Monitoring of natural complexes while developing and operating the fields of Timan-Pechora oil and gas province

Landscape monitoring is organized to monitor the state of natural complexes and their transformation. Monitoring of landscapes should ensure the identification of anthropogenic load, the dynamics of areas of anthropogenic impact, the degree of degradation of natural complexes. The Timan-Pechora oil and gas province is located on the territory of the Republic of Komi, the Nenets Autonomous Okrug and the adjacent water area of the Pechora Sea. The area of the province is 600 thousand km2. Currently, the development and extraction of mineral resources, mainly oil and gas, is actively underway in the territory under consideration. This is a complex process that requires the collaboration of many specialists, including ecologists. In the Bol'shezemel'skaya Tundra, the dominant part of tundra landscapes are extremely sensitive to anthropogenic influence and the unorganized use of the available space will soon lead to the complete loss of their own functions, and their restoration will take a huge amount of time. In this paper, a basic field study method was chosen as the main method to study the landscape. Thanks to route observations, a complex landscape characteristic of the territory was compiled.

The development of digital models of the soil cover in the western part of Bol’shezemel’skaya tundra

The methods of digital mapping are promising for creating soil maps on difficultly accessible territories. This study was aimed at searching of optimal approaches for digital mapping of the soil cover in poorly studied western part of the Bol’shezemel’skaya tundra on different scales. Medium-scale (1 : 200 000) and small-scale (1 : 1 M) soil maps served as the source of initial information about soils of this region; actual information of the state of the territory was obtained from remote sensing data (Landsat 8 scenes, Aug. 14, 2013) and digital elevation model ASTER GDEM v.2. After extraction of information and the choice of predictors, the analysis of digital soil cover models obtained with the use of different algorithms – Random Forest (RF), Multinomial Logistic Regression (MLR) and Linear Discriminant Analysis (LDA) – was performed. The coefficient of agreement between the newly developed digital models and the initial paper-based soil maps (kappa) was calculated. This test demonstrated that the RF algorithm ensures the best results, so the final digital maps were obtained using it. Averaged kappa values for the compared small- and medium-scale models were as follows: RF – 0.39 and 0.36; MLR – 0.31 and 0.31; and LDA – 0.28 and 0.18, respectively. After the preliminary correction of the initial medium-scale map, the kappa values somewhat increased (RF – 0.39, MLR – 0.35, LDA – 0.30). At the stage of evaluation of digital soil maps obtained with the use of RF algorithm, these maps and the initial soil maps were compared with independent point-size terrain data. The degree of agreement between these data and the new digital soil maps proved to be no less than that for the initial maps. For the initial and digital small-scale maps, it reached 24 and 26 %, respectively; for the initial and digital medium-scale maps, 54 and 43 %, respectively. After the preliminary correction of the initial medium-scale map, the degree of agreement between the digital model and terrain data improved considerably and reached 61 %. This method of digital soil mapping on the basis of analogous data seems to be optimal.