Gas Emission Craters (GEC) represent a new phenomenon in permafrost regions discovered in the north of West Siberia. In this study we use very-high-resolution Worldview satellite stereopairs and Resurs-P images to reveal and measure the geomorphic features that preceded and followed GEC formation on the Yamal and Gydan peninsulas. Analysis of DEMs allowed us to: (1) distinguish different terrain positions of the GEC, at the foot of a gentle slope (Yamal), and on an upper edge of a terrace slope; (2) notice that the formation of both Yamal and Gydan GECs were preceded by mound development; (3) measure a funnel-shaped upper part and a cylindrical lower part for each crater; (4) and measure the expansion and plan form modification of GECs. Although the general characteristics of both craters are similar, there are differences when comparing both key sites in detail. The height of the mound and diameter of the resulting GEC in Yamal exceeds that in Gydan; GEC-1 was surrounded by a well-developed parapet, while AntGEC did not show any considerable accumulative body. Thus, using very-high-resolution remote sensing data allowed us to discriminate geomorphic features and relief positions characteristic for GEC formation. GECs are a potential threat to commercial facilities in permafrost and indigenous settlements, especially because at present there is no statistically significant number of study objects to identify the local environmental conditions in which the formation of new GEC is possible.

Detailed analysis of five gas emission craters (GEC) found in the north of West Siberia is presented. Remote sensing data used in the study is verified by field surveys. Previous studies show that all of the GECs were preceded by mounds 2 to 6 m high and 20 to 55 m in diameter. GECs initially were 20–25 m in diameter, which increased in the first years of their existence. GECs are found in various environmental (shrublands or moss-grass tundra) and geomorphic (river valley, terrace, slopes) conditions. The objective of the paper is to identify common and differing geomorphologic and environmental characteristics of all the five GEC, and their mound-predecessors. The study is based on a compilation of DSMs before and after the GEC formation using very high-resolution satellite imagery stereo pairs compared to ArcticDEM project data. Diversity of terrain and environmental settings along with rather a narrow range of GEC and mound-predecessor morphometric parameters allows concluding that the mechanism of GEC formation is most likely similar for all the GEC and is controlled rather by internal geologic and cryolithologic structure than by any surface properties.

Работа посвящена анализу рельефа, предшествовавшего возникновению Антипаютинской воронки га-зового выброса на полуострове Гыдан, а также его изменению вследствие образования воронки. Для этого соз-даны разновременные цифровые модели рельефа, построенные на основе обработки стереопар космических снимков сверхвысокого пространственного разрешения как до, так и после образования воронки. С относи-тельной точностью 0,35-0,55 м получены морфометрические характеристики форм рельефа рассматривае-мого участка. Установлено, что воронка приурочена к бровке склона эрозионной формы -балки, врезанной в террасовидную поверхность высотой 30-50 м. Образованию Антипаютинской воронки предшествовало су-ществование бугра высотой около 2 м, диаметром основания около 20 м. Размеры бугра, а также началь-ный диаметр цилиндрической части воронки меньше, чем у изученной ранее подобной формы на Централь-ном Ямале. Для Антипаютинской воронки характерно отсутствие выраженных в рельефе (высотой более 1 м) аккумулятивных тел из выброшенного материала, зафиксированных на цифровой модели рельефа, что также отличает ее от Ямальской воронки. Полученные данные показали, что поиск бугров-предшественников воро-нок газового выброса не может основываться на размерах бугров из-за их значительных вариаций.Ключевые слова: воронка газового выброса, полуостров Гыдан, криогенный рельеф, дистанционное зондирование, стереосъемка, цифровая модель рельефа Одобрена к печати:

The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat in field conditions. In this research, we aimed to collect information on the hyperspectral data on wheat plants with different lesion degrees of STB&SNB and to create and train a neural network for the detection of lesions on leaves and ears caused by STB&SNB infection at the late stage of disease development. Spring wheat was artificially infected twice with Septoria pathogens in the stem elongation stage and in the heading stage. Hyperspectral reflections and brightness measurements were collected in the field on wheat leaves and ears on the 37th day after STB and the 30th day after SNB pathogen inoculation using an Ocean Insight “Flame” VIS-NIR hyperspectrometer. Obtained non-imaging data were pre-treated, and the perceptron model neural network (PNN) was created and trained based on a pairwise comparison of datasets for healthy and diseased plants. Both statistical and neural network approaches showed the high quality of the differentiation between healthy and damaged wheat plants by the hyperspectral signature. A comparison of the results of visual recognition and automatic STB&SNB estimation showed that the neural network was equally effective in the quality of the disease definition. The PNN, based on a neuron model of hyperspectral signature with a spectral step of 6 nm and 2000–4000 value datasets, showed a high quality of detection of the STB&SNB severity. There were 0.99 accuracy, 0.94 precision, 0.89 recall and 0.91 F-score metrics of the PNN model after 10,000 learning epochs. The estimation accuracy of diseased/healthy leaves ranged from 88.1 to 97.7% for different datasets. The accuracy of detection of a light and medium degree of disease was lower (38–66%). This method of non-imaging hyperspectral signature classification could be useful for the identification of the STB and SNB lesion degree identification in field conditions for pre-harvesting crop estimation.

In the last two decades in most regions of the country there has been a restoration of abandoned lands during the crisis of the 1990s. and higher yields due to a significant increase in state support for the agricultural sector and structural changes such as the emergence of agricultural holdings. As a result of modern reforms, Russia has become a leading player in the foreign food market. However, these positive developments take place against the background of a process of deepening regional differences in the productivity of the agricultural sector. The aim of the study is a comparative analysis of the dynamics of the withdrawal from circulation of sown areas in the Kirov province during the crisis of the 1990s. and post-crisis period 2000–2020. The analysis of spatio-temporal dynamics of the withdrawal from circulation and restoration of croplands was carried out by remote sensing methods for three agro-climatic zones and the main types of soils in the Kirov province. The main resource of the region is soddy-podzolic soils, which accounted for more than 77 % of the cropland in 1990 and about 70 % in 2020. The reduction in the area of cropland with this type of soil reached 90 % in the northern and 80 % in the central and southern zones, regardless of their differences in heat supply. Crisis period 1990–2000 characterized by the highest rate of withdrawal of agricultural land from circulation. In the post-crisis period, the reduction in sown areas only continued. Against this background, there is an extremely slight recovery of cropland (about 5 % of the 1990 level). There are natural differences in the restoration of sown areas in agro-climatic zones and by soil types, but they are poorly reflected in the overall negative dynamics of cropland, due to the low agro-climatic potential of the entire Kirov province.