• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.117-126
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• 2018

The purpose of this study is to analyze the spatial distribution characteristics and spatial changes of Automatic Weather Stations (AWS) in mountainous areas with altitude more than 200 meters in South Korea. In order to analyze the spatial distribution patterns, spatial analysis was performed on 203 Automatic Mountain Meteorology Observation Station (AMOS) points from 2012 to 2016 by Euclidean distance analysis, nearest neighbor index analysis, and Kernel density analysis methods. As a result, change of the average distance between 2012 and 2016 decreased up to 16.4km. The nearest neighbor index was 0.666632 to 0.811237, and the result of Z-score test was -4.372239 to -5.145115(P<0.01). The spatial distributions of AMOSs through Kernel density analysis were analyzed to cover 129,719ha/a station in 2012 and 50,914ha/a station in 2016. The result of a comparison between 2012 and 2016 on the spatial distribution has decreased about 169,399ha per a station for the past 5 years. Therefore it needs to be considered the mountainous regions with low density when selecting the site of AMOS.

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