• Korean Journal of Environmental Biology
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• v.29 no.4
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• pp.345-352
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• 2011

We investigated 12 plots using Herles' diversity of structure feature calculation method, which is based on the Shannon-Weaver-Index for estimation of species diversity. This study sought to facilitate a more systematic understanding of the structure of the forest stands in the Korean fir natural forest in Naeseorak. Although the change in the forest structure is dependent on the change in phase, factors of the natural forest were confirmed by associating individual structure features. As shown in the results of diversity of structure features, the diversity of the structure of the fir tree natural forest in Naeseorak was relatively low. The association between species diversity and overall factors (diversity) related with the change in the structure was found to be weak. The association between the number of trees and the diversity of forest structure stands was moderate, showing that the higher the number of trees, the less diverse the forest structure is. In most of the investigated plots, stem volume and volume of dead tree were associated with the height of natural regeneration, but these were not associated with other factors. Height of natural regeneration was found to be correlated to stand density, crown area and crown class, whereas tree height and BHD did not have any association with other factors. Overall, the results of the investigation are helpful in understanding the change in the structure of Korean fir natural forest. Further investigation with more plots is required.

• Journal of Korea Foresty Energy
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• v.24 no.2
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• pp.37-45
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• 2005

This study presents a method how to estimate the change of stand volume, the stand biomass and the carbon removals, using dynamic stand growth model according to whether the practices for forest management are implemented or not. As a result, it shows that the rate of stand change was significantly high if the practices were implemented. Consequently, the change of carbon removals was also high. The carbon removals at the stand where the practices were not implemented, was estimated about 0.27tC/ha. And the carbon removals at the stand where the practices were implemented, was estimated 166.02tC/ha(thinning from above) and 163.75tC/ha(thinning from below). It is confirmed that the thinning activities has a great influence on the change of carbon removals and there was little difference of the carbon removals between thinning types. From this result, it is proved that forest management like thinning activities is prerequisite condition to improve the carbon removals of stand.

• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.365-373
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• 2012

This study purposed to extract and assess the accuracy of assessment for deforestation area in Wonju city using medium resolution satellite image. The total size of deforestation area during the last nine years (2000-2008) was about 467 ha, and it was occurred annually about 52 ha. The most frequent form of deforestation was settlements (72%). Ninety percent of the size of deforestation was less than 2 ha in size. In addition, 79 percent of deforestation area was found within 500 m from the road network and within 100 m of the Forest/Non-forest boundary. This study compared the deforestation based on the administrative information (GIS deforestationI) with the deforestation (RS deforestation) extracted from the satellite imagery by vegetation indices (NDVI, NBR, NDWI). Extraction accuracy, mean-standard deviation${\times}1.5$ applied 3 by 3 filtering, showed reliable accuracy 35.47% k-value 0.20. However, error could be occurred because of the difference of land-use change and land-cover change. The actual rate of land-cover change deforestation area was 32% on administrative information. The 7.52% of forest management activities area was misjudged as deforestation by RS deforestation. Finally, the comparison of land-cover change deforestation (GIS deforestationII) with the RS deforestation accuracy, as a result NDVI mean-standard deviation${\times}2$ applied 3 by 3 filtering, showed improved accuracy 61.23%, k-value 0.23.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.14-26
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• 2016

This study analyzed the correlation between the standardized precipitation index(SPI) and forest fire occurrences using monthly accumulative rainfall data since 1970 and regional fire occurrence data since 1991. To understand the relationship between the SPI and forest fire occurrences, the correlations among the SPI of nine main observatory weather stations including Seoul, number of fire occurrences, and log of fire occurrences were analyzed. We analyzed the correlation of SPI with fire occurrences in the 1990s and 2000s and found that in the 1990s, the SPI of 3 months showed high correlation in Gyeonggi, Gangwon, and Chungnam, while the SPI of 6 months showed high correlation in Chungbuk, and the SPI of 12 months showed high correlation in Gyeongnam, Gyenongbuk, Jeonnam, and Jeonbuk. In the 2000s, the SPI of 6 months showed high correlation with the fire frequency in Gyeonggi, Chungnam, Chungbuk, Jeonnam, and Jeonbuk, whereas the fire frequency in western Gangwon was highly correlated with the SPI of 3 months and, in eastern Gangwon, Gyeongnam, and Gyenongbuk, with the SPI of 1 month. In the 1990s, distinct differences in the drought condition between the SPI of 3 months and 12 months in the northern and southern regions of Korean Peninsula were found, whereas the differences in both the SPI of 1 month and 6 months were found in the Baekdudaegan region except western Gangwon since the 2000s. Therefore, this study suggests that we can develop a model to predict forest fire occurrences by applying the SPI of 1-month and 6-month data in the future.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.309-310
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.225-226
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.240-240
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.243-243
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2012.07a
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• pp.93-96
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• 2012