• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.2
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• pp.86-96
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• 2006

Ecoclimap-1, a new complete surface parameter global database at a 1-km resolution, was previously presented. It is intended to be used to initialize the soil-vegetation- atmosphere transfer schemes in meteorological and climate models. Surface parameters in the Ecoclimap-1 database are provided in the form of a per-class value by an ecoclimatic base map from a simple merging of land cover and climate maps. The principal objective of this ecoclimatic map is to consider intra-class variability of life cycle that the usual land cover map cannot describe. Although the ecoclimatic map considering land cover and climate is used, the intra-class variability was still too high inside some classes. In this study, a new strategy is defined; the idea is to use the information contained in S10 NDVI SPOT/VEGETATION profiles to split a land cover into more homogeneous sub-classes. This utilizes an intra-class unsupervised sub-clustering methodology instead of simple merging. This study was performed to provide a new ecolimatic map over Northeast Asia in the framework of Ecoclimap-2 global database construction for surface parameters. We used the University of Maryland's 1km Global Land Cover Database (UMD) and a climate map to determine the initial number of clusters for intra-class sub-clustering. An unsupervised classification process using six years of NDVI profiles allows the discrimination of different behavior for each land cover class. We checked the spatial coherence of the classes and, if necessary, carried out an aggregation step of the clusters having a similar NDVI time series profile. From the mapping system, 29 ecosystems resulted for the study area. In terms of climate-related studies, this new ecosystem map may be useful as a base map to construct an Ecoclimap-2 database and to improve the surface climatology quality in the climate model.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Spatial Information Research
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• v.16 no.1
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• pp.19-32
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• 2008

Usually in South Korea, land cover type and topographic undulation are frequently changed even in a narrow area. However, most of researches using aerial LIDAR(LIght Detection And Ranging) data in abroad had been acquired in the study areas to be changed infrequently. This research was performed to explore reconstruction methodologies of 3D surface models considering the distribution of land cover type and topographic undulation. Composed of variously undulatory forests, rocky river beds and man-made land cover such as streets, trees, buildings, parking lots and so on, an area was selected for the research. First of all, the area was divided into three zones based on land cover type and topographic undulation using its aerial ortho-photo. Then, aerial LIDAR data was clipped by each zone and different 3D modeling processes were applied to each clipped data before integration of each models and reconstruction of overall model. These kinds of processes might be effectively applied to landscape management, forest inventory and digital map composition. Besides, they would be useful to resolve less- or over-extracted problems caused by simple rectangle zoning when an usual data processing of aerial LIDAR.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.127-139
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• 2017

Measurements of net ecosystem exchange (NEE) of $CO_2$ based on the eddy covariance technique provide reasonable carbon balance estimates in response to local environmental conditions. In South Korea, the forest ecosystems cover approximately 64% of the total area, thereby strongly affecting regional carbon balances. Cultivated croplands that cover about 17% of the total area should also be considered when calculating the carbon balance of the country. In this study, our objectives were (a) to quantify the range and seasonal variation of NEE at forest ecosystems, including deciduous, coniferous, and mixed forests, and agricultural ecosystems, including rice paddies and a potato field, in South Korea and (b) to compare NEE at ten Fluxnet sites that have the same or similar ecosystems as found in South Korea. The results showed that the forest and agricultural ecosystems were carbon sinks. In Korea, NEE at the forest ecosystems varied between -31 and $-362gC/m^2/yr$, and NEE at the croplands ranged from -210 to $-248gC/m^2/growing$ season. At the deciduous forest, NEE reached low values in late spring, early summer, and early autumn, while at the coniferous forest, it reached low values in spring, early summer, and mid autumn. The young mixed forest was a much stronger carbon sink than the old-growth deciduous and coniferous forests. During each crop growing season, beet had the lowest NEE value within six crops, followed by wither wheat, maize, rice, potato, and soybean. These results will be useful for designing and applying management strategies for the reduction of $CO_2$ emissions.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.653-660
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• 2017

