• Journal of Ecology and Environment
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• v.40 no.1
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• pp.55-65
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• 2016

Background: In this article, it was analyzed how snow melting affects the assembly of lichen and moss communities in a small area of the coastal region of Barton Peninsula, which is in maritime Antarctic. In the small area, even though there is a huge gap of difference of the environment between the snow-filled area and snow-melt one, the latter did not have distinctive environmental gradients. Results: Depending on the snow melting time, coverage and species diversity of lichens and mosses tend to increase remarkably. For species with significant changes depending on the snow-covered period, there are Andreaea regularis, crustose lichens, Placopsis contortuplicata, Usnea aurantiaco-atra, and snow algae. In this area, the process of vegetation assembly process has shown the directional development in the order of snow algae${\rightarrow}$crustose, lichen sub-formation${\rightarrow}$fruticose lichen, moss cushion sub-formation (Andreaea sociation)${\rightarrow}$fruticose lichen, and moss cushion sub-formation (Usnea sociation), according to the order of snow melting. These directional development stages are shown in gradual change in small area with the snow melting phenomena. However, in the snow-free area, where water is sufficiently supplied, it is expected that moss carpet sub-formation (Sanionia sociation) will be developed. Vegetation development in the small area with the snow melting phenomena, depending on differences of resistance on snow kill and moisture settled by species in according to the time of snow melting, tolerance model to form community is followed. Conclusions: The research results explain the development of vegetation in the Antarctic tundra and its spatial distribution according to the period for growth of lichens and mosses in the summer time by differences of snow melting in the small area. In the future, if research for the community development process in a large scale will be done, it will be helpful to figure out temporal and spatial dynamic of vegetation in the Antarctic tundra where snow and glaciers melt rapidly due to climatic warming.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Korean Journal of Environment and Ecology
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• v.11 no.3
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• pp.334-351
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• 1997

To study restoration model of evergreen broad-leaved forests in warm temperate region, vegetation structure was studied at Wando(Island) as a case study. Quercus acuta was a dominant species at evergreen broad-leaved forests in Wando(Island). Majority of evergreen broad-leaved forests was a thirty years old coppice forest. Reforested vegetation and deciduous broad-leaved forests was developed at a mid-slope districts and a piedmont. Deciduous broad-leaved forestsconsisted of Quercus serrata, Carpinus tschonoskii, Carpinus coreana, etc., was developed at a ridge and higher districts. Evergreen broad-leaved woody plants were growing at a forest floor of deciduous broad-leaved forests. The species over sixty percent of constanty ratio in forty seven plots were Ligustrum japonicum, Trachelospermum asiaticum var. intermedium, Quercus acuta and Eury japonica. The vascular plants in the Wando(Island) was summarized as 488 taxa which composed as 101 families, 321 genus, 426 species, 56 varieties, 5 forms and 1 hyvrid. Evergreen broad-leaved woody stecies was 32 taxa which composed as 23 genus, 30 species and 2 varieties. The species such as Liliope platyphylla of Liliaceae and Pueraria thunbergii of Leguminosae, etc. was recorded as the highest values for their widely distribution in the areas. On the contrary, and forty taxa of plants such as Viburnum erosum of Caprifoliaceae, Traceholospermum asiaticum var. intermedium was recorded as over 50% of constancy ratio. Two hundred and nine taxa of plants such as Juglans manshurica of Juglandaceae, Cornus walteri of Cornaceae and Rodotypos scandens of Rosaceae, etc. was showed the specific trends due to long-term artificial disturbance. The forest of Pinus thunbergii showde the highest species diversities(155 species per 600m$^{2}$), while the Cinnamomum japonicum-Tracheolospermum asiaticum var. intermedium community showed the lowest species diversities(23 species per 600m$^{2}$).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.405-416
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• 2019

Due to the development of information and communication technology, the production and processing speed of data is getting faster. To classify objects using machine learning, which is a field of artificial intelligence, data required for training can be easily collected due to the development of internet and geospatial information technology. In the field of geospatial information, machine learning is also being applied to classify or recognize objects using images and point clouds. In this study, the problem of manually constructing training data using existing digital map version 1.0 was improved, and the technique of classifying roads, buildings and vegetation using image and point clouds were proposed. Through experiments, it was possible to classify roads, buildings, and vegetation that could clearly distinguish colors when using true ortho-image with only RGB (Red, Green, Blue) bands. However, if the colors of the objects to be classified are similar, it was possible to identify the limitations of poor classification of the objects. To improve the limitations, random forest and support vector machine techniques were applied after band fusion of true ortho-image and normalized digital surface model, and roads, buildings, and vegetation were classified with more than 85% accuracy.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.476-482
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• 2014

In this study, we analyzed the effect of corn growth on the radar backscattering coefficient. At first, we measured the backscattering coefficients of various corn fields using a polarimetric scatterometer system. The backscattering coefficients of the corn fields were also computed using the 1st-order VRT(Vector Radiative Transfer) model with field-measured input parameters. Then, we analyzed the experimental and numerical backscattering coefficients of corn fields. As a result, we found that the backscatter from an underlying soil layer is dominant for early growing stage. On the other hand, for vegetative stage with a higher LAI(Leaf-Area-Index), the backscatter from vegetation canopy becomes dominant, and its backscattering coefficients increase as incidence angle increases because of the effect of leaf angle distribution. It was also found that the estimated backscattering coefficients agree quite well with the field-measured radar backscattering coefficients with an RMSE(Root Mean Square Error) of 1.32 dB for VV-polarization and 0.99 dB for HH-polarization. Finally, we compared the backscattering characteristics of vegetation and soil layers with various LAI values.

