• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.187-197
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• 2008

This study is to assess the impact of future land use change on hydrology and water quality in Gyungan-cheon watershed ($255.44km^2$) using SWAT (Soil and Water Assessment Tool) model. Using the 5 past Landsat TM (1987, 1991, 1996, 2004) and $ETM^+$ (2001) satellite images, time series of land use map were prepared, and the future land uses (2030, 2060, 2090) were predicted using CA-Markov technique. The 4 years streamflow and water quality data (SS, T-N, T-P) and DEM (Digital Elevation Model), stream network, and soil information (1:25,000) were prepared. The model was calibrated for 2 years (1999 and 2000), and verified for 2 years (2001 and 2002) with averaged Nash and Sutcliffe model efficiency of 0.59 for streamflow and determination coefficient of 0.88, 0.72, 0.68 for Sediment, T-N (Total Nitrogen), T-P (Total Phosphorous) respectively. The 2030, 2060 and 2090 future prediction based on 2004 values showed that the total runoff increased 1.4%, 2.0% and 2.7% for 0.6, 0.8 and 1.1 increase of watershed averaged CN value. For the future Sediment, T-N and T-P based on 2004 values, 51.4%, 5.0% and 11.7% increase in 2030, 70.5%, 8.5% and 16.7% increase in 2060, and 74.9%, 10.9% and 19.9% increase in 2090.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1369-1382
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• 2018

As the number of multispectral satellites increases, it is expected that it will be possible to acquire and use images for periodically. However, there is a problem of data discrepancy due to different overpass time, period and spatial resolution. In particular, the difference in band bandwidths became different reflectance even for images taken at the same time and affect uncertainty in the analysis of vegetation activity such as vegetation index. The purpose of this study is to estimate the band adjustment factor according to the difference of bandwidth with other multispectral satellites for the application of KOMPSAT-3 satellite in agriculture field. The Spectral band adjustment factor (SBAF) were calculated using the hyperspectral satellite images acquired in the desert area. As a result of applying SBAF to the main crop area, the vegetation index showed a high agreement rate of relative percentage difference within 3% except for the Hapcheon area where the zenith angle was 25. For the estimation of SBAF, this study used only one set of images, which did not consider season and solar zenith angle of SBAF variation. Therefore, long-term analysis is necessary to solve SBAF uncertainty in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.23-33
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• 2009

Satellite image is very usefully practiced to predict and analyze physical expansion and change of city. Physical expansion and change of city is closely related to the use of land, and continuous growth management focused on the use of land is essential for sustainable city growth. In this research, the change of land cover and land-use were analyzed with basic input data from 1985 to 2000 according to artificial satellite. Moreover, the land-use turnover rate was understood and expansion trend of urban sprawl in Busan metropolitan city and land-use characteristics of the expansion area. The results are, first, the area for urban region was expanded continuously but areas for agriculture area, forest area, and water area had different changes due to administrative district reform of Busan by each year. Second, the urbanization area in Busan was increased by 3.8% from $92.5km^2$ in 1985 to $167.5km^2$ in 2000. Third, the result of analysis on land-use turnover rate showed that agriculture area was turned into urbanized area the most, and forest area was followed by. Fourth, the result of analysis on database and overlay of buildings in Busan established in 2001 showed that agriculture area are had type 1 and 2 neighborhood living facilities (45.63%), apartment house in forest area (18.49%), and factory in water area (31.84%) with high ratio.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.105-123
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• 2018

Currently, satellite images act as essential and important data in water resources, environment, and ecology as well as information of geographic information system. In this paper, we will investigate basic characteristics of satellite images, especially application examples in water resources. In recent years, researches on spatial and temporal characteristics of large-scale regions utilizing the advantages of satellite imagery have been actively conducted for fundamental hydrological components such as evapotranspiration, soil moisture and natural disasters such as drought, flood, and heavy snow. Furthermore, it is possible to analyze temporal and spatial characteristics such as vegetation characteristics, plant production, net primary production, turbidity of water bodies, chlorophyll concentration, and water quality by using various image information utilizing various sensor information of satellites. Korea is planning to launch a satellite for water resources and environment in the near future, so various researches are expected to be activated on this field.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.27-35
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• 2018

