• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• 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.

• Journal of the Korean Geotechnical Society
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• v.34 no.5
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• pp.19-35
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• 2018

The necessity of predicting the spatial information of the site-specific seismic response, which is essential information for the comprehensive earthquake disaster countermeasures, is increasing for the mid-west urban areas where the earthquake-induced damages can be increased due to frequent occurrence of mid-scale earthquake such as 2016 Gyeongju Earthquake and 2017 Pohang Earthquake. Especially, researches on strategic securing of site survey datasets and understanding the site-specific site response characteristics were conducted for Gyeonggi-do, South Korea. In this study, a GIS-based framework for site-specific assessment of site response and induced vulnerable area in Gyeonggi-do, South Korea was proposed. Geo-Data based on GIS platform was constructed for regional estimation of geotechnical characteristics by collecting borehole and land coverage datasets. And the geo-spatial grid information was developed for deriving spatial distribution of geotechnical layer and site response parameters based on the optimization of the geostatistical interpolation method. Accordingly, base information for Improving earthquake preparedness measures was derived as seismic zonation map with administrative sub-units considering the quantitative site effect of Gyeonggi-do.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Environment and Ecology
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• v.20 no.3
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• pp.289-298
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• 2006

The purpose of this study was to investigate salt marsh flora and vegetation in the mouth of Mankyeong river estuary area where has a project for Sea Man Geum Reclaimed Land so that we can foster a foundation on restoration of an ecological habitat, development of applicable plants and establishment of a conservation policy after developing the reclaimed land for salt marsh vegetation which has great ecological value. As a result of this research, there are 10 families 25 genera 29 species and 3 varieties of vascular plants in the Mankyong-river estuary area. These are 0.76% among 4,191 of Korean vascular plants. There are also 5 families 6 genera 6 species and 1 varietiy of the naturalized plants which are 7 taxa in total and 3.85% of indicators of naturalized plants. Firstly, a district of low tide marsh has below 5% of vegetation coverage of Suaeda japonica and the vegetation cover was increasing rapidly while moving to a place of high tide marsh which is in the direction to a bank. In general, a range of from low tide marsh to high tide marsh is distributed with sequence of Suaeda japonica$\rightarrow$Suaeda maritima$\rightarrow$Suaeda japonica$\rightarrow$Aster tripolium$\rightarrow$Artemisia scoparia$\rightarrow$Carex scabrifolia$\rightarrow$Zoysia sinica$\rightarrow$Phragmites australis$\rightarrow$Phacelurus latifolius. Suaeda japonica has the highest dominance among the species composition and Aster tripolium, Phragmites australis, Artemisia scoparia, Carex scabrifolia and Phacelurus latifolius are distributed as zonation or patch. By the Z-M method eleven plant communities were recognized; Suaeda japonica, Suaeda japonica-Suaeda maritima, Suaeda maritima, Suaeda japonica-Aster tripolium, Aster tripolium, Phragmites australis, Carex scabrifolia, Phacelurus latifolius, Artemisia scoparia-Aster tripolium, Paspalum distichum var. indutum and Aster tripolium-Artemisia scoparia community. The actual vegetation map was constructed of the grounds of the communities classified and other data.