Two new advanced forms of spectrometry for space and commercial applications

Reagentless ultraviolet absorption spectrometry (UVAS) and Liquid Atomic Emission Spectrometry (LAES) represent new forms of spectrometry with extensive potential in both space and commercial applications. Originally developed under KSC sponsorship for monitoring nutrient solutions for the Controlled Ecological Life Support System (CELSS), both UVAS and LAES have extensive analytical capabilities for both organic and inorganic chemical compounds. Both forms of instrumentation involve the use of remote fiber optic probes and real-time measurements for on-line process monitoring. Commercial applications exist primarily in environmental analysis and for process control in the chemical, pulp and paper, food processing, metal plating, and water/wastewater treatment industries

System for automated environmental monitoring using remote sensing data of the Earth from open data sources

Environmental monitoring using remote sensing data requires an analyst to perform a large amount of routine work related to downloading, processing and analyzing data, especially in cases when the study area is covered with a large number of satellite imagery. The paper presents the results of the design and software implementation of the system that automates downloading and processing of remotely sensed data according to developed scenarios and, thus, greatly simplifies the processing of satellite imagery. It provides the description of tools for accessing data from the archive of the United States Geological Survey (USGS) and describes the data flow in the system. The paper gives an analysis of results obtained using the developed system on the example of monitoring the state of Siberian pine forests of the Tomsk region

Information-analytical and resources for diagnosing the underlying performance indicators of environmental management

Статтю присвячено комплексному та всебічному дослідженню проблем економічного забезпечення екологічного моніторингу в Україні. У статті запропоновано: а ) організаційно - економічні процедури; б) розраховані масштаби їх ресурсного забезпечення з витратами на створення цільової програми; в) методика еколого-економічного моніторингу, яка дає змогу визначити ключові показники - індикатори результативності на основі єдиного критеріального показника ідентифікації масштабів потенціалу сталого розвитку. Аргументована послідовність і етапи діагностики з метою здійснення об'єктивної еколого-економічної, організаційно-економічної та системно-універсальної оцінки результативності управління природокористуванням в Україні.Статья посвящена комплексному и всестороннему исследованию проблем экономического обеспечения экологического мониторинга в Украине. В статье предложены: а) организационно-экономические процедуры; б) рассчитаны масштабы их ресурсного обеспечения с затратами на создание целевой программы; в) методика эколого-экономического мониторинга, позволяющая установить ключевые показатели-индикаторы результативности на основе единого критериального показателя идентификации масштабов потенциала устойчивого развития. Аргументирована последовательность и этапы диагностики с целью осуществления объективной эколого-экономической, организационно-экономической и системно-универсальной оценки результативности управления природопользованием в Украине.The article is devoted to the complex and comprehensive research into the problems of economic ecological monitoring in Ukraine. The technique of ecological- economic monitoring , which allows you to define performance indicators for the environmental management based on a single criterion indicator identification scale sustainable development potential in the plane of the ecological , economic, organizational, economic and system- universal assessment. The article suggests: a) organizational and economic procedures, b) calculated the extent of their resource provision to the cost of creating the target program to simplify the evaluation of the main parameters of performance management implementation of environmental policy in the sphere of nature, c) the method of environmental-economic monitoring, allowing to establish key performance indicators performance indicators , based on a single criterion indicator identification scale capacity for sustainable development. Reasoned sequence stages of diagnosis and to implement the objective of eco-economic, organizational, economic and versatile system- performance evaluation of environmental management in Ukraine. Methodological bases of the system of environmental-economic monitoring process-closed type allow you to use: one scientific and methodical approach to construct algorithms for statistical processing of information, resources and functional, macroeconomic measurements, standardized methods for analysis of statistical databases, a single regulatory and methodological framework (Regulation Monitoring System ), which provides the reliability and comparability of quantitative measurement, reporting data and results of data processing

BIM and forecasting deformations in monitoring structures

BIM technologies are becoming more widely used, mainly in the design and operation of buildings and structures, and in most cases this is enough for trouble-free operation. Nevertheless, there is a category of buildings for which the monitoring of the technical condition should be an integral part of the construction and operation. These are the so-called public large-span structures. Unfortunately, the development of BIM technology in the Russian Federation is not at such a level as to answer questions about the behaviour of objects under changing environmental conditions and reveal hidden patterns in the monitoring data. Based on the analysis of literary sources, the authors reviewed various methods for identifying hidden patterns in geodetic measurement data when monitoring buildings and structures. It is noted that modern analysis methods are based on statistical processing of measurement results and on the statistical method of forecasting. However, there are attempts to apply models that take into account the design features and the temperature regime of the object. This type includes the two proposed models, which are used to model the three-dimensional coordinates of the strain marks in the 3D model and only the elevations of the marks in the 1-Z model. The article presents the rationale for the simulated geometric elements and properties of the object. The solution of the equations of both models and the analysis of the results and parameters of the model for measurement epochs are shown. The simulation is shown on the example of a real object, which was monitored by the authors in 2015-2016. The authors believe that the monitoring of large-span structures and the search for patterns of their behaviour should be an integral part of the BIM system for such structures

Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment

Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform

Near Real-time S-AIS: Recent Developments and Implementation Possibilities for Global Maritime Stakeholders

The Automatic identification System (AIS) has been mainly designed to improve safety and efficiency of navigation, environmental protection, coastal traffic monitoring simplifying identification and communication. Additionally, historical AIS data have been used in many other areas of maritime safety, economic and environmental research. The probability of the detection of terrestrial AIS signals from space was presented in 2003, following the advancements in micro satellite technology. Through constant development, research and cooperation between governmental and private sectors, Satellite AIS (S-AIS) has been continuously evolving. Advancements in signal and data processing techniques have resulted in an improved detection over vast areas outside of terrestrial range. Some of the challenges of S-AIS technology include satellite revisit times, message collision and ship detection probability. Data processing latency and lacking the continuous real-time coverage made it less reliable for end user in certain aspects of monitoring and data analysis. Recent developments and improvements by leading S-AIS service providers have reduced latency issues. Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry

A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

ArticleIn recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework

A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user

Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents

6-channel CMOS-based instrument for optical absorption spectroscopy and chemical identification

A multichannel portable instrument for on-chip optical absorption spectroscopy is presented. The system can house photonic chips having up to 6 sensing sites operating in parallel, allowing real-time simultaneous detection of multiple chemicals. A 6-channel CMOS lock-in front-end performs the amplification and demodulation of the signals from the integrated light detectors, while an FPGA is chosen for signal acquisition and analysis. A digital real-time ratiometric processing cancels out the effect of laser power fluctuations to achieve high sensitivity in monitoring the presence of the analytes, as demonstrated with the detection of an acetone sample. Compact size for portability, real-time parallel detection and flexible FPGA processing make this system suitable for environmental investigations on many different pollutants, both in the near- and mid-infrared wavelength range