Long-term uneven subsidence monitoring and risk prediction of ultra-large garbage landfills

doi:10.11947/j.AGCS.2026.20250489

Abstract

Abstract:

Landfills will continue to experience long-term settlement due to biodegradation and compression after closure, with strong spatial variability and complex temporal characteristics. Against the backdrop of the transition of current urban solid waste disposal facilities toward safer and more long-term management models, the evolution of surface settlement after landfill closure has become particularly important, urgently requiring surface deformation monitoring and risk assessment. This paper takes the Guangdong Xingfeng landfill, once the largest single treatment facility in Asia, as an example. It integrates Envisat and Sentinel-1 data, utilizes displacement tracking technology to process different satellite data to fill in deformation gaps during data gaps, and combines the small baseline subset technique to construct an 18-year long-term deformation sequence, while predicting future deformation trends. The monitoring results show significant spatially uneven settlement, with the degree of unevenness highly correlated with landfilling processes and waste types. The eastern landfill area exhibits severe surface settlement (4.4 m of cumulative settlement between 2008 and 2025), while the western emergency landfill area shows relatively stable surface changes (1.9 m of cumulative settlement between 2008 and 2025). The landfill settlement shows a strong correlation with climatic factors, with a 3~4 month lag effect, revealing the control of climate change on the surface stability of landfills. The deformation prediction results indicate that for a long period in the future, the landfill will remain in an uneven settlement state, with the eastern landfill area requiring 8~10 years to achieve surface stability and the western landfill area reaching surface stability in 5 years. This study achieves the identification of the full-cycle deformation characteristics of landfills through long-term deformation monitoring and quantifies the deformation characteristics of each division using deformation decomposition. Based on the ConvLSTM model, it evaluates the future deformation, risk characteristics, and surface stability time of the landfill. The research approach can provide a reference for the long-term full-cycle surface deformation and risk assessment of similar landfills.

Key words: InSAR, garbage landfills, deformation monitoring, ConvLSTM model, land safety

CLC Number:

P228

Qi LIU, Chen YU, Zhenhong LI, Chuang SONG, Xiaoning HU, Jie LI, Tenghui HU. Long-term uneven subsidence monitoring and risk prediction of ultra-large garbage landfills[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(6): 990-1002.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://xb.chinasmp.com/EN/10.11947/j.AGCS.2026.20250489

