A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. GF-1 Optical Image
2.2.2. Sentinel-1 A Radar Image
2.2.3. SRTM DEM Data
3. Methods
3.1. Image Pre-Processing Method
3.2. Vegetation Abnormal Information Extraction Method
- (1)
- Calculate the initial and values of each time phase. We count the cumulative percentage of each GNDVI value in the image at first and select the initial confidence interval based on empirical values. We first use the 5–95% confidence interval [43] as the initial value to try. Then, we calculate the initial and based on the left and right boundaries of the confidence interval;
- (2)
- Adjust the confidence interval for each time phase. We calculate the VC by using the values of and determined in step (1) and enhance results through the pseudo color density segmentation to visually judge the agreement degree of the bare land and vegetation area between in the VC map and in the original image. If not, repeat the above steps to redefine the confidence interval and perform the calculation again until the obtained result is the optimal fit;
- (3)
- Determine the final uniform confidence interval. In order to unify the thresholds of each time phase and obtain the consistent and best-fitting VC as much as possible, we comprehensively consider the confidence intervals of each phase and unify them to obtain the final confidence interval consistent with each time phase.
3.3. SBAS-InSAR Method
- (a)
- When there are N scenes of SAR images in the study area, each SAR image will be differentially interfered with by at least another N-1 scenes image to form an interference image pair. Finally, M interference image pairs will be obtained. Meanwhile, the image with most interference pairs is chosen as the main image, and the rest of the images are the slave images. The value range of M is shown in Formula (3) [51]:
- (b)
- Differential interferogram is collected from M interferometric pairs by using the InSAR phase deformation extraction method. The final interferogram is obtained through the phase filtering and unwrapping. The interference phase of the j-th interferogram can be expressed as Formula (4):
- (c)
- The G matrix is solved by using the SVD method through the least square rule, as shown in Formula (6):
- (d)
- Through the above steps, the optimal solution of the velocity vector can be obtained, and thus, the surface deformation information can be obtained. The surface deformation information still has the atmospheric delay and other errors, so they need to be filtered to obtain the final accurate surface deformation information [8].
4. Results
4.1. Evaluation of Image Preprocessing Results
4.1.1. The Spectral Curve of Image Fusion Features
4.1.2. GNDVI Results
4.2. Vegetation Abnormal Information Extraction Results Based on GF-1 Images
4.3. Surface Deformation Extraction Results Based on SBAS-InSAR
5. Discussion
6. Conclusions
- (1)
- This paper calculates the GNDVI index based on GF-1 time series data, and finally, obtains the vegetation coverage information of each scene. Through the multi-temporal qualitative and quantitative analysis of the extracted vegetation anomaly information, the VC decreased from 2013 to 2015. In reality, the landslide did occur in the study area in 2015, indicating that the early creep stage of landslides brings about a decrease in the VC. This verifies that the method of using vegetation anomaly information to monitor the Jizong Shed-Tunnel landslide is feasible. At the same time, it was discovered that there were two areas on the trailing edge of the landslide showing a downward trend in VC after 2017.
- (2)
- Through the SBAS-InSAR technology based on the Sentinel-1 data, the main deformation area is located at the rear edge of the landslide, and the surface subsidence rate ranges from 0 mm/year to 30 mm/year, indicating that the Jizong Shed-Tunnel landslide is in a slow creep stage.
