• Ecology and Resilient Infrastructure
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• v.2 no.2
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• pp.185-190
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• 2015

Greenhouse gas (GHG) emission from agricultural lands is recognized as one of important factors of global warming. The objective of this short communication was to evaluate the applicability of different soil GHG emission prediction models on agricultural systems in Korea. Four models, namely, DNDC, DAYCENT, EXPERT-N and COUP, were selected and the basic structure (e.g., components and sub-model), input variables, and output variables were compared. In particular, the availability and compilation of essential input variables were assessed. Major input variables needed for operating these predictive models were found to be available through database systems established by national organizations such as the Korea Meteorological Administration, the Korean Soil Information System, and the Rural Development Administration. However, in order to apply these models in Korea, it was necessary to calibrate and validate each of the models for the domestic landscape settings and climate conditions. In addition, field data of long-term monitoring of GHG emission from agricultural lands are limited and therefore should be measured.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1427-1435
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• 2023

Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, the anisotropic properties of the crop canopy surface can govern spectral reflectance and vegetation indices. In this study, we applied an ensemble of bidirectional reflectance distribution function (BRDF) models to high-resolution Sentinel-2 satellite imagery and compared the differences between correction results before and after reflectance. In the red and near-infrared (NIR) band reflectance images, BRDF-corrected outlier values appeared in certain urban and paddy fields of farmland areas and forest shadow areas. These effects were equally observed when calculating the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2). Furthermore, the outlier values in corrected NIR band were shown in pixels shadowed by mountain terrain. These results are expected to contribute to the development and improvement of BRDF models in high-resolution satellite images.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.4
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• pp.215-219
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• 2001

Land surface parameterization schemes for atmospheric models as well as decision support tools for ecosystem management require a frequent updating of land cover classification data for regional to global scales. Rice paddies have not been treated independently from other agricultural land classes in many classification systems, despite their atmospheric and ecological significance. A simple but improved method over conventional land cover classification schemes for rice paddy is suggested. Normalized difference vegetation index (NDVI) was calculated for the land area of South Korea at a 1km by 1 km resolution from the visible and the near-infrared channel reflectances of NOAA-AVHRR (Advanced Very High Resolution Radiometer). Monthly composite images of daily maximum NDVI were prepared for May and August, and used to classify 4 major land cover classes : urban, farmland, forests and water body. Among the pixels classified as "forests" in August, those classified as "water body" in May were assigned a "rice paddy" class. The distribution pattern of "rice paddy" pixels was very similar to the reported rice acreage of 1,455 Myons, which is the smallest administrative land unit in Korea. The correlation coefficient between the estimated and the reported acreage of Myons was 0.7, while 0.5 was calculated from the USGS classification.calculated from the USGS classification.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.79-85
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• 2009

Net radiation ($R_N$) is a driving force of biological and physical processes between the surface and the atmosphere and its knowledge is critical to weather forecasting and water resource management. The measurement of $R_N$ is, however, scarce and it is typically estimated from an empirical relationship. This study presented two different methods of $R_N$ estimation over three major plant functional types (i.e., a deciduous forest, a coniferous forest, and a farmland) in Korea. One is a linear regression method between $R_N$ and solar radiation and the other is a radiation balance method. The two methods were examined using the data collected in 2008 at the three sites. Based on the linear regression method over a year, $R_N$ was 70% of the incoming shortwave radiation ($R_S{\downarrow}$) for a deciduous forest, 79% for a coniferous forest, and 64% for a farmland, indicating that the relationship was plant functional type-specific. For the radiation balance method, the inclusion of longwave radiation component slightly improved $R_N$ estimations. Overall, there was a good agreement between the observed and the estimated $R_N$ from both methods, indicating a reliable applicability of the two methods in estimating $R_N$.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.109-120
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• 2007

