• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.431-440
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• 2005

Location, height and clear-length of individual tree can be measured directly by LiDAR Remote Sensing, and dbh(diameter at breast height) can be estimated indirectly by tree height measured by LiDAR. In addition, stand volume and stand biomass are computed from estimated growth factors. In this study, each estimated growth factor was compared to the field measurements to validate accuracy. The coefficient of determination of total tree heights was 0.66 for total trees, 0.68 for Pinus koraiensis, 0.66 for Larix leptolepis and 0.60 for Quercus spp. The coefficient of determination of clear-length was 0.79 for total trees, 0.73 for Pinus koraiensis, 0.79 for Larix leptolepis, 0.68 for Quercus spp. The coefficient of determination of dbh predicted was 0.73 for Pinus koraiensis, 0.73 for Larix leptolepis and 0.85 for Quercus spp. Moreover The coefficient of determination of basal area was 0.82 for Pinus koraiensis, 0.92 for Larix leptolepis and 0.95 for Quercus spp. Biomass per ha computed by growth factor using LiDAR was 40,306 dm/ha for Pinus koraiensis, 94,150 tdm/ha for Larix leptolepis and 94,481 tdm/ha for Quercus spp. by species.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.234-234
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• 2017

Recent abnormal weather, especially continued rainfall during sowing season causes difficulty in proper sowing of wheat and delayed sowing after November 15 is concerned about freezing damage during winter, resulting in reduction of wheat yield. To correspond government policy of crop sufficiency improvement and produce and supply raw wheat and barley steadily, expansion of cultivation area is necessary and spring sowing of wheat is required. To obtain basic information on the improvement of spring sown wheat and barley production, comparison and path coefficients analysis was conducted for yield and yield related components from autumn and spring sown wheat and barley. Path analyses were known as very useful in clarifying the effects of yield components on grain yield formation, which were not accurately reflected in simple correlation anaylses. Most cultivated 5 wheat and 9 barley cultivars were sown on October and February at Cheon-ju province according to standard sowing method. For the spring sowing of wheat and barley, the varieties having vernalization degree I~III are seeded in the mid of February and seeding rate is 200~250kg/ha which is increased by 25% than autumn sowing. N-fertilizer of 95 kg/ha and the same amount of P, K dressed in autumn are applied at once as basal fertilizer. The magnitude of direct effect in each yield components on yield was in sequence as follows. In autumn wheat, grain number per $spike{\geq}$ the number of spike per $m^2$>1000-grain weight and in spring wheat, grain number per $ spike{\geq}the$ number of spike per $m^2$> 1000-grain weight. In autumn naked barley, 1000-grain weight> the number of spike per $m^2$, grain number per spike and in spring barely, the number of spike per $m^2$> grain number per spike > 1000-grain weight. In autumn covered barley, grain number per spike>the number of spike per $m^2$ and in spring coverd barley, the number of spike per $m^2$> grain number per spike, 1000-grain weight. In autumn malt barley, the number of spike per $m^2$>1000-grain weight and in spring malt barley, the direct effects of three yield components were similar. According to the path analysis of yield components for spring sown wheat and barley, it was suggested that adequate number of spike per $m^2$ was most important factor for yield increase.

• Journal of Korean Society of Forest Science
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• v.65 no.1
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• pp.68-73
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• 1984

The purpose of this paper was to figure out the efficiencies of two sampling techniques, a simple random sampling and a two-stage P.P.S. (probability proportional to size) sampling, in estimating the volume of the mature coniferous stands near Salzburg, Austria. With black-and-white infrared photographs at a scale 1:10,000, the following four classes were considered; non-forest, young stands less than 40 years, mature beech and mature coniferous stands. After the classification, a field survey was carried out using a relascope with a BAF (basal area factor) 4. For the simple random sampling, 99 points were sampled, while for the P.P.S. sampling, 75 points were sampled in the mature coniferous stands. The following results were obtained. 1) The mean standing coniferous volume estimate was $422.0m^3/ha$ for the simple random sampling and $433.5m^3/ha$ for the P.P.S. sampling method. However, the difference was not statistically significant. 2) The required number of sampling points for a 5% sampling error were 170 for the two stage P.P.S. sampling, but 237 for the simple random sampling. 3) The two stage P.P.S. method reduced field survey time by 17% as compared to the simple random sampling.

• Journal of Korean Society of Forest Science
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• v.75 no.1
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• pp.1-18
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• 1986

This study was initiated to estimate productivity of Quercus variabilis stand. However the practical objective of this study was to provide some information to establish the basis of selecting the suitable site for Quercus variabilis. The productivity measured in terms of DBH, height, basal area and stem volume was hypothesized, respectively, to be a function of a group of factors. This study considered 32 factors, 20 of which were related to the forest environmental factors such as tree age, latitude, percent slope, etc. and the rest of which were related to soil factors such as soil moisture, total nitrogen, available $P_2O_5$, etc. The data on 4 productivity measurements of Quercus variabilis growth and related factors cited were collected from 99 sample plots in Kyeongbook and chungbook provinces. Some factors considered were, in nature, discrete variables and the others continuous variables. Each kind of factor was classified into 3 or 4 categories and total numbers of such categories were eventually amounted to 110. Then each category was treated as an independent variable. This is amounted to saying that individual variable was treated a dummy variable and assigned a value 1 or 0. However the first category of each factor was deleted from the normal equation for statistical consideration. First of all, each of 4 productivity measurements of Quercus variabilis growth was regressed and, at the same time, those 110 categories. Secondly, the partial correlation coefficients were measured between each pair of 4 productivity measurements and 32 individual foctors. Finally, the relative scores were estimated in order to derive the category ranges. The result of these statistical analyses could be summarized as follows: 1) Growth measurement in terms of height seems to be a more significant criterion for estimation of productivity of Quercus variabilis. 2) Productivity of forest on stocked land may better be estimated in terms of forest environmental factors, on the other hand, that of unstocked land may be estimated in terms of physio-chemical factors of soil. 3) The factors that a strongly positive relation to all growth factors of tree are age group, effective soil, soil moisture, etc. This implies that these factors might effectively be used for criteria for selecting the suitable site for Quercus variabilis. 4) Parent rock, latitude, total nitrogen, age group, effective soil depth, soil moisture, organic matter, etc., had more significant category range for tree growth. Therefore, the suitable site for Quercus variabilis may be selected, based on this information. In conclusion, the above results obtained by the multivariable analysis can be not only the important criteria for estimating the growth of Quercus variabilis but also the useful guidance for selecting the suitable sites and performing the rational of Quercus variabilis forest.