В работе представлены результаты интерферометрической обработки и анализа радиолокационных снимков оползневого склона на берегу р. Бурея, сделанных радиолокаторами с синтезированной апертурой (РСА) L-диапазона PALSAR, PALSAR-2 и РСА 
C-диапазона, установленным на борту спутника Sentinel-1B, в 2006–2018 гг. Показано ограничивающее влияние временнóй декорреляции радиолокационных сигналов, отраженных залесенной поверхностью берегового склона, вследствие которого из-за малого количества пригодных для интерферометрической обработки архивных данных был использован метод классической дифференциальной интерферометрии. Проведенные измерения скоростей подвижек показали, что сползание оползневого грунта в первую декаду XXI в. может характеризоваться стабильной скоростью движения в течение года. Заметное увеличение годового количества осадков во втором десятилетии в связи с сильными дождями в 2013, 2016 и 2018 гг. привело к значительному ускорению движения оползня в теплое время года с окончательным сходом оползневых масс в декабре 2018 г. Сделано предположение, что заполнение Бурейского водохранилища в 2003–2009 гг. с подъемом воды на 60 м в районе оползня и последующими сезонными колебаниями уровня воды спровоцировало оползневую активность после 2006 г. Совместный анализ интерферометрических измерений движения оползня с цифровыми моделями рельефа оползневого склона показал, что все выявленные методами радарной интерферометрии подвижки поверхности оползневого склона находятся в пределах локального понижения рельефа. Это понижение наблюдается на серии цифровых моделей рельефа, в т.ч. на полученной радиолокационным комплексом SRTM в 2000 г., задолго до наполнения водохранилища и начала работы гидроэлектростанции. Полученные результаты позволили сделать вывод о том, что катастрофическому обрушению 11 декабря 2018 г. предшествовал достаточно длительный этап медленных деформаций, вероятно, активизировавшихся после заполнения Бурейского водохранилища.

1. Бондур В.Г., Захарова Л.Н., Захаров А.И., Чимитдоржиев Т.Н., Дмитриев А.В., Дагуров П.Н., 2019. Мониторинг оползневых процессов с помощью космических интерферометрических радаров L-диапазона на примере обрушения склона берега реки Бурея. Исследование Земли из космоса, № 5, с. 3–14, https://doi.org/10.31857/S0205-9614201953-14.
2. Зеркаль О.В., Махинов А.Н., Кудымов А.В., Харитонов М.Е., Фоменко И.К., Барыкина О.С., 2019. Буреинский оползень
11 декабря 2018 г. Условия формирования и особенности механизма развития. ГеоРиск, Том XIII, № 4, с. 18–30, https://doi.org/10.25296/1997-8669-2019-13-4-18-30.
3. Кондратьева Л.М., 2019. Бурейский оползень и экологические риски. Вестник Дальневосточного отделения Российской академии наук, № 2, с. 45–55, https://doi.org/10.25808/08697698.2019.204.2.005.
4. Махинов А.Н., 2020. Крупный оползень и вызванное им цунами в Бурейском водохранилище. Геоморфология, № 3, с. 31–43, https://doi.org/10.31857/S0435428120030086.
5. Махинов А.Н., Ким В.И., Остроухов А.В., Матвеенко Д.В., 2019. Крупный оползень в долине реки Бурея и цунами в водохранилище Бурейской ГЭС. Вестник Дальневосточного отделения Российской академии наук, № 2, с. 35–44, https://doi.org/10.25808/08697698.2019.204.2.004.
6. Bamler R., Hartl P., 1998. Synthetic aperture radar interferometry. Inverse Problems, Vol. 14, No. 4, pp. R1–R54, https://doi.org/10.1088/0266-5611/14/4/001.
7. Bayer B., Schmidt D., Simoni A., 2017. The influence of external digital elevation models on PS-InSAR and SBAS results: implications for the analysis of deformation signals caused by slow moving landslides in the forthern Apennines (Italy). IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, pp. 2618–2631, https://doi.org/10.1109/TGRS.2017.2648885.
8. Berardino P., Fornaro G., Lanari R., Sansosti E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 11, pp. 2375–2383, https://doi.org/10.1109/TGRS.2002.803792.
