UAV-based monitoring of the thermal structure of heterogeneous landscapes

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

The paper presents a technique for measuring the temperature of an inhomogeneous underlying surface using unmanned aerial vehicles (UAVs). To test the proposed technique, measurements over various landscapes are presented: dunes in an arid zone, a temperate swamp, a subarctic city, and a combination of natural and anthropogenic landscapes in the Arctic. A measuring complex based on a DJI Mavic 2 Zoom quadrocopter with an installed Flir TAU2R thermal camera was used. Methods for correcting emerging hardware errors have been developed. To obtain detailed data on the spatial distribution of the surface brightness temperature, the orthomosaic construction method was used. Thermal maps of surfaces with relief inhomogeneities (dunes), moisture inhomogeneity (swamps), urban areas in polar and subpolar conditions were obtained at different times of the day. It is shown that thermal contrasts can reach the first ten degrees within an area of = 10–20 ha, both against the background of daytime heating and nighttime cooling of the surface, and could have a significant effect on the spatial distribution of the heat transfer characteristics of the atmosphere and the underlying surface. The developed methods are recommended for constructing surface thermal maps using thermal imaging technology.

全文:

受限制的访问

作者简介

M. Varentsov

Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Moscow

A. Varentsov

Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences

Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Moscow

I. Repina

Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences; Yugra State University

Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Moscow; Khanty-Mansiysk

A. Artamonov

Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences; Yugra State University

Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Khanty-Mansiysk

I. Drozd

Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences

Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Moscow

A. Mamonotov

Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences

Email: mikhail.varentsov@srcc.msu.ru
Moscow

V. Stepanenko

Lomonosov Moscow State University; Obukhov Institute of Atmospheric Physics of the Russian Academy of Sciences; Yugra State University

