Development of Distributed Research Center for analysis of regional climatic and environmental changes

We present an approach and first results of a collaborative project being carried out by a joint team of researchers from the Institute of Monitoring of Climatic and Ecological Systems, Russia and Earth Systems Research Center UNH, USA. Its main objective is development of a hardware and software platform prototype of a Distributed Research Center (DRC) for monitoring and projecting of regional climatic and environmental changes in the Northern extratropical areas. The DRC should provide the specialists working in climate related sciences and decision-makers with accurate and detailed climatic characteristics for the selected area and reliable and affordable tools for their in-depth statistical analysis and studies of the effects of climate change. Within the framework of the project, new approaches to cloud processing and analysis of large geospatial datasets (big geospatial data) inherent to climate change studies are developed and deployed on technical platforms of both institutions. We discuss here the state of the art in this domain, describe web based information-computational systems developed by the partners, justify the methods chosen to reach the project goal, and briefly list the results obtained so far

Open-Access Geographic Sources And Data For The Study And Management Of Natural Resources

The objective of this systematic review is to describe and analyze open geographic data provided by governmental sources in order to provide an overview of open geographic sources and data for the study and management of natural resources in Peru. For this purpose, the web was explored and scientific articles were reviewed, finding a huge cartographic archive offered by the Peruvian State. On the one hand, public institutions put their respective geoportals into operation, and on the other hand, the launching into space of the PeruSAT-1 satellite. This increased the supply of official geospatial information in the last five years. In addition, geotechnical data was found in raw and processed form from global initiatives. All of this documentary collection is available to the public in an open, free and free form in cyberspace, which can be used in the study of the use, restoration, conservation and valuation of ecosystems and other elements of the environment

Environmental Information: Placing Biodiversity Phenomena in an Ecological and Environmental Context

Environmental models are increasingly being used as surrogates to determine plant and animal species’ distributions for a range of uses. This use of models has become an important part of the recent science that has become known as biodiversity informatics. Because of the nature of species data, considerable effort has often been spent in managing the quality of those species data, but less time has generally been spent on determining the quality and efficacy of the environmental data against which the species data are being modeled. This paper examines a range of environmental data being used in species distribution modeling, and looks at how they are prepared, their quality and use, and some of the commonly encountered pitfalls and problems in using these data in species’ distribution modeling

Environmental science applications with Rapid Integrated Mapping and analysis System (RIMS)

The Rapid Integrated Mapping and analysis System (RIMS) has been developed at the University of New Hampshire as an online instrument for multidisciplinary data visualization, analysis and manipulation with a focus on hydrological applications. Recently it was enriched with data and tools to allow more sophisticated analysis of interdisciplinary data. Three different examples of specific scientific applications with RIMS are demonstrated and discussed. Analysis of historical changes in major components of the Eurasian pan-Arctic water budget is based on historical discharge data, gridded observational meteorological fields, and remote sensing data for sea ice area. Express analysis of the extremely hot and dry summer of 2010 across European Russia is performed using a combination of near-real time and historical data to evaluate the intensity and spatial distribution of this event and its socioeconomic impacts. Integrative analysis of hydrological, water management, and population data for Central Asia over the last 30 years provides an assessment of regional water security due to changes in climate, water use and demography. The presented case studies demonstrate the capabilities of RIMS as a powerful instrument for hydrological and coupled human-natural systems research

Early detection of weed in sugarcane using convolutional neural network

Weed infestation is an essential factor in sugarcane productivity loss. The use of remote sensing data in conjunction with Artificial Intelligence (AI) techniques, can lead the cultivation of sugarcane to a new level in terms of weed control. For this purpose, an algorithm based on Convolutional Neural Networks (CNN) was developed to detect, quantify, and map weeds in sugarcane areas located in the state of Alagoas, Brazil. Images of the PlanetScope satellite were subdivided, separated, trained in different scenarios, classified and georeferenced, producing a map with weed information included. Scenario one of the CNN training and test presented overall accuracy (0,983), and it was used to produce the final mapping of forest areas, sugarcane, and weed infestation. The quantitative analysis of the area (ha) infested by weed indicated a high probability of a negative impact on sugarcane productivity. It is recommended that the adequacy of CNN’s algorithm for Remotely Piloted Aircraft (RPA) images be carried out, aiming at the differentiation between weed species, as well as its application in the detection in areas with different culture crop

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Translate Data Into Meaning: integration of meteorology and geomatics to generate meaningful information for decision makers

A variety of actors at all scales and acting in different domains such as emergency management, agriculture, sports and leisure and commercial activities, are becoming more aware of the challenges and opportunities that meteorological data analysis poses for their operational goals. The increasing availability of meteorological data coupled with a rapid improvement in technology led to the widespread dissemination of the weather information to a variety of users on a regular basis. Particularly through the internet and mobile application all users, despite their varied background, can access to big amount of data with a high potential to gather essential input that can significantly help their decisions. At the same time, simply creating and disseminating information without context does not necessarily offer an added value to sèecific users. One of the main issues is related to the scientific approach of weather analysis and to the representation of results, which are hardly understandable for non-technical users and therefore not easily usable to make decisions. As a result, there are several researches aiming at finding new ways of supporting decision making by supplying easy to use information. The main objective of this thesis is therefore to provide guidance on how to identify and characterize the needs for meaningful and usable information among various users of meteorology, including members of the public, emergency managers, other government decision makers, and private-sector entities, both direct users and intermediaries. In particular a methodology for the integration of meteorological data and GIS capabilities is investigated and applied to three different end users having similarities and differences. Scientific analysis, results and cartographic products are adapted to specific requirements, experience and perceptions of the three different users