• Asia Marketing Journal
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• v.13 no.4
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• pp.159-189
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• 2012

What attracts consumers to bloggers' reviews? Consumers would be attracted both by the Bloggers' expertise (i.e., knowledge and experience) and by his/her unbiased manner of delivering information. Expertise and trustworthiness are both virtues of information sources, particularly when there is uncertainty in decision-making. Noting this point, we postulate that consumers' motives determine the relative weights they place on expertise and trustworthiness. In addition, our hypotheses assume that tie strength moderates consumers' expectation on bloggers' expertise and trustworthiness: with expectation on expertise enhanced for power-blog user-group (weak-ties), and an expectation on trustworthiness elevated for personal-blog user-group (strong-ties). Finally, we theorize that the effect of credibility on willingness to accept a review is moderated by tie strength; the predictive power of credibility is more prominent for the personal-blog user-groups than for the power-blog user groups. To support these assumptions, we conducted a field survey with blog users, collecting retrospective self-report data. The "gourmet shop" was chosen as a target product category, and obtained data analyzed by structural equations modeling. Findings from these data provide empirical support for our theoretical predictions. First, we found that the purposive motive aimed at satisfying instrumental information needs increases reliance on bloggers' expertise, but interpersonal connectivity value for alleviating loneliness elevates reliance on bloggers' trustworthiness. Second, expertise-based credibility is more prominent for power-blog user-groups than for personal-blog user-groups. While strong ties attract consumers with trustworthiness based on close emotional bonds, weak ties gain consumers' attention with new, non-redundant information (Levin & Cross, 2004). Thus, when the existing knowledge system, used in strong ties, does not work as smoothly for addressing an impending problem, the weak-tie source can be utilized as a handy reference. Thus, we can anticipate that power bloggers secure credibility by virtue of their expertise while personal bloggers trade off on their trustworthiness. Our analysis demonstrates that power bloggers appeal more strongly to consumers than do personal bloggers in the area of expertise-based credibility. Finally, the effect of review credibility on willingness to accept a review is higher for the personal-blog user-group than for the power-blog user-group. Actually, the inference that review credibility is a potent predictor of assessing willingness to accept a review is grounded on the analogy that attitude is an effective indicator of purchase intention. However, if memory about established attitudes is blocked, the predictive power of attitude on purchase intention is considerably diminished. Likewise, the effect of credibility on willingness to accept a review can be affected by certain moderators. Inspired by this analogy, we introduced tie strength as a possible moderator and demonstrated that tie strength moderated the effect of credibility on willingness to accept a review. Previously, Levin and Cross (2004) showed that credibility mediates strong-ties through receipt of knowledge, but this credibility mediation is not observed for weak-ties, where a direct path to it is activated. Thus, the predictive power of credibility on behavioral intention - that is, willingness to accept a review - is expected to be higher for strong-ties.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.335-344
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• 2023

Brachydiplax chalybea flavovittata, a climate-sensitive biological indicator species, was first observed and recorded at Jeju Island in Korea in 2010. Overwintering was recently confirmed in the Yeongsan River area. This study was aimed to predict the potential distribution patterns for the larvae of B. chalybea flavovittata and to understand its ecological characteristics as well as changes of population under global climate change circumstances. Data was collected both from the Global Biodiversity Information Facility (GBIF) and by field surveys from May 2019 to May 2023. We used for the distribution model among downloaded 19 variables from the WorldClim database. MaxEnt model was adopted for the prediction of potential and future distribution for B. chalybea flavovittata. Larval distribution ranged within a region delimited by northern latitude from Jeju-si, Jeju Special Self-Governing Province (33.318096°) to Yeoju-si, Gyeonggi-do (37.366734°) and eastern longitude from Jindo-gun, Jeollanam-do (126.054925°) to Yangsan-si, Gyeongsangnam-do (129.016472°). M type (permanent rivers, streams and creeks) wetlands were the most common habitat based on the Ramsar's wetland classification system, followed by Tp type (permanent freshwater marshes and pools) (45.8%) and F type (estuarine waters) (4.2%). MaxEnt model presented that potential distribution with high inhabiting probability included Ulsan and Daegu Metropolitan City in addition to the currently discovered habitats. Applying to the future scenarios by Intergovernmental Panel on Climate Change (IPCC), it was predicted that the possible distribution area would expand in the 2050s and 2090s, covering the southern and western coastal regions, the southern Daegu metropolitan area and the eastern coastal regions in the near future. This study suggests that B. chalybea flavovittata can be used as an effective indicator species for climate changes with a monitoring of their distribution ranges. Our findings will also help to provide basic information on the conservation and management of co-existing native species.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.4
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• pp.225-231
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• 2023

