Mountain Landslide Monitoring Using a DS-InSAR Method Incorporating a Spatio-Temporal Atmospheric Phase Screen Correction Model Article Swipe
Shipeng Guo
,
Xiaoqing Zuo
,
Jihong Zhang
,
Yang Xu
,
Cheng Zhi Huang
,
Xuefu Yue
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16224228
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/rs16224228
The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs16224228
- OA Status
- gold
- Cited By
- 2
- References
- 56
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4404327973Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs16224228Digital Object Identifier
- Title
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Mountain Landslide Monitoring Using a DS-InSAR Method Incorporating a Spatio-Temporal Atmospheric Phase Screen Correction ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-13Full publication date if available
- Authors
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Shipeng Guo, Xiaoqing Zuo, Jihong Zhang, Yang Xu, Cheng Zhi Huang, Xuefu YueList of authors in order
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https://doi.org/10.3390/rs16224228Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs16224228Direct OA link when available
- Concepts
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Interferometric synthetic aperture radar, Remote sensing, Landslide, Environmental science, Geology, Synthetic aperture radar, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-11-13 |
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| referenced_works | https://openalex.org/W3047462331, https://openalex.org/W2768638539, https://openalex.org/W1998904650, https://openalex.org/W2051227254, https://openalex.org/W2075550759, https://openalex.org/W2003128404, https://openalex.org/W1982603755, https://openalex.org/W1970235400, https://openalex.org/W2013991066, https://openalex.org/W2141414687, https://openalex.org/W4249744610, https://openalex.org/W2066132495, https://openalex.org/W2787761199, https://openalex.org/W1631937297, https://openalex.org/W2038566498, https://openalex.org/W2969843439, https://openalex.org/W2144819091, https://openalex.org/W6855112332, https://openalex.org/W2047029664, https://openalex.org/W2152657318, https://openalex.org/W6755069264, https://openalex.org/W6808468067, https://openalex.org/W2550595154, https://openalex.org/W2121773078, https://openalex.org/W1975289769, https://openalex.org/W2065822370, https://openalex.org/W2123632763, https://openalex.org/W6842756001, https://openalex.org/W4361027156, https://openalex.org/W4388572003, https://openalex.org/W2910709819, https://openalex.org/W2599092069, https://openalex.org/W1983777277, https://openalex.org/W2112183468, https://openalex.org/W6787425887, https://openalex.org/W2004736578, https://openalex.org/W2908287662, https://openalex.org/W4310808610, https://openalex.org/W2922064494, https://openalex.org/W1664171571, https://openalex.org/W4308509717, https://openalex.org/W2890787798, https://openalex.org/W4393158139, https://openalex.org/W1672745209, https://openalex.org/W1972002878, https://openalex.org/W2888706946, https://openalex.org/W2152012906, https://openalex.org/W2794760869, https://openalex.org/W2110940994, https://openalex.org/W2767948554, https://openalex.org/W2806372340, https://openalex.org/W2894757765, https://openalex.org/W4293318828, https://openalex.org/W3114284068, https://openalex.org/W4213277427, https://openalex.org/W4384916586 |
| referenced_works_count | 56 |
| abstract_inverted_index.a | 33 |
| abstract_inverted_index.28 | 59 |
| abstract_inverted_index.DS | 99 |
| abstract_inverted_index.In | 25 |
| abstract_inverted_index.We | 55 |
| abstract_inverted_index.as | 73 |
| abstract_inverted_index.by | 17, 180 |
| abstract_inverted_index.cm | 146, 150, 170 |
| abstract_inverted_index.in | 49, 87 |
| abstract_inverted_index.of | 2, 32, 67, 98, 131, 140, 148, 162, 175 |
| abstract_inverted_index.on | 58 |
| abstract_inverted_index.to | 42, 63, 91, 168, 184, 197 |
| abstract_inverted_index.we | 28, 191 |
| abstract_inverted_index.(1) | 75 |
| abstract_inverted_index.(2) | 119 |
| abstract_inverted_index.(3) | 152 |
| abstract_inverted_index.The | 0, 70, 96, 120, 153 |
| abstract_inverted_index.and | 22, 38, 105, 113, 127, 134, 171 |
| abstract_inverted_index.are | 72 |
| abstract_inverted_index.has | 14 |
| abstract_inverted_index.map | 44 |
| abstract_inverted_index.our | 68 |
| abstract_inverted_index.the | 30, 39, 45, 65, 77, 125, 135, 158, 163, 173, 176, 193 |
| abstract_inverted_index.use | 31 |
| abstract_inverted_index.was | 101, 143 |
| abstract_inverted_index.(DS) | 37 |
| abstract_inverted_index.1.19 | 145 |
| abstract_inverted_index.1.58 | 149 |
| abstract_inverted_index.1.62 | 169 |
| abstract_inverted_index.1.88 | 167 |
| abstract_inverted_index.SHPs | 89 |
| abstract_inverted_index.been | 15 |
| abstract_inverted_index.both | 18 |
| abstract_inverted_index.five | 106 |
| abstract_inverted_index.from | 166 |
| abstract_inverted_index.mean | 137, 159 |
