• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.272-289
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• 2004

The spatial resolution of local weather and climate information for agronomic practices exceeds the current weather service scale. To supplement the insufficient spatial resolution of official forecasts and observations, gridded climate data are frequently generated. Most ecological models can be run using gridded climate data to produce ecosystem responses at landscape scales. In this lecture, state of the art techniques derived from geospatial climatology, which can generate gridded climate data by spatially interpolating point observations at synoptic weather stations, will be introduced. Removal of the urban effects embedded in the interpolated surfaces of daily minimum temperature, incorporation of local geographic potential for cold air accumulation into the minimum temperature interpolation scheme, and solar irradiance correction for daytime hourly temperature estimation are presented. Some experiences obtained from their application to real landscapes will be described.

• Atmosphere
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• v.22 no.4
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• pp.437-447
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• 2012

There is still large uncertainty in estimating aerosol indirect effect despite ever-escalating efforts and virtually exponential increase in published studies concerning aerosol-cloud-precipitation interactions (CAPI). Probably most uncertainty comes from a wide range of observational scales and different platforms inappropriately used, and inherent complex chains of CAPI. Therefore, well-designed field campaigns and data analysis are required to address how to attribute aerosol signals along with clouds and precipitation to the microphysical effects of aerosols. Basically, aerosol influences cloud properties at the microphysical scales, "process scale", but observations are generally made of bulk properties over a various range of temporal and spatial resolutions, "analysis scale" (McComiskey & Feingold, 2012). In the most studies, measures made within the wide range of scales are erroneously treated as equivalent, probably resulting in a large uncertainty in associated with CAPI. Therefore, issues associated with the disparities of the observational resolution particular to CAPI are briefly discussed. In addition, the dependence of CAPI on the cloud environment such as stability and adiabaticity, and observation characteristics with varying situations of CAPI are also addressed together with observation framework optimally designed for the Korean situation. Properly designed and observation-based CAPI studies will likely continue to accumulate new evidences of CAPI, to further help understand its fundamental mechanism, and finally to develop improved parameterization for cloud-resolving models and large scale models.

• Journal of Korean Society on Water Environment
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• v.32 no.1
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• pp.70-79
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• 2016

Based on the soil moisture data assimilation suggested in the first paper (I), we estimated root zone soil moisture and evaluated drought severity using remotely sensed (RS) data. We tested the impacts of various spatial resolutions on soil moisture variations, and the model outputs showed that resolutions of more than 2-3 km resulted in over-/under-estimation of soil moisture values. Thus, we derived the 2 km resolution-scaled soil moisture dynamics and assessed the drought severity at the study sites (Chungmi-cheon sites 1 and 2) based on the estimated soil/root parameters and weather forcings. The drought indices at the sites were affected mainly by precipitation during the spring season, while both the precipitation and land surface characteristics influence the spatial distribution of drought during the rainy season. Also, the drought severity showed a periodic cycle, but additional research on drought cycles should be conducted using long-term historical data. Our proposed approach enabled estimation of daily root zone soil moisture dynamics and evaluation of drought severity at various spatial scales using MODIS data. Thus, this approach will facilitate efficient management of water resources.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.221-241
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• 2005

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1477-1491
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• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.60-71
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• 1999

The micrometeorological tower flux network is the cornerstone of the global terrestrial vegetation monitoring. The eddy covariance technique used for tower fluxes is derived from the conservation of mass and is most applicable for steady-state conditions over flat, extended, and uniform vegetation. This technique allows us to obtain surface fluxes of energy budget components, greenhouse and trace gases, and other pollutants. The quality-controlled flux data are invaluable to validate various models with temporal scales ranging from minutes to years and spatial scales ranging from a few meters to hundreds of kilometers. In this paper, we review the theoretical background of this important eddy covariance technique, examine the measurement criteria and corrections, and finally suggest some measurement strategies that may facilitate coordinated flux measurements among different disciplines and provide a strong infrastructure for the global flux network.