This study was carried out to investigate the growth characteristics, yield and chemical compositions of whole crop barley (Hordeum vulgare L.) according to mixing ratio of chemical fertilizer (CF) and liquid swine manure (LSM) in the paddy field cultivation. The experimental design was arranged in a randomized block design with five treatments and three replications. The manure fertilizer ratio of five treatments were CF 100% (T1), CF 70% + LSM 30% (T2), CF 50% + LSM 50% (T3), CF 30% + LSM 70% (T4), and LSM 100% (T5) of whole crop barley. At this time, the application of liquid swine manure was based solely on the nitrogen. Plant length was higher at T1 as compared to other treatments (T2, T3, T4 and T5). Fresh yield, dry matter yield and total digestive nutrients (TDN) yield were the highest in T1, whereas the lowest in T5 treatment (p<0.05). Chemical compositions (crude protein, crude fat, neutral detergent fiber, acid detergent fiber and TDN) did not show significant difference among treatments. Ca and Na contents were significantly lower at T1 as compared to other treatments (T2, T3, T4 and T5). However, Mg and P contents were significantly higher at T1 as compared to other treatments(p<0.05). There was no significant difference in total free sugar content among T2, T3, T4 and T5 treatments, but the chemical fertilizer (T1) was significantly lower than the other treatments (p<0.01). Considering the above results, liquid swine manure application showed lower dry matter yield and TDN yield than chemical fertilizer, but higher free sugar content. Therefore, in order to increase the productivity of whole crop barley, it is considered desirable to mix liquid fertilizer with chemical fertilizer, taking into account the decomposition rate and insufficient components (P, K) of the liquid swine manure.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.697-714
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• 2023

The Tracksite of Cretaceous Dinosaurs and Pterosaurs in Jeongchon, Jinju was discovered in late 2017 during the construction of the Ppuri industry complex. This site is a natural heritage site with a high paleontological value, as it preserves fossils of various types of dinosaurs, pterosaurs, and animal traces at a dense concentration. In this study, we surveyed that physical weathering such as joint, crack, scaling, exfoliation, and fragmentation occurred through field research in the fossil site, and conducted basic research on conservation science to reduce the damage. To this end, among the eight levels identified after excavation, the rocks of Level 3, which yielded a large number of theropod footprint fossils, and Level 4, which yielded pterosaur footprint fossils, were analyzed for material characteristics and evaluation of the effectiveness of consolidation and adhesion. This results showed that the rocks in the Level 3 stratum were dark gray siltstone and the rocks in the Level 4 stratum were dark gray shale, which contained a large amount of calcite and were composed of quartz, plagioclase, mica, alkali feldspar, and other clay minerals, which are likely to be damaged by rainfall under external conditions. As a result of conducting an artificial weathering experiment by dividing the probationary sample into four groups: untreated, consolidation treatment, anti-swelling treatment, and adhesive treatment, the consolidation and the swelling inhibitor showed an effect immediately after treatment, but did not show a blocking effect under a freezing-thawing environment. The adhesive showed that the adhesive effect was maintained even under freezing-thawing conditions. In order to preserve the fossil sites at Jeongchon in the future, in addition to temporary measures to block the inflow of moisture, practical measures such as the construction of protective facilities should be prepared.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.729-744
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• 2023

The value of lithium has significantly increased due to the rising demand for electric cars and batteries. Lithium is primarily found in pegmatites, hydrothermally altered tuffaceous clays, and continental brines. Globally, groundwater-fed salt lakes and oil field brines are attracting attention as major sources of lithium in continental brines, accounting for about 70% of global lithium production. Recently, deep groundwater, especially geothermal water, is also studied for a potential source of lithium. Lithium concentrations in deep groundwater can increase through substantial water-rock reaction and mixing with brines. For the exploration of lithim in deep groundwater, it is important to understand its origin and behavior. Therefore, based on a nationwide preliminary study on the hydrogeochemical characteristics and evolution of thermal groundwater in South Korea, this study aims to investigate the distribution of lithium in the deep groundwater environment and understand the geochemical factors that affect its concentration. A total of 555 thermal groundwater samples were classified into five hydrochemical types showing distinct hydrogeochemical evolution. To investigate the enrichment mechanism, samples (n = 56) with lithium concentrations exceeding the 90th percentile (0.94 mg/L) were studied in detail. Lithium concentrations varied depending upon the type, with Na(Ca)-Cl type being the highest, followed by Ca(Na)-SO₄ type and low-pH Ca(Na)-HCO₃ type. In the Ca(Na)-Cl type, lithium enrichment is due to reverse cation exchange due to seawater intrusion. The enrichment of dissolved lithium in the Ca(Na)-SO₄ type groundwater occurring in Cretaceous volcanic sedimentary basins is related to the occurrence of hydrothermally altered clay minerals and volcanic activities, while enriched lithium in the low-pH Ca(Na)-HCO₃ type groundwater is due to enhanced weathering of basement rocks by ascending deep CO₂. This reconnaissance geochemical study provides valuable insights into hydrogeochemical evolution and economic lithium exploration in deep geologic environments.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.521-535
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• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.