This paper presents and validates a remote sensing methodology for afforestation and reforestation monitoring. This methodology is based on an integrated analysis of modern radar datasets, including interferometric data from the Alaska SAR Facility (ASF) and others. The study area is located in Eastern Siberia, where young trees are surrounded by mature pine stands. The proposed approach combines two key components: 1) Biomass dynamics assessment using L-band SAR data (co- and cross-polarization); 2) Canopy height monitoring via two interferometric techniques. Comparative analysis of biomass dynamics revealed that temporal analysis of backscatter (sigma degrees) provides more accurate biomass estimates than dual-polarization H-Alpha decomposition. Furthermore, backscatter time-series processing can be automated using the Google Earth Engine (GEE) platform. For canopy height, both interferometric methods demonstrated their efficacy. Particularly, L-band InSAR (co-polarized HH) with an extremely long 2114-d baseline detected a 2-3 cm increase in scattering phase center height for young trees. This demonstrates the fundamental possibility of using L-band interferometric pairs with very long time baselines to monitor the radial growth of boreal forest main branches. C-band Stacking-InSAR was applied for the first time to estimate vertical growth rates in young coniferous stands, revealing a canopy height increase of up to 3.5 cm/yr during periods of 54% higher precipitation. The proposed framework, leveraging multi-frequency SAR datasets, enables comprehensive and near-real-time monitoring of reforestation processes. Results on biomass and height dynamics refine carbon sequestration estimates, supporting climate modeling and sustainable forest management.

This paper proposes a method for comprehensive assessing of the state and dynamics of young forest growth through joint analysis of multispectral optical images and satellite radar data. Previous research showed that separate analysis of vegetation (using the NDVI index) and radar (using the RVI index) data produced significantly different forecasts of reforestation levels after a fire. Therefore, we propose evaluating the temporal dynamics of reforestation through the NDVI–RVI plane, comparing against control areas of coniferous and mixed forests and also a treeless area. We demonstrate that these areas create a movable triangular zone, where changes in vegetation indices indicate an increase in forest projective cover, aboveground biomass growth, and replacement/restoration of species. To describe such dynamics, we introduce two quantitative indices: the degree of reforestation index (DRI) and the ratio of species index (RSI). To process the data and scale the results, we used the modern functionality of the Google Earth Engine (GEE) cloud platform. We obtained NDVI values from Landsat-5 and -8 data during the 2007–2020 winter (snow) period, minimizing the influence of soil and grass on assessing deciduous and evergreen coniferous plantations’ crown dynamics. Additionally, we obtained the radar vegetation index RVI from synthetic aperture satellite radar (SAR) data of ALOS‑1 PALSAR‑1 (2006–2010) and ALOS‑2 PALSAR‑2 (2016–2020) with the help of the GEE cloud platform to study forest vegetation biomass changes.

Accurate estimation of young forest height is essential for assessing the carbon sequestration potential of vast Siberian boreal forests recovering from wildfires. Satellite radar interferometry, particularly PolInSAR, is a promising tool for this task. However, its application in winter conditions and over sparse young forests remains underexplored. This study proposes a novel method for estimating the height of sparse young pine (Pinus sylvestris) stands using fully polarimetric bistatic TerraSAR-X/TanDEM-X data acquired in winter. The method is based on an analysis of the multimodal distribution of the unwrapped interferometric phase of the surface scattering component, which was isolated via PolInSAR decomposition. We hypothesize that the phase centers correspond to the snow-covered ground (located between tree groups) and the rough surface formed by the upper layer of branches and needles (of the tree groups). The results demonstrate that the difference between the dominant modes of the surface scattering phase distribution correlates with the height of young trees. However, the measurable height difference is limited by the interferometric height of ambiguity. Furthermore, a temporal analysis of the phase and meteorological data revealed a strong correlation between sudden phase shifts and daytime temperature rises around 0 degrees C. This is interpreted as the formation of a layered snowpack structure with a dense ice crust. This study confirms the potential of X-band PolInSAR for monitoring the structure of young Siberian forests in winter but also highlights a significant limitation: the critical impact of snowpack metamorphism, particularly melt-freeze cycles, on the interferometric phase. The proposed method is only applicable to certain forest regeneration stages where tree height does not exceed the ambiguity limit and snow conditions are stable.