| abstract_inverted_index.root | 136 |
| abstract_inverted_index.same | 78 |
| abstract_inverted_index.than | 109 |
| abstract_inverted_index.this | 26 |
| abstract_inverted_index.(GPS) | 188 |
| abstract_inverted_index.8.27% | 182 |
| abstract_inverted_index.Radar | 13 |
| abstract_inverted_index.Under | 76 |
| abstract_inverted_index.eight | 103 |
| abstract_inverted_index.error | 139 |
| abstract_inverted_index.model | 41, 123 |
| abstract_inverted_index.phase | 20 |
| abstract_inverted_index.ratio | 94 |
| abstract_inverted_index.rural | 4 |
| abstract_inverted_index.small | 114 |
| abstract_inverted_index.test. | 95 |
| abstract_inverted_index.tests | 57 |
| abstract_inverted_index.times | 104, 107 |
| abstract_inverted_index.using | 8 |
| abstract_inverted_index.China. | 54 |
| abstract_inverted_index.Diqing | 50 |
| abstract_inverted_index.Global | 185 |
| abstract_inverted_index.System | 187 |
| abstract_inverted_index.higher | 85, 108 |
| abstract_inverted_index.images | 62 |
| abstract_inverted_index.method | 156 |
| abstract_inverted_index.noise. | 24 |
| abstract_inverted_index.points | 100 |
| abstract_inverted_index.sample | 79 |
| abstract_inverted_index.square | 138 |
| abstract_inverted_index.study, | 27 |
| abstract_inverted_index.subset | 116 |
| abstract_inverted_index.values | 142 |
| abstract_inverted_index.Tibetan | 51 |
| abstract_inverted_index.delays, | 133 |
| abstract_inverted_index.density | 97 |
| abstract_inverted_index.driving | 194 |
| abstract_inverted_index.factors | 195 |
| abstract_inverted_index.instead | 147 |
| abstract_inverted_index.propose | 29 |
| abstract_inverted_index.rapidly | 43 |
| abstract_inverted_index.reduced | 157 |
| abstract_inverted_index.screens | 21 |
| abstract_inverted_index.spatial | 126 |
| abstract_inverted_index.surface | 47 |
| abstract_inverted_index.Aperture | 12 |
| abstract_inverted_index.Finally, | 190 |
| abstract_inverted_index.Position | 186 |
| abstract_inverted_index.accuracy | 174 |
| abstract_inverted_index.baseline | 115 |
| abstract_inverted_index.combined | 34 |
| abstract_inverted_index.compared | 90, 183 |
| abstract_inverted_index.follows: | 74 |
| abstract_inverted_index.improved | 172 |
| abstract_inverted_index.interval | 82 |
| abstract_inverted_index.measured | 141 |
| abstract_inverted_index.methods, | 117 |
| abstract_inverted_index.methods. | 69 |
| abstract_inverted_index.mountain | 5 |
| abstract_inverted_index.proposed | 121, 154 |
| abstract_inverted_index.solution | 179 |
| abstract_inverted_index.standard | 160 |
| abstract_inverted_index.temporal | 128 |
| abstract_inverted_index.validate | 64 |
| abstract_inverted_index.velocity | 178 |
| abstract_inverted_index.Synthetic | 11 |
| abstract_inverted_index.conducted | 56 |
| abstract_inverted_index.considers | 124 |
| abstract_inverted_index.corrected | 164 |
| abstract_inverted_index.detection | 1 |
| abstract_inverted_index.deviation | 161 |
| abstract_inverted_index.landslide | 6, 46, 198 |
| abstract_inverted_index.potential | 3 |
| abstract_inverted_index.scatterer | 36, 111 |
| abstract_inverted_index.Autonomous | 52 |
| abstract_inverted_index.challenged | 16 |
| abstract_inverted_index.confidence | 81 |
| abstract_inverted_index.estimation | 83 |
| abstract_inverted_index.likelihood | 93 |
| abstract_inverted_index.persistent | 110 |
| abstract_inverted_index.properties | 130 |
| abstract_inverted_index.summarized | 192 |
| abstract_inverted_index.(PZTD-NEF). | 151 |
| abstract_inverted_index.Prefecture, | 53 |
| abstract_inverted_index.SENTINEL-1A | 61 |
| abstract_inverted_index.atmospheric | 19 |
| abstract_inverted_index.conclusions | 71 |
| abstract_inverted_index.conditions, | 80 |
| abstract_inverted_index.decoherence | 23 |
| abstract_inverted_index.deformation | 177 |
| abstract_inverted_index.distributed | 35 |
| abstract_inverted_index.generalized | 92 |
| abstract_inverted_index.identifying | 88 |
| abstract_inverted_index.performance | 86 |
| abstract_inverted_index.time-series | 9 |
| abstract_inverted_index.variability | 129 |
| abstract_inverted_index.contributing | 196 |
| abstract_inverted_index.demonstrated | 84 |
| abstract_inverted_index.instability. | 199 |
| abstract_inverted_index.tropospheric | 132 |
| abstract_inverted_index.approximately | 102, 144, 181 |
| abstract_inverted_index.displacements | 7, 48 |
| abstract_inverted_index.effectiveness | 66 |
| abstract_inverted_index.measurements. | 189 |
| abstract_inverted_index.respectively. | 118 |
| abstract_inverted_index.interferograms | 165 |
| abstract_inverted_index.interferometry | 112 |
| abstract_inverted_index.Prophet_ZTD-NEF | 40, 122, 155 |
| abstract_inverted_index.full-resolution | 60 |
| abstract_inverted_index.interferometric | 10 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5101624901 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I10660446 |
| citation_normalized_percentile.value | 0.88943625 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |