• Spatial Information Research
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• v.20 no.4
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• pp.47-55
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• 2012

Normalized Difference Vegetation Index (NDVI) has been used to measure and monitor plant growth, vegetation cover, and biomass from multispectral satellite data. It is also a valuable index in forest applications, providing forest resource information. In this research, an approach for monitoring forest change using MODIS NDVI time series data is explored. NDVI difference-based approaches for a specific point in time have possible accuracy problems and are lacking in monitoring long-term forest cover change. It means that a multi-time NDVI pattern change needs to be considered. In this study, an efficient methodology to consider long-term NDVI pattern is suggested using a harmonic model. The suggested method reconstructs MODIS NDVI time series data through application of the harmonic model, which corrects missing and erroneous data. Then NDVI pattern is analyzed based on estimated values of the harmonic model. The suggested method was applied to 49 NDVI time series data from Aug. 21, 2009 to Sep. 6, 2011 and its usefulness was shown through an experiment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.198-221
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• 2021

After large-scale reforestation in the 1960s and 1970s, forests in Korea have gradually been aging. Net ecosystem CO₂ exchange of old-growth forests is theoretically near zero; however, it can be a CO₂ sink or source depending on the intervention of disturbance or management. In this study, we report the CO₂ budget dynamics of the Gwangneung deciduous old-growth forest (GDK) in Korea and examined the following two questions: (1) is the preserved GDK indeed CO₂ neutral as theoretically known? and (2) can we explain the dynamics of CO₂ budget by the common mechanisms reported in the literature? To answer, we analyzed the 15-year long CO₂ flux data measured by eddy covariance technique along with other biometeorological data at the KoFlux GDK site from 2006 to 2020. The results showed that (1) GDK switched back-and-forth between sink and source of CO₂ but averaged to be a week CO₂ source (and turning to a moderate CO₂ source for the recent five years) and (2) the interannual variability of solar radiation, growing season length, and leaf area index showed a positive correlation with that of gross primary production (GPP) (R²=0.32~0.45); whereas the interannual variability of both air and surface temperature was not significantly correlated with that of ecosystem respiration (RE). Furthermore, the machine learning-based model trained using the dataset of early monitoring period (first 10 years) failed to reproduce the observed interannual variations of GPP and RE for the recent five years. Biomass data analysis suggests that carbon emissions from coarse woody debris may have contributed partly to the conversion to a moderate CO₂ source. To properly understand and interpret the long-term CO₂ budget dynamics of GDK, new framework of analysis and modeling based on complex systems science is needed. Also, it is important to maintain the flux monitoring and data quality along with the monitoring of coarse woody debris and disturbances.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.125-135
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• 2022

In this study, growth changes of the diameter at breast height (DBH), height, basal area, volume, and biomass of Korean white pine (Pinus koraiensis Siebold & Zucc.) on a plantation were examined via long-term monitoring. In addition, this study was performed to provide the basic data for timber production in line with DBH class by comparing the growth of the relative DBH size. Growth characteristics according to DBH class were analyzed by categorizing trees into five classes based on sorted DBH rankings: class I (1%-20%; upper 20%), class II (21%-40%), class III (41%-60%), class IV (61%-80%), class V (81%-100%; lower 20%). A total class (0%-100%) was also used. Total increment and mean annual increment (MAI) were calculated using data from nine measurements taken over 39 years. Tree characteristics based on average values and stand characteristics based on unit area per hectare were examined. According to the total increments of variables, the differences in DBH, basal area, volume, and biomass among classes I-V increased over time, whereas the height difference did not continually increase. According to MAI, the maximum DBH value was 0.92 cm·yr^-1 at age 23 in class I, whereas the maximum value in all trees was 0.69 cm·yr^-1 at age 17. The maximum value of height MAI for class I was 0.52 m·yr^-1 at age 23, whereas that for all trees was 0.49 m·yr^-1 at age 20. In terms of basal area, volume, and biomass growth at tree-and stand-level, the maximum MAI of class I and all trees was not observed during the measurement period. Therefore, additional long-term monitoring data are required to determine the maximum MAI of the variables.

• Journal of Korean Society of Forest Science
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• v.103 no.3
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• pp.422-430
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• 2014

This study was conducted to investigate growth changes of DBH, height, crown width, volume, stem biomass, and dead trees after thinning treatments with different thinning intensity for Korean white pine (Pinus koraiensis) plantation. First thinning was applied with 29~69% intensity based on number of trees in 19-yearold plantation, and trees were measured three times: right after thinning (19-year-old), 5 years later (24-yearold), 12 years later (29-year-old). In the case of DBH growth, average DBH growth of heavily thinned plots was 19.6~19.9% higher 5 years later, and 13.3~24.7% higher 12 years later, compared to that of unthinned plots. Initial diameter growth rate was higher than late growth rate. The proportion of large pole candidates ($DBH{\geq}25cm$) was 31% in heavily thinned plot while only 2% was shown in unthinned plot. No difference was shown in height growth depending on plots, and average crown width growth in heavily thinned plots was 30.6~33.3% higher, 5 years later, and 35.0~40.0% higher, 12 years later, compared to that of unthinned plots. Average volume growth of individual trees in heavily thinned plots was 39.8~46.8% higher, 5 years later, and 23.0~52.0% higher, 12 years later, compared to unthinned plots. The maximum volume and biomass per unit area were shown in unthinned plot; the volume and biomass 5 and 12 years later after thinning were $133m^3/ha$ (51 kg/ha) and $344m^3/ha$ (132 kg/ha), respectivily. The significant difference appeared in crown width, volume, and biomass depending on thinning intensity. No dead trees occurred in heavily thinned plots for 12 years after thinning, while mortality rate in unthinned plots was 27.9~37.8%. As a result of analyzing annual increment using cores to determine the timing of second thinning, it suggested that second thinning be suitable around 10 years after first thinning.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.1
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• pp.9-15
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• 2000

The objective of this study is to evaluate the use of RADARSAT and Landsat TM data for the monitoring of rice growth. The relationships between backscatter coefficients($\sigma$$^{0}$ ) of RADARSAT data and digital numbers (DN) of Landsat TM and rice growth parameters were investigated. Radar backscatter coefficients were calculated by calibration process and then compared with rice growth parameters; plant height, leaf area index (LAI), and fresh and dry biomass. When radar backscatter coefficient ($\sigma$$^{0}$ ) of rice was expressed as a function of time, it is shown that the increasing trend ranged from -22--20dB to -9--8dB as growth advances. The temporal variation of backscatter coefficient was significant to interpret rice growth. According to the relationship between leaf area index and backscatter coefficient, backscatter coefficient underestimated leaf area index at the beginning of life history and overestimated, at the reproductive stage. The same increasing trend between biomass and backscatter coefficient was shown. From these results, RADARSAT data appear positive to the monitoring of rice growth. Each band of time-series Landsat TM data had a significant trend as a rice crop grows during its life cycle. Spectral indices, NDVI[(TM4-TM3)/(TM4+TM3)] and RVI(TM4/TM2), derived from Landsat TM equivalent bands had the same trend as leaf area index.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.435-449
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• 2022

Forest canopy height is an indispensable vertical structure parameter that can be used for understanding forest biomass and carbon storage as well as for managing a sustainable forest ecosystem. Plot-based field surveys, such as the national forest inventory, have been conducted to provide estimates of the forest canopy height. However, the comprehensive nationwide field monitoring of forest canopy height has been limited by its cost, lack of spatial coverage, and the inaccessibility of some forested areas. These issues can be addressed by remote sensing technology, which has gained popularity as a means to obtain detailed 2- and 3-dimensional measurements of the structure of the canopy at multiple scales. Here, we reviewed both international and domestic studies that have used remote sensing technology approaches to estimate the forest canopy height. We categorized and examined previous approaches as: 1) LiDAR approach, 2) Stereo or SAR image-based point clouds approach, and 3) combination approach of remote sensing data. We also reviewed upscaling approaches of utilizing remote sensing data to generate a continuous map of canopy height across large areas. Finally, we provided suggestions for further advancement of the Korean forest canopy height estimation system through the use of various remote sensing technologies.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.27-42
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• 2016