9. Blonda P., Satalino G., Alberga V., Wasowski J., Parise M., Chiaradia M.T., Viggiano R., Pappalepore M., 1999. Soft computing techniques for data classification in a landslide-prone area of Italy. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS’99), Hamburg, Germany, 1999, Vol. 3, pp. 1600–1602, https://doi.org/10.1109/IGARSS.1999.772032.
10. Fadhillah M.F., Achmad A.R., Lee C.-W., 2022. Improved combined scatterers interferometry with optimized point scatterers (ICOPS) for interferometric synthetic aperture radar (InSAR) time-series analysis. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, ID 5220014, https://doi.org/10.1109/TGRS.2021.3138763.
11. Ferretti A., Fumagalli A., Novali F., Prati C., Rocca F., Rucci A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, Issue 9, pp. 3460–3470, https://doi.org/10.1109/TGRS.2011.2124465.
12. Ferretti A., Prati C., Rocca F., 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, Issue 1, pp. 8–20, https://doi.org/10.1109/36.898661.
13. Fruneau B., Achache J., Delacourt C., 1996. Observation and modelling of the Saint-Étienne-de-Tinée landslide using SAR interferometry. Tectonophysics, Vol. 265, Issue 3–4, pp. 181–190, https://doi.org/10.1016/S0040-1951(96)00047-9.
14. Goel K., Adam N., 2014. A distributed scatterer interferometry approach for precision monitoring of known surface deformation phenomena. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, Issue 9, pp. 5454–5468, https://doi.org/10.1109/TGRS.2013.2289370.
15. Hooper A., Zebker H., Segall P., Kampes B., 2004. A new method for measuring deformation on volcanoes and other natural InSAR persistent scatterers. Geophysical Research Letters, Vol. 31, Issue 23, ID L23611, https://doi.org/10.1029/2004GL021737.
16. Lv X., Yazici B., Zeghal M., Bennett V., Abdoun T., 2014. Joint-scatterer processing for time-series InSAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, Issue 11, pp. 7205–7221, https://doi.org/10.1109/TGRS.2014.2309346.
17. Pepe A., Yang Y., Manzo M., Lanari R., 2015. Improved EMCF-SBAS processing chain based on advanced techniques for the noise filtering and selection of small baseline multilook DInSAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, Issue 8, pp. 4394–4417, https://doi.org/10.1109/TGRS.2015.2396875.
18. Rodriguez E., Martin J.M.,1992. Theory and design of interferometric synthetic aperture radars. IEE Proceedings F (Radar and Signal Processing), Vol. 139, Issue 2, pp. 147–159, https://doi.org/10.1049/ip-f-2.1992.0018.
19. Xia Y., 2008. CR-Based SAR-interferometry for landslide monitoring. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS-2008), Boston, MA, USA, 2008, ID 10445445, https://doi.org/10.1109/IGARSS.2008.4779226.
20. Xia Y., Kaufmann H., Guo X., 2002. Differential SAR interferometry using corner reflectors. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS-2002), Toronto, Canada, 2002, Vol. 2, pp. 1243–1246, https://doi.org/10.1109/IGARSS.2002.1025902.
21. Xue F., Lv X., Dou F., Yun Y., 2020. A review of time-series interferometric SAR techniques: a tutorial for surface deformation analysis. IEEE Geoscience Remote Sensing Magazine, Vol. 8, Issue 1, pp. 22–42, https://doi.org/10.1109/MGRS.2019.2956165.
22. Zakharov A., Zakharova L., 2022. The Bureya landslide recent evolution according to spaceborne SAR interferometry data. Remote Sensing, Vol. 14, Issue 20, ID 5218, https://doi.org/10.3390/rs14205218.
23. Zhang L., Sun Q., Hu J., 2018. Potential of TCPInSAR in monitoring linear infrastructure with a small dataset of SAR images: application of the Donghai Bridge, China. Applied Sciences, Vol. 8, Issue 3, ID 425, https://doi.org/10.3390/app8030425.

ЗАХАРОВА Л.Н.
Фрязинский филиал Института радиотехники и электроники им. В.А. Котельникова РАН, г. Фрязино, Московская область, Россия, ludmila@sunclass.ire.rssi.ru
Адрес: пл. Введенского, д. 1, г. Фрязино, Московская область, 141190, Россия
ЗАХАРОВ А.И.*
Фрязинский филиал Института радиотехники и электроники им. В.А. Котельникова РАН, г. Фрязино, Московская область, Россия, aizakhar@ire.rssi.ru
СИНИЛО В.П.