- (3)
- After superimposing the abnormal vegetation area in the optical data with the surface deformation information in the radar data and performing time series analysis and accuracy assessment, it is found that the vegetation abnormality and the change trend of the surface deformation are basically consistent. When the surface deformation of the landslide decreases, the VC also shows a downward trend. When the deformation accelerates, the change in VC also intensifies. Even when the decline in the deformation is not large, the vegetation growth status can reflect these changes, which indicates the effectiveness and reliability of using vegetation abnormalities to monitor the Jizong Shed-Tunnel landslide, and the results of the two methods are similar. This method can provide new ways and ideas for the high-mountain landslide monitoring in southwestern China and can make up for some of the shortcomings of existing landslide monitoring methods.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Li, W.; Xu, Q.; Lu, H.; Dong, X.; Zhu, Y. Tracking the Deformation History of Large-Scale Rocky Landslides and Its Enlightenment. Geomat. Inf. Sci. Wuhan Univ. 2019, 44, 1043–1053. [Google Scholar] [CrossRef]
- Murakmi, S.; Nishigaya, T.; Tien, T.; Sakai, N.; Lateh, H.; Azizat, N. Development of historical landslide database in Peninsular Malaysia. In Proceedings of the 2014 IEEE 2nd International Symposium on Telecommunication Technologies (ISTT), Langkawi, Malaysia, 24–26 November 2014; pp. 149–153. [Google Scholar] [CrossRef]
- Balbi, E.; Terrone, M.; Faccini, F.; Scafidi, D.; Barani, S.; Tosi, S.; Crispini, L.; Cianfarra, P.; Poggi, F.; Ferretti, G. Persistent Scatterer Interferometry and Statistical Analysis of Time-Series for Landslide Monitoring: Application to Santo Stefano d’Aveto (Liguria, NW Italy). Remote Sens. 2021, 13, 3348. [Google Scholar] [CrossRef]
- Mourin, M.; Ferdaus, A.; Hossain, M. Landslide Susceptibility Mapping in Chittagong District of Bangladesh using Support Vector Machine integrated with GIS. In Proceedings of the 2018 International Conference on Innovation in Engineering and Technology (ICIET), Dhaka, Bangladesh, 27–28 December 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Zhao, J. Spatial-Temporal Distribution and Disaster Analysis of Landslide Disasters in Typical Karst Areas in My Country. Master’s Thesis, Shandong Normal University, Shandong, China, 2014. [Google Scholar]
- Zhang, L.; Dai, K.; Deng, J.; Ge, D.; Liang, R.; Li, W.; Xu, Q. Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR. Remote Sens. 2021, 13, 3662. [Google Scholar] [CrossRef]
- Lv, G.; Zhu, Y. Analysis of the surge process of the Fuquan landslide hitting the pond in Guizhou. Chin. J. Geol. Hazard Contr. 2017, 28, 1–5. [Google Scholar] [CrossRef]
- Yang, J. Research on Slope Deformation Monitoring Based on InSAR Technology. Master’s Thesis, University of Electronic Science and Technology of China, Sichuan, China, 2019. [Google Scholar]
- Tang, Y.; Wang, L.; Ma, G.; Jia, H.; Jin, X. Using domestic remote sensing satellites for emergency monitoring of Jinsha River landslide disaster. J. Remote Sens. 2019, 23, 72–81. [Google Scholar] [CrossRef]
- Wu, M.; Pi, X.; Wu, X.; Liu, H.; Ma, L. Analysis of Meteorological Causes and Meteorological Service of Landslides in Songtao County. Mod. Agr. Technol. 2020, 24, 172–173+177. [Google Scholar]