http://xb.chinasmp.com/EN/Y2026/V55/I6/990

Figures/Tables 13

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 2

Fig. 5

Fig. 6

Tab. 3

Fig. 7

Tab. 4

Fig. 8

Fig. 9

References 47

[1]	戴福初, 李军, 张晓晖. 城市建设用地与地质环境协调性评价的GIS方法及其应用[J]. 地球科学, 2000, 25(2): 209-214.
	DAI Fuchu, LI Jun, ZHANG Xiaohui. GIS methodology and its application to evaluation of coordination between urban land use and geo-environmental potential[J]. Earth Science, 2000, 25(2): 209-214.
[2]	袁文祥, 陈善平, 邰俊, 等. 我国垃圾填埋场现状、问题及发展对策[J]. 环境卫生工程, 2016, 24(5): 8-11.
	YUAN Wenxiang, CHEN Shanping, TAI Jun, et al. Present situation, problems and development countermeasures of landfill in China[J]. Environmental Sanitation Engineering, 2016, 24(5): 8-11.
[3]	宋薇, 吴彬彬, 陶其阳. 我国生活垃圾填埋场封场后管理现状与建议[J]. 环境卫生工程, 2018, 26(2): 73-75, 79.
	SONG Wei, WU Binbin, TAO Qiyang. Status and suggestion on post-closure management of municipal solid waste landfill in China[J]. Environmental Sanitation Engineering, 2018, 26(2): 73-75, 79.
[4]	MOR S, RAVINDRA K. Municipal solid waste landfills in lower-and middle-income countries: environmental impacts, challenges and sustainable management practices[J]. Process Safety and Environmental Protection, 2023, 174: 510-530.
[5]	IQBAL A, LIU X M, CHEN G H. Municipal solid waste: review of best practices in application of life cycle assessment and sustainable management techniques[J]. Science of the Total Environment, 2020, 729: 138622.
[6]	ZHANG Shuai, LÜ Yunhong, YANG Haiben, et al. Monitoring and quantitative human risk assessment of municipal solid waste landfill using integrated satellite-UAV-ground survey approach[J]. Remote Sensing, 2021, 13(22): 4496.
[7]	WALL D K, ZEISS C. Municipal landfill biodegradation and settlement[J]. Journal of Environmental Engineering, 1995, 121(3): 214-224.
[8]	HOSSAIN M S, GABR M A, BARLAZ M A. Relationship of compressibility parameters to municipal solid waste decomposition[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2003, 129(12): 1151-1158.
[9]	DUAN Z H, SCHEUTZ C, KJELDSEN P. Trace gas emissions from municipal solid waste landfills: a review[J]. Waste Management, 2021, 119: 39-62.
[10]	ZHANG Xiwen, SONG Jing, PENG Jian, et al. Landslides-oriented urban disaster resilience assessment: a case study in ShenZhen, China[J]. Science of the Total Environment, 2019, 661: 95-106.
[11]	KE Han, MA Pengcheng, LAN Jiwu, et al. Field behaviors of a geogrid reinforced MSW slope in a high-food-waste-content MSW landfill: a case study[J]. Geotextiles and Geomembranes, 2021, 49(2): 430-441.
[12]	NANDA S, BERRUTI F. Municipal solid waste management and landfilling technologies: a review[J]. Environmental Chemistry Letters, 2021, 19(2): 1433-1456.
[13]	MA Shijun, ZHOU Chuanbin, PAN Jingjin, et al. Leachate from municipal solid waste landfills in a global perspective: characteristics, influential factors and environmental risks[J]. Journal of Cleaner Production, 2022, 333: 130234.
[14]	王罗春, 赵由才, 陆雍森. 垃圾填埋场稳定化评价[J]. 环境卫生工程, 2001, 9(4): 157-159, 166.
	WANG Luochun, ZHAO Youcai, LU Yongsen. Evaluating of refuse landfill site stabilization[J]. Environmental Sanitation Engineering, 2001, 9(4): 157-159, 166.
[15]	KWARTENG A Y, AL-ENEZI A. Assessment of Kuwait's Al-qurain landfill using remotely sensed data[J]. Journal of Environmental Science and Health, Part A, 2004, 39(2): 351-364.
[16]	AL-YAQOUT A F, HAMODA M F, ZAFAR M. Characteristics of wastes, leachate, and gas at landfills operated in arid climate[J]. Practice Periodical of Hazardous, Toxic, and Radioactive Waste Management, 2005, 9(2): 97-102.
[17]	XIE Y K, XUE J F. Quantifying the long-term settlement of municipal solid wastes in landfills based on global navigation satellite system[C]//Proceedings of the 9th International Conference on Environmental Geotechnics. Chania: ISSMGE, 2023: 451.
[18]	ADAMCOVÁ D, BARTON S, OSINSKI P, et al. Analytical modelling of MSW landfill surface displacement based on GNSS monitoring[J]. Sensors, 2020, 20(21): 5998.
[19]	BAEK W K, JUNG H S, JO M J, et al. Ground subsidence observation of solid waste landfill park using multi-temporal radar interferometry[J]. International Journal of Urban Sciences, 2019, 23(3): 406-421.
[20]	DU Yanan, FU Haiqiang, LIU Lin, et al. Continued monitoring and modeling of Xingfeng solid waste landfill settlement, China, based on multiplatform SAR images[J]. Remote Sensing, 2021, 13(16): 3286.
[21]	李晨程, 江利明, 吴宇轩, 等. 基于时序InSAR技术的武汉大型垃圾填埋场形变监测与分析[J]. 武汉大学学报(信息科学版), 2025, 50(8): 1694-1704.
	LI Chencheng, JIANG Liming, WU Yuxuan, et al. Monitoring ground displacement of large-scale landfills in Wuhan using time-series InSAR technology[J]. Geomatics and Information Science of Wuhan University, 2025, 50(8): 1694-1704.
[22]	史绪国, 徐金虎, 蒋厚军, 等. 时序InSAR技术三峡库区藕塘滑坡稳定性监测与状态更新[J]. 地球科学, 2019, 44(12): 4284-4292.
	SHI Xuguo, XU Jinhu, JIANG Houjun, et al. Slope stability state monitoring and updating of the Outang landslide, Three Gorges area with time series InSAR analysis[J]. Earth Science, 2019, 44(12): 4284-4292.
[23]	SONG Chuang, YU Chen, LI Zhenhong, et al. Triggering and recovery of earthquake accelerated landslides in central Italy revealed by satellite radar observations[J]. Nature Communications, 2022, 13: 7278.
