雷达相干性与NDVI经验模型

doi:10.13474/j.cnki.11-2246.2021.0109

测绘通报 ›› 2021, Vol. 0 ›› Issue (4): 45-51,59.doi: 10.13474/j.cnki.11-2246.2021.0109

雷达相干性与NDVI经验模型

刘智勇¹, 张晨², 刘泽楷¹, 袁俊健¹, 祁宏昌¹, 潘屹峰³, 朱焕廉⁴, 吴希文⁵, 王华⁵

1. 广东电网有限责任公司广州供电局, 广东广州 510620;
2. 广州地理研究所, 广东广州 510075;
3. 中科云图科技有限公司, 广东广州 510075;
4. 深圳建设工程质量监测中心, 广东深圳 518052;
5. 广东工业大学, 广东广州 510006

收稿日期:2020-09-30 出版日期:2021-04-25 发布日期:2021-04-30
作者简介:刘智勇(1972-),男,硕士,主要研究方向为输电线路运维管理。E-mail:913401372@qq.com
基金资助:
中国南方电网有限责任公司广州供电局有限公司重点科技项目（080000KK52190001）；广州市科技计划（201902010033）；广东省自然科学基金（2018A030310538）

Empirical relationship between radar coherence and NDVI

LIU Zhiyong¹, ZHANG Chen², LIU Zekai¹, YUAN Junjian¹, QI Hongchang¹, PAN Yifeng³, ZHU Huanlian⁴, WU Xiwen⁵, WANG Hua⁵

1. Guangdong Power Grid Co., Ltd., Guangzhou Power Supply Bureau, Guangzhou 510620, China;
2. Guangzhou Institute of Geography, Guangzhou 510075, China;
3. ZhongkeYuntu Technology Co., Ltd., Guangzhou 510075, China;
4. Shenzhen Construction Engineering Quality Monitoring Center, Shenzhen 518052, China;
5. Guangdong University of Technology, Guangzhou 510006, China

Received:2020-09-30 Online:2021-04-25 Published:2021-04-30

摘要/Abstract

摘要： 植被的覆盖程度是造成雷达影像失相干的重要因素。通常，在森林等植被覆盖严重的地区，相干性相对较低，而在城市等植被覆盖率较低的地区，相干性较高。本文基于2017年珠江三角洲地区的MODIS归一化植被指数（NDVI）与Sentinel-1雷达卫星影像相干性，建立线性回归和幂函数回归模型，并利用两种模型预测该地区2016年的相干性，最后通过F分布检验及残差分布比较两者的精度。试验结果表明，总体上幂函数模型的精度在拟合及预测方面均高于线性函数模型，因此，本文建议以幂函模型作为相干性及NDVI的经验模型。利用幂函数经验模型不仅能够较好地预测2016年珠三角地区的相干性，同时在预测云南地区的相干性中也获得了较高的精度，因此证明该经验模型具有一定的推广价值。

关键词: 归一化植被指数, 相干性, InSAR, 幂函数, 线性函数

Abstract: The degree of vegetation coverage is an important factor that causes the loss of coherence between radar images. Generally, in areas with severe vegetation coverage such as forests, coherence is relatively low, while in areas with low vegetation coverage such as cities, coherence is relatively high. We establish linear regression and power function regression models based on MODIS normalized vegetation index (NDVI) and InSAR in the Pearl River Delta region in 2017. We use these two models to predict the coherence in this region in 2016 and use F distribution to test the accuracy of the models. The results show that the accuracy of the power function regression model is higher than the linear function in both fitting and prediction. Therefore, we suggest the power function model as an optimal empirical model of coherence and NDVI. Our results show that the power model can well predict the coherence not only in the Pearl River Delta in 2016, but also can predict that in high precision in Yunnan. Therefore, we suggest the empirical model might be widely used in other regions.

Key words: normalized vegetation index, coherence, InSAR, power function, linear function

中图分类号:

P237

刘智勇, 张晨, 刘泽楷, 袁俊健, 祁宏昌, 潘屹峰, 朱焕廉, 吴希文, 王华. 雷达相干性与NDVI经验模型[J]. 测绘通报, 2021, 0(4): 45-51,59.

LIU Zhiyong, ZHANG Chen, LIU Zekai, YUAN Junjian, QI Hongchang, PAN Yifeng, ZHU Huanlian, WU Xiwen, WANG Hua. Empirical relationship between radar coherence and NDVI[J]. Bulletin of Surveying and Mapping, 2021, 0(4): 45-51,59.