Using Sentinel-1 satellite radio interferometry data, the geodynamics in the area of the epicenter of the destructive Mw = 6.8 earthquake that occurred in Morocco on September 8, 2023, were studied using the Stacking-InSAR method applied to 801 interferograms. Over the period from January 2019 to September 2023, local surface subsidence with an average speed of 1.5 cm/year was discovered, and the maximum speed was identified in 2023 and amounted to 24 cm/year, for areas with a developed melioration system located above aquifers. Based on an integrated analysis of changes in the water equivalent thickness, measured from satellite gravimetric data for 2000–2023, and the amount of precipitation, it was found that the surface subsidence was due to a huge irrigation draft. Assuming the similarity of shapes of isoseists of earthquakes with close epicenters, a comparison of the isoseists of earthquakes that occurred in 2014 and 2023 was carried out, which made it possible to identify the expansion of the contours of the isoseists towards the descending surface areas for the earthquake from 2023. This process, along with the tectonic movements of the Eurasian and Nubian plates, is believed to increase the stress-strain state between two aquifers, what caused the Mw = 6.8 earthquake in Morocco on September 8, 2023.

Nowadays, global remote sensing studies of tropical forest parameters are relevant for assessing carbon sequestration, whereas boreal forests receive little attention. This is due to the current idea that forests with greater aboveground biomass absorb more carbon. However, new research indicates that rapidly growing young forests take up more carbon than mature ones. Therefore, it is necessary to develop universal methods of remote reforestation/afforestation monitoring. The existing reforestation methods rely on the separate analysis of multispectral optical images and radar data. Here, we propose a method for analyzing the joint dynamics of NDVI (or the Normalized Burn Ratio, NBR) and the radar vegetation index (RVI) on a 2D plot for a test reforestation site. NDVI and NBR time series were derived from Landsat-5,8 data, and the RVI was derived from ALOS-1,2 and PALSAR-1,2 for 2007–2020 using the resources of Google Earth Engine. The quantitative parameters to evaluate the degree of reforestation and changes in the species composition of young trees have been suggested. The suggested method enables a more thorough evaluation of reforestation by measuring the coupled dynamics of the projective cover of young trees and aboveground biomass.

The brief communication demonstrates the potential for quantitative assessment of forest canopy height dynamics in mature and young pine forests on a plain using the method of weighted summing of time series of unwrapped interferometric phases. The latter were obtained using a modern approach based on cloud computations. By comparing the rates of canopy height growth for the years 2017, 2018, and 2019, it has been confirmed that the growth rate is influenced by the amount of precipitation in May-July of the respective year.

The geodynamics at the epicenter of the destructive Mw = 6.8 earthquake that occurred in Morocco on September 8, 2023, was studied by the Stacking-InSAR method applied to 801 interferograms based on the Sentinel-1 synthetic aperure radar (SAR) data. Over the period from January 2019 to September 2023, local subsidence of the surface with an average velocity of 1.5 cm/yr was discovered. The maximum velocity obtained in 2023 reached 24 cm/yr in the areas with a developed melioration system located above aquifers. Based on the integrated analysis of variations in the water equivalent thickness measured from the 2000–2023 satellite gravimetric data and the amount of precipitation, the surface subsidence was found to be due to a significant withdrawal of water from aquifers. Assuming similar shapes of isoseists of earthquakes with close epicenters, the isoseists of the earthquakes that occurred in 2014 and 2023 were compared. The data obtained made it possible to identify the expansion of isoseist contours toward the descending surface areas of the 2023 earthquake. This process, along with the tectonic movements of the Eurasian and Nubian plates, is believed to have increased the stress–strain state between two aquifers and finally caused the Mw = 6.8 earthquake in Morocco on September 8, 2023.

Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term heaving mounds that turn into craters as a result are relevant. A workflow for detecting and assessing anomalous dynamics of heaving mounds in the Arctic regions is proposed. Areas with anomalous increase of ALOS-2 PALSAR-2 synthetic aperture radar (SAR) backscattering intensity are detected in the first stage. These increases take place due to sudden changes in local terrain slopes when the scattering surface (mound slope) turns toward the radar. Radar backscattering intensity also rises due to depolarization at newly formed frost cracks. Validation of the detected anomaly is carried out at the second stage through a comparison of multi-temporal digital elevation models obtained from bistatic radar interferometry TerraSAR-X/TanDEM-X data. At the final stage, the deformations are assessed within the detected areas using differential SAR interferometry (DInSAR) technique by ALOS-2 PALSAR-2 data. The magnitude of the heaving along the line of sight (LOS) was 22–24 cm in the period from January 2019 to January 2020. In general, effectiveness for detecting the perennial heaving mounds and the rate assessment of their increase were demonstrated in the suggested workflow.

Displacement velocity fields of the block-fault structure are constructed and the main geodynamic processes in the area of the East Anatolian fault are revealed based on the results of processing of 437 radar interferograms obtained from the Sentinel-1 radar in the period from the beginning of 2018 to disastrous seismic activity in February 2023 in Turkey by Stacking InSAR method. Anomalous block displacements along this fault have been identified, which are timed to the earthquake of January 24, 2020 (M = 6.7). Zones of stress-strain state of the main blocks in the period preceding the earthquake have been established using cluster analysis of time series of velocity fields. It is shown that the epicenters of February 2023 earthquakes are located in these zones. It is concluded that it is necessary to use such a technique to assess the stress-strain state in order to predict seismic activity.

Assessment of the processes of afforestation and restoration of forests after fires is relevant for a significant territory of Russia, including the problem of carbon neutrality. The paper considers the possibilities of radar monitoring of the afforestation process based on the Cloud-Pottier decomposition of L-band data time series with dual polarization. Preliminary segmentation is based on the minimum values of the radar backscatter over the entire observation period. This makes it possible to distinguish treeless areas and sparsely wooded areas into a separate class, both existing before the start of the study and formed later. Next, Cloud-Pottier polarimetric decomposition is performed to obtain the parameters H (entropy) and α (scattering angle) and form time series from them. Studies have shown the principal possibility of afforestation dynamics monitoring on the H-α plane, where the points of the test areas form characteristic time tracks. A mature dense forest, whose characteristics are considered permanent, was used as a reference for estimating the changes rate on the H-α plane.

An estimate of the processes of afforestation and forest restoration after fires is relevant for a significant territory of Russia, including due to the issue of carbon neutrality. In this paper we examine the possibilities of radar monitoring of the afforestation process based on the Cloude–Pottier decomposition of dual-polarization L-band data time series. Preliminary segmentation is performed based on the minimal radar backscatter values for the entire observation period. This makes it possible to separate into a separate class treeless areas and open forests, both those that existed before the beginning of the study and those that formed later. Next, polarimetric decomposition is performed by the Cloude–Pottier method to obtain parameters H (entropy) and α (scattering angle) and form time series from them. Research showed the fundamental possibility of monitoring forest dynamics on the H-α plane, where the points of the test plots form characteristic time tracks. A mature dense forest, the characteristics of which are assumed to be constant, was used as a reference for estimating the rate of change on the H-α plane.