- Gili, J.A.; Corominas, J.; Rius, J. Using Global Positioning System Techniques in Landslide Monitoring. Eng. Geol. 2000, 55, 167–192. [Google Scholar] [CrossRef]
- Zhu, Y.; Zhou, S.; Lu, T. Research on the Application of GPS in the Slope Safety Monitoring Technique Using in Opencast Uuranium Mine Based on VBA. In Proceedings of the 2009 International Forum on Information Technology and Applications, Chengdu, China, 15–17 May 2009; pp. 760–763. [Google Scholar] [CrossRef]
- Lytvyn, M.; Pöllabauer, C.; Troger, M.; Landfahrer, K.; Hörmann, L.; Steger, C. Real-Time landslide monitoring using single-frequency PPP: Proof of concept. In Proceedings of the 2012 6th ESA Workshop on Satellite Navigation Technologies (Navitec 2012) & European Workshop on GNSS Signals and Signal Processing, Noordwijk, The Netherlands, 5–7 December 2012; pp. 1–6. [Google Scholar] [CrossRef]
- Jiang, S.; Wen, B.; Zhao, C.; Li, R.; Li, Z. Kinematics of a giant slow-moving landslide in Northwest China: Constraints from high resolution remote sensing imagery and GPS monitoring. J. Asian Earth Sci. 2016, 123, 34–46. [Google Scholar] [CrossRef]
- Peng, F.; Nie, G.; Xue, C.; Wu, S.; Li, H.; Wang, J.; Liu, W. Application research of GPS/BDS precision single point positioning technology in landslide deformation monitoring. Nav. Position. Tim. 2019, 6, 103–112. [Google Scholar] [CrossRef]
- Notti, D.; Cina, A.; Manzino, A.; Colombo, A.; Bendea, I.H.; Mollo, P.; Giordan, D. Low-Cost GNSS Solution for Continuous Monitoring of Slope Instabilities Applied to Madonna Del Sasso Sanctuary (NW Italy). Sensors 2020, 20, 289. [Google Scholar] [CrossRef] [Green Version]
- Šegina, E.; Peternel, T.; Urbančič, T.; Realini, E.; Zupan, M.; Jež, J.; Caldera, S.; Gatti, A.; Tagliaferro, G.; Consoli, A.; et al. Monitoring Surface Displacement of a Deep-Seated Landslide by a Low-Cost and near Real-Time GNSS System. Remote Sens. 2020, 12, 3375. [Google Scholar] [CrossRef]
- Akbarimehr, M.; Motagh, M.; Haghshenas-Haghighi, M. Slope Stability Assessment of the Sarcheshmeh Landslide, Northeast Iran, Investigated Using InSAR and GPS Observations. Remote Sens. 2013, 5, 3681–3700. [Google Scholar] [CrossRef] [Green Version]
- Zhang, H.; Liu, S.; Wang, R. Landslide displacement field monitoring method based on high-resolution images and ASIFT algorithm. J. Northeast. Univ. Nat. Sci. 2017, 38, 1468–1472+1476. [Google Scholar] [CrossRef]
- Bamler, R.; Hartl, P. Synthetic aperture radar interferometry. Inverse Probl. 1998, 14, R1. [Google Scholar] [CrossRef]
- Huang, Y.; Yu, M.; Xu, Q.; Sawada, K.; Moriguchi, S.; Yashima, A.; Liu, C.; Xue, L. InSAR-derived digital elevation models for terrain change analysis of earthquake-triggered flow-like landslides based on ALOS/PALSAR imagery. Environ. Earth Sci. 2015, 73, 7661–7668. [Google Scholar] [CrossRef]
- He, Y. Application of High-Resolution Remote Sensing and InSAR Technology in the Identification and Monitoring of Loess Landslides. Master’s Thesis, Chang’an University, Shanxi, China, 2016. [Google Scholar] [CrossRef]
- Huang, J.; Xie, M.; Wang, L. Study on deformation monitoring of Baige landslide based on SBAS-InSAR technology. Yangtze Riv. 2019, 50, 101–105. [Google Scholar] [CrossRef]
- Jiang, Y.; Xu, Q.; Lu, Z. Landslide Displacement Monitoring by Time Series InSAR Combining PS and DS Targets. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September–2 October 2020; pp. 1011–1014. [Google Scholar] [CrossRef]
