• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.160-168
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• 2012

This study generated regression models to estimate the carbon storage and uptake from the urban deciduous landscape trees through a direct harvesting method, and established essential information to quantify carbon reduction from urban greenspace. Tree species for the study included Acer palmatum, Zelkova serrata, Prunus yedoensis, and Ginkgo biloba, which are usually planted as urban landscape trees. Tree individuals for each species were sampled reflecting various diameter sizes at a given interval. The study measured biomass for each part including the roots of sample trees to compute the total carbon storage per tree. Annual carbon uptake per tree was quantified by analyzing radial growth rates of stem samples at breast height. The study then derived a regression model easily applicable in estimating carbon storage and uptake per tree for the 4 species by using diameter at breast height(dbh) as an independent variable. All the regression models showed high fitness with $r^2$ values of 0.94~0.99. Carbon storage and uptake per tree and their differences between diameter classes increased as the diameter sizes got larger. The carbon storage and uptake tended to be greatest with Zelkova serrata in the same diameter sizes, followed by Prunus yedoensis and Ginkgo biloba in order. A Zelkova serrata tree with 15cm in dbh stored about 54kg of carbon and annually sequestered 7 kg, based on a regression model for the species. The study has broken new grounds to overcome limitations of the past studies which substituted, due to a difficulty in direct cutting and root digging of urban landscape trees, coefficients from the forest trees such as biomass expansion factors, ratios of below ground/above ground biomass, and diameter growth rates. Study results can be useful as a tool or skill to evaluate carbon reduction by landscape trees in urban greenspace projects of the government.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1427-1435
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• 2023

Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, the anisotropic properties of the crop canopy surface can govern spectral reflectance and vegetation indices. In this study, we applied an ensemble of bidirectional reflectance distribution function (BRDF) models to high-resolution Sentinel-2 satellite imagery and compared the differences between correction results before and after reflectance. In the red and near-infrared (NIR) band reflectance images, BRDF-corrected outlier values appeared in certain urban and paddy fields of farmland areas and forest shadow areas. These effects were equally observed when calculating the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2). Furthermore, the outlier values in corrected NIR band were shown in pixels shadowed by mountain terrain. These results are expected to contribute to the development and improvement of BRDF models in high-resolution satellite images.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.163-173
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• 2022

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.4
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• pp.316-326
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• 2014

Determination of the late marginal heading date (LMHD), which would allow estimation of the late marginal seeding date and the late marginal transplanting date, would help identification of potential double cropping areas and, as a result, establishment of cropping systems. The objective of this study was to determine the LMHD at 51 sites in Korea. For these sites, weather data were obtained from 1971 to 2000 and from 1981 to 2010, which represent past and current normal climate conditions, respectively. To examine crop productivity on the LMHD, climatic yield potential (CYP) was determined to represent the potential yield under a given climate condition. The LMHD was calculated using accumulated temperature for 40 days with threshold values of $760^{\circ}C$, $800^{\circ}C$, $840^{\circ}C$ and $880^{\circ}C$. The value of CYP on a given LMHD was determined using mean temperature and sunshine duration for 40 days from the LMHD. The value of CYP on the LMHD was divided by the maximum value of CYP (CYPmax) in a season to represent the relative yield on the LMHD compared with the potential yield in the season. Our results indicated that the LMHD was delayed at most sites under current normal conditions compared with past conditions. Spatial variation of the LMHD differed by the threshold temperature. Overall, the minimum value of CYP/CYPmax was 81.8% under all of given conditions. In most cases, the value of CYP/CYPmax was >90%, which suggested that yield could be comparable to the potential yield even though heading would have occurred on the LMHD. When the LMHD could be scheduled later without considerable reduction in yield, the late marginal transplanting date could also be delayed accordingly, which would facilitate doublecropping in many areas in Korea. Yield could be affected by sudden change of temperature during a grain filling period. Yet, CYP was calculated using mean temperature and sunshine duration for 40 days after heading. Thus, the value of CYP/CYPmax may not represent actual yield potential due to change of the LMHD, which suggested that further study would be merited to take into account the effect of weather events during grain filling periods on yield using crop growth model and field experiments.

• Korean Journal of Ecology and Environment
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• v.49 no.1
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• pp.11-21
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• 2016

Cymbidium kanran Makino is being threatened in its own habitats due illegal collecting and habitat changes by vegetation growth along historical landuse change. In this study, we established habitat restoration model for conservation of C. kanran based on ecological diagnosis. Through exploration to Jeju Island in 2014 and 2015, we identified 27 unknown habitats of C. kanran and in there, abiotic variables and vegetation structure and composition were quantified. Altitudinal distribution of C. kanran was between 200 m~700 m a.s.l. and compared to distribution in 2004, Area of Occupation (AOO) decreased at 82%. Specific habitat affinity was not observed by evenly found in mountain slope and valley and summergreen and evergreen broadleaved forests, but likely more abundant in valley habitats with higher soil and ambient moisture. Total of 96 individual of C. kanran was observed with an average density of $942.6individuals\;ha^{-1}$. The plants showed relatively short leaf length (average=$10.7cm{\pm}1.1cm$) and small number of pseudo bulbs ($1.2{\pm}0.2$). Flowering and fruiting individuals were not observed in field. C. kanran was classified into endangered plant species as CR (Critically Endangered) category by IUCN criteria. Phenotypic plasticity of C. kanran was likely support to sustain in more shaded habitat environment and recent habatat changes to closed canopy and low light availability may exhibit negatively effects to C. kanran's life history. Restoring C. kanran habitat should create open environment as grassland and low woody species density.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.55-64
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• 2019

The flowering seasons can be advanced due to climate change that would cause an abnormally warm winter. Such warm winter would increase the frequency of crop damages resulted from sudden occurrences of low temperature before and after the vegetative growth stages, e.g., the period from germination to flowering. The degree and pattern of freezing damage would differ by the development stage of each individual fruit tree even in an orchard. A critical temperature, e.g., killing temperature, has been used to predict freeze damage by low-temperature conditions under the assumption that such damage would be associated with the development stage of a fruit flower bud. However, it would be challenging to apply the critical temperature to a region where spatial variation in temperature would be considerably high. In the present study, a phenological model was used to estimate major bud development stages, which would be useful for prediction of regional risks for the freeze damages. We also derived a linear function to calculate a probabilistic freeze risk in spring, which can quantitatively evaluate the risk level based solely on forecasted weather data. We calculated the dates of freeze damage occurrences and spatial risk distribution according to main production areas by applying the spring freeze risk function to apple, peach, and pear crops in 2018. It was predicted that the most extensive low-temperature associated freeze damage could have occurred on April 8. It was also found that the risk function was useful to identify the main production areas where the greatest damage to a given crop could occur. These results suggest that the freezing damage associated with the occurrence of low-temperature events could decrease providing early warning for growers to respond abnormal weather conditions for their farm.