The information on flowpath, storage, residence time, and interactions of water and carbon transport in a catchment is the prerequisite to the understanding and predicting of water and carbon cycling in the mountainous landscapes of Korea. In this paper, along with some up-to-date results, we present the principal methods that are currently used in HydroKorea and CarboKorea research to obtain such information. Various catchment hydrological processes have been examined on the basis of the water table fluctuations, the end-member mixing model, the cross correlation analysis, and cosmogenic radioactive isotope activity. In the Gwangneung catchment, the contribution of surface discharge was relatively large, and the changes in the amount, intensity and patterns of precipitation affected both the flowpath and the mean residence time of water. Particularly during the summer monsoon, changes in precipitation patterns and hydrological processes in the catchment influenced the carbon cycle such that the persistent precipitation increased the discharge of dissolved organic carbon (DOC) concentrated in the surface soil layer. The improved understanding of the hydrological processes presented in this report will enable a more realistic assessment of the effects of climate changes on the water resource management and on the carbon cycling in forest catchments.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.3
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• pp.169-180
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• 2014

Chemical weathering of forest soils can reduce atmospheric $CO_2$ concentration over geologic time scales, providing many essential elements for life. Although many studies have been conducted on the effects of elevated atmospheric $CO_2$ on forest carbon storage using open top chambers and FACE (Free air $CO_2$ enrichment) facilities since the 1990s, studies on chemical weathering of forest soils under elevated $CO_2$ are relatively rare. Here I review on how elevated atmospheric $CO_2$ can affect the chemical weathering of forest soils and suggest directions on future research. Despite the recent advances in chemical weathering of forest soils under elevated atmospheric $CO_2$, it is still not clear how the large volume of forest soils would react under the condition. Future studies on weathering of forest soils covering large areas from the tropics to the polar regions with carefully monitored pre-treatment data would provide key information on how soils, the Earth's life sustaining engine, change under climate change.

• Korean Journal of Plant Resources
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• v.37 no.2
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• pp.137-148
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• 2024

The purpose of this study is to investigate the weather, soil characteristics, and location environment of Pimpinella brachycarpa natural habitats in order to gather the essential information for the conservation of these habitats. P. brachycarpa are distributed throughout Korea and are mainly found to grow in shady and humid areas between 500 and 1,200 m above sea level. The average annual temperature in Mt. Duta was 13.1℃, and the average annual precipitation in Mt. Jungwon was 1,509 mm, which was higher than in other regions. The pH ranged from 4.42 to 4.97, indicating slight acidity. The total N content ranged from 0.18% to 0.68%, and the available P ranged from 13.43 to 531.56 mg/kg, demonstrating notable regional variations. The species diversity index (H') was highest at Mt. Ilwol, measuring 1.713. The evenness (J') ranged from 0.983 to 0.993, and the dominance (D') ranged from 0.007 to 0.017. The similarity index was very low, averaging 24.86%, and it was divided into communities of Wilson's elm (Ulmus davidiana var. japonica) and communities of Korean maple (Acer pseudo-siebodianum).

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.755-770
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• 2023

Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.4 s.31
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• pp.21-26
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• 2004

Annually repeated flood damage is bringing about the rapid increasement of the loss of human lives and properly, and the enormous social cost for the restoration work is required. The annual amount of flood damage caused by the abnormal climate is reached about 1% of GDP, so more effective management of the flood control operation is needed to prevent the repetitive flood occurred same region. Also, the systematic planning and management or river improvement works are keenly required for the more effective execution of government working budget that expense to recover or to prevent the flood damage. The several organizations of government - Ministry of Construction & Transportation, Ministry of Government Administration and Home Affair, Ministry of Agriculture and Forestry, Ministry of Environment, Ministry of Science & Technology, Korea Meteorological Adminstration and so on - are promoting the flood prevention operations, but those promotion system makes it difficult to maintain the consistency of works. In this study, we try to develop the Web-GIS system prototype that will be able to effectively manage the nationwide river improvement works and to establish a framework that will be able to maintain the consistency of river improvement works. To achieve the study goals, we analysed current system of flood prevention operations, gathered related documents, had interviews with many government employees, and developed the Web-GIS system prototype.