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.

Landscape monitoring studies of the North Caucasian geochemical province

The data on the geochemical features of the bedrocks and soils of the province are given. Considerable attention is paid to regional abundances, as well as enrichment and dispersion factors of the chemical elements in landscapes. Using the example of the North Caucasus, it is shown that for such indicators as phytomass, geological, geomorphological, and geobotanical features, it is possible to make a preliminary outlining of regional structures corresponding to geochemical provinces. At the same time, a subsequent geochemical study of these structures remains mandatory. Upon determining certain geochemical associations, geochemical provinces can be basically distinguished; to a large extent, geochemical properties of these accumulated and scattered associations of elements contribute to the regional soil geochemistry. The results of long-term monitoring studies of the North Caucasus geochemical province have shown that the key features of the regional landscapes are due to the composition of bedrock and the presence of a large number of ore deposits and occurrences. The data obtained are the basis for assessing the state of the environment in conditions of increasing anthropogenic impact, and the established regional abundances can be used to assess the degree of pollution in agricultural, residential, and mining landscapes.

A Machine Learning Approach to the Recognition of Brazilian Atlantic Forest Parrot Species

Avian survey is a time-consuming and challenging task, often being conducted in remote and sometimes inhospitable locations. In this context, the development of automated acoustic landscape monitoring systems for bird survey is essential. We conducted a comparative study between two machine learning methods for the detection and identification of 2 endangered Brazilian bird species from the Psittacidae species, the Amazona brasiliensis and the Amazona vinacea. Specifically, we focus on the identification of these 2 species in an acoustic landscape where similar vocalizations from other Psittacidae species are present. A 3-step approach is presented, composed of signal segmentation and filtering, feature extraction, and classification. In the feature extraction step, the Mel-Frequency Cepstrum Coefficients features were extract and fed to the Random Forest Algorithm and the Multilayer Perceptron for training and classifying acoustic samples. The experiments showed promising results, particularly for the Random Forest algorithm, achieving accuracy of up to 99%. Using a combination of signal segmentation and filtering before the feature extraction steps greatly increased experimental results. Additionally, the results show that the proposed approach is robust and flexible to be adopted in passive acoustic monitoring systems.

Effective ecological monitoring requires a multi-scaled approach.

Environmental monitoring data is fundamental to our understanding of environmental change and is vital to evidence-based policy and management. However, different types of ecological monitoring, along with their different applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecological monitoring, leading to poor program development, implementation, and outcomes. In an effort to develop a more consistent and clear understanding of environmental monitoring programs we review previous monitoring classifications and support the widespread adoption of three succinct categories of monitoring, namely targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables us to address questions related to where and when environmental change is occurring. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the cause of environmental change. This classification system is ideal because it can incorporate different interests and objectives, and as well as different spatial scales and temporal frequencies. It is both comprehensive and flexible, while also providing valuable structure and consistency across distinct ecological monitoring programs. To support our argument, we examined the ability of each monitoring type to inform on six key types of questions that are routinely posed to ecological monitoring programs, such as where and when change is occurring, what is the magnitude of that change, and how to manage that change. As we demonstrate, each type of ecological monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, users can compare how well different types of monitoring can answer different ecological questions, allowing scientists and managers to design programs best suited to their needs. Finally and most importantly, we assert that for our most serious environmental challenges, it is essential that we include information at each of these monitoring scales to inform on all facets of environmental change. This will be best achieved through close collaboration between practitioners of each form of monitoring. With a renewed understanding of the importance of each monitoring type along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.

КАРТОГРАФИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ГЕОЭКОЛОГИЧЕСКОГО МОНИТОРИНГА БАССЕЙНА ОЗЕРА БАЙКАЛ

Разрабатываемая методология геоэкологического мониторинга основана на выявлении антропогенных источников вещества и наблюдении связей между ними и объектами среды путем рассмотрения объектов в качестве смесей, а источников в качестве их компонентов, с использованием подходов ландшафтного мониторинга. Обоснованы методические подходы к разработке картографического обеспечения мониторинга структуры загрязнения и распределения загрязнителей в бассейне озера Байкал. Для обоснования сети наблюдений и контроля, экстраполяции результатов мониторинга на территории, не охваченные непосредственными наблюдениями, показа оперативной информации о состоянии геосистем и экосистем требуется картографическая основа. Методика картографирования базируется на основных положениях учения о геосистемах В.Б. Сочавы. Методические приемы геоинформационного картографирования были адаптированы применительно к бассейну оз. Байкал. Модель SRTM 4й версии, взятая за основу для получения изолиний рельефа, была преобразована и приведена к масштабу 1:5 000000, а все цифровые слои были интегрированы в единую картографическую проекцию и систему координат (WGS 84). Сопоставление пространственно привязанных слоев рельефа и гидрографической сети в QGIS позволило разграничить бассейны крупных, средних и малых притоков Байкала. Инструментами выявления структуры хозяйственной деятельности служат методы ландшафтногеохимического синтеза, картографирования агроландшафтов, ландшафтного планирования и ретроспективного картографического анализа геосистем с длительной историей хозяйственного освоения. Выявление структуры загрязнения Байкала, его притоков и водосборного бассейна в целом осуществляется путем анализа пространственного и временного распределения загрязнителей в компонентах природной среды. Картографирование дифференциации загрязнителей проводится с использованием современных методов геоинформационного анализа и моделирования, которые реализованы в программе ArcGIS 10. Расчет составов выбросов неизвестных источников проводится с применением методов многомерного анализа. Проанализированы основные параметры ландшафтногеохимической дифференциации бассейна озера Байкал, составлена карта дифференциации поверхностных вод по способности вод к самоочищению, разработана схема районирования территории по способности обеспечивать тот или иной состав вод. The developed methodology of geoenvironmental monitoring is based on identification of anthropogenic sources of substance and the observation of communications between them and the objects of the environment by consideration of the objects as mixes, and the sources as their components, with use of approaches of landscape monitoring. Methodical approaches to development of cartographic ensuring of monitoring of pollution and distribution of pollutants in the basin of Lake Baikal are proved. The cartographic basis is necessary for justification of the network of observations and monitoring, extrapolation of results of monitoring on the territories which are not captured by immediate observations, display of operational information on a condition of geosystems and ecosystems. The technique of mapping is based on original positions of V.B. Sochava doctrine about geosystems. Methodical techniques of geoinformation mapping were adapted in relation to the basin of Lake Baikal. The SRTM model of the 4th version taken as a basis for receiving isolines of a relief was transformed and scaled by 1:5,000,000. All digital layers were integrated into a uniform cartographic projection and a frame (WGS 84). Comparison of spatially attached layers of a relief and a hydrographic network in QGIS allowed us to differentiate the pools of the large, average and small inflows of Baikal. The methods of landscapegeochemical synthesis, mapping of agrolandscapes, landscape planning and the retrospective cartographic analysis of geosystems with the long history of economic development served as the instruments of identification of the structure of economic activity. Identification of pollution structure of Lake Baikal, its inflows and a catchment basin in general is carried out by the analysis of spatial and temporary distribution of pollutants in environment components. Mapping of pollutants differentiation is carried out with use of the modern methods of the geoinformational analysis and the model operation, which are realized in the ArcGIS 10 program. Calculation of compositions for the emissions of unknown sources is carried out with application of methods of the multidimensional analysis. Key parameters of landscapegeochemical differentiation of the basin of Lake Baikal are analyzed. The map of differentiation of the surface water on ability of waters to selfcleaning has been compiled. The scheme of regionalization for the territory on ability to provide this or that composition of waters has been developed.