• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1423-1444
• /
• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.

• Resources Recycling
• /
• v.31 no.5
• /
• pp.67-76
• /
• 2022

The furniture industry has a high possibility to create value-added and a high potential to create new occupations due to the characteristics of the industry, which mainly consists of small and medium-sized enterprises (SMEs). However, the used furniture, which has sufficient reuse value, is also crushed and used as solid refuse fuel (SRF) recently. Besides, the number of waste treatment companies continues to decrease, and it occurs congestion of wood waste. As a way to solve the issue, a business model development of remanufacturing used furniture can be suggested as an alternative due to its high circular economic efficiency. Remanufacturing business including furniture industry creates positive effects in various aspects such as economic, environmental and job creation. In other words, remanufacturing is an effective recycling way to reduce input resources and energy in the production process. The results of economic analysis show that the expected annual revenue from the single worker furniture remanufacturing site was 104 million won which is 3.11 times more than the average income of a single-worker household in Korea and its B/C ratio was estimated about 30 which means high business feasibility. Revenue through furniture remanufacturing also showed 320 times higher than that of SRF production from the perspective of weight. In addition, it is shown that the GHGs reduction from the furniture remanufacturing is 2.2 ton CO₂-eq. per year, which is similar to the amount of GHGs absorption effect of 937 pine trees or 622 Korean oak trees annually. Thus the results of this study demonstrate that it is important to adopt an appropriate recycling method considering the economic and environmental effects at the end-of-life stage.

• Journal of Korean Society of Forest Science
• /
• v.112 no.1
• /
• pp.71-82
• /
• 2023

Forest biomass is used as a representative indicator of forest size, maturity, and productivity. Therefore, quantitative evaluation is important for management and harvest as well as the evaluation of ecosystem functions and services including CO₂ absorption. The allometric equation is a widely used method for estimating the value of each component through the relative growth rate of plants. Recently, studies indicated that the relative growth of trees is changing because of the increased CO₂ concentration in the atmosphere and the resulting climate change, raising the need to review the previously developed relative growth models and coefficients. In this study, the height-diameter at breast height (DBH) relationships of four major tree species in Korea [(Pinus densiflora (PD), Larix kaempferi (LK), Quercus variabilis (QV), and Quercus mongolica (QM)] were analyzed using the 5th-7th National Forest Inventory (NFI) data. Furthermore, these results were compared with the present yield table from the National Institute for Forest Science. This analysis revealed that the expected height for the same DBH increased as the NFI progressed. For example, in model analysis, the expected heights for PD, LK, QV, and QM for DBH of 25 cm were 12.48, 19.17, 14.47, and 13.19 m, respectively, in the 5th NFI data. In the 7th NFI data, these values were estimated as 13.61 (+9.1%), 21.58 (+12.7%), 15.76 (+8.9%), and 13.93 m (+5.6%), respectively. These results indicate that the major tree species in South Korean forests currently are more vigorous in height growth than in diameter growth when compared to the height-DBH development trends by tree species identified through past survey data.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1565-1576
• /
• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.