• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.63-71
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• 2021

Windbreak forests, which have a windproof effect against strong winds, are known to be effective in reducing the spread of odors and dust emitted from livestock farms. The effect of reducing the spread of odors and dust can be estimated through numerical models such as computational fluid dynamics, which require aerodynamic coefficients of the windbreaks for accurate prediction of their performance. In this study, we aimed to evaluate the aerodynamic coefficients, Co, C₁, C₂, and α, of two windbreaks, Thuja occidentalis and a mesh net, through wind tunnel experiments. The aerodynamic coefficients were derived by the relation between the incoming wind speed and the pressure loss due to the windbreaks which was measured by differential pressure sensors. In order to estimate the change in the aerodynamic coefficient concerning various leaf density, the experiments were conducted repeatedly by removing the leaves gradually in various stages. The results showed that the power law regression model more suitable for coefficient evaluation compared to the Darcy-Forchheimer model.

• Journal of Bio-Environment Control
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• v.29 no.4
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• pp.448-455
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• 2020

This study was carried out to investigate reduction of the frost damage using the mesh net used for the purpose of non-bagged cultivation. A device measuring the weight of frost was developed and installed in both the control and the experimental, and the effect of frost reduction was evaluated with their weights. As a result, weight of frost in the control was reduced from 37% to 59% with mesh net on the day the frost was observed. In addition, the device for automatically observing the amount of frost was developed and the height of the windbreak of the frost measuring device was determined to be 30 cm through wind tunnel experiment. The results of this study are expected to reduce frost damage during the flowering season of fruit trees by installing mesh net and it is expected to be used as basic data for agricultural use of mesh net.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1399-1414
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• 2018

Natural forests are un-manned forests where the artificial forces of people are not applied to the formation of forests. On the other hand, artificial forests are managed by people for their own purposes such as producing wood, preventing natural disasters, and protecting wind. The artificial forests enable us to enhance economical benefits of producing more wood per unit area because it is well-maintained with the purpose of the production of wood. The distinction surveys have been performed due to different management methods according to forests. The distinction survey between natural forests and artificial forests is traditionally performed via airborne remote sensing or in-situ surveys. In this study, we suggest a classification method of forest types using satellite imagery to reduce the time and cost of in-situ surveying. A classification map of natural forest and artificial forest were generated using KOMPSAT-3, 3A, 5 data by employing artificial neural network (ANN). And in order to validate the accuracy of classification, we utilized reference data from 1/5,000 stock map. As a result of the study on the classification of natural forest and plantation forest using artificial neural network, the overall accuracy of classification of learning result is 77.03% when compared with 1/5,000 stock map. It was confirmed that the acquisition time of the image and other factors such as needleleaf trees and broadleaf trees affect the distinction between artificial and natural forests using artificial neural networks.