[24]	GONZÁLEZ F. InSAR-based mapping of ground deformation caused by industrial waste disposals: the case study of the Huelva phosphogypsum stack, SW Spain[J]. Bulletin of Engineering Geology and the Environment, 2022, 81(8): 304.
[25]	EMIL M K, SULTAN M, ALAKHRAS K, et al. Countrywide monitoring of ground deformation using InSAR time series: a case study from Qatar[J]. Remote Sensing, 2021, 13(4): 702.
[26]	GABRIEL A K, GOLDSTEIN R M, ZEBKER H A. Mapping small elevation changes over large areas: differential radar interferometry[J]. Journal of Geophysical Research: Solid Earth, 1989, 94(B7): 9183-9191.
[27]	刘国祥. InSAR原理与应用[M]. 北京: 科学出版社, 2019.
	LIU Guoxiang. Principle and application of InSAR[M]. Beijing: Science Press, 2019.
[28]	YU C, LI Z H, PENNA N T. Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections[J]. Remote Sensing of Environment, 2020, 251: 112097.
[29]	BERARDINO P, FORNARO G, LANARI R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383.
[30]	LI Z, YU C, XIAO R, et al. Entering a new era of InSAR: advanced techniques and emerging applications[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(1): 1-4.
[31]	GOLDSTEIN R M, WERNER C L. Radar interferogram filtering for geophysical applications[J]. Geophysical Research Letters, 1998, 25(21): 4035-4038.
[32]	JIACHENG X, XIUFENG H, STEIN A, et al. Deformation monitoring of the embankments using multitemporal InSAR: a case study of the Kangshan embankment[J]. Journal of Geodesy and Geoinformation Science, 2025, 8(1): 12-29.
[33]	YU C, LI Z H, PENNA N T. Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model[J]. Remote Sensing of Environment, 2018, 204: 109-121.
[34]	YU C, LI Z H, PENNA N T, et al. Generic atmospheric correction model for interferometric synthetic aperture radar observations[J]. Journal of Geophysical Research: Solid Earth, 2018, 123(10): 9202-9222.
[35]	COSTANTINI M. A novel phase unwrapping method based on network programming[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 813-821.
[36]	LI Jia, LI Zhiwei, DING Xiaoli, et al. Investigating mountain glacier motion with the method of SAR intensity-tracking: removal of topographic effects and analysis of the dynamic patterns[J]. Earth-Science Reviews, 2014, 138: 179-195.
[37]	LIU Xiaojie, ZHAO Chaoying, ZHANG Qin, et al. Deformation of the Baige landslide, Tibet, China, revealed through the integration of cross-platform ALOS/PALSAR-1 and ALOS/PALSAR-2 SAR observations[J]. Geophysical Research Letters, 2020, 47(3): e2019GL086142.
[38]	YIN Yueping, LIU Xiaojie, ZHAO Chaoying, et al. Multi-dimensional and long-term time series monitoring and early warning of landslide hazard with improved cross-platform SAR offset tracking method[J]. Science China Technological Sciences, 2022, 65(8): 1891-1912.
[39]	刘晓杰. 星载雷达遥感广域滑坡早期识别与监测预测关键技术研究[D]. 西安: 长安大学, 2022.
	LIU Xiaojie. Research on key technologies for early identification, monitoring and forecasting of wide-area landslides with spaceborne radar remote sensing[D]. Xi'an: Changan University, 2022.
[40]	胡敏云, 陈云敏, 温振统. 城市垃圾填埋场垃圾土压缩变形的研究[J]. 岩土工程学报, 2001, 23(1): 123-126.
	HU Minyun, CHEN Yunmin, WEN Zhentong. Study on compression deformation of waste soil in municipal solid waste landfill[J]. Chinese Journal of Geotechnical Engineering, 2001, 23(1): 123-126.
[41]	ZEKKOS D, DATA S, FEI X C. Coupled process modeling for energy generation from municipal solid waste degradation: Laboratory-scale and field-scale simulations[J]. Japanese Geotechnical Society Special Publication, 2021, 9(11): 485-503.
[42]	柯瀚, 刘骏龙, 陈云敏, 等. 不同压力下垃圾降解压缩试验研究[J]. 岩土工程学报, 2010, 32(10): 1610-1615.
	KE Han, LIU Junlong, CHEN Yunmin, et al. Biodegradation-compression tests on municipal solid waste subjected to different vertical pressures[J]. Chinese Journal of Geotechnical Engineering, 2010, 32(10): 1610-1615.
[43]	罗袆沅, 许强, 蒋亚楠, 等. 基于时序InSAR与机器学习的大范围地面沉降预测方法[J]. 地球科学, 2024, 49(5): 1736-1745.
	LUO Yiyuan, XU Qiang, JIANG Yanan, et al. The prediction method of large-scale land subsidence based on multi-temporal InSAR and machine learning[J]. Earth Science, 2024, 49(5): 1736-1745.
[44]	SHI Xingjian, CHEN Zhourong, WANG Hao, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting[J]. Advances in neural information processing systems, 2015, 28: 6352419.
[45]	国家质量监督检验检疫总局, 中国国家标准化管理委员会. 生活垃圾填埋场稳定化场地利用技术要求：GB/T 25179—2010[S]. 北京: 中国标准出版社, 2011.
	General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. Technical requirements for site utilization after stabilization in municipal solid waste landfill: GB/T 25179—2010[S]. Beijing: Standards Press of China, 2011.
[46]	国家市场监督管理总局, 国家标准化管理委员会. 地质灾害危险性评估规范：GB/T 40112—2021[S]. 北京: 中国标准出版社, 2021.
	State Administration for Market Regulation, Standardization Administration of the People's Republic of China. Specifications for risk assessment of geological hazard: GB/T 40112—2021[S]. Beijing: Standards Press of China, 2021.
[47]	SHIN Y, LEE H. Subsidence of a coal ash landfill in a power plant observed by applying PSInSAR to Sentinel-1 SAR data[J]. Remote Sensing, 2023, 15(17): 4127.

