• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.257-263
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• 2016

Objective of this study was to evaluate the carbon budget on 40 years old pear orchard at Naju. For carbon budget assessment, we measured the soil respiration, net ecosystem productivity of herbs, pear biomass and net ecosystem exchange. In 2015, pear orchard released about $25.6ton\;CO_2\;ha^{-1}$ by soil respiration. And $27.9ton\;CO_2\;ha^{-1}$ was sequestrated by biomass growth. Also about $12.6ton\;CO_2\;ha^{-1}$ was stored at pruning branches and about $5.2ton\;CO_2\;ha^{-1}$ for photosynthesis of herbs. As a result, 25.6 ton of $CO_2$ per ha is annually released to atmosphere. At the same time about 45.7 ton of $CO_2$ was sequestrated from atmosphere. When it sum up the amount of $CO_2$ release and sequestration, approximately $20.1ton\;CO_2\;ha^{-1}$ was sequestrated by pear orchard in 2015, and it showed no significant differences with net ecosystem exchanges ($17.8ton\;CO_2\;ha^{-1}\;yr^{-1}$) by eddy covariance method with the same period. Continuous research using various techniques will help the understanding of $CO_2$ dynamics in agroecosystem and it can be able to present a new methodology for assessment of carbon budget in woody crop field. Futhermore, it is expected that the this study can be used as the basic data to be recognized as a carbon sink.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of Biosystems Engineering
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• v.28 no.6
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• pp.517-524
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• 2003

There have been worldwide research and development efforts to automate various processes of bio-production and those efforts will be expanded with priority given to tasks which require high intensive labor or produce high value-added product and tasks under hostile environment. In the field of bio-production capabilities of the versatility and robustness of automated system have been major bottlenecks along with economical efficiency. This paper introduces a new concept of automation based on tole-operation, which can provide solutions to overcome inherent difficulties in automating bio-production processes. Operator(farmer), computer, and automatic machinery share their roles utilizing their maximum merits to accomplish given tasks successfully. Among processes of greenhouse watermelon cultivation tasks such as pruning, watering, pesticide application, and harvest with loading were chosen based on the required labor intensiveness and functional similarities to realize the proposed concept. The developed system was composed of 5 major hardware modules such as wireless remote monitoring and task control module, wireless remote image acquisition and data transmission module, gantry system equipped with 4 d.o.f. Cartesian type robotic manipulator, exchangeable modular type end-effectors, and guided watermelon loading and storage module. The system was operated through the graphic user interface using touch screen monitor and wireless data communication among operator, computer, and machine. The proposed system showed practical and feasible way of automation in the field of volatile bio-production process.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.392-404
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• 2019

The purpose of this study was to identify the impact of various income sources on income inequality of forestry households. Data from the Forestry Household Economy Survey from 2013-2016 were analyzed using the Gini coefficient decomposition method via income source. In particular, the income inequality analysis of forestry households was broken down into separate analyses based on group, i.e., the whole of forestry households and the five income quintile classes. The results of the analyses showed that income inequality of forestry households is primarily affected by forestry and nonforestry incomes and income quintile class. Moreover, income inequality of the highest income quintile class was largely affected by forestry income compared with other sources of income, whereas that of other income quintile classes was largely affected by nonforestry income. Therefore, in order to reduce income inequality in forestry households, it is necessary to increase the proportion of forestry income in the lower four quintile classes. Given that the income of the lowest quintile class is negative, it is necessary to devise ways to improve the proportion and quantity of forestry income. At the same time, as forestry income increases, a policy alternative is also required to improve inequality in forestry income.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.485-496
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• 2022

