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Thermal Insulation Panels with Bio-Based Adhesives
No full textEnvironment friendly insulation panels were manufactured and tested made of pine wood fibers and glued with a bio-based adhesive, called dextran. The aim of this work was to determine relevant technical properties of panels fabricated with this new glue type. In the panels, the ratio of dry glue content was varied from 30% to 60% with 10% steps which runs served as comparison basis. The density of the insulation panels was set to 70 kg/m3, 100 kg/m3 and 125 kg/m3. Beside their thermal conductivity, the compression strength, the bonding strength, and the wetting angle of the adhesive were measured. With the variation of panel densities, the thermal conductivity is in a narrow range of 0.039 to 0.042 W/mK. The resistance to compression at 10% strain was measured to be 0.3 MPa, 0.35 MPa and 0.4 MPa in the panels with 70 kg/m3, 100 kg/m3 and 125 kg/m3, respectively. The wetting angle of the adhesives seemed to correlate only weakly with the bonding strength, and the glue`s wetting ability diminished with the increase of the glue content. The results seem to be competitive if compared with the traditionally used glass and rockwool, and foam insulation materials. © The Authors, published by EDP Sciences
Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning
No full textForest damage has become more frequent in Hungary in the last decades, and remote sensing offers a powerful tool for monitoring them rapidly and cost-effectively. A combined approach was developed to utilise high-resolution ESA Sentinel-2 satellite imagery and Google Earth Engine cloud computing and field-based forest inventory data. Maps and charts were derived from vegetation indices (NDVI and Z∙NDVI) of satellite images to detect forest disturbances in the Hungarian study site for the period of 2017–2020. The NDVI maps were classified to reveal forest disturbances, and the cloud-based method successfully showed drought and frost damage in the oak-dominated Nagyerdő forest of Debrecen. Differences in the reactions to damage between tree species were visible on the index maps; therefore, a random forest machine learning classifier was applied to show the spatial distribution of dominant species. An accuracy assessment was accomplished with confusion matrices that compared classified index maps to field-surveyed data, demonstrating 99.1% producer, 71% user, and 71% total accuracies for forest damage and 81.9% for tree species. Based on the results of this study and the resilience of Google Earth Engine, the presented method has the potential to be extended to monitor all of Hungary in a faster, more accurate way using systematically collected field-data, the latest satellite imagery, and artificial intelligence
School students’ perceptions of trees and wooden objects in their immediate home environment: Possible implications for school environments
No full textWe present a county-wide statistically representative survey with 430 Hungarian school students demonstrating that students who have trees in the parental home environment show more positive attitude towards trees and wood than their peers who do not have any. We aimed at finding out about the current attitudes of the upcoming generation with regard to the use of wood and wood materials, and also about the question as to whether they would consciously use wood materials in the future. Results show that school students who have trees in their parental home environment show significantly higher scores on the cognitive, affective, and conative domains of attitude towards trees and wood compared to those who do not have trees in their parental home environment. The present study contributes to outdoor environmental education research in that we provide quantitative evidence for the positive effect of the immediate environment of children on the attitude towards wood and trees. The findings suggest the imperative need for further novel educational interventions in school gardens related to trees and wooden objects. © The Author(s) 2024