参考文献

[1] HOOPER A, BEKAERT D, SPAANS K, et al. Recent advances in SAR interferometry time seriesanalysis for measuring crustal deformation[J]. Tectonophysics, 2012,514(1):1-13.
[2] MASSONNET D, ROSSI M, CARMONA C, et al.The displacement field of the Landersearthquake mapped by radar interferometry[J]. Nature,1993, 364(6433):138-142.
[3] WANG H, GE L, XU C, et al. 3-D coseismic displace-ment field of the 2005 Kashmir earthquake inferred from satellite radar imagery[J]. Earth, planets space, 2007, 59(5):343-349.
[4] WANG H, XU C, GE L. Coseismic deformation and slip distribution of the 1997 Mw7.5 Manyi, Tibet, earthquake from InSAR measurements[J]. Journal of Geodynamics, 2007, 44(3):200-212.
[5] HENDERSON S T, PRITCHARD M E. Time-dependent deformation of Uturuncu volcano, Bolivia, constrained by GPS and InSAR measurements and implications for source models[J]. Geosphere, 2017, 13(6):1834-1854.
[6] LU Z, FIELDING E, PATRICK M R, et al. Estimating lava volume by precision combination of multiple baseline spaceborne and airborne interferometric synthetic aperture radar:the 1997 eruption of Okmok volcano, Alaska[J]. Geoscience and Remote Sensing, 2003, 41(6):1428-1436.
[7] MASTERLARK T, HANEY M, DICKINSON H, et al. Rheologic and structural controls on the deformation of Okmok volcano, Alaska:FEMs, InSAR, and ambient noise tomography[J]. Journal of Geophysical Research Solid Earth, 2010, 115(B02409):1-22.
[8] NG H M, GE L, LI X, et al. Mapping land subsidence in Jakarta, Indonesia using persistent scatterer interferometry (PSI) technique with ALOS PALSAR[J]. International Journal of Applied Earth Observations & Geoinformation, 2012, 18:232-242.
[9] NG H M, GE L, LI X, et al. Monitoring ground deformation in Beijing, China with persistent scatterer SAR interfero-metry[J]. Journal of Geodesy, 2012, 86(6):375-392.
[10] NG H M, GE L, YAN L, et al. Mapping accumulated mine subsidence using small stack of SAR differential interferograms in the Southern coalfield of New South Wales, Australia[J]. Engineering Geology, 2010, 115(1-2):1-15.
[11] ZHAO C, LU Z, ZHANG Q, et al. Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA[J]. Remote Sensing of Environment, 2012, 124(9):348-359.
[12] YAGUE-MARTINEZ N, PRATS-IRAOLA P, GONZALEZ F R, et al. Interferometric Processing of Sentinel-1 TOPS Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4):2220-2234.
[13] HAVIVI S, SCHVARTZMAN I, MAMAN S, et al. Com-bining TerraSAR-X and Landsat images for emergency response in urban environments[J]. Remote Sensing, 2018, 10(5): 802.
[14] ARAB-SEDZE M, HEGGY E, BRETAR F, et al. Quan-tification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements[J]. Remote Sens Environ, 2014, 152:202-216.
[15] TOUZI R, LOPES A, BRUNIQUEL J, et al. Coherence estimation for SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 135-149.
[16] FERRETTI A, MONTIGUARNIERI A, PRATI C, et al. InSAR principles: guidelines for SAR interferometry processing and interpretation[J]. Journal of Financial Stability,2007, 10(10): 156-162.
[17] CARLSON T N, GILLIES R R, PERRY E M. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover[J]. Remote Sensing Reviews, 1994, 9(1): 161-173.
[18] ROSEN P A, GURROLA E, SACCO G F, et al. The InSAR scientific computing environment[C]//Proceedings of the 9th European Conference on Synthetic Aperture Radar. [S.l.]: VDE, 2012.

雷达相干性与NDVI经验模型

Empirical relationship between radar coherence and NDVI

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	周志伟, 程翔, 周伟, 郝卫峰, 肖海斌, 陈鸿杰, 杨魁. 地基SAR在滑坡形变监测中的应用[J]. 测绘通报, 2022, 0(7): 60-63.
[2]	葛鹏飞, 刘辉, 陈蜜, 李昱, 丁瑞力, 刘菲. 时序InSAR监测京雄城际铁路河北段地面沉降[J]. 测绘通报, 2022, 0(7): 64-70.
[3]	史健存, 杨泽发, 吴立新. 优化先验模型参数的矿区多轨InSAR三维形变监测[J]. 测绘通报, 2022, 0(7): 71-77.
[4]	兰进京, 曲逸穹, 都伟冰, 高鑫, 马丹丹, 郑岩超. 典型矿业城市地表形变遥感监测及沉降特征分析[J]. 测绘通报, 2022, 0(6): 98-103.
[5]	朱邦彦, 唐超, 任志忠, 马状, 张琪, 李云. 基于PS-InSAR技术的珠海市地表形变监测与驱动力分析[J]. 测绘通报, 2022, 0(6): 108-113.
[6]	张家勇, 邹银先, 刘黔云, 张楠, 龚伟, 李康伦. 利用时序InSAR进行毕节市潜在滑坡识别与形变监测[J]. 测绘通报, 2022, 0(6): 121-124.
[7]	曾学宏, 赵义花. 利用SBAS-InSAR技术分析西宁市地面沉降监测及驱动因素[J]. 测绘通报, 2022, 0(6): 137-142.
[8]	张玉鹏, 张永红, 刘青豪. 利用GRACE数据进行京津冀地区地面沉降与地下水耦合分析[J]. 测绘通报, 2022, 0(5): 89-94,125.
[9]	李佳豪, 周吕, 马俊, 杨飞, 咸凌霄. 利用PS-InSAR技术进行城市地铁沿线形变监测与机理分析[J]. 测绘通报, 2022, 0(4): 20-25.
[10]	逯中香, 樊彦国, 李国胜. 利用稀少卫星影像控制点生产1:10 000 DOM——以阿拉善盟测区为例[J]. 测绘通报, 2022, 0(3): 138-142,156.
[11]	黄佳宾, 郭云开, 罗伟茂. 利用Sentinel-1监测武汉市地面沉降[J]. 测绘通报, 2021, 0(9): 53-58.
[12]	王建业, 梁小龙, 金喜, 白泽朝, 齐二恒. 黄土填方区地表沉降时序InSAR监测分析[J]. 测绘通报, 2021, 0(8): 158-161.
[13]	翁昌凯, 肖添. SBAS技术支持下的临沧市地表时序形变分析[J]. 测绘通报, 2021, 0(7): 81-85.
[14]	祝昕刚, 明生. 时序InSAR技术应用于高压输电线路附近的地表形变灾害防控[J]. 测绘通报, 2021, 0(7): 92-97.
[15]	吴绿川, 王剑辉, 符彦. 基于InSAR技术和光学遥感的贵州省滑坡早期识别与监测[J]. 测绘通报, 2021, 0(7): 98-102.