• Journal of Plant Biology
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• v.16 no.1_2
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• pp.1-5
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• 1973

Six young trees of Alnus tinctoria grown in isolation, each having different growing stage, were selected and the surface area of their roots, stems and leaves was determined. Each of the roots of more than 0.2mm in diameter and stems was cut at intervals of 10cm and their surface area was calculated with 2$\pi$rl from the average diameter (2r) of both sections (upper and lower) by making cylindrical estimation of the cut pieces. The leaf area measured was only one side area, and the volume of cut piece and amount of dry matter of each organ were also measured. The percentage to the surface area of the whole plant body by each organ was 4-12% in root, 7-9% in stem and 69-89% in leaf, respectively. There was relatively a little individual difference. However, the surface area ratios of root and stem showed a slightly increasing tendency while that of leaf decreasing according to the growing stage. The ratio of sum leaf area index (LAIi) was 2.3-4.0$m^2$/$m^2$-and that of the surface area index(SaIi) was 0.16-0.33$m^2$/$m^2$, respectively. It has been known that the stem surface area(SAI) to the leaf area index(LAI) is within the range of 31-53%, but the SAIi is within the range of 8-11% of the LAIi.

• Spatial Information Research
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• v.18 no.5
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• pp.27-36
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• 2010

This study observed distribution of vegetation to confirm change of tundra-taiga boundary. Tundra-taiga boundary is used to observe the transfer of vegetation pattern because it is very sensitive to human activity, natural disturbances and climate change. The circumpolar tundra-taiga boundary could observe reaction about some change. Reaction and confirmation about climate change were definite than other place. This study used Leaf Area Index(LAI) 8-Day data in August from 2000 to 2009 that acquire from Terra satellite MODerate resolution Imaging Spectroradiometer(MODIS) sensor and used K$\"{o}$ppen Climate Map, Global Land Cover 2000 for reference data. This study conducted analysis of spatial distribution in low density vegetated areas and inter-annual / zonal analysis for using the long period data of LAI. Change of LAI was confirmed by analysis based on boundary value of LAI in study area. Development of vegetation could be confirmed by area of grown vegetation($730,325km^2$) than area of reduced vegetation ($22,372km^2$) in tundra climate. Also, area was increased with the latitude $64^{\circ}$ N~$66^{\circ}$ N as the center and around the latitude $62^{\circ}$ N through area analysis by latitude. Vegetation of tundra-taiga boundary was general increase from 2000 to 2009. While area of reduced vegetation was a little, area of vegetation growth and development was increased significantly.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.56 no.3
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• pp.255-263
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• 2011

Projected increases in atmospheric $CO_2$ concentration ([$CO_2$]) and temperature ($T_a$) have the potential to alter in rice growth and yield. However, little is known about whether $T_a$ warming with elevated [$CO_2$] modify plant architecture. To better understand the vertical profiles of leaf area index (LAI) and the flag leaf morphology of rice grown under elevated $T_a$ and [$CO_2$], we conducted a temperature gradient field chamber (TGC) experiment at Gwangju, Korea. Rice (Oryza sativa L. cv. Dongjin1ho) was grown at two [$CO_2$] [386 (ambient) vs 592 ppmV (elevated)] and three $T_a$ regimes [26.8 ($\approx$ambient), 28.1 and $29.8^{\circ}C$] in six independent field TGCs. While elevated $T_a$ did not alter total LAI, elevated [$CO_2$] tended to reduce (c. 6.6%) the LAI. At a given canopy layer, the LAI was affected neither by elevated [$CO_2$] nor by elevated $T_a$, allocating the largest LAI in the middle part of the canopy. However, the fraction of LAI distributed in a higher and in a lower layer was strongly affected by elevated $T_a$; on average, the LAI distributed in the 75-90 cm (and 45-60 cm) layer of total LAI was 9.4% (and 35.0%), 18.8% (25.9%) and 18.6% (29.2%) in ambient $T_a$, $1.3^{\circ}C$ and $3.0^{\circ}C$ above ambient $T_a$, respectively. Most of the parameters related to flag leaf morphology was negated with elevated [$CO_2$]; there were about 12%, 5%, 7.5%, 15% and 21% decreases in length (L), width (W), L:W ratio, area and mass of the flag leaf, respectively, at elevated [$CO_2$]. However, the negative effect of elevated [$CO_2$] was offset to some extent by $T_a$ warming. All modifications observed were directly or indirectly associated with either stimulated leaf expansion or crop phenology under $T_a$ warming with elevated [$CO_2$]. We conclude that plant architecture and flag leaf morphology of rice can be modified both by $T_a$ warming and elevated [$CO_2$] via altering crop phenology and the extent of leaf expansion.

