• Rural Digital Europe
• 2014-2023close
• Riga Technical University Repository close
• European University of Technology close
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Abstract The purpose of this research was to investigate a landowner’s status as a lessor in land consolidation on agricultural areas in Europe. The research was based on surveys designed for land consolidation experts from Cyprus, Finland, France, Germany, the Netherlands, Latvia, Estonia and statistical data of the areas. The lease of agricultural land is common in Europe. However, there are differences related to how common the lease is and what the rental value of land is. The variation is based on factors such as differences in the markets, historical development of the agricultural lands and current legislation. The lessors have rights as landowners in land consolidation, but in case the land is used by third parties, lessor’s status is more limited compared to other landowners’. Similarly the costs of land consolidation typically are paid by landowners, not by users. The lessors are commonly indifferent and suspicious about the land consolidation. They are occasionally suspicious, especially before or at the beginning of the process, but later on they seem to be less suspicious. The lessors’ experience of land consolidation is based commonly on a fear of additional costs for their investment.

Abstract Geometrical accuracy of remote sensing data often is ensured by geometrical transforms based on Ground Control Points (GCPs). Manual selection of GCP is a time-consuming process, which requires some sort of automation. Therefore, the aim of this study is to present and evaluate methodology for easier, semi-automatic selection of ground control points for urban areas. Custom line scanning algorithm was implemented and applied to data in order to extract potential GCPs for an image analyst. The proposed method was tested for classical orthorectification and special object polygon transform. Results are convincing and show that in the test case semi-automatic methodology is able to correct locations of 70 % (thermal data) – 80 % (orthophoto images) of buildings. Geometrical transform for subimages of approximately 3 hectares with approximately 12 automatically found GCPs resulted in RSME approximately 1 meter with standard deviation of 1.2 meters.

Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass) and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.

Abstract This paper focuses on the quantitative examination of the wage share in the countries of the EU from 2008 to 2012, applying a division of national economy by three main sectors. The aim of the paper is to investigate the effect of employment structure and its changes, and the changes of the wage share inside the sectors on the aggregate wage share and its alterations during a short-term period. The conducted research showed that the levels of the weighted wage share, corresponding to the employment structure, were: the lowest – in agriculture, the highest – in the service sector. Furthermore, the shift of the aggregate wage share was mostly affected by the decline in the primary and secondary sectors, which was generally based on the decrease of employed people. The results also displayed that in several countries the wage shares declined because of the reduction of the wages and salaries rather than value-added.

Salsola kali is considered extremely valuable as an energy crop worldwide because it adapts easily to environments with strong abiotic stresses (hydric, saline and alkaline) and produces large amounts of biomass in drylands. This species is categorized as an important weed in Argentina. The aim of this work was to design an agro-ecological zoning model for tumbleweed in Argentina, employing a Geography Information System. Based on the bioclimatic requirements for the species and the climatic data for Argentina (1981–2010 period), an agro-climatic suitability map was drawn. This map was superimposed on the saline and alkaline soil maps delineated by the Food and Agriculture Organization for dry climates, generating the agro-ecological zoning on a scale of 1 : 500 000. This zoning revealed very suitable and suitable cultivation areas on halomorphic soils. The potential growing areas extend from N of the Salta province (approximately 22° S) to the Santa Cruz province (50° S). The use of tumbleweed on halomorphic soils under semi-arid to arid conditions, for the dual purpose of forage use and source of lignocellulosic material for bioenergy, could improve agricultural productivity in these lands. Furthermore, it could also contribute to their environmental sustainability, since the species can be used to reclaim saline soils over the years. Based on international bibliography, the authors outlined an agro-ecological zoning model. This model may be applied to any part of the world, using the agro-ecological limits presented here. Fil: Falasca, Silvia Liliana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Pitta Alvarez, Sandra Irene. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Micología y Botánica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Micología y Botánica; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental; Argentina Fil: Ulberich, Ana Cristina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Humanas. Centro de Investigaciones Ecogeográficas y Ambientales; Argentina

The construction and development of aerial vehicle or flying robot systems with remote sensing have become a vision of environmental monitoring in agriculture, ice condition, pollution, military, and territory. The new concept of environmental monitoring is superior to satellite imagery because of its accessibility, flexibility, applicability, and economy. Resulting from the expectable advantages, aerial vehicles have been extensively researched for their potential to carry out various missions. Recently, several developments have been engaged for aerial vehicles to conduct tasks in battlefield, transportation, exploration, and disaster prevention. Although the aforementioned researches can verify the applicability of aerial vehicle systems, most aerial vehicles lack the flexibility to accomplish other than predefined tasks. Moreover, the limitations of aerial vehicle performance and photographic technique also impede the development of such system. Therefore, with the introducing of multi-vehicle system, human intelligence, novel aerial vehicle design, and real-time photogrammetry system in outdoor environments, the efficiency and quality of monitoring can be improved significantly.

Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.

Abstract The paper covers research findings on mussel farming and the analysis of current situation in mussel farming in the Baltic Sea. The mussel farming has a development potential in the Baltic Sea region. Some developers have chosen progressive activities to achieve the aim. For example, in Sweden the development of mussel farming is suggested as one of the instruments for reducing eutrophication. Several countries in the Baltic Sea region are in the beginning phase of the mariculture development. The following research methods were used in the research: studies of scientific publications, case studies and document studies on some important factors impeding the development of mussel farming in the Baltic Sea.