Abstract
Thermal infrared remote sensing technology based on unmanned aerial vehicle (UAV) was applied to estimate the spatial distribution of ground surface temperatures on permafrost slopes and evaluate the thermal influence of nearby engineering infrastructure. This paper presents a method that uses a miniature UAV with a thermal infrared sensor to collect thermal images with high temporal–spatial resolution. Moreover, spatial analysis is used to effectively evaluate the relationship between engineering infrastructure and permafrost slopes in the Qinghai–Tibet Engineering Corridor (QTEC), China. To test the method, aerial measurements were collected from 11:00 to 17:00 in July and August of 2017 at two permafrost slopes along the QTEC, where the Qinghai–Tibet Highway (QTH), Qinghai–Tibet Railway (QTR), and electric towers were built on permafrost slopes. The differences of ground surface temperature between the highway and the surrounding soil were largest at 11:00 and 17:00; the differences were smaller at noon to approximately 15:00 when the difference was minimal, and the differences began to increase after 15:00. The distances of the thermal influence of the highway, railway, and electric towers on the surrounding permafrost slopes are approximately 12–14, 8–10, and 2–4 m, respectively. The results indicate that the degree of influence of engineering structures on permafrost slopes is as follows: QTH > QTR > electric towers. This study is the first to use UAV-based thermal infrared remote sensing to evaluate the thermal dynamics of permafrost slopes along the QTEC. These results may provide new insights into the future design, construction, and maintenance of engineering structures on permafrost slopes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aicardi I, Chiabrando F, Lingua AM, Noardo F, Piras M, Vigna B (2016) A methodology for acquisition and processing of thermal data acquired by UAVs: a test about subfluvial springs’ investigations. Geomat Nat Haz Risk 8:5–17. https://doi.org/10.1080/19475705.2016.1225229
Cheng G and Wu T (2007) Responses of permafrost to climate change and their environmental significance, Qinghai–Tibet Plateau. Journal of Geophysical Research: Earth Surface 112. https://doi.org/10.1029/2006jf000631
Guo L, Yu Q, You Y, Wang X, Li X, Yuan C (2016) Cooling effects of thermosyphons in tower foundation soils in permafrost regions along the Qinghai–Tibet Power Transmission Line from Golmud, Qinghai Province to Lhasa, Tibet Autonomous Region, China. Cold Reg Sci Technol 121:196–204. https://doi.org/10.1016/j.coldregions.2015.07.011
Han F, Yu W, Yi X, Hu D, Lu Y (2016) Thermal regime of paved embankment in permafrost regions along the Qinghai–Tibet Engineering Corridor. Appl Therm Eng 108:330–338. https://doi.org/10.1016/j.applthermaleng.2016.07.126
Kenner R, Phillips M, Hauck C, Hilbich C, Mulsow C, Bühler Y, Stoffel A, Buchroithner M (2017) New insights on permafrost genesis and conservation in talus slopes based on observations at Flüelapass, Eastern Switzerland. Geomorphology 290:101–113. https://doi.org/10.1016/j.geomorph.2017.04.011
Lu B, He Y (2017) Species classification using unmanned aerial vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. Isprs J Photogramm 128:73–85. https://doi.org/10.1016/j.isprsjprs.2017.03.011
Luo L, Ma W, Zhang Z, Zhuang Y, Zhang Y, Yang J, Cao X, Liang S, Mu Y (2017) Freeze/thaw-induced deformation monitoring and assessment of the slope in permafrost based on terrestrial laser scanner and GNSS. Remote Sens 9:198. https://doi.org/10.3390/rs9030198
Luo L, Ma W, Zhuang Y, Zhang Y, Yi S, Xu J, Long Y, Ma D, Zhang Z (2018) The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai–Tibet Engineering Corridor. Ecol Indic 93:24–35. https://doi.org/10.1016/j.ecolind.2018.04.067
Ma W, Niu F, Akagawa S, Jin D (2006) Slope instability phenomena in permafrost regions of Qinghai–Tibet Plateau, China. Landslides 3:260–264. https://doi.org/10.1007/s10346-006-0045-0