Forest accounts for almost 64 percents of total land cover in South Korea. For inventorying, monitoring, and managing such large area of forest, application of remote sensing and geographic information system (RS/GIS) technology is essential. On the basis of spectral characteristics of satellite imagery, forest cover and tree species can be classified, and forest cover map can be prepared. Using three dimensional data of LiDAR(Light Detection and Ranging), tree location and tree height can be measured, and biomass and carbon stocks can be also estimated. In addition, many indices can be extracted using reflection characteristics of land cover. For example, the level of vegetation vitality and forest degradation can be analyzed with VI (vegetation Index) and TGSI (Top Grain Soil Index), respectively. Also, pine wilt disease and o ak w ilt d isease c an b e e arly detected and controled through understanding of change in vegetation indices. RS and GIS take an important role in assessing carbon storage in climate change related projects such as A/R CDM, REDD+ as well. In the field of climate change adaptation, impact and vulnerability can be spatio-temporally assessed for national and local level with the help of spatio-temporal data of GIS. Forest growth, tree mortality, land slide, forest fire can be spatio-temporally estimated using the models in which spatio-temporal data of GIS are added as influence variables.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.176-182
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• 2020

National Institute of crop Science of the Rural Development Administration (RDA) has conducted long-term monitoring studies to find out the relationship between crop yield and climatic factors for major food crops including rice. Rice growth and y ield have been monitored in 17 regions where the branches of the National Institute of Crop science and the Provincial Agricultural Research and Extension Service locate. The data obtained from monitoring studies for rice growth and yield include the observation of vegetative growth status and yield components, which include leaf number, biomass and the weight of 1000 grains. These data have been collected from rice fields where standard management procedures have been applied. The observation data for crop growth and yield monitoring studies from 1999 to 2019 are open to public through agricultural science library operated by RDA.

• Journal of Wetlands Research
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• v.16 no.2
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• pp.245-250
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• 2014

This study developed allometry equation and estimated the aboveground-biomass of Quercus glauca, a warm-temperature, evergreen broad-leaved tree, growing in Kotzawal wetland located on Jeju Island. The allometric equations between DBH(diameter at breast height) and dry weights of stems (Ws), branches (Wb), leaves (Wl) and aboveground biomass (Wab) of Q. glauca were as follows: logWs=2.4042logDBH-1.3045, logWb=2.6436logDBH-1.6232, logWl =1.5428logDBH-1.3692 and logWab=2.3324logDBH-0.9181. The allometric equations between $D^2H$ and Ws, Wb, Wl, and Wab of Q.glauca were as follows : logWs=$0.853logD^2H-1.4252$, logWb=$0.8453logD^2H-1.5834$, logWl=$0.5328logD^2H-1.4073$ and logWab=$0.8453logD^2H-1.0327$. The $R^2$ between DBH and Ws, Wb, Wl and Wab were 0.9873, 0.9711, 0.7979 and 0.993, respectively. The $R^2$ between $D^2H$ and Ws,Wb,Wl and Wab were 0.9841, 0.9174, 0.7537 and 0.9876, respectively. There was no significant difference between observed and calculated values of the allomatric equations from DBH and $D^2H$(p>0.05, Kolmogorov-Smirnov test). Thus, to estimate the aboveground biomass of Q. glauca, use of DBH and $D^2H$ as an independent variables in the allometric equation is recommended.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.203-217
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• 2024

The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.