• Journal of Bio-Environment Control
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• v.25 no.3
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• pp.146-152
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• 2016

Due to complicate canopy structures of Irwin mangoes grown in greenhouses, it is difficult to determine their growth parameters accurately. Leaf area, leaf fresh weight, and leaf dry weight are widely used as indicators to diagnose the tree growth. Therefore, it is necessary to establish models that can non-destructively estimate these growth indicators. The objective of this study was to establish regression models to estimate leaf area, leaf fresh weight, and leaf dry weight of Irwin mangoes (Mangifera indica L. cv. Irwin) by using leaf length, leaf width, petiole length, and SPAD value. The input values of leaf length, leaf width, petiole length, and SPAD value of 6-year old Irwin mangoes were measured, and the corresponding output values of leaf area, leaf fresh weight, and leaf dry weight were also measured. After 14 models were selected among the existing models, coefficients of the models were estimated by regression analysis. Three models with higher $R^2$ and lower RMSE values selected. In validation the $R^2$ values for the selected models were 0.967, 0.743, and 0.567 in the leaf area, leaf fresh weight, and leaf dry weight models, respectively. It is concluded that this models will be helpful to conveniently diagnose the growth of the Irwin mango.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.1
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• pp.15-19
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• 1985

This study was carried out to develope the equations for estimating the areas of leaflet, leaf, and total leaf for 1, 2, 3, 4, 5, and 6 years old ginseng, Panax ginseng, grown in field. The highest correlation coefficient was found between leaflet area and product of leaflet length and width(LW) in all leaflets although leaflet shape varied somewhat according to the position and plant age. It was possible to estimate area of the leaf, and total leaf by one central leaflet in a compound leaf. The equations for estimating the leafet, leaf areas of 1 year differ to those of over 2 years old plant, but there was no difference among those of 2, 3, 4, 5, and 6 years. The equations for 1 year old are A =0.64 LW, A' =A/0.38, and for 2, 3, 4, 5, and 6 years old, A =0.60 LW, A' =A/0.32, A" =A' x number of leaves of central leaflet(A), leaf(A') and total leaf areas(A"), respectively. The estimation of leaflet, leaf, total leaf areas of ginseng plant grown under 20% light-transmittance rate was possible by using the equations mentioned.

• Journal of Sericultural and Entomological Science
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• v.8
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• pp.11-25
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• 1968

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and seven characters namely branch length. branch diameter, node number per branch, total branch weight, branch weight except leaves, leaf weight and leaf area, were studied. The formulae to estimate the leaf yield of mulberry trees are as follows: 1. Varietal differences were appeared in means, variances, standard devitations and standard errors of seven characters studied as shown in table 1. 2. Y$_1$=a$_1$X$_1$${\times}$P$_1$......(l) where Y$_1$ means yield per l0a by branch number and leaf weight determination. a$_1$.........leaf weight per branch. X$_1$.......branch number per plant. P$_1$........plant number per l0a. 3. Y$_2$=(a$_2$${\pm}$S. E.${\times}$X$_2$)+P$_1$.......(2) where Y$_2$ means leaf yield per l0a by branch length and leaf weight determination. a$_2$......leaf weight per meter of branch length. S. E. ......standard error. X$_2$....total branch length per plant. P$_1$........plant number per l0a as written above. 4. Y$_3$=(a$_3$${\pm}$S. E${\times}$X$_3$)${\times}$P$_1$.....(3) where Y$_3$ means of yield per l0a by branch diameter measurement. a$_3$.......leaf weight per 1cm of branch diameter. X$_3$......total branch diameter per plant. 5. Y$_4$=(a$_4$${\pm}$S. E.${\times}$X$_4$)P$_1$......(4) where Y$_4$ means leaf yield per 10a by node number determination. a$_4$.......leaf weight per node X$_4$.....total node number per plant. 6. Y$\sub$5/= {(a$\sub$5/${\pm}$S. E.${\times}$X$_2$)Kv}${\times}$P$_1$.......(5) where Y$\sub$5/ means leaf yield per l0a by branch length and leaf area measurement. a$\sub$5/......leaf area per 1 meter of branch length. K$\sub$v/......leaf weight per 100$\textrm{cm}^2$ of leaf area. 7. Y$\sub$6/={(X$_2$$\div$a$\sub$6/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$......(6) where Y$\sub$6/ means leaf yield estimated by leaf area and branch length measurement. a$\sub$6/......branch length per l00$\textrm{cm}^2$ of leaf area. X$_2$, K$\sub$v/ and P$_1$ are written above. 8. Y$\sub$7/= {(a$\sub$7/${\pm}$S. E. ${\times}$X$_3$)}${\times}$K$\sub$v/${\times}$P$_1$.......(7) where Y$\sub$7/ means leaf yield estimates by branch diameter and leaf area measurement. a$\sub$7/......leaf area per lcm of branch diameter. X$_3$, K$\sub$v/ and P$_1$ are written above. 9. Y$\sub$8/= {(X$_3$$\div$a$\sub$8/${\pm}$S. E.)}${\times}$K$\sub$v/${\times}$P$_1$.......(8) where Y$\sub$8/ means leaf yield estimates by leaf area branch diameter. a$\sub$8/......branch diameter per l00$\textrm{cm}^2$ of leaf area. X$_3$, K$\sub$v/, P$_1$ are written above. 10. Y$\sub$9/= {(a$\sub$9/${\pm}$S. E.${\times}$X$_4$)${\times}$K$\sub$v/}${\times}$P$_1$......(9) where Y$\sub$7/ means leaf yield estimates by node number and leaf measurement. a$\sub$9/......leaf area per node of branch. X$_4$, K$\sub$v/, P$_1$ are written above. 11. Y$\sub$10/= {(X$_4$$\div$a$\sub$10/$\div$S. E.)${\times}$K$\sub$v/}${\times}$P$_1$.......(10) where Y$\sub$10/ means leaf yield estimates by leaf area and node number determination. a$\sub$10/.....node number per l00$\textrm{cm}^2$ of leaf area. X$_4$, K$\sub$v/, P$_1$ are written above. Among many estimation methods. estimation method by the branch is the better than the methods by the measurement of node number and branch diameter. Estimation method, by branch length and leaf area determination, by formulae (6), could be the best method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.166-169
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• 2004