Maes W, Huete A, Steppe K (2017) Optimizing the processing of UAV-based thermal imagery. Remote Sens 9:476. https://doi.org/10.3390/rs9050476
Mandanici E, Conte P, Girelli V (2016) Integration of aerial thermal imagery, LiDAR data and ground surveys for surface temperature mapping in urban environments. Remote Sens 8:880. https://doi.org/10.3390/rs8100880
Mu Y, Wang G, Yu Q, Li G, Ma W, Zhao S (2016) Thermal performance of a combined cooling method of thermosyphons and insulation boards for tower foundation soils along the Qinghai–Tibet Power Transmission Line. Cold Reg Sci Technol 121:226–236. https://doi.org/10.1016/j.coldregions.2015.06.006
Niu F, Luo J, Lin Z, Fang J, Liu M (2016) Thaw-induced slope failures and stability analyses in permafrost regions of the Qinghai–Tibet Plateau, China. Landslides 13:55–65. https://doi.org/10.1007/s10346-014-0545-2
Niu F, Luo J, Lin Z, Liu M, Yin G (2014) Thaw-induced slope failures and susceptibility mapping in permafrost regions of the Qinghai–Tibet Engineering Corridor, China. Nat Hazards 74:1667–1682. https://doi.org/10.1007/s11069-014-1267-4
Parmentier B, McGill BJ, Wilson AM, Regetz J, Jetz W, Guralnick R, Tuanmu M-N, Schildhauer M (2015) Using multi-timescale methods and satellite-derived land surface temperature for the interpolation of daily maximum air temperature in Oregon. Int J Climatol 35:3862–3878. https://doi.org/10.1002/joc.4251
Peng CH, Ouyang H, Gao Q, Jiang Y, Zhang F, Li J, Yu Q (2007) Environment—building a “green” railway in China. Science 316:546–547. https://doi.org/10.1126/science.1134134
Santesteban LG, Di Gennaro SF, Herrero-Langreo A, Miranda C, Royo JB, Matese A (2017) High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agr Water Manage 183:49–59. https://doi.org/10.1016/j.agwat.2016.08.026
Sheng Y, Zhang J, Liu Y, Wu J (2002) Thermal regime in the embankment of Qinghai–Tibetan Highway in permafrost regions. Cold Reg Sci Technol 35:35–44. https://doi.org/10.1016/s0165-232x(02)00026-5
Wang Q, Jin H, Zhang T, Cao B, Peng X, Wang K, Xiao X, Guo H, Mu C, Li L (2017) Hydro-thermal processes and thermal offsets of peat soils in the active layer in an alpine permafrost region, NE Qinghai–Tibet plateau. Glob Planet Chang 156:1–12. https://doi.org/10.1016/j.gloplacha.2017.07.011
Wu Q, Zhang T (2008) Recent permafrost warming on the Qinghai–Tibetan Plateau. J Geophys Res-Atmos 113. https://doi.org/10.1029/2007jd009539
Yang Y, Wu Q, Jiang G, Zhang P (2017) Stable isotopic stratification and growth patterns of ground ice in permafrost on the Qinghai–Tibet Plateau, China. Permafrost Periglac 28:119–129. https://doi.org/10.1002/ppp.1892
Zhang M, Pei W, Zhang X, Lu J (2015) Lateral thermal disturbance of embankments in the permafrost regions of the Qinghai–Tibet Engineering Corridor. Nat Hazards 78:2121–2142. https://doi.org/10.1007/s11069-015-1823-6
Zhang Z, Wu Q, Liu Y, Gao S (2017) Characteristics of water and heat changes in near-surface layers under influence of engineering interface. Appl Therm Eng 125:986–994. https://doi.org/10.1016/j.applthermaleng.2017.07.087
Zhao R, Li Z, Feng G, Wang Q, Hu J (2016) Monitoring surface deformation over permafrost with an improved SBAS-InSAR algorithm: with emphasis on climatic factors modeling. Remote Sens Environ 184:276–287. https://doi.org/10.1016/j.rse.2016.07.019
Acknowledgements
This research was supported by the National Natural Science Foundation of China (41301508, 41630636, and 60973137). The authors would like to express their gratitude to the editors and anonymous reviewers for the insightful comments and suggestions that have helped improve this paper.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Luo, L., Ma, W., Zhao, W. et al. UAV-based spatiotemporal thermal patterns of permafrost slopes along the Qinghai–Tibet Engineering Corridor. Landslides 15, 2161–2172 (2018). https://doi.org/10.1007/s10346-018-1028-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-018-1028-7