- Qiu, Z.; Shen, P. Encyclopedia of China Water Conservancy, 2nd ed.; China Water Conservancy and Hydropower Press: Beijing, China, 2006; p. 576. [Google Scholar]
- Ouyang, C.; An, H.; Zhou, S.; Wang, Z.; Su, P.; Wang, D.; Cheng, D.; She, J. Insights from the failure and dynamic characteristics of two sequential landslides at Baige village along the Jinsha River, China. Landslides 2019, 16, 1397–1414. [Google Scholar] [CrossRef]
- Ouyang, C.; Zhao, W.; An, H.; Zhou, S.; Wang, D.; Xu, Q.; Li, W.; Peng, D. Early identification and dynamic processes of ridge-top rockslides: Implications from the Su Village landslide in Suichang County, Zhejiang Province, China. Landslides 2019, 16, 799–813. [Google Scholar] [CrossRef]
- Lu, T.; Zeng, H.; Luo, Y.; Wang, Q.; Shi, F.; Sun, G.; Wu, Y.; Wu, N. Monitoring vegetation recovery after China’s May 200 Wenchuan earthquake using Landsat TM time-series data: A case study in Mao County. Ecol. Res. 2012, 27, 955–966. [Google Scholar] [CrossRef]
- He, B.; Zhang, X. Remote sensing survey and development trend analysis of landslide on Sichuan-Tibet Highway 102. Chin. J. Geol. Hazard Contr. 2015, 26, 103–109. [Google Scholar] [CrossRef]
- Piroton, V.; Schlöge, R.; Barbier, C.; Havenith, H. Monitoring the recent activity of landslides in the Mailuu-Suu Valley (Kyrgyzstan) using radar and optical remote sensing techniques. Geosciences 2020, 10, 164. [Google Scholar] [CrossRef]
- Xun, Z.; Zhao, C.; Kang, Y.; Liu, X.; Liu, Y.; Du, C. Automatic extraction of potential landslides by integrating an optical remote sensing image with an InSAR-derived deformation map. Remote Sens. 2022, 14, 2669. [Google Scholar] [CrossRef]
- Guo, X.; Guo, Q.; Feng, Z. The relationship between landslide creep and vegetation anomalies on remote sensing images. J. Remote Sens. 2020, 24, 776–786. [Google Scholar] [CrossRef]
- Guo, X.; Guo, Q.; Feng, Z. Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data. Front. Earth Sci. 2021, 9, 706998. [Google Scholar] [CrossRef]
- Ge, C.; He, B. The application of geological route selection principles in the survey and design of the section from Jiegu to Batang Airport in Yushuzhou, National Highway 214. Highw. Trans. Sci. Tech. 2014, 10, 122–126. [Google Scholar]
- Ghassemian, H. A review of remote sensing image fusion methods. Inf. Fusion 2016, 32, 75–89. [Google Scholar] [CrossRef]
- Sun, W.; Chen, B.; Messinger, D. Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Opt. Eng. 2014, 53, 013107. [Google Scholar] [CrossRef] [Green Version]
- Shah, V.; Younan, N.; King, R. An efficient pansharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1323–1335. [Google Scholar] [CrossRef]
- Aiazzi, B.; Baronti, S.; Selva, M. Improving component substitution Pansharpening through multivariate regression ofMS+ Pan data. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3230–3239. [Google Scholar] [CrossRef]
- Guo, L.; Yang, J.; Shi, L.; Zhan, Y.; Zhao, D.; Zhang, C.; Sun, J.; Ji, J. Comparison of SPOT6 remote sensing image fusion methods. Remote Sens. Land Res. 2014, 26, 71–77. [Google Scholar] [CrossRef]
- Jiang, H.; Xing, X.; Liang, L.; Wang, M. Study on Pansharpening auto-fusion arithmetic and application. Geo. Spat. Inform. Tech. 2008, 31, 72–78. [Google Scholar] [CrossRef]
- Li, M. Research on Remote Sensing Estimation Method of Vegetation Coverage. Master’s Thesis, Graduate University of Chinese Academy of Sciences (Institute of Remote Sensing Applications), Beijing, China, 2003. [Google Scholar]