Т. Н. Чимитдоржиев 1 *, А. В. Дмитриев 1 , Ж. Д. Номшиев 1 Радиолокационный мониторинг залесения с использованием декомпозиции Клауда-Потье для двойной поляризации1 Институт физического материаловедения СО РАН, г.Улан

В докладе представлены результаты анализа нерегулярных временных рядов радарного вегетационного индекса dpRVI для соснового леса и соснового подроста в окрестностях г. Улан-Удэ. Индексы dpRVI рассчитаны с помощью облачных технологий GEE по данным с двойной поляризацией ALOS PALSAR-1/2 за 2007-2019 гг. Для тестовых участков произведена оценка сезонных вариаций dpRVI. Установлен значимый долговременный тренд роста dpRVI для соснового молодняка y=0,0027t 0,0289 (r=0,50), где t - возраст лесного подроста, отсчитываемый от момента лесного пожара 2003 г., уровень значимости 0,05 и 0,01. The report presents the results of the analysis of irregular time series of the radar vegetation index dpRVI for pine forests and pine undergrowth in the vicinity of Ulan-Ude. The dpRVI indices were calculated using GEE cloud technologies based on data with double polarization ALOS PALSAR-1/2 for 2007-2019. Seasonal variations of the DPVI were estimated for the test plots. A significant long-term growth trend of DPVI for pine young y=0.0027t 0.0289 (r=0.50) has been established, where t is the age of forest undergrowth, counted from the time of the 2003 forest fire, the significance levels are 0.05 and 0.01.

Displacement velocity fields of the block-fault structure are constructed and the main geodynamic processes in the area of the East Anatolian Fault (EAF) are revealed based on the results of the processing of 437 radar interferograms obtained from the Sentinel-1 radar in the period from early 2018 until the beginning of the destructive seismic activity in February 6, 2023 in Turkey by the Stacking-InSAR method. Anomalous block displacements along this fault have been identified; they are timed to the earthquake of January 24, 2020 (Mw = 6.7). Zones of stress–strain state of the main blocks in the period preceding the earthquake have been determined using the cluster analysis of time series of velocity fields. It is shown that the epicenters of the February 2023 earthquakes are located in these zones. A conclusion is made about the necessity of using such a technique to estimate the stress–strain state in order to predict seismic activity.

The method of forest restoration monitoring after wildfires is proposed. It is based on the joint use of ALOS-2 PALSAR-2 polarimetric radar data to assess the temporal dynamics of physical scattering mechanisms and the analysis of long time series of normalized difference vegetation index (NDVI) calculated with help of Google Earth Engine (GEE) cloud platform. This made it possible to quantitatively analyze the process of post fire reforestation in terms of changes in the projective coverage and geometric dimensions of forest undergrowth. Such a new approach will make it possible further to take into account more accurately the role of boreal forests as one of the largest carbon stocks.

To identify heaving mounds with anomalous topography and microrelief dynamics, including slopes and fissure structures, a technique for analyzing multi-temporal radar images in the L-wavelength range with cross- and matched horizontal polarizations is proposed. Using the example of the Yamal heaving mound, which turned into a crater as a result of gas outburst in the summer of 2020, the forward slope angles were calculated: 22°‒24° in 2017‒2018 and up to 26°‒28° in January 2020. It was established using the Fisher–Snedecor criterion that the zone of significant changes in cross-polarization is four times larger than a similar zone of changes at consistent horizontal polarization. It is concluded that this effect of increasing the area of depolarization of the radar signal is associated with an increase in microrelief inhomogeneities, i.e., with the formation of crack structures along the heaving mound and around it.

This communication is devoted to the methodology of remote complex analysis of forest restoration after strong wildfires. It is proposed to quantify the projective leaf/needles area index by multispectral optical images. The increase in dimensions of trunks and branches commensurate with a radar wavelength of 24 cm is estimated using radar polarimetric data. It is shown that the growth’s potential of aboveground biomass in different spots of test site ranges from 35 to 70% in the case under consideration. Such a new approach will make it possible to further consider more accurately the role of boreal forests as one of the largest carbon stocks.