• Korean Journal of Remote Sensing
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• 제37권3호
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제39권6호
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• pp.691-702
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• 2019

Flood inundation map has various important roles in terms of municipal planning, timely dam operation, economic levee design, and building flood forecasting systems. Considering that the riparian areas adjacent to national rivers with high potential flood vulnerability conventionally imposed special cares to justify applications of recently available two- or three-dimensional flood inundation numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. On the contrary, local streams usually could not have benefits from recent technological advances, instead they inevitably have relied upon time-consuming manual drawings or have accepted DEMs with poor resolutions or inaccurate 1D numerical models for producing inundation maps due mainly to limited budgets and suitable techniques. In order to efficiently and cost-effectively provide a series of flood inundation maps dedicatedly for the local streams, this study proposed an OpenGIS-based flood mapping tool named Open Flood Mapper (OFM). The spatial accuracy of flood inundation map derived from the OFM was validated throughout comparison with an inundation trace map acquired after typhoon Nari in Hancheon basin located in Jeju Island. Also, a series of inundation maps from the OFM were comprehensively investigated to track the burst of flood in the extreme flood events.

• Asian Journal of Atmospheric Environment
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• 제12권3호
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• pp.232-243
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• 2018

The construction, development and maintenance of an economically, environmentally and socially sustainable campus involves the integration of measuring tools and technical information that invites and encourages the community to know the actual state to generate positive actions for reducing the negative impacts over the local environment. At the Universidad Nacional de Colombia - Campus $Bogot{\acute{a}}$, a public area with daily traffic of more than 25000 people, the Environmental Management Bureau has committed with the monitoring of the noise pollution and air quality, as support to the campaigns aiming to reduce the pollutant emissions associated to the student's activities and campus operation. The target of this study is based in the implementation of mobile air quality and sonometry monitoring equipment, the mapping of the actual air quality and noise pollution inside the university campus as a novel methodology for a sub-area inside a megacity. This results and mapping are proposed as planning tool for the institution administrative sections. A mobile Kunak$^{(R)}$ Air & OPC air monitoring station with the capability to measure particulate matter $PM_{10}$, $PM_{2.5}$, Ozone ($O_3$), Sulfur Oxide ($SO_2$), Carbon Monoxide (CO) and Nitrogen Oxide ($NO_2$) as well as Temperature, Relative Humidity and Latitude and Longitude coordinates for the data georeferenciation; and a sonometer Cirrus$^{(R)}$ 162B Class 2 were used to perform the measurements. The measurements took place in conditions of academic activity and without it, with the aim of identify the impacts generated by the campus operation. Using the free code geographical information software QGIS$^{(R)}$ 2.18, the maps of each variable measured were developed, and the impacts generated by the operation of the campus were identified qualitative and quantitively. For the measured variables, an increase of around 21% for the $L_{Aeq}$ noise level and around 80% to 90% for air pollution were detected during the operation period.

• Journal of Wetlands Research
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• 제23권2호
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• pp.154-162
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• 2021

The naturally opened Han river estuary is a place where the flows of the Han river, Imjin river, Yaesung river meet with West Sea of Korea, so the hydrodynamic mechanism(Impact-Response) structure of Han river estuary is complex. Continuous observation and measurement due to the morphological characteristics at the estuary are required to maintain the estuary environment and river management facilities. However, the Sannam wetland(the study area) is in the military operation area. Therefore, Sannam wetland has the limited access under the control from military office. In 2020, there had a natural disaster due to flooding in August and COVID-19, and it made a survey hard. The noncontact survey technique, the analysis of LANDSAT images at Sannam wetland, was applied to analyze riverbed fluctuation and morphological transformation around Sannam wetland. LANDSAT images obtained from EarthExplorer, USGS and analyzed by QGIS. The analysis was performed based on the area and the distance near Sannam wetland. As a result, an erosion was happened on the downstream of the study area, and the upstream of the study area did not have any serious sediment transport. Considering the resolution of LANDSAT images, this noncontect survey technique is applicable to manage the study area. From the analysis of LANDSAT images, it is assumed that the tidal effect is greater than the inflow from the upstream. The pattern change of tidal response causes the damage of the river facilities near the Hangang river estuary.

