• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.1
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• pp.30-42
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• 2016

Climate change are known to have had enormous impacts on plant phenology and thus to have damage on other species which are interacted within ecosystem. In Korea, however, it is difficult to analyze the relationship between climate and phenology due to the limitation of measurement data of plant phenological records. In this study, to be effective analysis of SOG(start of growing season), we used phenological transition dates by using satellite data. Then, we identified the most influential variable in variation of SOG throughout the relationship between SOG and temperature factors. As a result, there is a strong correlation between the SOG and April temperature, TSOGmin($3^{\circ}C$, 12days). This study is expected to be used for predicting plant phenological change using climate change scenario data.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6261-6265
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• 2015

Background: Even though rarely found in Indonesia, the incidence of multiple myeloma (MM) is increasing every year. Bone involvement of MM is the most often a clinical disorder which leads to worsening clinical conditions and low quality of life of patients. Purpose: To determine the clinicopathology profile of bone involvement of MM patients in Indonesia. Materials and Methods: The cross-sectional study of MM conducted at Dharmais National Cancer Hospital (DNCH) by collecting data from medical records of MM patients who came to DNCH in period 2008-2012. Results: There were 39 MM patients all with age above 60 years. There were 56.4% male and 43.6% female patients. Most were diagnosed at stage III (32.4%), and 41% had obesity. The comorbid conditions were anemia (82.9%), hypoalbuminemia (60%), increased creatinine level (38.5%), increased ${\beta}2$ microglobulin level (94.1%), increased LDH level (23.1%) and plasmocytes above 30% (65%), but only 4.2% patients presented with hypercalcemia. Meanwhile, bone involvement occurred in 76.9% of MM patients with 4 lesions on average and a maximum of 16 lesions. The locations of bone lesions were spine (70%), skull (70%), pelvis (33.3%), humerus (30%), and femur (30%). Conclusions: The incidence of MM in Indonesia is increasing annually with bone involvement in more than three-fourths, but interestingly without hypercalcemia.

• Safety and Health at Work
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• v.2 no.3
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• pp.260-272
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• 2011

Objectives: The promotion of health and safety (H&S) awareness among hospital staff can be applied through various methods. The aim of this study was to assess the risk level of physical hazards in the hospital sector by combining workers' perception, experts' evaluation and objective measurements. Methods: A cross-sectional study was designed using multiple triangulation. Hospital staff (n = 447) filled in an H&S questionnaire in a general hospital in Athens and an oncology one in Thessaloniki. Experts observed and filled in a checklist on H&S in the various departments of the two hospitals. Lighting, noise and microclimate measurements were performed. Results: The staff's perception of risk was higher than that of the experts in many cases. The measured risk levels were low to medium. In cases of high-risk noise and lighting, staff and experts agreed. Staff's perception of risk was influenced by hospital's department, hospital's service, years of working experience and level of education. Therefore, these factors should be taken into account in future studies aimed at increasing the participation of hospital workers. Conclusion: This study confirmed the usefulness of staff participation in the risk assessment process, despite the tendency for staff to overestimate the risk level of physical hazards. The combination of combining staff perception, experts' evaluation and objective measures in the risk assessment process increases the efficiency of risk management in the hospital environment and the enforcement of relevant legislation.

• Korean Journal of Head & Neck Oncology
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• v.24 no.2
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• pp.155-161
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• 2008

Head and neck squamous cell carcinoma(HNSCC) still has poor outcome, and laryngeal cancer is the most frequent subtype of HNSCC. Therefore, there is a need to develop novel treatments to improve the outcome of patients with HNSCC. It is critical to gain further understanding on the molecular and chromosomal alteration of HNSCC to identify novel therapeutic targets but genetic etiology of squamous cell carcinoma of the larynx is so complex that target genes have not yet been clearly identified. Array based CGH(array-CGH) allows investigation of general changes in target oncogenes and tumor suppressor genes, which should, in turn, lead to a better understanding of the cancer process. In this study, We used genomic wide array-CGH in tissue specimens to map genomic alterations found in laryngeal squamous cell carcinomas. As results, gains of MAP2, EPHA3, EVI1, LOC389174, NAALADL2, USP47, CTDP1, MASP1, AHRR, and KCNQ5, with losses of SRRM1L, ANKRD19, FLJ39303, ZNF141, DSCAM, GPR27, PROK2, ARPP-21, and B3GAT1 were observed frequently in laryngeal squamous cell carcinoma tissue specimens. These data about the patterns of genomic alterations could be a basic step for understanding more detailed genetic events in the carcinogenesis and also provide information for diagnosis and treatment in laryngeal squamous cell carcinoma. The high resolution of array-CGH combined with human genome database would give a chance to find out possible target genes which were gained or lost clones.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.365-365
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• 2019

