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The occurrence patterns of forest insect pests and diseases have been altered by global events such as climate change. Recent developments in improved monitoring methods and tools for data analyses provide new opportunities to understand the causes and consequences of such changes. Using a variety of management tools, forest pest management programs can mitigate the influence of global changes on forest health. The goal of this Special Issue is to improve our understanding of the root causes of changes that have induced global changes. Fifteen papers are included in this Special Issue, covering several issues in forest pest management. One paper reviews the causes of Korean oak wilt, and another paper discusses fourteen invasive tree pests in Russia. The remaining thirteen papers cover issues related to the monitoring and management of forest pests. These studies provide a better understanding of the causes of change in the patterns of forest pests under the influence of global changes. These reviews also contribute to the development of forest-pest-management strategies to mitigate such impacts on forests due to global changes. [ABSTRACT FROM AUTHOR]

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen's slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed. [ABSTRACT FROM AUTHOR]

Atmospheric correction of satellite remote sensing data is a prerequisite for a large variety of applications, including time series analysis and quantitative assessment of the Earth's vegetation cover. It was earlier reported that an atmospherically corrected KMSS-M (Meteor-M #2) dataset was produced for Russia and neighboring countries. The methodology adopted for atmospheric correction was based on localized histogram matching of target KMSS-M and MODIS reference gap-free and date-matching imagery. In this paper, we further advanced the methodology and quantitatively assessed Level-2 surface reflectance analysis-ready datasets, operatively produced for KMSS-2 instruments over continental scales. Quantitative assessment was based on accuracy, precision, and uncertainty (APU) metrics produced for red and near-infrared bands of the KMSS-2 instrument based on a reference derived from a MODIS MOD09 reconstructed surface reflectance. We compared error distributions at 5%, 20%, and 50% levels of cloudiness and indicated that the cloudiness factor has little impact on the robustness of the atmospheric correction regardless of the band. Finally, the spatial and temporal gradients of accuracy metrics were investigated over northern Eurasia and across different seasons. It was found that for the vast majority of observations, accuracy falls within the −0.010–0.035 range, while precision and uncertainty were below 0.06 for any band. With the successful launch of the most recent Meteor-M #2.3 with a new KMSS-2 instrument onboard, the efficiency and interoperability of the constellation are expected to increase. [ABSTRACT FROM AUTHOR]

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenges by proposing an effective pipeline for building segmentation that utilizes freely available Sentinel-2 data with 10 m spatial resolution. The approach we use combines a super-resolution (SR) component with a semantic segmentation component. As a result, we simultaneously consider and analyze SR and building segmentation tasks to improve the quality of the infrastructure analysis through medium-resolution satellite data. Additionally, we collected and made available a unique dataset for the Russian Federation covering area of 1091.2 square kilometers. The dataset provides Sentinel-2 imagery adjusted to the spatial resolution of 2.5 m and is accompanied by semantic segmentation masks. The building footprints were created using OpenStreetMap data that was manually checked and verified. Several experiments were conducted for the SR task, using advanced image SR methods such as the diffusion-based SR3 model, RCAN, SRGAN, and MCGR. The MCGR network produced the best result, with a PSNR of 27.54 and SSIM of 0.79. The obtained SR images were then used to tackle the building segmentation task with different neural network models, including DeepLabV3 with different encoders, SWIN, and Twins transformers. The SWIN transformer achieved the best results, with an F1-score of 79.60. [ABSTRACT FROM AUTHOR]