- Kaufman, Y.; Tanre, D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans. Geosci. Remote Sens. 1992, 30, 261–270. [Google Scholar] [CrossRef]
- Li, M.; Wu, B.; Yan, C.; Zhou, W. Estimation of Vegetation Fraction in the Upper Basin of Miyun Reservoir by Remote Sensing. Res. Sci. 2004, 26, 153–159. [Google Scholar] [CrossRef]
- Gheorghe, M.; Armaş, I. Comparison of multi-temporal differential interferometry techniques applied to the measurement of Bucharest City Subsidence. Procedia Environ. Sci. 2016, 32, 221–229. [Google Scholar] [CrossRef] [Green Version]
- Zhou, S.; Ouyang, C.; Huang, Y. An InSAR and depth-integrated coupled model for potential landslide hazard assessment. Acta Geotech. 2022, 17, 3613–3632. [Google Scholar] [CrossRef]
- Ferretti, A.; Prati, C.; Rocca, F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2202–2212. [Google Scholar] [CrossRef]
- Intrieri, E.; Raspini, F.; Fumagalli, A.; Lu, P.; Conte, S.; Farina, P.; Allievi, J.; Ferretti, A.; Casagli, N. The Maoxian landslide as seen from space: Detecting precursors of failure with Sentinel-1 data. Landslides 2018, 15, 123–133. [Google Scholar] [CrossRef] [Green Version]
- Berardino, P.; Fornaro, G.; Lanari, R.; Sansosti, E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens. 2002, 40, 2375–2383. [Google Scholar] [CrossRef] [Green Version]
- Ma, S.; Xu, C.; Shao, X.; Xu, X.; Liu, A. A Large Old Landslide in Sichuan Province, China: Surface Displacement Monitoring and Potential Instability Assessment. Remote Sens. 2021, 13, 2552. [Google Scholar] [CrossRef]
- Liu, X.; Zhao, C.; Zhang, Q.; Lu, Z.; Li, Z.; Yang, C.; Zhu, W.; Liu, J.; Chen, L.; Liu, C. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Eng. Geol. 2021, 284, 106033. [Google Scholar] [CrossRef]
- Zhao, R.; Li, Z.; Feng, G.; Wang, Q.; Hu, J. Monitoring surface deformation over permafrost with an improved SBAS-InSAR algorithm: With emphasis on climatic factors modeling. Remote Sens. Environ. 2016, 184, 276–287. [Google Scholar] [CrossRef]
- Peng, J.; Zhang, C. Remote sensing monitoring of vegetation cover based on Gaofen-1 remote sensing image—Taking Xiamen as an example. Remote Sens. Land Res. 2019, 31, 137–142. [Google Scholar] [CrossRef]
- Standard of Classification for Geological Hazard. Available online: https://www.cgs.gov.cn/ddztt/jqthd/fzjz/xmjz/bzgf/202006/P020200602403973808311.pdf (accessed on 21 June 2022).
Number | Image Time | Number | Image Time | Number | Image Time |
---|---|---|---|---|---|
1 | 5 November 2013 | 4 | 22 December 2016 | 7 | 24 November 2019 |
2 | 8 November 2014 | 5 | 11 November 2017 | 8 | 30 November 2020 |
3 | 16 November 2015 | 6 | 19 November 2018 |
Number | Image Time | Polarization | Number | Image Time | Polarization |
---|---|---|---|---|---|
1 | 18 March 2017 | VV | 30 | 12 February 2019 | VV |
2 | 30 March 2017 | VV | 31 | 8 March 2019 | VV |
3 | 23 April 2017 | VV | 32 | 1 April 2019 | VV |
4 | 17 May 2017 | VV | 33 | 25 April 2019 | VV |
5 | 10 June 2017 | VV | 34 | 19 May 2019 | VV |
6 | 4 July 2017 | VV | 35 | 12 June 2019 | VV |
7 | 9 August 2017 | VV | 36 | 6 July 2019 | VV |
8 | 2 September 2017 | VV | 37 | 30 July 2019 | VV |
9 | 26 September 2017 | VV | 38 | 23 August 2019 | VV |
10 | 20 October 2017 | VV | 39 | 16 September 2019 | VV |
11 | 13 November 2017 | VV | 40 | 10 October 2019 | VV |
12 | 7 December 2017 | VV | 41 | 3 November 2019 | VV |
13 | 31 December 2017 | VV | 42 | 27 November 2019 | VV |
14 | 24 January 2018 | VV | 43 | 21 December 2019 | VV |