가뭄은 전 세계적으로 농업을 비롯한 사회, 경제적으로 큰 피해를 주는 자연 재해이며, 향후 피해 저감을 위해 가뭄의 경향을 파악하고 지역별 가뭄 특성을 파악할 필요가 있다. 위성영상을 활용한 가뭄 판단은 광역적 범위를 대상으로 다양한 밴드를 활용한 데이터를 주기적이고 일정한 수준으로 취득 가능하다는 장점이 있다. 농업 가뭄 분야의 위성영상 활용은 미계측 지역에 대한 정확한 데이터 취득이 어려운 지점데이터의 단점을 보완할 수 있다. 위성영상을 활용한 가뭄 지수로는 Leaf Area Index (LAI), Vegetation Health Index (VHI), Enhanced Vegetation Index (EVI) 등 다양한 지수들이 있으며, 본 연구에서는 단기 가뭄 판단에 활용되고 있는 Evaporative Stress Index (ESI)를 활용하였다. 국내 행정구역 기반의 가뭄 판단을 위해 Moderate Resolution Imaging Spectramadiometer (MODIS)위성의 MOD16A2 영상을 사용하였다. MOD16A2는 land surface temperature (LST)과 LAI의 계산을 통한 실제 증발산량과 FAO-56 Penman-Monteith 공식을 사용한 잠재증발산량을 포함한 다양한 데이터를 8일 주기의 500m 해상도로 제공하고 있다. 2001년부터 2018년까지 500m 해상도의 ESI를 산정하였으며, 국내의 과거 가뭄 경향 분석과 지역별 특성 파악을 위한 표준화를 수행하였다. 그 결과 과거 극심한 가뭄이 있었던 해 (2000-2001년, 2015-2017년 등)에 대한 농업 가뭄 경향 분석이 가능하였으며, 지역별 특성을 파악한 결과 상습가뭄 지역에서 가뭄 경향을 확인하였다. 농업 가뭄 분야에서 ESI의 활용은 가뭄 조기 경보 시스템 개발 및 위성영상 기반 가뭄 모니터링 기술 개발 등에 활용 가능할 것으로 기대된다.

• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.2
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• pp.103-108
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• 2023

Due to the recent impact of global warming, heavy rainfall and droughts have been occurring regardless of the season, affecting the growth of Italian ryegrass (IRG), a winter forage crop. Particularly, delayed sowing due to frequent heavy rainfall or autumn droughts leads to poor growth and reduced winter survival rates. Therefore, techniques to improve yield through additional sowing in spring have been implemented. In this study, the growth of IRG sown in Spring and Autumn was compared and analyzed using vegetation indices during the months of April and May. Spectral data was collected using an Unmanned Aerial Vehicle (UAV) equipped with a hyperspectral sensor, and the following vegetation indices were utilized: Normalized Difference Vegetation Index; NDVI, Normalized Difference Red Edge Index; NDRE (I), Chlorophyll Index, Red Green Ratio Index; RGRI, Enhanced Vegetation Index; EVI and Carotenoid Reflectance Index 1; CRI1. Indices related to chlorophyll concentration exhibited similar trends. RGRI of IRG sown in autumn increased during the experimental period, while IRG sown in spring showed a decreasing trend. The results of RGRI in IRG indicated differences in optical characteristics by sowing seasons compared to the other vegetation indices. Our findings showed that the timing of sowing influences the optical growth characteristics of crops by the results of various vegetation indices presented in this study. Further research, including the development of optimal vegetation indices related to IRG growth, is necessary in the future.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Fisheries and Aquatic Sciences
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• v.27 no.2
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• pp.87-99
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• 2024

Global climate change, followed by an increase in anthropogenic activities in aquatic ecosystems, and species invasions, has resulted in a decline in aquatic organism biodiversity. The Batanghari River, Sumatra's longest river, is polluted by mercury-containing illegal gold mining waste (PETI), industrial pollution, and domestic waste. Several studies have provided evidence suggesting a decline in fish biodiversity within the Batanghari River. However, a comprehensive evaluation of the present status of biodiversity in this river is currently lacking. The species under investigation were identified through various molecular-based identification methods, as well as morphological identification, which involved the use of neighbor-joining (NJ) trees. All collected specimens were initially identified using morphological techniques and subsequently confirmed with molecular barcoding analysis. Morphological and DNA barcoding identification categorized all specimens (1,692) into 36 species, 30 genera and 16 families, representing five orders. A total of 36 DNA barcodes were generated from 30 genera using a 650-bp-long fragment of the mitochondrial cytochrome oxidase subunit I (COI) gene. Based on the Kimura two-parameter model (K2P), The minimum and maximum genetic divergences based on K2P distance were 0.003 and 0.331, respectively, and the average genetic divergence within genera, families, and orders was 0.05, 0.12, 0.16 respectively. In addition, the average interspecific distance was approximately 2.17 times higher than the mean intraspecific distance. Our results showed that the COI barcode enabled accurate fish species identification in the Batanghari River. Furthermore, the present work will establish a comprehensive DNA barcode library for freshwater fishes along Batanghari River and be significantly useful in future efforts to monitor, conserve, and manage fisheries in Indonesia.

• Korean Journal of Agricultural and Forest Meteorology
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• v.13 no.2
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• pp.69-78
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• 2011

Rice is one of the world's staple foods. Paddy rice fields have unique biophysical characteristics that the rice is grown on flooded soils unlike other crops. Information on the spatial distribution of paddy fields and the timing of irrigation are of importance to determine hydrological balance and efficiency of water resource management. In this paper, we detected the timing of irrigation and spatial distribution of paddy fields using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Aqua satellite. The timing of irrigation was detected by the combined use of MODIS-based vegetation index and Land Surface Water Index (LSWI). The detected timing of irrigation showed good agreement with field observations from two flux sites in Korea and Japan. Based on the irrigation detection, a land cover map of paddy fields was generated with subsidiary information on seasonal patterns of MODIS enhanced vegetation index (EVI). When the MODISbased paddy field map was compared with a land cover map from the Ministry of Environment, Korea, it overestimated the regions with large paddies but underestimated those with small and fragmented paddies. Potential reasons for such spatial discrepancies may be attributed to coarse pixel resolution (500 m) of MODIS images, uncertainty in parameterization of threshold values for discarding forest and water pixels, and the application of LSWI threshold value developed for paddy fields in China. Nevertheless, this study showed that an improved utilization of seasonal patterns of MODIS vegetation and water-related indices could be applied in water resource management and enhanced estimation of evapotranspiration from paddy fields.