Nowadays, the leading role of data from sensors to monitor crop irrigation practices is indisputable. The combination of ground and space monitoring data and agrohydrological modeling made it possible to evaluate the effectiveness of crop irrigation. This paper presents some additions to recently published results of field study at the territory of the Privolzhskaya irrigation system located on the left bank of the Volga in the Russian Federation, during the growing season of 2012. Data were obtained for 19 crops of irrigated alfalfa during the second year of their growing period. Irrigation water applications to these crops was carried out by the center pivot sprinklers. The actual crop evapotranspiration and its components being derived with the SEBAL model from MODIS satellite images data. As a result, a time series of daily values of evapotranspiration and transpiration were obtained for the area occupied by each of these crops. To assess the effectiveness of irrigation of alfalfa crops, six indicators were used based on the use of data on yield, irrigation depth, actual evapotranspiration, transpiration and basal evaporation deficit. The series of indicators estimating irrigation effectiveness were analyzed and ranked. The obtained rank values were used to analyze the similarity and non-similarity of indicators of irrigation effectiveness of alfalfa crops. As a result of this analysis, the opportunity to assess irrigation effectiveness with the help of data from ground and space-based sensors was proved. [ABSTRACT FROM AUTHOR]

This paper discusses the potential of using infrared remote sensing data to determine geocryological conditions in the northern part of the Yenisei Ridge in Russia. Landsat-8 thermal infrared images and land surface data were used for our analysis. The obtained thermal characteristics were compared with vegetation indices calculated for the period of active vegetation growth along several surface transects. Surface observations included geobotanical descriptions, phytomass estimations, measurements of thickness of the seasonally thawed layer, and visual identification of different effects of permafrost on the components of the taiga landscape. The obtained surface temperatures differed depending of forest type due to their bio-productivity characteristics on sporadic permafrost as the most important factor of forest growth conditions within the southern part of the cryolithozone. The differences in the thermal characteristics are due to varying degree of permafrost influence on boreal vegetation growth. The surface temperature was used as indicator to quantify the relationship between the latent heat and the sensible heat fluxes for the corresponding landscape. The areas with higher surface temperatures were usually characterized by higher sensible heat flux due to lower evapotranspiration of the plant canopy. The forest types with the highest evapotranspiration had usually the lowest surface temperatures. Such forest types are also the most fire-resistant systems, and have the highest water-discharge potential. This is characteristic of the forests under the lowest impact of permafrost (thawed soils or the presence of the permafrost layer at lower depths). Such types of forests have higher ecosystem service potential (e.g., fire-resistance and stock formation). [ABSTRACT FROM AUTHOR]

The retreat rates of Arctic coasts have increased in recent decades at many sites, and an essential part of coasts considered accumulative before have turned erosional due to global climate changes and construction in the coastal zone. In this paper, we study a 7 km long coastal section of the western Gydan Peninsula in a new construction area. Based on the interpretation of multi-temporal satellite imagery, we assessed coastal dynamics in distinct periods from 1972 to 2020. We analyzed the geological structure of the coast as well as changes in hydrometeorological parameters with time, and considering the human impact, we proposed the main drivers of spatial and temporal variations of coastal dynamics. The studied low-lying sandy accumulative marine terrace was more or less stable in the period before construction (1972–2014). However, with the area's development, the coast dynamics changed drastically: in 2014–2017, three-quarters of the studied area experienced retreat, and the average retreat rate amounted to 5.8 m/yr, up to 28.5 m/yr near the construction sites. We relate this coastal erosion intensification to human impact combined with the growth of hydrometeorological forcing. Although coastal erosion slowed down after 2017, the retreat trend remained. In the coming years, with Arctic climate warming, erosion of the studied coast will continue. [ABSTRACT FROM AUTHOR]

Under the influence of various forces, the conflict between Russia and Ukraine is violent and changeable. The obtaining of battlefield data by conventional means is difficult but necessary in order to ensure security, reliability, and comprehensiveness. The use of remote sensing technology can make up for the deficiencies of conventional methods. By using night-time light data, the total number of night-time lights in the built-up areas of Ukrainian cities within 36 days of the outbreak of the Russian–Ukrainian conflict is compiled in this paper. Furthermore, the dynamic changes in night-time light at the national, regional, and urban scales are analyzed by using the night-time light ratio index and the dynamic degree model combined with the time-series night-time light data. The results show that (1) after the outbreak of the war, more than 60% of the night-time lights in Ukrainian cities were lost. In terms of the night-time light recovery speed, the night-time lights in the pro-Russian areas recovered significantly faster, followed by Russian-controlled areas, and the recovery speed in areas of conflict was the lowest. (2) Decision-making by belligerents affects non-combatant activities and thus corresponds to light at night. The loss of night-time light will be reduced if military operations are reduced and mitigated if humanitarian operations are increased. (3) The changes in night-time light reflect the changes in the conflict situation well. When the conflict between Russia and Ukraine intensifies, the overall change of night-time light shows a downward trend. In this context, night-time light data can be used as an effective source to deduce and predict battlefield situations. [ABSTRACT FROM AUTHOR]

