Woo-Kyun Lee

Allometry and biomass of Korean pine (Pinus koraiensis) in central Korea.

Aboveground tree biomass of Korean pine (Pinus koraiensis Sieb. et Zucc.) was determined for a natural forest of Korean pine and mixed deciduous trees and seven age classes of plantation forests in central Korea. Regression analyses of the dry weights of stem wood, stem bark, branches, and needles versus diameter at breast height were used to calculate regression equations of the form of log Y = a + b log X. Biomass of Korean pine in the mixed forest was 118 Mg ha(-1), and biomass in the plantations was linearly related to stand age, ranging from 52.3 Mg ha(-1) in 11 to 20-year-old stands to 317.9 Mg ha(-1) in 71 to 80-year-old stands. The proportions of stem wood and stem bark in the total aboveground biomass decreased with stand age while those of branch and needle increased. Specific leaf area of Korean pine ranging from 35.2 to 52.1 cm2 g(-1) was significantly different among crown positions and needle ages; in general, lower crown position and current needles had the greatest surface area per unit dry weight.

Modeling stem profiles for Pinus densiflora in Korea

A new taper model is presented for Pinus densiflora in Korea. The new variable-exponent model describes well the gradually changing tree form along the stem. The changing exponent of the new model can be used to graphically compare different stem forms among tree groups. And various form indices numerically expressing stem form are derived from the new model. Five form indices: (1) taper rate of the butt section, (2) inflection point, (3) parabolic or paraconic range, (4) minimum exponent, and (5) relative height at the minimum exponent, are useful analytical tools for numerically comparing stem forms and stratifying trees into different form groups.

Estimating Crown Variables of Individual Trees Using Airborne and Terrestrial Laser Scanners

In this study, individual tree height (TH), crown base height (CBH), crown area (CA) and crown volume (CV), which were considered as essential parameters for individual stem volume and biomass estimation, were estimated by both an airborne laser scanner (ALS) and a terrestrial laser scanner (TLS). These ALS- and TLS-derived tree parameters were compared because TLS has been introduced as an instrument to measure objects more precisely. ALS-estimated TH was extracted from the highest value within a crown boundary delineated with the crown height model (CHM). The ALS-derived CBH of individual trees was estimated by k-means clustering method using the ALS data within the boundary. The ALS-derived CA was calculated simply with the crown boundary, after which CV was computed automatically using the crown geometric volume (CGV). On the other hand, all TLS-derived parameters were detected manually and precisely except the TLS-derived CGV. As a result, the ALS-extracted TH, CA, and CGV values were underestimated whereas CBH was overestimated when compared with the TLS-derived parameters. The coefficients of determination (R2) from the regression analysis between the ALS and TLS estimations were approximately 0.94, 0.75, 0.69 and 0.58, and root mean square errors (RMSEs) were approximately 0.0184 m, 0.4929 m, 2.3216 m2 and 13.2087 m3 for TH, CBH, CA and CGV, respectively. Thereby, the error rate decreased to 0.0089, 0.0727 and 0.0875 for TH, CA and CGV via the combination of ALS and TLS data.

Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Fine Root Dynamics in Thinned and Limed Pitch Pine and Japanese Larch Plantations