• Atmosphere
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• v.26 no.3
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• pp.413-421
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• 2016

Climate Analysis Seoul (CAS) which provides gridded data relevant for thermal assessment was applied to one of the urban green areas, the Seonjeongneung, in Seoul, Korea. The thermal environment in the Seonjeongneung was evaluated from the CAS simulation for the five heat-wave issued cases during the last five years (2011~2015). The CAS has been improved continuously since it was developed. An updated version with a higher resolution of the CAS simulation domain and an addition of the vegetation information was used in this study. The influence of vegetation in the Seonjeongneung is estimated through the amount of the cold air generation ($Q_{ca}$) and air temperature deviation at each grid points, which are calculated by incorporating Geographic Information System (GIS) analysis on the simulation domain and meteorological analysis with the METeorology and atmospheric PHOtochemistry mesoscale MODel (MetPhoMod) in the CAS. The average amount of the cold air generation ($Q_{ca}$) at the Seonjeongneung is about $25.5m^3m^{-2}h^{-1}$ for the whole cases, and this value is similar to the ones in a forest or a well-wooded region. The average value of the total air temperature deviation (TD) is $-2.54^{\circ}C$ at the Seonjeongneung for the five cases. However, this cooling effect of the urban green area disappeared when the region is replaced by high-rise buildings in the CAS simulation. The $Q_{ca}$ drastically decreases to about $1.1m^3m^{-2}h^{-1}$ and the average TD shows an increase of $1.14^{\circ}C$ for the same events. This result shows that the vegetation in the Seonjeongneung supposes to keep down temperature during the heat-wave issued day and the average cooling effect of the green region is $3.68^{\circ}C$ quantitatively from the TD difference of the two simulations. The cooling effect represented with the TD difference is larger than $0.3^{\circ}C$ within 200 m distance from the boundary of the Seonjeongneung. Further improvements of the thermodynamical and advection processes above the model surface are required to consider more accurate assessment of the cooling effect for the urban green area.

• Korean Journal of Environment and Ecology
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• v.29 no.5
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• pp.777-784
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• 2015

This study verified the correlations between vegetation factors, such as the number of individual species and species diversity, and soil factors in Chamaecyparis obtusa (CO) stands in Jeollanam-do. Also, the site environmental factors that affect the annual growth of CO ring width were analyzed. Positive correlations were found between the species diversity index and Cation Exchange Capacity (CEC), available phosphate, and exchangeable $K^+$ (P<0.01). In addition, strong positive correlations were also found between the number of species that appeared in the study site and CEC, available phosphorus, exchangeable $K^+$ and exchangeable $Mg^{2+}$ (P<0.01). Tree ring growth showed strong correlations with the nutrient holding capacity and fertility of soil, including available phosphate, exchangeable $K^+$, CEC, and electrical conductivity (P<0.01). The explanatory variables of tree ring growth in CO were composed of exchangeable $K^+$, organic matter content, and soil pH. The regression model had a high level of explanatory power, 74.4%. In this model, the annual growth of CO ring width increased when exchangeable $K^+$ and organic matter content were higher but decreased when soil pH was lower. According to the analysis, it is found that the annual growth of CO ring width was significantly affected by soil fertility, including available phosphate, exchangeable $K^+$, CEC, and electrical conductivity. In addition, the soil fertility of CO stands seems to be significantly affected by the supply of fallen leaves from the understory vegetation of CO.

• Journal of Korean Society of Forest Science
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• v.85 no.2
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• pp.210-224
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• 1996

The SLODSVAT consists of interrelated submodels that simulate : the transfer of radiation, water vapour, sensible heat, carbon dioxide and momentum in two canopy layers determined by environmental conditions and ecophysiological properties of the vegetation ; uptake and storage of water in the "root-stem-leaf" system of plants ; interception of rainfall by the canopy layers and infiltration and storage of rain water in the four soil layers. A comparison of the results of modeling experiments and field micro-climatic observations in a spruce forest(Picea abies [L].Karst) in the Soiling hills(Germany) shows, that the SLODSVAT can describe and simulate the short-term(diurnal) as well as the long-term(seasonal) variability of water vapour and sensible heat fluxes adequately to natural processes under different environmental conditions. It proves that it is possible to estimate and predict the transpiration and evapotranspiration rates for spruce forest ecosystems on the patch and landscape scales for one vegetation period, if certain meteorological, botanical and hydrological information for the structure of the atmospheric boundary layer, the canopy and the soil are available.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.64-75
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• 2012

To predict the future distribution of forest vegetation, the present forest stand distributions of South Korea were represented by multinomial logit model with the following environmental variables: summer average precipitation, the coldest month average temperature, elevation, degree of base saturation, and soil organic matter. The future forest community was predicted by applying the MIROC3.2 hires A1B scenario. The future climate data were downscaled by statistically method. The coldest month average temperature increased $4.4^{\circ}C$, $6.0^{\circ}C$, and $9.4^{\circ}C$, and 3 months average precipitation changed -1.2%, 5.7%, and 5.3% for 2020s, 2050s, and 2080s respectively. For the projected summer precipitation and the coldest temperature, the future deciduous and mixed forests in the study area increased 56.9% and 8.3% and the coniferous forest decreased 11.2% in 2080s based on present.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.