Email: mikhail.varentsov@srcc.msu.ru
俄罗斯联邦, Moscow; Moscow; Khanty-Mansiysk

参考

  1. Афонин А. В., Таджибаев А. И., Титков В. В. Инфракрасная термография в энергетике. Под ред. Ньюпорта Р. К., Таджибаева А. И. Т. 1. Основы инфракрасной термографии. СПб.: СПЭИПК, 2000. 240 с.
  2. Варенцов М. И., Грищенко М. Ю., Константинов П. И. Сопоставление наземных и космических разномасштабных температурных данных на примере городов Российской Арктики для зимних условий // Исследования Земли из космоса. 2021. Т. 2021. № 2. С. 64–76.
  3. Варенцов М. И., Репина И. А., Глазунов А. В., Самсонов Т. Е., Константинов П. И., Степаненко В. М., Артамонов А. Ю., Дебольский А. В., Печкин А. С., Соромотин А. В. Особенности пограничного слоя атмосферы г. Надыма по данным экспериментальных измерений и вихреразрешающего моделирования // Вестник Московского университета. Сер. 5. География. 2022. № 6. С. 64–78.
  4. Глазунов А. В., Степаненко В. М. Вихреразрешающее моделирование стратифицированных турбулентных течений над неоднородными природными ландшафтами // Известия РАН. Физика атмосферы и океана. 2015. Т. 51. № 4. С. 403–415.
  5. Головацкая Е. А., Дюкарев Е. А., Ипполитов И. И., Кабанов М. В. Влияние ландшафтных и гидрометеорологических условий на эмиссию СО2 в торфоболотных экосистемах // Доклады Академии Наук. 2008. № 4. С. 1–4.
  6. Госсорг Ж. Инфракрасная термография. Основы, техника, применение: пер. с франц. М.: Мир, 1988. 416 с.
  7. Киселев М. В. Воропай Н. Н., Дюкарев Е. А., Прейс Ю. И. Температурный режим осушенных и естественных болот в засушливые и переувлажненные годы // CITES’2019. 2019. С. 188–191.
  8. Курамагомедов Б. М., Алексеенко Н. А., Медведев А. А. Тепловая съемка с беспилотных летательных аппаратов в географических исследованиях // Огарёв-Online. 2015. Т. 24. № 65.
  9. Молчанов А. Г. Газообмен сфагнума при различных уровнях поверхностных грунтовых вод // Экология. 2015. № 3. С. 182
  10. Мосеев Д. С., Кряучюнгас В. В., Игловский С. А. Флора некоторых районов западной части Шпицбергена в начале вегетационного периода // Arct. Environ. Res. 2014. № 3. С. 94–100.
  11. Репина И. А., Варенцов М. И., Чечин Д. Г., Артамонов А. Ю., Бодунков Н. Е., Калягин М. Ю., Живоглотов Д. Н., Шевченко А. М., Варенцов А. И., Куксова Н. Е., Степаненко В. М., Шестакова А. А. Использование беспилотных летательных аппаратов для исследования атмосферного пограничного слоя // Инноватика и экспертиза. 2020. Т. 2. № 30. С. 20–39.
  12. Тарасова М. А., Варенцов М. И., Степаненко В. М. Параметризации взаимодействия атмосферы с городской поверхностью: обзор и перспективы развития // Известия РАН. Физика атмосферы и океана. 2023. Т. 59. № 2. С. 1–22.
  13. Чечин Д. Г., Артамонов А. Ю., Бодунков Н. Е., Живоглотов Д. Н., Зайцева Д. В., Калягин М. Ю., Кузнецов Д. Д., Кунашук А. А., Шевченко А. М., Шестакова А. А. Опыт исследования турбулентной структуры атмосферного пограничного слоя с помощью беспилотного летательного аппарата // Известия РАН. Физика атмосферы и океана. 2021. Т. 57. № 5. С. 602–610.
  14. Шелехов А. П., Афанасьев А. Л., Шелехова Е. А., Кобзев А. А., Тельминов А. Е., Молчунов А. Н., Поплевина О. Н. Использование малоразмерных БПЛА для измерения турбулентности в атмосфере // Известия РАН. Физика атмосферы и океана. 2021. Т. 57. № 5. С. 611–624.
  15. Шутко А. М. СВЧ-радиометрия водной поверхности и почвогрунтов. М.: Наука, 1986. 190 с.
  16. Эткин В. С., Шарков Е. А. Возможности дистанционного исследования поверхности Земли при помощи радиофизических систем // Космические исследования земных ресурсов. М.: Наука, 1976. С. 99–105.
  17. Abolt C., Caldwell T., Wolaver B., Pai H. Unmanned aerial vehicle-based monitoring of groundwater inputs to surface waters using an economical thermal infrared camera // Opt. Eng. 2018.V. 57. № 5. P. 053113–053113.
  18. Arola A. Parameterization of Turbulent and Mesoscale Fluxes for Heterogeneous Surfaces // J. Atmos. Sci. 1999. V. 56. № 4. P. 584–598.
  19. Avissar R., Pielke R. A. A Parameterization of Heterogeneous Land Surfaces for Atmospheric Numerical Models and Its Impact on Regional Meteorology // Mon. Weather Rev. 1989. V. 117. № 10. P. 2113–2136.