ABSTRACT To investigate fine root dynamics after thinning (50% of standing tree) and liming calcium magnesium carbonate[CaMg(CO3)2] 2 Mg ha− 1, a 2-year study was performed in 40-year-old pitch pine (Pinus rigida Mill.) and 44-year-old Japanese larch (Larix leptolepis Gord.) plantations in central Korea. Mean total fine root mass (kg ha− 1± SE) in the control, thinned, and limed plots were 1234 ± 32, 1346 ± 67, and 1134 ± 40 for the pitch pine plantation and 1655 ± 48, 1953 ± 58, and 1868 ± 70 for the Japanese larch plantation, respectively. Live fine root mass of pitch pine at 0-10 cm soil depth decreased after thinning and liming. In addition, liming significantly increased dead fine root mass of Japanese larch. Fine root production (kg ha− 1 yr− 1± SE) in the control, thinned and limed plots was 1108 ± 148, 2077 ± 262, and 1686 ± 103 for the pitch pine plantation and 1762 ± 103, 1886 ± 277, and 2176 ± 271 for the Japanese larch plantation, respectively. Fine root turnover rates increased after liming for both plantations. Fine root nitrogen (N) and phosphorus (P) concentrations of Japanese larch (1.012% of N and 0.073% of P) were higher than those of pitch pine (0.809% of N and 0.046% of P) in the control. Also N and P inputs into soil through fine root turnover increased after treatments. Results indicated that comparing fine root dynamics among forest types and after forest management practices might influence differences in soil fertility and underground nutrient cycling.

Effects of thinning on soil nitrogen mineralization in a Japanese larch plantation

Abstract We measured soil nitrogen (N) mineralization and litter decomposition for four different thinning intensities [control (C), 10% (T10), 20% (T20), 40% (T40) thinning] during the first year after treatment in a 15‐year‐old Japanese larch plantation in central Korea. Initial soil moisture content increased in the thinned plots. Air temperature also increased with thinning intensity, however, soil temperature did not. Annual net N mineralization rates (mg N kg‐1) for C, T10, T20, and T40 were 6.7, 7.2, 6.6, and 11.5, respectively. The heavily thinned plot showed higher annual N mineralization than control, lightly and moderately thinned plots, but differences were not statistically significant. Except for the T40 treatment, the ratios of annual net nitrification to annual net N mineralization were greater than 100%. Dry weight and N concentration in decomposing needle litter did not show any significant patterns with thinning intensity. The current results indicated that various levels of thinning fo...

Changes in the distribution of South Korean forest vegetation simulated using thermal gradient indices.

To predict changes in South Korean vegetation distribution, the Warmth Index (WI) and the Minimum Temperature of the Coldest Month Index (MTCI) were used. Historical climate data of the past 30 years, from 1971 to 2000, was obtained from the Korea Meteorological Administration. The Fifth-Generation National Center for Atmospheric Research (NCAR) /Penn State Mesoscale Model (MM5) was used as a source for future climatic data under the A1B scenario from the Special Report on Emission Scenario (SRES) of the Intergovernmental Panel on Climate Change (IPCC). To simulate future vegetation distribution due to climate change, the optimal habitat ranges of Korean tree species were delimited by the thermal gradient indices, such as WI and MTCI. To categorize the Thermal Analogy Groups (TAGs) for the tree species, the WI and MTCI were orthogonally plotted on a two-dimensional grid map. The TAGs were then designated by the analogue composition of tree species belonging to the optimal WI and MTCI ranges. As a result of the clustering process, 22 TAGs were generated to explain the forest vegetation distribution in Korea. The primary change in distribution for these TAGs will likely be in the shrinkage of areas for the TAGs related to Pinus densiflora and P. koraiensis, and in the expansion of the other TAG areas, mainly occupied by evergreen broad-leaved trees, such as Camellia japonica, Cyclobalanopsis glauca, and Schima superba. Using the TAGs to explain the effects of climate change on vegetation distribution on a more regional scale resulted in greater detail than previously used global or continental scale vegetation models.

Estimating the spatial pattern of human-caused forest fires using a generalized linear mixed model with spatial autocorrelation in South Korea