• Journal of the Korean Institute of Landscape Architecture
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• v.23 no.3
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• pp.155-166
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• 1995

The aim of this paper was to investigate images of neighborhood parks and pergola facilities. 280 individuals responded to the 19 semantic differential pollar terms for all four parks. A 7-point semantic differential scale was used. Results from the data analysis suggest several important findings; 1. Mean scores were significantly different in most of S.D.scales according to park and pergola facilities types. As compared with park, S.D.scales values of pergola varied with its visual identity in the spatial composition of park. 2. Data from the factor analysis revealed that four dimensions appeared for all park and pergola types : "Evaluation ", "Potentiality", "Variety", and "Familiarity". 3. In terms of the "Familiarity" dimension, Omok park and its pergola were viewed a ssignificantly more familiar than any of the other three areas. 4. Parks were perceived more positive than pergolas images in the dimensions of "Evaluation ", "Variety", and "Familiarity". 5. Regression analysis showed that "Evaluation "and "Variety" dimensions were mainly related to visual preference.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.4
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• pp.401-411
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• 2005

This study was conducted to develop a method to be able to estimate the vehicular emissions according to spatial scales-Seoul province, 25 counties and hundreds of grids $(1km{\times}1km)$. First, the emissions at each spatial scale was calculated by using the road network and the travel volume and speed of each link modeled by travel demand model (TDM). Second, the emission at each spatial scale was calculated on the basis of average speeds estimated by using three kinds of averaging method. These are called the provincial, volume-delay function (VDF) and zonal method, respectively. Third, three kinds of emissions and those by TDM are compared each other at three spatial scales. In Seoul (provincial scale), three kinds of emissions are less than those by TDM, but the differences of TDM from three speed averaging methods (SAMs) are small. The relative ratios of three SAMs to TDM are $88\~90\%\;in\;CO,\;99\~100\%\;in\;NOx,\;84\~85\%$ in VOCs. At county scale, NOx among three pollutants showed the highest correlation between TDM and three SAMs and the zonal method among three SAMs was proven to be the highest correlation with TDM. NOx showed the coefficients $(R^2)$ greater than 0.9 in all three SAMs but CO and VOC showed the coefficients $(R^2)$ greater than 0.9 in only zonal method. Slopes of co..elations of all pollutants showed the values close to '1' in zonal method. In the other two SAMs, slopes of NOx showed the values close to '1', but those of CO and VOC showed the values less than 0.85. At grid scale, correlations between TDM and three SAMs were not high. CO showed $0.68\~0.77\;in\;R^2s\;and\;58\~0.68$ in slopes. NOx showed $0.90\~0.94\;in\;R^2s\;and\;0.86\~0.94$ in slopes. VOC showed $0.56\~0.70\;in\;R^2s\;and\;0.48\~0.57$ in slopes. There are not high correlations between TDM and three SAMs in grid scale. This study showed that there is the most suitable method for calculating the average travel speed at each spatial scale and it is thought that the zonal method is more suitable than the VDF or provincial method.

• Spatial Information Research
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• v.11 no.3
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• pp.227-240
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• 2003

The primary focus of this study is to provide a general methodology which can be utilized to understand and analyze environmental issues such as long term ecosystem dynamics and land use/cover change by development of 2D dynamic landscape models and model-based simulation. Change processes in land cover and ecosystem function can be understood in terms of the spatial and temporal distribution of land cover resources. In development of a system to understand major processes of change and obtain predictive information, first of all, spatial heterogeneity is to be taken into account because landscape spatial pattern affects on land cover change and interaction between different land cover types. Therefore, the relationship between pattern and processes is to be included in the research. Landscape modeling requires different approach depending on the definition, assumption, and rules employed for mechanism behind the processes such as spatial event process, land degradation, deforestration, desertification, and change in an urban environment. The rule-based models are described in the paper for land cover change by natural fires. Finally, a case study is presented as an example using spatial modeling and simulation to study and synthesize patterns and processes at different scales ranging from fine-scale to global scale.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.466-469
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• 2005

In this study e-map component was designed and developed to possibly overlay with all kinds of thematic maps in various scales and provide the all detailed information by using high-resolution satellite image and GIS. Also, this system has powerful map composition tool to display map such as legend, scale bar, index map and so on. For this, this e-map component was designed by using UML and developed based on Windows 2000 and implemented by using Visual Basic 6.0 as development programming language, Map Objects 2.1 of ESRI as GIS component. Through this system, the forest officials could generate more detailed topography and desired thematic map. In addition, the data consistency in DBMS could be maintained by using SDE (Spatial Database Engine) for their job and share the standard forest database with others in real time.