The SCS-CN (Soil Conservation Service-Curve Number) method has been practically applied for estimating the effective precipitation. The CN is used to be determined according to the land use condition based on the US standard. However, there are two distinctive differences between U.S. and Korean land use conditions: mountainous (forest) and rice paddy area that cover more than 70% of the Korean territory. The previous work proposed to use 79 for rice paddy area, regardless of the soil type. Because US SCS's goal was originally to increase crops, the SCS classification standard provides only for woods and there are no criteria to distinguish the wood and forest. To determine the CN for forest, alternatively the U.S. Forest Service criteria have been employed in practice considering hydrologic condition class. In this study, we investigated the change of the forest CN using the observed rainfall - runoff data within the target area. The results indicated that the CN for forest was suitable for HC=1, and the corresponding CNs were redetermined between 54 and 55.

• Korean Journal of Environment and Ecology
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• v.32 no.1
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• pp.97-104
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• 2018

This study investigated the effect of forest type changes in Daegu, the hottest city in Korea, on the land surface temperature (LST). The LST change by forest type was analyzed by 2scene of Landsat TM image from 1990 to 2007. The land cover types were classified into 4 types; forest areas, urban areas, cultivated areas and other areas, and water areas. The forest areas were further classified into the coniferous tree areas and the broadleaf tree areas. The result of the statistical analysis of the LST change according to the forest type showed that the LST increased when the forest was changed to the urban area. The LST increased by about $0.6^{\circ}C$ when a broadleaf tree area was changed to an urban area and about $0.2^{\circ}C$ when a coniferous tree area was changed to an urban area. This was the temperature change as the result of the simple type change for 17 years. The temperature change was larger when considering both cases of the forest type being retained and changed. The LST increased by $2.3^{\circ}C$ more when the broadleaf tree areas were changed to the urban areas than when broadleaf trees were maintained. The LST increased by $1.9^{\circ}C$ more when the coniferous tree areas were changed to the urban areas than when the coniferous tree areas were maintained. The LST increased by $0.4^{\circ}C$ more when the broadleaf tree areas were destroyed than when the coniferous tree areas were destroyed. The results confirmed that the protection of broadleaf trees in urban forests was more effective for mitigating climate change.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.23-29
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• 2014

Open- to closed canopy stage and it's ecological characteristics in vegetation succession are commonly described, but poorly understood in Korea. Vegetation development on structure, environment and understory abundance were studied for 16 yr in post-clearcut Pinus densiflora forests in the southern Gangwon-do province by applying space-for-time approach. We sampled 210 plots (10 for structure and 200 for understory) for four seral stages (1yr, 3yr, 10yr and 16yr). After clear-cut, mean stem density increased gradually to $5,714{\pm}645$ stems/ha after 16 years and mean basal area was also from $5.5{\pm}0.7m^2/ha$ after 10 years and doubled at $10.0{\pm}1.6m^2/ha$ in 16 years. Woody debris and bared soil on the forest floor peaked at 11--- after 10 years and at 10.3--- after 3 years, respectively. In understory mean cover declined with all growth form groups following succession, but in richness, forb specie increased with structural development during 16 years. Our study suggested that overstory development did not suppressed whole understory properties especially in richness, thus appeared to act as a filter selectively constraining the understory characteristics. However only long-term studies are essential for elucidating patterns and processes that cannot be inferred form short-term or space-for-time researches. Strong negative relationship between overstory and understory characteristics in conventional models surely reexamined.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.661-670
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• 2012

Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1943-1947
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• 2007

The objective of this research was to find a direct and indirect method to estimate land surface temperature (LST) efficiently, using Landsat images and in-situ measurement. Agricultural fields including paddy fields have long been known to have multi-functions beneficial to the environment and ecology of the urban surrounding areas. Among these functions, the ambient temperature cooling (ATC) effect are widely acknowledged. However, quantitative and regional assessment of such effect has not had many investigations. Thermal remote sensing has been used over urban areas to assess ATC effect, to perform land cover classifications and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of paddy fields and urban climates, focusing primarily on the ATC effect. Landsat satellite images were used to determine the surface temperatures of different land cover types of a $441km^2$ study area in Cheongju, Korea. The results show that the ATC are a function of paddy area percentage in Landsat pixels. Pixels with higher paddy area percentage have more significant cooling effect.