  20. Bartlett P. A., McCaughey J.H., Lafleur P. M., Verseghy D. L. A comparison of the mosaic and aggregated canopy frameworks for representing surface heterogeneity in the Canadian boreal forest using CLASS: a soil perspective // J. Hydrol. 2002. V. 266. № 1–2. P. 15–39.
  21. Bellvert J., Zarco-Tejada P.J., Girona J., Fereres E. J.P.A. Mapping crop water stress index in a Pinot-noir vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle // Precision agriculture. 2014. V. 15. P. 361–376.
  22. Canisius F., Wang S., Croft H., Leblanc S. G., Russell H. A.J., Chen J., Wang R. A UAV-Based Sensor System for Measuring Land Surface Albedo: Tested over a Boreal Peatland Ecosystem // Drones. 2019. V. 3. № 1. P. 27.
  23. Chilson P. B., Bell T. M., Brewster K. A., Azevedo G. B.H. De Carr F. H., Carson K., Doyle W., Fiebrich C. A., Greene B. R., Grimsley J. L., Kanneganti S. T., Martin J., Moore A., Palmer R. D., Pillar-Little E.A., Salazar-Cerreno J.L., Segales A. R., Weber M. E., Yeary M., Droegemeier K. K. Moving towards a network of autonomous UAS atmospheric profiling stations for observations in the earth’s lower atmosphere: The 3D mesonet concept // Sensors. 2019. V. 19. № 12.
  24. Coll C., García-Santos V., Niclòs R., Caselles V. Test of the MODIS land surface temperature and emissivity separation algorithm with ground measurements over a rice paddy // IEEE Trans. Geosci. Remote Sens. 2016. V. 54. № 5. P. 3061–3069.
  25. De Vrese P., Schulz J.-P., Hagemann S. On the Representation of Heterogeneity in Land-Surface–Atmosphere Coupling // Boundary-Layer Meteorol. 2016. V. 160. № 1. P. 157–183.
  26. Feng L., Tian H., Qiao Z., Zhao M., Liu Y. Detailed variations in urban surface temperatures exploration based on unmanned aerial vehicle thermography // IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2019. V. 13. P. 204–216.
  27. Garcia-Santos V., Cuxart J., Jimenez M. A., Martinez-Villagrasa D., Simo G., Picos R., Caselles V. Study of Temperature Heterogeneities at Sub-Kilometric Scales and Influence on Surface-Atmosphere Energy Interactions // IEEE Trans. Geosci. Remote Sens. 2019. V. 57. № 2. P. 640–654.
  28. Ho H. C., Knudby A., Sirovyak P., Xu Y., Hodul M., Henderson S. B. Mapping maximum urban air temperature on hot summer days // Remote Sens. Environ. 2014. V. 154. P. 38–45.
  29. Kelly J., Kljun N., Olsson P. O., Mihai L., Liljeblad B., Weslien P., Klemedtsson L., Eklundh L. Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera // Remote Sens. 2019. V. 11. P. 567.
  30. Koster R. D., Suarez M. J. A Comparative Analysis of Two Land Surface Heterogeneity Representations // J. Clim. 1992. V. 5. № 12. P. 1379–1390.
  31. Kraaijenbrink P. D.A., Shea J. M., Litt M., Steiner J. F., Treichler D., Koch I., Immerzeel W. W. Mapping surface temperatures on a debris-covered glacier with an unmanned aerial vehicle // Front. Earth Sci. 2018. V. 6. P. 64.
  32. Kral S. T., Reuder J., Vihma T., Suomi I., Haualand K. F., Urbancic G. H., Greene B. R., Steeneveld G. J., Lorenz T., Maronga B., Jonassen M. O., Ajosenpää H., Båserud L., Chilson P. B., Holtslag A. A.M., Jenkins A. D., Kouznetsov R., Mayer S., Pillar-Little E.A., Rautenberg A., Schwenkel J., Seidl A. W., Wrenger B. The innovative strategies for observations in the arctic atmospheric boundary layer project (ISOBAR) unique finescale observations under stable and very stable conditions // Bull. Am. Meteorol. Soc. 2021. V. 102. № 2. P. E218–E243.
  33. Kupriianova I., Kupriianova I. V., Kaverin A. A., Filippov I. V., Ilyasov D. V., Lapshina E. D., Logunova E. V., Kulyabin M. F. The main physical and geographical characteristics of the Mukhrino field station area and its surroundings // Environmental Dynamics and Global Climate Change. 2023. V. 13. № 4. P. 215–252.
  34. Lee D. H., Park J. H. Developing inspection methodology of solar energy plants by thermal infrared sensor on board unmanned aerial vehicles // Energies. 2019. V. 12. № 15. P. 2928.
  35. Lee T. R., Buban M., Dumas E., Baker C. B. A new technique to estimate sensible heat fluxes around micrometeorological towers using small unmanned aircraft systems // J. Atmos. Ocean Technol. 2017. V. 34. № 9. P. 2103–2112.
  36. Li D., Bou‐Zeid E., Barlage M., Chen F., Smith J. A. Development and evaluation of a mosaic approach in the WRF-Noah framework // J. Geophys. Res. Atmos. 2013a. V. 118. № 21. P. 11.918–11.935.