Most forest fires in Korea are spatially concentrated in certain areas and are highly related to human activities. These site-specific characteristics of forest fires are analyzed by spatial regression analysis using the R-module generalized linear mixed model (GLMM), which can consider spatial autocorrelation. We examined the quantitative effect of topology, human accessibility, and forest cover without and with spatial autocorrelation. Under the assumption that slope, elevation, aspect, population density, distance from road, and forest cover are related to forest fire occurrence, the explanatory variables of each of these factors were prepared using a Geographic Information System-based process. First, we tried to test the influence of fixed effects on the occurrence of forest fires using a generalized linear model (GLM) with Poisson distribution. In addition, the overdispersion of the response data was also detected, and variogram analysis was performed using the standardized residuals of GLM. Second, GLMM was applied to consider the obvious residual autocorrelation structure. The fitted models were validated and compared using the multiple correlation and root mean square error (RMSE). Results showed that slope, elevation, aspect index, population density, and distance from road were significant factors capable of explaining the forest fire occurrence. Positive spatial autocorrelation was estimated up to a distance of 32 km. The kriging predictions based on GLMM were smoother than those of the GLM. Finally, a forest fire occurrence map was prepared using the results from both models. The fire risk decreases with increasing distance to areas with high population densities, and increasing elevation showed a suppressing effect on fire occurrence. Both variables are in accordance with the significance tests.

The necessity and availability of noise-free daily satellite-observed NDVI during rapid phenological changes in terrestrial ecosystems in East Asia

General, global, long-term, and comprehensive phenological observations are required to evaluate the variability of photosynthetic activities due to environmental changes in terrestrial ecosystems. The observation of seasonal changes and detection of interannual variation in canopy phenology over regional and global scales require satellite data with high temporal resolution (i.e. a daily time step). However, satellite data often include noise caused by snow cover on vegetation, cloud contamination, and atmospheric aerosols. To accurately detect the timing of leaf-expansion and leaf-fall, which occur rapidly, and their rates, it is necessary to examine the observational frequency of noise-free satellite-observed vegetation index data during each phenological period. In this study, we investigated the spatio-temporal distribution of the number of observational days (NUMdays) in the Terra/MODIS (Moderate Resolution Imaging Spectroradiometer)-observed daily high-quality normalized difference vegetation index (NDVIhigh) data with no effects of snow cover, cloud contamination, or atmospheric noise. These data were examined for each month over 10 years in the various ecosystems of East Asia. To ground-truth the relationship between the Terra/MODIS-observed daily NDVIhigh data and canopy surface images, we performed a long-term continuous field study in a cool-temperate deciduous broad-leaved forest in central Japan. During the leaf-expansion and leaf-fall periods, the NUMdays for NDVIhigh data in southern Russia, northeastern China, the Tibetan Plateau, Korea, and maritime Japan was about 3–7 for each month. The NUMdays for NDVIhigh data exceeded 10 for each month in arid regions during the growing season and in the subtropical region including northeastern India, Myanmar, and southwestern China during the dry season. In contrast, the NUMdays for NDVIhigh data was almost 0 for each month in southeastern China throughout the year and in the subtropical region during the southeastern monsoon season (July and August). By considering observations from both the Terra/MODIS and Aqua/MODIS satellites, the NUMdays for NDVIhigh data in the deciduous broad-leaved forest in Japan was increased by 40% compared with only Terra/MODIS satellite observations. Our findings indicate that daily NDVI data from multiple satellites detect the seasonal changes in the various ecosystems of East Asia more accurately than 8-day or biweekly composite NDVI data.

Comparison of spatial interpolation techniques for predicting climate factors in Korea

A variety of statistical interpolation techniques have been used with climate factors. This study compared three statistical interpolation techniques such as PRISM, Kriging, and IDW with temperature and precipitation. Mean monthly cumulative values on temperature and precipitation from 1977 to 2006, which were provided by Korea Meteorological Administration, were used for this study. The aim of this study is to find better appropriate process which can consider to the topographical characteristics of Korea and to produce high‐resolution climate‐maps using statistical models. As a result, Kriging showed gradual and smooth pattern, while IDW showed rather irregularly distributed with discontinuous borders. And PRISM generated the most detailed pattern. This paper will contribute to coping with climate‐change such as urban heat island, flood, disaster, and to make alterative suggestion for the meteorological factors on forest and vegetation models in the future.