This study is for the software implementation to generate atmospheric and surface reflectance products from RapidEye satellite imagery. The software is an extension based on Orfeo Toolbox (OTB) and an open-source remote sensing software including calibration modules which use an absolute atmospheric correction algorithm. In order to verify the performance of the program, the accuracy of the product was validated by a test image on the Radiometric Calibration Network (RadCalNet) site. In addition, the accuracy of the surface reflectance product generated from the KOMPSAT-3A image, the surface reflectance of Landsat Analysis Ready Data (ARD) of the same site, and near acquisition date were compared with RapidEye-based one. At the same time, a comparative study was carried out with the processing results using QUick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool supported by a commercial tool for the same image. Similar to the KOMPSAT-3A-based surface reflectance product, the results obtained from RapidEye Extension showed accuracy of agreement level within 5%, compared with RadCalNet data. They also showed better accuracy in all band images than the results using QUAC or FLAASH tool. As the importance of the Red-Edge band in agriculture, forests, and the environment applications is being emphasized, it is expected that the utilization of the surface reflectance products of RapidEye images produced using this program will also increase.

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.25-37
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• 2023

According to the 「Rural Spatial Reconstruction and Regeneration Support Act」, enacted on March 29, 2024, all local governments are required to establish a 'Rural Spatial Reconstruction and Regeneration Plan' (hereinafter referred to as the 'Rural Spatial Plan'). In order for the 'Rural Spatial Plan' to be appropriately established, this study analyzed the supply and demand of spatial data from the perspective of user stakeholders and derived implications for improving rural spatial planning data utilization. In conclusion, three key recommendations come from this result. Firstly, it is necessary to establish an integrated DB for rural spatial planning data. This can solve the problem of low awareness of scattered data-providing websites, reduce the processing time of non-GIS data, and reduce the time required to acquire data by securing the availability of data search and download. In particular, research should be conducted on the establishment of a spatial analysis simulation system to support stakeholders' decision-making, considering that many stakeholders have difficulty in spatial analysis because spatial analysis techniques were not actively used in rural projects before the implementation of the rural agreement system in 2020. Secondly, research on how to improve data acquisition should be conducted in each data sector. The data sector group with the lowest ease of receiving are 'Local Community Domain', 'Changes in Domestic and International Conditions', and 'Provision and Utilization of Daily Life Services'. Lastly, in-depth research is needed on how to raise each rural spatial planning data supply stakeholder to the position of player. Stakeholders of 'University Institutions' and 'Public Enterprises and Research Institutes' should give those who participate in the formulation of rural spatial plans access to the raw data collected for public work. Stakeholders of 'Private company' need to come up with realistic measures to build a data pool centered on consultative bodies between existing private companies and then prepare a step-by-step strategy to fully open it by participating various stakeholders. In order to induce 'Village Residents and Associations' stakeholders to play a leading role as owners and producers of data, personnel should be trained to collect and record data related to the village. In addition, support measures should be prepared to continue these activities.

• Journal of Mushroom
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• v.17 no.3
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• pp.119-125
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• 2019

This study aims to report the results for the analysis of the growth environment by applying smart farm technology to "Chunchu No 2" farmers in order to develop an optimal growth model for precision cultivation of bottle-grown oyster mushrooms. The temperature, humidity, carbon dioxide concentration, and illumination data were collected and analyzed using an environmental sensor installed to obtain growth environment data from the oyster mushroom cultivator. Analysis of the collected temperature data revealed that the temperature at the time of granulation was $19.5^{\circ}C$ after scraping, and the mushroom was generated and maintained at about $21^{\circ}C$ until the bottle was flipped. When the fruiting body grew and approached harvest time, mushrooms were harvested while maintaining the temperature between $14^{\circ}C$ and $18^{\circ}C$. The humidity was maintained at almost 100% during the complete growth stage. Carbon dioxide concentration gradually increased until 3 days after the beginning of cultivation, and then increased rapidly to almost 5,500 ppm. From the 6th day, carbon dioxide concentration was gradually decreased through ventilation and was maintained at 1,600 ppm during harvest. Light intensity of 8 lux was irradiated up to day 6 after seeding, and growth was then continued while periodically irradiating 4 lux light. The fruiting body characteristics of "Chunchu No 2" cultivated in the farmhouse were as follows: pileus diameter of 26.5 mm and thickness of 4.9 mm, stipe thickness of 8.9 mm, and length of 68.7 mm. The fruiting body yield was 166.8 g/850 ml, and the individual weight was 12.8 g/10 units.