Фрязинский филиал Института радиотехники и электроники им. В.А. Котельникова РАН, г. Фрязино, Московская область, Россия, sinilo@ire.rssi.ru

The paper presents the results of interferometric processing and analysis of radar images of the landslide slope on the Bureya River bank acquired by L-band synthetic aperture radars (SARs) PALSAR, PALSAR-2 and C-band SAR installed on the Sentinel-1B satellite in 2006–2018. It was shown that the negative effects of the temporal decorrelation of the radar signals scattered by the forested slope surface reduce a number of the SAR data pairs applicable for the interferometric processing. For that reason, the classical differential SAR interferometry method was used in the study. Displacement velocity measurements showed that in the first decade of the 21st century the landslide motion may be characterized by a stable velocity throughout the year. Significant growth of precipitation rate in the second decade due to heavy rainfall in 2013, 2016 and 2018 led to acceleration of the landslide movement in the warm season with the final catastrophic landfall in December 2018. Presumably, Bureya Reservoir filling in 2003–2009 followed by 60 m rise of water level in the landslide vicinity and subsequent seasonal fluctuations in the water level triggered landslide activity after 2006. Joint analysis of interferometric measurements and digital elevation models (DEMs) of the landslide slope demonstrated that all displacements detected by radar took place within the local depression. This depression appears on the SRTM DEMs acquired in 2000, long before the reservoir was filled and the hydroelectric power plant began operating. The obtained results allowed us to conclude that the catastrophic collapse on
11 December 2018 was preceded by a rather long stage of slow deformations that activated probably after the filling of the Bureya Reservoir.

1. Bondur V.G., Zakharova L.N., Zakharov A.I., Chimitdorzhiev T.N., Dmitriev A.V., Dagurov P.N., 2019. Monitoring of landslide processes by means of L-band radar interferometric observations: Bureya River bank caving case. Issledovanie Zemli iz Kosmosa, No. 5, pp. 3–14, https://doi.org/10.31857/S0205-9614201953-14. (in Russian)
2. Zerkal O.V., Makhinov A.N., Kudymov A.V., Kharitonov M.E., Fomenko I.K., Barykina O.S., 2019. Bureya landslide on 11 December 2018. Conditions of the formation and features of the development mechanism. GeoRisk World, Vol. XIII, No. 4, pp. 18–30, https://doi.org/10.25296/1997-8669-2019-13-4-18-30. (in Russian)
3. Kondratyeva L.M., 2019. Bureysky landslide and ecological risks. Vestnik of the Far East Branch of the Russian Academy of Sciences, No. 2, pp. 45–55, https://doi.org/10.25808/08697698.2019.204.2.005. (in Russian)
4. Makhinov A.N., 2020. Large tsunami-generated landslide in the Bureysky Reservoir. Geomorfologiya, No. 3, pp. 31–43, https://doi.org/10.31857/S0435428120030086. (in Russian)
5. Makhinov A.N., Kim V.I., Ostroukhov A.V., Matveenko D.V., 2019. Large landslide in the valley of the Bureya River and tsunami in the reservoir of the Bureya hydroelectric power station. Vestnik of the Far East Branch of the Russian Academy of Sciences, No. 2, pp. 35–44, https://doi.org/10.25808/08697698.2019.204.2.004. (in Russian)
6. Bamler R., Hartl P., 1998. Synthetic aperture radar interferometry. Inverse Problems, Vol. 14, No. 4, pp. R1–R54, https://doi.org/10.1088/0266-5611/14/4/001.
7. Bayer B., Schmidt D., Simoni A., 2017. The influence of external digital elevation models on PS-InSAR and SBAS results: implications for the analysis of deformation signals caused by slow moving landslides in the Northern Apennines (Italy). IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, pp. 2618–2631, https://doi.org/10.1109/TGRS.2017.2648885.
8. Berardino P., Fornaro G., Lanari R., Sansosti E., 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 11, pp. 2375–2383, https://doi.org/10.1109/TGRS.2002.803792.
9. Blonda P., Satalino G., Alberga V., Wasowski J., Parise M., Chiaradia M.T., Viggiano R., Pappalepore M., 1999. Soft computing techniques for data classification in a landslide-prone area of Italy. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS’99), Hamburg, Germany, 1999, Vol. 3, pp. 1600–1602, https://doi.org/10.1109/IGARSS.1999.772032.
