• Rural Digital Europe
• 2014-2023close
• Open Access close
• European University of Technology close

Abstract Infrared thermography (IRT) has met an extensive popularity among the non-destructive technologies for building diagnostics, especially with the increasing concerns of energy minimisation and low energy consumption of the building sector. Its popularity for a broad range of applications can be attributed to its non-contact safe nature, its usefulness and effectiveness, as well as the energy and cost savings it can achieve. This paper reviews the state-of-the-art literature and research regarding the passive and active infrared thermography. The fundamentals of IRT are thoroughly explained and the thermographic process for building diagnostics is presented. This work also presents the fields of applicability of IRT with a focus on the building sector, as well as the advantages, limitations and potential sources of errors of IRT employment. Additionally previous non-destructive testing (NDT) studies that employed passive, active pulsed, and active lock-in thermographies for building diagnostics are presented. A review of the thermal image analysis methods and the future trends of thermal imaging are also included in this work. It can be concluded that while IRT is a useful tool for the characterisation of defects in the building sector, there is great prospect for the development of more advanced, effective and accurate approaches that will employ a combination of thermography approaches.

Abstract Agriculture is the second most significant source of greenhouse gas (GHG) emissions, with approximately 24 % of Latvia’s total GHG emissions in 2014. Emissions from agricultural soils contributed major share of the total emissions – 59.6 %, enteric fermentation emissions was second largest source – 32 %. The share of manure management emissions was evaluated as 7.5 % of total emissions in the sector, remaining 0.9 % of emissions refer to liming and urea application. GHG emissions increased in 2014 by 3.3 % comparing to 2013 due to increase of cattle, sheep and fur animal numbers. Statistics also showed increase of synthetic N fertilizer consumption approximately by 4.6 %, sown area by 0.3 % and lime application to soils 42.9 % [1]. Bearing in mind significant share of agricultural emissions in total GHG emissions in Latvia and the growing emission trend the additional attention is necessary for evaluation of impacts of agriculture on the environment. The purpose of this study is to explore indicators for assessing eco-efficiency in the Latvian agriculture sector. First the paper describes methods which can be used for measuring eco-efficiency, second availability of activity data and third, presents calculations of selected indicators for eco-efficiencies.

Proceedings of SPIE - The International Society for Optical Engineering, Volume 9229, 2014, Article number 922912 Accurate assessment of water use is an important issue in a globally changing climate and environment, where water is becoming a scarce but essential resource. The concept "?Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage, respectively. This indicator provides valuable information for a global assessment of how water resources are used. Remote sensing (RS) provides physically-based, worldwide, and consistent spatial information over space and time, and has been used in hydrological applications in order to estimate relevant variables at different temporal and spatial scales. The paper focuses on exploring and exploiting the potential of using RS techniques and data for WF assessment in agriculture. Based on recent papers initiated in this research topic the investigation focuses on how variables needed in the calculation of water footprint are obtained (based on non RS and on RS approaches), on identifying the inputs required for estimating the WF of crops and whether it is feasible to integrate various RS approaches. The results of this study demonstrate the usefulness of satellite data for water footprint assessment, which were obtained by the Remote Sensing Working Group in the framework of the ESSEM COST Action ES1106, "Assessment of EUROpean AGRIculture WATer use and trade under climate change"? (EURO-AGRIWAT). © 2014 SPIE.

In this article we will present a transdisciplinary approach to carbon sequestration in agricultural soils. The software provides a method proposed to measure the amount of carbon that can be captured from different soil types and different crop. The application has integrated an intuitive interface, is portable and calculate the number of green certificates as a reward for farmers financial support for environmental protection. We plan to initiate a scientific approach to environmental protection through financial incentives for agriculture fits in EU rules by taxing big polluters and rewarding those who maintain a suitable environment for the development of ecological and competitive agriculture.

The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR), ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1–3), various regression models are examined for more than 70 different vegetation indices (Steps 4–5). The specific data analysis indicated that the red-edge position (REP) hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6), providing comparable results with the in situ GPR measurements (Step 7). Other vegetation indices, such as the normalized difference vegetation index (NDVI), have also been examined, providing significant correlation between the two datasets (R = 0.50). The model is then projected to a high-resolution image over the area of interest (Step 8). The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9). The overall outcomes document that fusion models between various types of remote sensing datasets frequently used to support archaeological research can further expand the current capabilities and applications for the detection of buried archaeological remains.

Part 4: Building Information Modeling (BIM); International audience; Large-scale 3D city building models have been widely used in urban planning, intelligent transportation, military simulation and other fields. The traditional ways of modeling generally have common problems such as low efficiency, waste of manpower and time consumption. How to find a rapid approach to automatically complete large-scale 3D modeling is a very hot research topic. In this paper we propose a novel approach of procedural modeling of buildings with CityEngine, which is combined with ArcGIS technology for the geographic information. This approach produces extensive architectural 3D models with high visual quality and geometric details at low cost. It includes following two contents concretely. At first, directly writing computer generated architecture (CGA) shape grammar to complete procedural modeling of building and other objects. Secondly, using facade modeling based on two-dimensional images to generate architectural model as well as creating high geometry details. It is validated that this novel approach of procedural modeling is a significant step forward that reduces a lot of modeling times by CGA shape grammars.

Existen numerosas aplicaciones de IoT donde los gateways recopilan los datos en bruto de sensores y los envían a una nube para su procesamiento. Una alternativa consiste en hacer gran parte del procesamiento en el propio gateway y minimizar el envío de datos y la carga computacional en la nube; esta estrategia se denomina edge-computing y puede lograr un mayor ahorro de energía y reducción del ancho de banda empleado en las comunicaciones. El objetivo del proyecto es diseñar e implementar un gateway edge-computing basado en Raspberry Pi usando Python y tecnologías estándar; el gateway edgecomputing recopila datos de numerosos sensores IoT y hace un preprocesamiento y tratamiento de dichos datos minimizando el envío de datos a una nube. Se propone un caso de uso basado en sensores IoT de temperatura y humedad usando comunicación Bluetooth Low Energy. Escuela Técnica Superior de Ingeniería de Telecomunicación Universidad Politécnica de Cartagena