The powerful explosive eruptions with large volumes of volcanic ash pose a great danger to the population and jet aircraft. Global experience in monitoring volcanoes and observing changes in the parameters of their thermal anomalies is successfully used to analyze the activity of volcanoes and predict their danger to the population. The Kamchatka Peninsula in Russia, with its 30 active volcanoes, is one of the most volcanically active regions in the world. The article considers the thermal activity in 2015–2022 of the Klyuchevskoy, Sheveluch, Bezymianny, and Karymsky volcanoes, whose rock composition varies from basaltic andesite to dacite. This study is based on the analysis of the Value of Temperature Difference between the thermal Anomaly and the Background (the VTDAB), obtained by manual processing of the AVHRR, MODIS, VIIRS, and MSU-MR satellite data in the VolSatView information system. Based on the VTDAB data, the following "background activity of the volcanoes" was determined: 20 °C for Sheveluch and Bezymianny, 12 °C for Klyuchevskoy, and 13–15 °C for Karymsky. This study showed that the highest temperature of the thermal anomaly corresponds to the juvenile magmatic material that arrived on the earth's surface. The highest VTDAB is different for each volcano; it depends on the composition of the eruptive products produced by the volcano and on the character of an eruption. A joint analysis of the dynamics of the eruption of each volcano and changes in its thermal activity made it possible to determine the range of the VTDAB for different phases of a volcanic eruption. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes. [ABSTRACT FROM AUTHOR]

This paper considers spatiotemporal distribution and seasonal variability of aerosol optical depth (AOD) of the atmosphere at the 0.55 μm wavelength in the atmosphere over the Baikal region of Russia based on long-term data (2005–2019) from satellite observations (MODIS/AQUA). A comparison of satellite AOD values with the AERONET record at the Geophysical Observatory of Institute of Solar-Terrestrial Physics of Siberian Brunch of Russian Academy of Science was performed. The results show that interannual AOD variability is mainly due to forest fires. The highest atmospheric transparency was in 2010, 2013 and 2016, and the lowest was in 2008, 2012 and 2014. It is noted that AOD decreased with latitude with a gradient ΔAOD = 0.002 ÷ 0.001 per degree of latitude. The mean seasonal variations in AOD at the six satellite overpass points were characterized by spring (April) and summer (July) highs and low AOD values in autumn. From June to November, the drop in AOD monthly means was more than 60%. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality ( mAP = 0.75 , true positive rate TPR = 0.61 ) and a well-specified detection uncertainty (false alarm ratio FAR = 0.36 ). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics. [ABSTRACT FROM AUTHOR]

The investigation of hard-to-reach areas that are prone to landslides is challenging. The research of landslide hazards can be significantly advanced by using remote sensing data obtained from an unmanned aerial vehicle (UAV). Operational acquisition and high detail are the advantages of UAV data. The development of appropriate automated algorithms and software solutions is necessary for quick decision-making based on the received heterogeneous spatial data characterising various aspects of the environment. This article introduces the first phase of a long-term study about landslide detection and prediction that aims to develop an automatic algorithm for detecting potentially hazardous landslide areas, using data obtained by UAV surveys. As a part of the project, the selection of appropriate techniques was implemented and a landslide susceptibility (LS) map of the study site was developed. This paper presents the outcomes of the applied indirect heuristic approach of landslide susceptibility assessment using an analytical hierarchy process (AHP) in a GIS environment, based on UAV data. The results obtained have been tested on a real-world entity. [ABSTRACT FROM AUTHOR]

Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters. [ABSTRACT FROM AUTHOR]

The present study was carried out to describe the characteristic features of potentially toxic metals (PTMs) in the soil of industrial city, Kolchugino–Vladimir Region–Russia. The mass fractions in mg/kg of Cu, Pb, Zn, and Cd were measured by atomic absorption spectrometer (AAS). Multivariate statistical analysis and hierarchical cluster analysis (HCA), were performed. The obtained results of the potentially toxic elements were mapped using ArcGIS. A total of four pollution indices were calculated to identify the areas with significant pollution associations. The data analysis showed that locations 14 (urban area) and 16 (non-ferrous metallurgy plant) contribute significantly to pollution. Maximum likelihood method was used to classify the land-use and land-cover (LULC). The analysis shows that locations 14 and 16 are belonging to the industrial area on LULC. Great attention should be paid to the control and regulation of waste disposal into the environment, which in turn, has an adversely impact on human health. By using the data, it could help to identify areas where waste control measures need to be implemented, such as increasing recycling or introducing more waste control legislation. It could also help to identify areas where waste control efforts have been successful. Data can be used by government, policymakers, and stakeholders for future planning and R&D activities. [ABSTRACT FROM AUTHOR]

In this study, we have analyzed tree stand disturbance by hthe Siberian Silk Moth (Dendrolimus sibiricus Tschetverikov (Lepidoptera: Lasiocampidae)) in Central Siberia (Krasnoyarsk region, Russia) in 2015–2020. We considered two plots that experienced silk moth outbreaks in 2015–2018 and 2018–2020 and used satellite data (Terra/MODIS, Landsat/ETM/OLI), field forest inventory data, a meteorological data set, and a vegetation cover vector layer. Silk moth-disturbed areas were classified using NDVI, which was calculated for each 15-day period during the growing season (April–September). We obtained formalized descriptions of the temporal dynamics of the disturbed area. Next, we classified the degree of disturbance of the forest stand after the impact of the silk moth by the threshold method according to the ranges of NDVI anomalies. Based on the generalized data from the forest inventory, we performed a correlation analysis of the relationship between the main characteristics of forests and the classes of disturbance. Finally, using a series of regression equations, we described a procedure for predicting the degree of impact on the stand during the time of silk moth outbreaks in the dark-needle coniferous stands of Central Siberia. [ABSTRACT FROM AUTHOR]

The long-term spectral characteristics of the bare soil surface (BSS) in the BLUE, GREEN, RED, NIR, SWIR1, and SWIR2 Landsat spectral bands are poorly studied. Most often, the RED and NIR spectral bands are used to analyze the spatial heterogeneity of the soil cover; in our opinion, it is outmoded and seems unreasonable. The study of multi-temporal spectral characteristics requires the processing of big remote sensing data based on artificial intelligence in the form of convolutional neural networks. The analysis of BSS belongs to the direct methods of analysis of the soil cover. Soil degradation can be detected by ground methods (field reconnaissance surveys), modeling, or digital methods, and based on the remote sensing data (RSD) analysis. Ground methods are laborious, and modeling gives indirect results. RSD analysis can be based on the principles of calculation of vegetation indices (VIs) and on the BSS identification. The calculation of VIs also provides indirect information about the soil cover through the state of vegetation. BSS analysis is a direct method for analyzing soil cover heterogeneity. In this work, the informativeness of the long-term (37 years) average spectral characteristics of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 bands of the Landsat 4–8 satellites for detecting areas of soil degradation with recognition of the BSS using deep machine learning methods was estimated. The objects of study are the spectral characteristics of kastanozems (dark chestnut soils) in the south of Russia in the territory of the Morozovsky district of the Rostov region. Soil degradation in this area is mainly caused by erosion. The following methods were used: retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis. Six new maps of the average long-term spectral brightness of the BSS have been obtained. The information content of the BSS for six spectral bands has been verified on the basis of ground surveys. The informativeness was determined by the percentage of coincidences of degradation facts identified during the RSD analysis, and those determined in the field. It has been established that the spectral bands line up in the following descending order of information content: RED, NIR, GREEN, BLUE, SWIR1, SWIR2. The accuracy of degradation maps by band was determined as: RED—84.6%, NIR—82.9%, GREEN—78.0%, BLUE—78.0%, SWIR1—75.5%, SWIR2—62.2%. [ABSTRACT FROM AUTHOR]

