze surface deformation over time, enabling the derivation of spatial-temporal deformation gradients. Hazard and vulnerability assessment factors are calculated using an analytic hierarchy process, integrating data such as nighttime light remote sensing, land use, and Chinese building height datasets.A macroscopic risk assessment is conducted, with supplementary microscopic-levelanalysis to assess building risks and identify potential high-risk areas. Comparison experiments verify the effectiveness of the research.
Conclusion
Significant deformation disparities are identified between the eastern Chaoyang District and the northwestern Tongzhou District. In addition, high-risk areas are observed around the Capital International Airport region and the vicinity of Anding South Street. Therefore, the study highlights the importance of multisource data for effectively monitoring differential deformation to ensureurban safe.
- time-series InSAR /
- Beijing Plain /
- spatial deformation gradient /
- night-time light remote sensing /
- risk assessment /
- urban building
注释:

所有作者声明不存在利益冲突。

HTML全文

图 1 北京平原范围示意图

Figure 1. Schematic map of Beijing Plain

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图 2 城市区域风险评估技术路线图

Figure 2. Technical roadmap of urban area risk assessment

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图 3 垂直向累计形变图(a)、水平向累计形变图(b)、垂直向空间形变梯度图(c)和水平向空间形变梯度图(d)

Figure 3. Vertical cumulative deformation(a), horizontal cumulative deformation(b), vertical spatial deformation gradient(c) and horizontal spatial deformation gradient(d)

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图 4 相位梯度堆叠图

Figure 4. Phase-gradient stacking

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图 5 宏观危险性(a)和易损性(b)等级分类结果图

Figure 5. Classification of macro hazard(a) and vulnerability(b)

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图 6 宏观风险性等级评估分类结果图

Figure 6. Classification of macro risk assessment

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图 7 建筑物危险性(a)和易损性等级分类结果图(b)

Figure 7. Classification of buildings hazard(a) and vulnerability(b)

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图 8 建筑物风险性等级评估分类结果图

Figure 8. Classification of buildings risk assessment

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图 9 风险性等级评估结果对比图

a.对比实验宏观结果; b.多源数据宏观结果；c.对比实验微观结果；d.多源数据微观结果

Figure 9. Comparison of risk assessment results

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表 1 宏观危险性分级标准和权重

Table 1. Macro hazard classification standards and weights

危险性因子	危险性分级标准				权重
危险性因子	非常高(H3)	高(H2)	中(H1)	低(H0)	权重
水平向累计形变/m	≤-0.06或 ≥0.06	(-0.06, -0.04]或 [0.04, 0.06)	(-0.04, -0.02]或 [0.02, 0.04)	(-0.02, 0.02)	0.08
垂直向累计形变/m	≤-0.15或 ≥0.06	(-0.15, -0.08]或 [0.04, 0.06)	(-0.08, -0.05]或 [0.02, 0.04)	(-0.05, 0.02)	0.16
垂直向空间形变梯度(弧度形变)/10^-4	≥1.2	[0.8, 1.2)	[0.4, 0.8)	[0, 0.4)	0.20
水平向空间形变梯度(弧度形变)/10^-4	≥1.0	[0.5, 1.0)	[0.3, 0.5)	[0, 0.3)	0.32
相位梯度堆叠	≥0.8	[0.5, 0.8)	[0.3, 0.5)	[0, 0.3)	0.24

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表 2 宏观易损性分级标准和权重

Table 2. Macro vulnerability classification standards and weights

易损性因子	易损性分级标准				权重
易损性因子	非常高(V3)	高(V2)	中(V1)	低(V0)	权重
人口密度/(人·km^-2)	>80 000	(8 000, 80 000]	(800, 8 000]	[0, 800]	0.2
GDP/亿元	>5 000	(500, 5 000]	(50, 500]	[0, 50]	0.2
夜光遥感强度	>800	(500, 800]	(300, 500]	[0, 300]	0.2
建筑物高度/m	>20	(10, 20]	(5, 10]	[0, 5]	0.4

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表 3 宏观风险评估分级

Table 3. Macro risk assessment classification

易损性等级	危险性等级
易损性等级	H0	H1	H2	H3
V0	R0	R0	R1	R2
V1	R0	R1	R2	R3
V2	R1	R2	R3	R4
V3	R2	R3	R4	R4
注：R0，R1，R2，R3，R4分别为宏观风险非常低、低、中、高、非常高5个风险性等级，下同

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表 4 建筑物易损性分级标准和权重

Table 4. Classification standards and weights of buildings vulnerability

易损性因子	易损性分级标准				权重
易损性因子	非常高(V3)	高(V2)	中(V1)	低(V0)	权重
夜光遥感强度	>500	(400, 500]	(300, 400]	(200, 300]	0.5
建筑物体积/m³	>50 000	(10 000, 50 000]	(5 000, 10 000]	(0, 5 000]	0.5

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表 5 建筑物风险评估分级

Table 5. Buildings risk assessment classification

易损性等级	危险性等级
易损性等级	H0	H1	H2	H3
V0	R0	R0	R0	R1
V1	R0	R0	R1	R2
V2	R0	R1	R2	R3
V3	R1	R2	R3	R3

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表 6 对照实验评价因子分级

Table 6. Classification of control experiment evaluation factors

评价因子	风险性分级标准
评价因子	高(R3)	中(R2)	低(R1)	非常低(R0)
空间形变梯度/10^-4	≥1.2(H3)	[0.8, 1.2)(H2)	[0.4, 0.8)(H1)	[0, 0.4)(H0)
人口密度/(人·km^-2)	>80 000(V3)	(8 000, 80 000](V2)	(800, 8 000](V1)	[0, 800](V0)

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