Mapping of West Siberian taiga wetland complexes using Landsat imagery: implications for methane emissions

High latitude wetlands are important for understanding climate change risks because these environments sink carbon and emit methane. Fine scale heterogeneity of wetland landscapes pose challenges for producing the greenhouse gas flux inventories based on point observations. To reduce uncertainties at the regional scale, we mapped wetlands and water bodies in the taiga zone of West Siberia on a scene-by-scene basis using a supervised classification of Landsat imagery. The training dataset was based on high-resolution images and field data that were collected at 28 test areas. Classification scheme was aimed at methane inventory applications and included 7 wetland ecosystem types composing 9 wetland complexes in different proportions. Accuracy assessment based on 1082 validation polygons of 10 × 10 pixels indicated an overall map accuracy of 79 %. The total area of the wetlands and water bodies was estimated to be 52.4 Mha or 4-12 % of the global wetland area. Ridge-hollow complexes prevail in WS's taiga, occupying 33 % of the domain, followed by forested bogs or "ryams" (23 %), ridge-hollow-lake complexes (16 %), open fens (8 %), palsa complexes (7 %), open bogs (5 %), patterned fens (4 %), and swamps (4 %). Various oligotrophic environments are dominant among the wetland ecosystems, while fens cover only 14 % of the area. Because of the significant update in the wetland ecosystem coverage, a considerable revaluation of the total CH4 emissions from the entire region is expected. A new Landsat-based map of WS's taiga wetlands provides a benchmark for validation of coarse-resolution global land cover products and wetland datasets in high latitudes

Net ecosystem exchange and energy fluxes measured with the eddy covariance technique in a western Siberian bog

Very few studies of ecosystem-atmosphere exchange involving eddy covariance data have been conducted in Siberia, with none in the western Siberian middle taiga. This work provides the first estimates of carbon dioxide (CO2) and energy budgets in a typical bog of the western Siberian middle taiga based on May-August measurements in 2015. The footprint of measured fluxes consisted of a homogeneous mixture of tree-covered ridges and hollows with the vegetation represented by typical sedges and shrubs. Generally, the surface exchange rates resembled those of pinecovered bogs elsewhere. The surface energy balance closure approached 100 %. Net CO2 uptake was comparatively high, summing up to CO2 gCm(-2) for the four measurement months, while the Bowen ratio was seasonally stable at 28 %. The ecosystem turned into a net CO2 source during several front passage events in June and July. The periods of heavy rain helped keep the water table at a sustainably high level, preventing a usual drawdown in summer. However, because of the cloudy and rainy weather, the observed fluxes might rather represent the special weather conditions of 2015 than their typical magnitudes.Peer reviewe

Automation of complex text CAPTCHA recognition using conditional generative adversarial networks

With the rapid development of Internet technologies, the problems of network security continue to worsen. So, one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (fully automated public Turing test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessary to perform a simple task, such as entering a word displayed in an image, solving a basic arithmetic equation, etc. However, the most widely used type of CAPTCHA is still the text type. In the recent years, the development of computer vision and, in particular, neural networks has contributed to a decrease in the resistance to hacking of text CAPTCHA. However, the security and resistance to recognition of complex CAPTCHA containing a lot of noise and distortion is still insufficiently studied. This study examines CAPTCHA, the distinctive feature of which is the use of a large number of different distortions, and each individual image uses its own different set of distortions, that is why even the human eye cannot always recognize what is depicted in the photo. The purpose of this work is to assess the security of sites using the CAPTCHA text type by testing their resistance to an automated solution. This testing will be used for the subsequent development of recommendations for improving the effectiveness of protection mechanisms. The result of the work is an implemented synthetic generator and discriminator of the CGAN architecture, as well as a decoder program, which is a trained convolutional neural network that solves this type of CAPTCHA. The recognition accuracy of the model constructed in the article was 63 % on an initially very limited data set, which shows the information security risks that sites using a similar type of CAPTCHA can carry

Relationship of methane consumption with the respiration of soil and grass-moss layers in forest ecosystems of the southern taiga in Western Siberia

The consumption of methane by some soils in the southern taiga of Western Siberia was studied by the static chamber method in the summer of 2013. The median of the specific CH4 flux through the soil was −0.05 mg C/(m2 h) for the entire set of measurements (the negative flux indicates the consumption of methane by the soil). A statistically significant (R2 = 0.81) linear relationship has been found between the specific CH4 flux to the soil and the total respiration of the soil and the grass-moss layers in the studied forest ecosystems. The quantitative theoretical explanation of this relationship is based on the plant-associated and free methanotrophy