  37. Li Z. L., Tang B. H., Wu H., Ren H., Yan G., Wan Z., Trigo I. F., Sobrino J. A. Satellite-derived land surface temperature: Current status and perspectives // Remote Sens. Environ. 2013b. V. 131. P. 14–37.
  38. Malbéteau Y., Johansen K., Aragon B., Al-Mashhawari S.K., McCabe M. F. Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects // Remote Sens. 2021. V. 13. № 16. P. 3255.
  39. Medvedev A., Telnova N., Alekseenko N., Koshkarev A., Kuznetchenko P., Asmaryan S., Narykov A. UAV-Derived Data Application for Environmental Monitoring of the Coastal Area of Lake Sevan, Armenia with a Changing Water Level // Remote Sens. 2020. V. 12. P. 3821.
  40. Molod A., Salmun H., Waugh D. W. A New Look at Modeling Surface Heterogeneity: Extending Its Influence in the Vertical // J. Hydrometeorol. 2003. V. 4. № 5. P. 810–825.
  41. Molod A., Salmun H., Waugh D. W. The Impact on a GCM Climate of an Extended Mosaic Technique for the Land–Atmosphere Coupling // J. Clim. 2004. V. 17. № 20. P. 3877–3891.
  42. Nishar A., Richards S., Breen D., Robertson J., Breen B. Thermal infrared imaging of geothermal environments and by an unmanned aerial vehicle (UAV): A case study of the Wairakei – Tauhara geothermal field, Taupo, New Zealand // Renew. Energy. 2016. V. 86. P. 1256–1264.
  43. Nunez M., Oke T. R. The Energy Balance of an Urban Canyon // J. Appl. Meteorol. 1977. V. 16. P. 11–19.
  44. Oke T. R., Mills G., Christen A., Voogt J. A. Urban Climates. Cambridge: Cambridge University Press, 2017. 509 с.
  45. Part IV: Physical Processes // IFS Documentation CY47R1. 2020. P. 1–228.
  46. Rautenberg A., Schön M., Berge K., Mauz M., Manz P., Platis A., Kesteren B., Suomi I., Kral S. T., Bange J. The Multi-Purpose Airborne Sensor Carrier MASC-3 for Wind and Turbulence Measurements in the Atmospheric Boundary Layer // Sensors. 2019. V. 19. № 10. P. 2292.
  47. Ryan J. C., Hubbard A., Box J. E., Brough S., Cameron K., Cook J. M., Cooper M., Doyle S. H., Edwards A., Holt T., Irvine-Fynn T., Jones C., Pitcher L. H., Rennermalm A. K., Smith L. C., Stibal M., Snooke N. Derivation of high spatial resolution albedo from UAV digital imagery: Application over the Greenland ice sheet // Front. Earth Sci. 2017. V. 5. № May. P. 1–13.
  48. Segales A. R., Greene B. R., Bell T. M., Doyle W., Martin J. J., Pillar-Little E.A., Chilson P. B. The CopterSonde: an insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research // Atmos. Meas. Tech. 2020. V. 13. № 5. P. 2833–2848.
  49. Shelekhov A., Afanasiev A., Shelekhov E., Kobzev A., Tel’minov A., Molchunov A., Poplevina O. Low-Altitude Sensing of Urban Atmospheric Turbulence with UAV // Drones. 2022. V. 6. P. 61.
  50. Shelekhov A., Afanasiev A., Shelekhova E., Kobzev A., Tel’minov A., Molchunov A., Poplevina O. High-Resolution Profiling of Atmospheric Turbulence Using UAV Autopilot Data // Drones. 2023. V. 7. P. 412.
  51. Sizov O., Fedorov R., Pechkina Y., Kuklina V., Michugin M., Soromotin A. Urban Trees in the Arctic City: Case of Nadym // Land. 2022. V. 11. P. 531.
  52. Stewart I. D., Oke T. R., Krayenhoff E. S. Evaluation of the “local climate zone” scheme using temperature observations and model simulations // Int. J. Climatol. 2014. V. 1080. P. 1062–1080.
  53. Varentsov M., Stepanenko V., Repina I., Artamonov A., Bogomolov V., Kuksova N., Marchuk E., Pashkin A., Varentsov A. Balloons and Quadcopters: Intercomparison of Two Low-Cost Wind Profiling Methods // Atmosphere. 2021. V. 12. № 3. P. 380.
  54. Varentsov M., Konstantinov P., Repina I., Artamonov A., Pechkin A., Soromotin A., Esau I., Baklanov A. Observations of the urban boundary layer in a cold climate city // Urban Clim. 2023. V. 47. P. 101351.
  55. Wan Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product // Remote Sens. Environ. 2014. V. 140. P. 36–45.
  56. Weng Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends // ISPRS J. Photogramm. Remote Sens. 2009. V. 64. № 4. P. 335–344.
  57. Yu W., Ma M. Scale mismatch between in situ and remote sensing observations of land surface temperature: Implications for the validation of remote sensing LST products // IEEE Geosci. Remote Sens. Lett. 2015. V. 12. № 3. P. 497–501.