15 | 17 February 2018 | VV | 44 | 14 January2020 | VV |
16 | 13 March 2018 | VV | 45 | 7 February2020 | VV |
17 | 6 April 2018 | VV | 46 | 2 March 2020 | VV |
18 | 30 April 2018 | VV | 47 | 26 March 2020 | VV |
19 | 24 May 2018 | VV | 48 | 19 April 2020 | VV |
20 | 17 June 2018 | VV | 49 | 13 May2020 | VV |
21 | 11 July 2018 | VV | 50 | 6 June 2020 | VV |
22 | 4 August 2018 | VV | 51 | 30 June 2020 | VV |
23 | 28 August 2018 | VV | 52 | 24 July 2020 | VV |
24 | 21 September 2018 | VV | 53 | 17 August 2020 | VV |
25 | 15 October 2018 | VV | 54 | 10 September 2020 | VV |
26 | 8 November 2018 | VV | 55 | 4 October 2020 | VV |
27 | 2 December 2018 | VV | 56 | 28 October 2020 | VV |
28 | 26 December 2018 | VV | 57 | 21 Nevember 2020 | VV |
29 | 19 January 2019 | VV |
GNDVI | MS | NND | GS | PCA | HPF | Pansharpening |
---|---|---|---|---|---|---|
Mean | 0.27 | 0.27 | 0.26 | 0.28 | 0.28 | 0.28 |
Max | 0.68 | 0.69 | 0.75 | 0.72 | 0.74 | 0.74 |
Min | 0.03 | 0.03 | −0.39 | −0.17 | −0.22 | −0.27 |
Image Time | Fv ≤ 0.85 Pixels Number | Fv ≤ 0.85 Pixels Percentage |
---|---|---|
5 November 2013 | 21,103 | 76.39% |
8 November 2014 | 22,317 | 80.78% |
16 November 2015 | 23,525 | 85.16% |
22 December 2016 | 22,506 | 81.47% |
11 November 2017 | 22,138 | 80.14% |
19 November 2018 | 22,280 | 80.65% |
24 November 2019 | 22,522 | 81.53% |
30 November 2020 | 22,547 | 81.62% |
Speed Grade | Landslide Type | Sliding Speed Threshold | Destructive Force Description |
---|---|---|---|
1 | Very slow | <0.016 m/year | No damage will occur to buildings that have been protected in advance. |
2 | Slightly slow | 0.016 m/year~1.6 m/year | Some permanent buildings are not damaged;even if the building cracks due to sliding, it is repairable. |
3 | Slow speed | 1.6 m/year~13 m/month | If the slip time is short and the movement of the edge of the landslide is distributed over a wide area, the road and fixed structures can be preserved after several major repairs. |
4 | Medium speed | 13 m/month~1.8 m/h | Fixed buildings at a certain distance from the foot of the landslide can’t be damaged; the buildings located on the upper part of the sliding body are extremely damaged. |
5 | Fast speed | 1.8 m/h~3 m/min | It has time for escape and evacuation; houses, property and equipment are damaged by landslide. |
6 | High fast | 3 m/min~5 m/s | The destructive power of the disaster is large, and due to its high speed, it is impossible to transfer all personnel, resulting in some casualties. |
7 | Super fast | >5 m/s | The destructive force is huge, the surface buildings are completely destroyed, and the impact or disintegration of the sliding body causes huge casualties. |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, Q.; Tong, L.; Wang, H. A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide. Remote Sens. 2022, 14, 5640. https://doi.org/10.3390/rs14225640
Guo Q, Tong L, Wang H. A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide. Remote Sensing. 2022; 14(22):5640. https://doi.org/10.3390/rs14225640
Chicago/Turabian StyleGuo, Qing, Lianzi Tong, and Hua Wang. 2022. "A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide" Remote Sensing 14, no. 22: 5640. https://doi.org/10.3390/rs14225640
APA StyleGuo, Q., Tong, L., & Wang, H. (2022). A Monitoring Method Based on Vegetation Abnormal Information Applied to the Case of Jizong Shed-Tunnel Landslide. Remote Sensing, 14(22), 5640. https://doi.org/10.3390/rs14225640