Remote sensing tasks play a very important role in the domain of sensing and measuring, and can be very specific. Advances in computer vision techniques allow for the extraction of various information from remote sensing satellite imagery. This information is crucial in making quantitative and qualitative assessments for monitoring of forest clearing in protected areas for power lines, as well as for environmental analysis, in particular for making assessments of carbon footprint, which is a highly relevant task. Solving these problems requires precise segmentation of the forest mask. Although forest mask extraction from satellite data has been considered previously, no open-access applications are able to provide the high-detailed forest mask. Detailed forest masks are usually obtained using unmanned aerial vehicles (UAV) that set particular limitations such as cost and inapplicability for vast territories. In this study, we propose a novel neural network-based approach for high-detailed forest mask creation. We implement an object-based augmentation technique for a minimum amount of labeled high-detailed data. Using this augmented data we fine-tune the models, which are trained on a large forest dataset with less precise labeled masks. The provided algorithm is tested for multiple territories in Russia. The F 1 - s c o r e , for small details (such as individual trees) was improved to 0.929 compared to the baseline score of 0.856 . The developed model is available in an SAAS platform. The developed model allows a detailed and precise forest mask to be easily created, which then be used for solving various applied problems. [ABSTRACT FROM AUTHOR]

Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest fires and other wildfires by remote sensing. Using the example of catastrophic fires in July–August 2010 in the Moscow region (the center of European Russia), in the present study, we consider the results of peat-fire detection using Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots, peat maps, and analysis of land cover pre- and post-fire according to Landsat-5 TM data. A comparison of specific (for detecting fires) and non-specific vegetation indices showed the difference index ΔNDMI (pre- and post-fire normalized difference moisture Index) to be the most effective for detecting burns in peatlands according to Landsat-5 TM data. In combination with classification (both unsupervised and supervised), this index offered 95% accuracy (by ground verification) in identifying burnt areas in peatlands. At the same time, most peatland fires were not detected by Terra/Aqua MODIS data. A comparison of peatland and other wildfires showed the clearest differences between them in terms of duration and the maximum value of the fire radiation power index. The present results may help in identifying peat (underground) fires and their burnt areas, as well as accounting for carbon losses and greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data. [ABSTRACT FROM AUTHOR]

Thermodenudation on the Kara seacoast, the Yugorsky Peninsula, Russia, is studied by analyzing remote-sensing data. Landforms resulting from the thaw of tabular ground ice, referred to as thermocirques, are formed due to polycyclic retrogressive thaw slumps, during the last decade 2010–2020. We calculate the retreat rate of the thermocirque edge using various statistical approaches. We compared thermocirque outlines by the end of each time interval defined by the dates of available very-high-resolution imagery. Six thermocirques within two key sites on the Yugorsky peninsula are monitored. We correlate each of the thermocirque edge's retreat rates to various climatic parameters obtained at the Amderma weather station to understand the interrelation patterns better. As a result, we find a very low correlation between the retreat rate of each thermocirque and summer warmth, rainfall, and wave action. In general, the activity of thermodenudation decreases in time from the previous decade (2001–2010) to 2010–2020, and from 2010 towards 2020, although the summer warmth trend increases dramatically. A single thermocirque or series of thermocirques expand in response to environmental and geological factors in coastal retreat caused by thermodenudation. [ABSTRACT FROM AUTHOR]