卫星	轨道	极化方式	成像模式	开始日期	截止日期	影像数量
Envisat	升轨	VV	IM	2008-01-07	2010-09-26	33
Sentinel-1	升轨	VV	IW	2015-06-15	2025-05-17	273

点名	P11	P12	P13	P14	P15	P16	P17	P21	P22	P23
标准差	99.28	140.33	111.12	153.59	204.63	175.86	104.28	56.86	38.24	21.70
标准差（校正）	36.71	76.24	81.95	114.64	170.27	151.39	84.91	55.96	33.25	17.35

特征点	温度相关性（Pearson系数）	温度相关性（Spearman系数）	降水相关性（Pearson系数）	降水相关性（Spearman系数）	温度时滞/d	降水时滞/d
P11	0.72	0.68	0.69	0.64	115	108
P12	0.75	0.81	0.74	0.82	124	104
P13	0.76	0.63	0.79	0.58	105	106
P14	0.69	0.74	0.64	0.69	111	118
P15	0.66	0.59	0.69	0.54	115	124
P16	0.59	0.71	0.61	0.67	118	132
P17	0.62	0.56	0.57	0.52	101	99
P21	0.58	0.66	0.59	0.73	132	114
P22	0.64	0.78	0.54	0.76	119	110
P23	0.53	0.61	0.51	0.49	119	124

模型	RMSE/mm	R²
ConvLSTM	21.2	0.892
LSTM	25.8	0.831
GRU	26.4	0.816
Transformer	28.2	0.789
ARIMA	32.5	0.724