• Journal of Ecology and Environment
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• v.31 no.2
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• pp.153-159
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• 2008

The timing of the canopy phenology onset (CPO hereafter) indicates the initiation of the growing season, with rapid increases in exchange rates of carbon dioxide and water vapor between vegetation and atmosphere. The CPO is regarded as a potential indicator of ecosystem responses to global warming, but the CPO shows considerable spatial variation depending on the species composition and local temperature regime. at a given geographic location. In this study, we evaluated the utility of satellite observation data for detection of the timing of the CPO. Leaf area indices (LAI) obtained from the Moderate Resolution Imaging Spectrora-diometer (MODIS) were utilized to detect and map the onset dates from 2001 to 2006. The reliability of MODIS-based onset dates was evaluated with ground measured cherry blossom flowering data from national weather stations. The MODIS onset dates preceded the observed flowering dates by 8 days and were linearly related with a correlation coefficient of 0.58 (p < 0.05). In spite of the coarse spatial (1 km) and temporal (8 days) resolutions of MODIS LAI, the MODIS-based onset dates showed reasonable ability to predict flowering dates.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.649-654
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• 2005

This study proposes a particular approach to assess information about NDVI(Normalized Difference Vegetation Index) and LAI(Leaf Area Index) from the spectroradiometer and NOAA/AVHRR satellite data. AVHRR data were collected in twelves months over a one year period in 2004. We calculated 10-day composite NDVI using daily composite AVHRR surface reflectance products(1km spatial resolution). The 10-day composite NDVI have a great effect on the plant growth conditions. Considerably, NDVI was increased by developing muscle fiber tissue from April to May. Then the NDVI increased until the August and then decreased until February. The highest month was at August and the lower month was at December. The difference NDVI analysis using December and another months data was conducted, the results were provided information on the variation of vegetation coverage. The result suggest that a relationship established between the LAI and NDVI in 2004.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.6
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• pp.463-467
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• 2004

This study was carried out to estimate of leaf area index (LAI) rapidly using plant canopy analyzer, comparing with specific leaf area (SLA) and leaf area ratio (LAR) in rice from 2001 to 2002 at Honam Agricultural Research Institute in Iksan Korea. The relationship between LAI values taken by plant canopy analyzer and by leaf area meter showed high correlation at each growth stages of rice. LAIs obtained by plant canopy analyzer were highly correlated with that by leaf area meter which were the highest in Dongjinbyeo and the lowest in Hapcheon1. Specific leaf area (SLA) of all rice cultivars were tend to decrease remarkably with the progress of growth stage. It was found that the SLA of Dasanbyeo was the highest and that of Hapcheon1 was the lowest among rice cultivars tested. Leaf area ratios (LARs) was also decreased with the progress of growth stage.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.571-585
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• 2017

The number of spaceborne optical sensors including red-edge band has been increasing since red-edge band is known to be effective to enhance the information content on biophysical characteristics of vegetation. Considering that the Agriculture and Forestry Satellite is planning to carry an imaging sensor having red-edge band, we tried to analyze the current status and potential of red-edge band. As a case study, we analyzed the effect of using red-edge band and tried to find the optimum band width and wavelength region of the red-edge band to estimate leaf area index (LAI) of very dense tree canopy. Field spectral measurements were conducted from April to October over two tree species (white oak and pitch pine) having high LAI. Using the spectral measurement data, total 355 red-edge bands reflectance were simulated by varying five band width (10 nm, 20 nm, 30 nm, 40 nm, 50 nm) and 71 central wavelength. Two red-edge based spectral indices(NDRE, CIRE) were derived using the simulated red-edge band and compared with the LAI of two tree species. Both NDRE and CIRE showed higher correlation coefficients with the LAI than NDVI. This would be an alternative to overcome the limitation of the NDVI saturation problem that NDVI has not been effective to estimate LAI over very dense canopy situation. There was no significant difference among five band widths of red-edge band in relation to LAI. The highest correlation coefficients were obtained at the red-edge band of center wavelength near the 720 nm for the white oak and 710 nm for the pitch pine. To select the optimum band width and wavelength region of the red-edge band, further studies are necessary to examine the relationship with other biophysical variables, such as chlorophyll, nitrogen, water content, and biomass.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.137-143
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• 2008

Rapid canopy closure (RCC) is one of the physiological attributes that may enhance genetic yield potential of rice (Oryza sativa L.) in a growing season. Crop growth before canopy closure could be described by an exponential equation of $y\;=\;{\alpha}{\cdot}{\exp}({\beta}{\cdot}t)$ where $\alpha$ is the crop leaf area index (LAI) or shoot dry weight (DW), t is the thermal time, $\beta$ is the LAI or DW at the beginning of the exponential growth and is the relative growth rate of LAI ($m^2m^{-2}^{\circ}C^{-1}$) or DW($gg^{-2}^{\circ}C^{-1}$). Field experiment using 22 cultivars revealed that the exponential growth phase before canopy closure can be divided into two sections; an earlier section during which crop dry weight and LAI of varieties are highly dependent on $\alpha$ and a second section where crop dry weight and LAI are highly dependent on $\beta$. Grain weight had significantly positive correlation with $\alpha$ parameter and dry weight and LAI during early exponential phase. The parameter $\beta$ of the exponential growth curve had positive and significant correlation with the LAI and dry weight during the late exponential growth phase, grain number per unit area, and grain yield. There was genotypic difference for RCC parameters, $\alpha$ and $\beta$, indicating the possibility of genetic improvement for these traits.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.