10. Fadhillah M.F., Achmad A.R., Lee C.-W., 2022. Improved combined scatterers interferometry with optimized point scatterers (ICOPS) for interferometric synthetic aperture radar (InSAR) time-series analysis. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, ID 5220014, https://doi.org/10.1109/TGRS.2021.3138763.
11. Ferretti A., Fumagalli A., Novali F., Prati C., Rocca F., Rucci A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, Issue 9, pp. 3460–3470, https://doi.org/10.1109/TGRS.2011.2124465.
12. Ferretti A., Prati C., Rocca F., 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, Issue 1, pp. 8–20, https://doi.org/10.1109/36.898661.
13. Fruneau B., Achache J., Delacourt C., 1996. Observation and modelling of the Saint-Étienne-de-Tinée landslide using SAR interferometry. Tectonophysics, Vol. 265, Issue 3–4, pp. 181–190, https://doi.org/10.1016/S0040-1951(96)00047-9.
14. Goel K., Adam N., 2014. A distributed scatterer interferometry approach for precision monitoring of known surface deformation phenomena. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, Issue 9, pp. 5454–5468, https://doi.org/10.1109/TGRS.2013.2289370.
15. Hooper A., Zebker H., Segall P., Kampes B., 2004. A new method for measuring deformation on volcanoes and other natural InSAR persistent scatterers. Geophysical Research Letters, Vol. 31, Issue 23, ID L23611, https://doi.org/10.1029/2004GL021737.
16. Lv X., Yazici B., Zeghal M., Bennett V., Abdoun T., 2014. Joint-scatterer processing for time-series InSAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, Issue 11, pp. 7205–7221, https://doi.org/10.1109/TGRS.2014.2309346.
17. Pepe A., Yang Y., Manzo M., Lanari R., 2015. Improved EMCF-SBAS processing chain based on advanced techniques for the noise filtering and selection of small baseline multilook DInSAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, Issue 8, pp. 4394–4417, https://doi.org/10.1109/TGRS.2015.2396875.
18. Rodriguez E., Martin J.M.,1992. Theory and design of interferometric synthetic aperture radars. IEE Proceedings F (Radar and Signal Processing), Vol. 139, Issue 2, pp. 147–159, https://doi.org/10.1049/ip-f-2.1992.0018.
19. Xia Y., 2008. CR-Based SAR-interferometry for landslide monitoring. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS-2008), Boston, MA, USA, 2008, ID 10445445, https://doi.org/10.1109/IGARSS.2008.4779226.
20. Xia Y., Kaufmann H., Guo X., 2002. Differential SAR interferometry using corner reflectors. Proceedings of the IEEE International geoscience and remote sensing Symposium (IGARSS-2002), Toronto, Canada, 2002, Vol. 2, pp. 1243–1246, https://doi.org/10.1109/IGARSS.2002.1025902.
21. Xue F., Lv X., Dou F., Yun Y., 2020. A review of time-series interferometric SAR techniques: a tutorial for surface deformation analysis. IEEE Geoscience Remote Sensing Magazine, Vol. 8, Issue 1, pp. 22–42, https://doi.org/10.1109/MGRS.2019.2956165.
22. Zakharov A., Zakharova L., 2022. The Bureya landslide recent evolution according to spaceborne SAR interferometry data. Remote Sensing, Vol. 14, Issue 20, ID 5218, https://doi.org/10.3390/rs14205218.
23. Zhang L., Sun Q., Hu J., 2018. Potential of TCPInSAR in monitoring linear infrastructure with a small dataset of SAR images: application of the Donghai Bridge, China. Applied Sciences, Vol. 8, Issue 3, ID 425, https://doi.org/10.3390/app8030425.

LIUDMILA N. ZAKHAROVA
Fryazino Branch of the Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences; Fryazino, Moscow Region, Russia; ludmila@sunclass.ire.rssi.ru
Address: Bld. 1, Vvedensky Sq., 141190, Fryazino, Moscow Region, Russia
ALEXANDER I. ZAKHAROV*
Fryazino Branch of the Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences; Fryazino, Moscow Region, Russia; aizakhar@ire.rssi.ru
VICTOR P. SINILO
Fryazino Branch of the Kotelnikov Institute of Radioengineering and Electronics, Russian Academy of Sciences; Fryazino, Moscow Region, sinilo@ire.rssi.ru