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Satellite images of four polygons for which the thermal survey was carried out: sand dunes in the vicinity of Naryn-Khuduk settlement (a), upper bog in the vicinity of Mukhrino research station (b), central part of Nadym (c), Barentsburg settlement (d). Nadym (c), Barentsburg settlement (d). The green colour shows the routes of the quadrocopter flight over these polygons during the survey

下载 (766KB)
索引源数据
3. Fig. 2. Photo of the FLIR Tau 2 thermal imager (a) and measurement system based on the DJI Mavic 2 Zoom quadcopter with Drone Experts suspension (b). Photo from https://dronexpert.nl/en/

下载 (110KB)
索引源数据
4. Fig. 3. Schematic of the correction algorithm. Numbers in parentheses advise the number of the equation in the text. Dotted line indicates optional correction steps, the necessity of which is determined by expert judgement for each shooting

下载 (448KB)
索引源数据
5. Fig. 4. Example of application of the correction algorithm to the survey data of the upper bog surface in the area of Mukhrino station in the evening of 17 June 2022: (a) time series of initial temperature values (coloured curves on the graph, the colour corresponds to the scanning band in Fig. 6) and values corrected at the stage of Algorithm No. 1 (top), time series of temperature difference between neighbouring points (bottom); (b) identified scanning bands, dotted lines indicate transitions between bands; (c) temperature dependence on position along the scanning bands and the results of its approximation by local-linear regression at the stage of Algorithm No. 3

下载 (535KB)
索引源数据
6. Fig. 5. Results of different stages of surface temperature image stitching and orthomosaic construction in Agisoft Metashape: image georeferencing (a), sparse point cloud (b), dense point cloud (c), orthomosaic (d)

下载 (1MB)
索引源数据
7. Fig. 6. Thermal orthomosaics plotted from the original thermal scheme data (a) and from the data after application of the first (b) and second (c) and third (d) steps of the correction algorithm for the marsh polygon at the Mukhrino research station on the evening of 17 June 2022 (17:30). The mean (mean), standard deviation (std) and the difference of the 99th and 1st percentiles (IQR) are given below each orthomosaic

下载 (1MB)
索引源数据
8. Fig. 7. Thermal orthomosaics for natural polygons: for the barchan zone in the area of Naryn-Khuduk settlement (Kalmykia) according to the survey data in the morning (a) and afternoon (b) on 22 July 2021, for the upland bog at the Mukhrino research station based on night (c) and day (d) survey data from 16-17 June 2022. The mean (mean), standard deviation (std) and the difference between the 99th and 1st percentiles (IQR) are given below each orthomosaic. (a) Barchans, morning (22.07.2021, 08:25), (b) Barchans, day (22.07.2021, 14:15), (c) High Marsh, night (16.06.2022, 23:30), (c) High Marsh, day (17.06.2022, 11:25)

下载 (1MB)
索引源数据
9. Fig. 8. Thermal orthomosaics for polygons with anthropogenically altered surface: for the central part of Nadym (YNAO) based on night (a) and day (b) survey data on 11 August 2021, for the territory of Barentsburg settlement (Svalbard archipelago, Norway) based on night (c) and day (d) survey data on 10 September 2021. The mean (mean), standard deviation (std) and the difference of 99th and 1st percentiles (IQR) are given below or to the side of each orthomosaic. (a) Nadym, night (11.08.2021, 01:00), (b) Nadym, day (11.08.2021, 14:15), (c) Barentsburg settlement, night (10.09.2021, 23:45), (d) Barentsburg settlement, day (10.09.2021, 14:10)

下载 (1MB)
索引源数据

版权所有 © Russian Academy of Sciences, 2024

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。