• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.325-338
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• 2023

Korean fir (Abies koreana Wilson) is one of the most important environmental indicator tree species for assessing climate change impacts on coniferous forests in the Korean Peninsula. However, due to the nature of alpine and subalpine regions, it is difficult to conduct regular field surveys of Korean fir, which is mainly distributed in regions with altitudes greater than 1,000 m. Therefore, this study analyzed the vegetation change trend of Korean fir using regularly observed remote sensing data. Specifically, normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature (LST), and precipitation data from Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievalsfor GPM from September 2003 to 2020 for Hallasan and Jirisan were used to analyze vegetation changes and their association with environmental variables. We identified a decrease in NDVI in 2020 compared to 2003 for both sites. Based on the NDVI difference maps, areas for healthy vegetation and high mortality of Korean fir were selected. Long-term NDVI time-series analysis demonstrated that both Hallasan and Jirisan had a decrease in NDVI at the high mortality areas (Hallasan: -0.46, Jirisan: -0.43). Furthermore, when analyzing the long-term fluctuations of Korean fir vegetation through the Hodrick-Prescott filter-applied NDVI, LST, and precipitation, the NDVI difference between the Korean fir healthy vegetation and high mortality sitesincreased with the increasing LST and decreasing precipitation in Hallasan. Thissuggests that the increase in LST and the decrease in precipitation contribute to the decline of Korean fir in Hallasan. In contrast, Jirisan confirmed a long-term trend of declining NDVI in the areas of Korean fir mortality but did not find a significant correlation between the changes in NDVI and environmental variables (LST and precipitation). Further analyses of environmental factors, such as soil moisture, insolation, and wind that have been identified to be related to Korean fir habitats in previous studies should be conducted. This study demonstrated the feasibility of using satellite data for long-term monitoring of Korean fir ecosystems and investigating their changes in conjunction with environmental conditions. Thisstudy provided the potential forsatellite-based monitoring to improve our understanding of the ecology of Korean fir.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.316-328
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• 2021

In this study, the leaf Na content prediction model for spring potato was established using 400-1000 nm hyperspectral sensor to develop the multispectral sensor for the salinity monitoring in reclaimed land. The irrigation conditions were standard, drought, and salinity (2, 4, 8 dS/m), and the irrigation amount was calculated based on the amount of evaporation. The leaves' Na contents were measured 1st and 2nd weeks after starting irrigation in the vegetative, tuber formative, and tuber growing periods, respectively. The reflectance of the leaves was converted from 5 nm to 10 nm, 25 nm, and 50 nm of FWHM (full width at half maximum) based on the 10 nm wavelength intervals. Using the variance importance in projections of partial least square regression(PLSR-VIP), ten band ratios were selected as the variables to predict salinity damage levels with Na content of spring potato leaves. The MLR(Multiple linear regression) models were estimated by removing the band ratios one by one in the order of the lowest weight among the ten band ratios. The performance of models was compared by not only R2, MAPE but also the number of band ratios, optimal FWHM to develop the compact multispectral sensor. It was an advantage to use 25 nm of FWHM to predict the amount of Na in leaves for spring potatoes during the 1st and 2nd weeks vegetative and tuber formative periods and 2 weeks tuber growing periods. The selected bandpass filters were 15 bands and mainly in red and red-edge regions such as 430/440, 490/500, 500/510, 550/560, 570/580, 590/600, 640/650, 650/660, 670/680, 680/690, 690/700, 700/710, 710/720, 720/730, 730/740 nm.