In the mandatory application of the CPTED, only negative parts of urban parks and vegetation were reflected. Therefore, this study tries to present the positive effects of urban parks and vegetation. The purpose of this study is to demonstrate the effects of urban parks and vegetation on crime occurrence and to suggest the planning implications of this to CPTED based on theory related to crime, environmental psychology, and crime occurrence analysis. This study used the crime occurrence data of Seoul, NDVI, population, distance from urban park, floating population, and the like. This study collected data from the Statistics Korea, the local government, and Landsat 8 satellite images provided by the USGS and created data of environmental variables and social variables by district using ArcGIS and statistical program. Literature analysis, correlation analysis, regression analysis, and geographically weighted regression were used to determine the relationship between crime occurrence and environmental variables, and to discuss its implication. It was found that crime occurrence has a relationship with the total population (${\beta}=.663$), the number of amusement facilities (${\beta}=.447$) and the area of a police station jurisdiction (${\beta}=.395$). This confirms that a crime rate is low when the floating population is large (${\beta}=-.241$) and vegetation vitality is high (NDVI, ${\beta}=-.281$). Vegetation vitality (NDVI) is effective in lowering violence through psychological stabilization, strengthening territoriality and improving regional image. The implications for the allocation of urban park and vegetation, program and management plan of urban park and vegetation to reduce crime occurrence have therefore been presented.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.67-79
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• 2020

Plans to mitigate overheating in urban areas requires the identification of the characteristics of the thermal environment of the city. The key information is the distribution of higher and lower temperatures (referred to as "hotspot" or "coldspot", respectively) in the city. This study aims to identify the areas within Jeonju City that are suffering from increasing land surface temperatures (LST) and the factors linked to such this phenomenon. To identify the hot and cold spots, Local Moran's I and Getis-Ord Gi^* were calculated for the LST based on 2017 images taken using the thermal band of the Landsat 8 satellite. Hotspot analysis revealed that hotspot regions, (the areas with a high concentration of Land Surface Temperature) are located in the old town area and in industrial districts. To figure out the factors linked to the hotspots, a correlation analysis, and a regression analysis taking into account environmental covariates including Normalized Difference Vegetation Index (NDVI) and land cover. The values of NDVI showed that it had the strongest effect on the lowering LSTs. The results of this study are expected to provide directions for urban thermal environment designing and policy development to mitigate the urban heat island effect in the future.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.169-182
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• 2022

This study was conducted to predict the distribution of rare·endemic plants (Sophora koreensis Nakai) in the border forests where wildfire damage occurred and to quantify the damage. For this purpose, we tried to derive more accurate results through forest area damage (NBR) according to the Burn severity of wildfires, damage by tree species type (Vegetation map), and MaxEnt model. For Burn severity analysis, satellite imagery (Landsat-8) was used to analyze Burn severity (ΔNBR2016-2015) and to derive the extent of damage. To prepare the Vegetation map, the land cover map prepared by the Ministry of Environment, the Vegetation map prepared by the Korea Forest Service, and the vegetation survey conducted by itself were conducted to prepare the clinical map before and after the forest fire. Lastly, for MaxEnt model analysis, the AUC value was derived by using the habitat coordinates of Sophora koreensis Nakai based on the related literature and self-report data. As a result of combining the Maxent model analysis data with the Burn severity data, it was confirmed that 45.9% of the 44,760 m² of habitat (predicted) area of Sophora koreensis Nakai in the wildfire damaged area or 20,552 m², was damaged.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.50-61
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• 2010

This study was carried out to find out the way to build a comprehensive wetland ecosystem database using the technique of remote sensing and Geographic Information System. A Landsat TM image (taken in Oct. 30, 2002), Kompsat-2 images (Jan. 17, 2008 & Nov. 20, 2008), LiDAR(Mar. 1, 2009) were used for the primary source for the image analysis. Field surveys were conducted March to August of 2009 to help image analysis and examine the results. An actual wetland vegetation map was created based on the field survey. Satellite images were analyzed by unsupervised and supervised classification methods and finally categorized into such classes as Phragmites australis community, mixed community, sand beach, Scirpus planiculmis community and non-vegetation intertidal area. The map of wetland productivity was developed based on the productivity of Phragmites australis and the relationship to the proximity of adjacent water bodies. The developed 3 dimensional wetland map showed such several potential applications as flood inundation, birds flyway viewsheds and benthos distribution. Considering these results, we concluded that it is possible to use the remote sensing and GIS techniques for producing wetland ecosystem spatial database and these techniques are very effective for the development of the national wetland inventory in Korea.