NDVI has been used to estimate several ecological variables including leaf area index (LAI). Global MODIS LAI data are partially produced by empirical model that is based on the assumption of high correlation between NDVI and LAI. This study attempts to evaluate the MODIS empirical model by comparing with the result obtained from field LAI measurement and Landsat ETM+ reflectance. MODIS LAI product and ancillary data were analyzed over a small forest watershed near the Seoul metropolitan area. The relationship between NDVI of ETM+ and field measured LAI did not correspond to MODIS LAI estimation. Since the study area is mostly covered by very dense and fully closed forest, the correlation between NDVI and LAI might not be high. Although MODIS LAI product has great potential for global environment studies, it needs to be cautious to use them in regional and local area in particular for the forest of dense canopy situation.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.46-46
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• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2004.11a
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• pp.80-85
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• 2004

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1087-1091
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• 2008

Evapotranspiration (ET) is an important factor while simulating daily streamflow in hydrological models. The LAI (Leaf Area Index) value reflecting the conditions of vegetation generally affects considerably in the estimation of ET, for example, when using FAO Penman Monteith equation. Recently in evaluating the vegetation condition as a fixed quantity, the remotely sensed LAIs from MODIS satellite data are avaliable, and the time series values of spatial LAI coupled with land use classes are utilized for ET evaluation. The 4 years (2001-2004) MODIS LAI data were prepared for the evaluation of continuous hydrological model, SLURP (Semi-distributed Land Use-based Runoff Processes). The model was applied for simulating the dam inflow of Chungjudam watershed ($6661.58\;km^2$) located in the upstream of Han river basin of South Korea. From the model results, the FAO Penman Monteith ET was affected by the MODIS LAIs. Especially for the ET of deciduous forest, the Total ET was 33.9 % lager than coniferous forest for the 3.8 % lager of LAI. The watershed average LAI caused a 7.0 % decrease in average soil moisture of the watershed and 14.3 % decrease of ground water recharge.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.1
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• pp.1-15
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• 2016

Annual litterfall production and leaf area index (LAI, $m^2/m^2$) were estimated using litter traps in Gwangneung, Mt. Taewha and Mt. Gariwang. Annual total litter fall production including branch, bark, others was the highest in Gwangneung($7497.3{\pm}326.5kg/ha/yr$), which had the highest basal area at late successional stage, and followed by Mt. Taewha($5929.1{\pm}225.8kg/ha/yr$) and Mt. Gariwang($3,210.1{\pm}220.1kg/ha/yr$). Mt. Gariwang had the lowest litterfall production due to high elevation and short growing season even with the higher stand density and basal area than Mt. Taewha. Similarly, LAI, which was calculated by multiplying the mass of leaf litter with specific leaf area, was the highest in Gwangneung($5.99{\pm}0.69$) and followed by Mt. Taewha($5.20{\pm}0.24$) and Mt. Gariwang($4.06{\pm}0.42$) and the upper canopy species had the highest leaf area index in every sites (Gwangneung : 4.72, Mt. Taewha : 3.08, Mt. Gariwang : 2.19). However, species specific LAI estimation based on the relationship between basal area and leaf area was limited due to upper canopy species non-proportionality of basal area with LAI. In addition, the comparison between direct and indirect LAI measurement showed the importance of canopy clumping, especially at high density. Our study emphasized the necessity of direct LAI measurement using litter fall traps especially at temperate deciduous forest with diverse species.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.25-28
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• 2002

The aim of the present study was the evaluation of methods for estimating the vegetation information in the field on the basis of spectral reflectance measured farm field, in particular the estimation of Leaf Area Index(LAI). Variability in tissue optical properties was wavelength-dependent. For rice and bean, the lowest variation was in the visible spectral region and the highest in the near-infrared. The structural attributes of ecosystems determine the relative contribution of tissue and canopy factors that drive variation in a reflectance signal.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1076-1080
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• 2003

$CO_2$ uptake of vegetation is one of the important variables in order to estimate photosynthetic activity, plant growth and carbon budget estimations. The objective of this research was to develop a new estimation method of $CO_2$ uptake of vegetation based on airborne hyperspectral remote sensing measurements in combination with a photosynthetic rate curve model. In this study, a compact airborne spectrographic imager (CASI) was used to obtain image over a field that had been set up to study the $CO_2$ uptake of corn on August 7, 2002. Also, a field survey was conducted concurrently with the CASI overpass. As a field survey, chlorophyll a content, photosynthetic rate curve, Leaf area, dry biomass and light condition were measured. The developed estimation method for $CO_2$ uptake consists of three major parts: a linear mixture model, an enhanced big leaf model and a photosynthetic rate curve model. The Accuracy of this scheme indicates that $CO_2$ uptake of vegetation could be estimated by using airborne hyperspectral remote sensing data in combination with a physiological model.