• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.346-352
• /
• 2022

Mountain meteorology in South Korea that is covered mountains with complex terrain is important for understanding and managing the forest disaster and forest ecosystems. In particular, recent changes in dryness and/or rainfall intensity due to climate change may cause an increase in the possibility of forest disasters. Therefore, accurate monitoring of mountain meteorology is needed for efficient forest management. Korea Forest Service (KFS) is establishing the Automatic Mountain Meteorology Observation Stations (AMOS) in the mountain regions since 2012. 464 AMOSs are observing various meteorological variables such as air temperature, relative humidity, wind speed and direction, precipitation, soil temperature, and air pressure for every minute, which is conducted the quality control (QC) to retain data reliability. QC process includes the physical limit test, step test, internal consistency test, persistence test, climate range test, and median filter test. All of AMOS observations are open to use, which can be found from the Korean Mountain Meteorology Information System (KoMIS, http://mtweather.nifos.go.kr/) of the National Institute of Forest Science and the Public Data Portal (https://public.go.kr/). AMOS observations with guaranteed quality can be used in various forest fields including the public safety, forest recreation, forest leisure activities, etc., and can contribute to the advancement of forest science and technology. In this paper, a series of processes are introduced to collect and use the AMOS dataset in the mountain region in South Korea.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2019.08a
• /
• pp.122-123
• /
• 2019

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.244-255
• /
• 2022

This study estimated and evaluated the high resolution (1km) gridded mountain meteorology data of daily mean, maximum and minimum temperature based on ASOS (Automated Surface Observing System), AWS (Automatic Weather Stations) and AMOS (Automatic Mountain Meteorology Observation System) in South Korea. The ASOS, AWS, and AMOS meteorology data which were located above 200m was classified as mountainous area. And the ASOS, AWS, and AMOS meteorology data which were located under 200m was classified as non-mountainous area. The bias-correction method was used for correct air temperature over complex mountainous area and the performance of enhanced daily coefficients based on the AMOS and mountainous area observing meteorology data was evaluated using the observed daily mean, maximum and minimum temperature. As a result, the evaluation results show that RMSE (Root Mean Square Error) of air temperature using the enhanced coefficients based on the mountainous area observed meteorology data is smaller as 30% (mean), 50% (minimum), and 37% (maximum) than that of using non-mountainous area observed meteorology data. It indicates that the enhanced weather coefficients based on the AMOS and mountain ASOS can estimate mean, maximum, and minimum temperature data reasonably and the temperature results can provide useful input data on several climatological and forest disaster prediction studies.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.318-329
• /
• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.18 no.4
• /
• pp.208-220
• /
• 2016

Automatic Mountain Meteorology Observation System (AMOS) is an important ingredient for several climatological and forest disaster prediction studies. In this study, we select the optimal sites for AMOS in the mountain areas of Honam and Jeju in order to prevent forest disasters such as forest fires and landslides. So, this study used spatial dataset such as national forest map, forest roads, hiking trails and 30m DEM(Digital Elevation Model) as well as forest risk map(forest fire and landslide), national AWS information to extract optimal site selection of AMOS. Technical methods for optimal site selection of the AMOS was the firstly used multifractal model, IDW interpolation, spatial redundancy for 2.5km AWS buffering analysis, and 200m buffering analysis by using ArcGIS. Secondly, optimal sites selected by spatial analysis were estimated site accessibility, observatory environment of solar power and wireless communication through field survey. The threshold score for the final selection of the sites have to be higher than 70 points in the field assessment. In the result, a total of 159 polygons in national forest map were extracted by the spatial analysis and a total of 64 secondary candidate sites were selected for the ridge and the top of the area using Google Earth. Finally, a total of 26 optimal sites were selected by quantitative assessment based on field survey. Our selection criteria will serve for the establishment of the AMOS network for the best observations of weather conditions in the national forests. The effective observation network may enhance the mountain weather observations, which leads to accurate prediction of forest disasters.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_1
• /
• pp.715-727
• /
• 2023

To provide detailed and appropriate meteorological information in mountainous areas, the Korea Forest Service has established an Automatic Mountain Meteorology Observation Station (AMOS) network in major mountainous regions since 2012, and 464 stations are currently operated. In this study, we proposed an optimal kriging technique with lapse rate correction to produce gridded temperature data suitable for Korean forests using AMOS point observations. First, the outliers of the AMOS temperature data were removed through statistical processing. Then, an optimized theoretical variogram, which best approximates the empirical variogram, was derived to perform the optimal kriging with lapse rate correction. A 500-meter resolution Kriging map for temperature was created to reflect the elevation variations in Korean mountainous terrain. A blind evaluation of the method using a spatially unbiased validation sample showed a correlation coefficient of 0.899 to 0.953 and an error of 0.933 to 1.230℃, indicating a slight accuracy improvement compared to regular kriging without lapse rate correction. However, the critical advantage of the proposed method is that it can appropriately represent the complex terrain of Korean forests, such as local variations in mountainous areas and coastal forests in Gangwon province and topographical differences in Jirisan and Naejangsan and their surrounding forests.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.427-435
• /
• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.8 no.1
• /
• pp.36-44
• /
• 2006

In this study, we constructed a forest fire danger map in the Yeongdong area of Gangwon-do and Northeastern area of Gyeongsangbuk-do using a forest fire rating model and geographical information system (GIS). We investigated the appropriate positions of the automatic weather station (AWS) and a comprehensive network solution (a system including measurement, communication and data processing) for the establishment of an optimum mountain meteorological observation network system (MMONS). Also, we suggested a possible plan for combining the MMONS with unmanned monitoring camera systems and wireless relay towers operated by local governments and the Korea Forest Service for prevention of forest fire.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.22 no.3
• /
• pp.21-36
• /
• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.