• Journal of Environmental Health Sciences
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• 제48권6호
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• pp.298-305
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• 2022

Background: The concentration of air pollutants as measured by the Air Quality Monitoring System (AQMS) is not an accurate population exposure level since actual human activities and temporal and spatial variability need to be considered. Therefore, to increase the accuracy of exposure assessment, the population should be considered. However, it is difficult to obtain population data due to limitations such as personal information. Objectives: The existing population defined in this study is the number of people in each region's grid. The purpose is to provide a methodology for evaluating exposure to PM_2.5 through existing population data provided by the National Geographic Information Institute. Methods: The selected study period was from October 26 to October 28, 2021. Using PM_2.5 concentration data measured at the Sensor-based Air Monitoring Station (SAMS) installed in Guro-gu and Wonju-si, the concentration for each grid was estimated by applying inverse distance weights through QGIS version 3.22. Considering the existing population, population-weighted average concentration (PWAC) was calculated and the exposure level of the population was compared by region. Results: The outdoor PM_2.5 concentration as measured through the SAMS was high in Wonju-si on all three days. Wonju-si showed an average 22% higher PWAC than Guro-gu. As a result of comparing the PWAC and outdoor PM_2.5 concentration by region, the PWAC in Guro-gu was 1~2% higher than the observed value, but it was almost the same. Conversely, observations of Wonju-si were 10.1%, 11.3%, and 8.2% higher than PWAC. Conclusions: It is expected that the Geographic Information System (GIS) method and the existing population will be used to evaluate the exposure level of a population with a narrow activity radius in further research. In addition, based on this study, it is judged that research on exposure to environmental pollutants and risk assessment methods should be expanded.

• Journal of the Korean GEO-environmental Society
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• 제25권1호
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• pp.5-11
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• 2024

Ground subsidence mainly occurs in urban areas with high population density, so it is necessary to clearly identify the cause of occurrence and prepare in advance. The main cause of ground subsidence is reported to be the creation of cavities in the ground due to damage to underground pipes, but the property information and influencing factors of underground pipes to predict and prepare for ground subsidence are not properly established. Therefore, in this study, factors showing a significant correlation with the occurrence of ground subsidence were selected among the underground facility property information and a regression equation was proposed through logistic regression analysis. For this purpose, data on underground structures and ground subsidence history information in the target area were collected, and the target area was divided into girds of 100m x 100m in size using QGIS. The underground facility attribute information and ground subsidence history information contained within the gird were extracted. Then, preprocessing was performed to construct a dataset and correlation analysis was performed. As a result, factors excluding the year of sewer pipes and communication pipes and the average depth of communication pipes, heat pipes, and gas pipes were found to have a significant correlation with ground subsidence. In addition, a regression equation for whether ground subsidence occurred in the target area is proposed through logistic regression analysis.

• Journal of the Korean Association of Geographic Information Studies
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• 제27권1호
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• Journal of The Korean Society of Grassland and Forage Science
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• 제42권2호
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Korean Journal of Environment and Ecology
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• 제36권1호
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• pp.102-111
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• 2022

Among protected areas, National Parks are rich in biodiversity, and the benefits of ecosystem services provided to human are higher than the others. Ecosystem service evaluation is being used to manage the value of national parks based on objective and scientific data. Ecosystem services are classified into four services: supporting, provisioning, regulating and cultural. The purpose of this study is to evaluate habitat quality among supporting services. Habitat Quality Model of InVEST was used to analyze. The coefficients of sensitivity and habitat initial value were reset by reflecting prior studies and the actual conditions of protected areas. Habitat quality of 21 national parks except Hallasan National Park was analyzed and mapped. The value of habitat quality was evaluated to be between 0 and 1, and the closer it is to 1, the more natural it is. As a result of habitat quality analysis, Seoraksan and Taebaeksan National Parks (0.90), Jirisan and Odaesan National Parks (0.89), and Sobaeksan National Park (0.88) were found to be the highest in the order. As a result of comparing the area and habitat quality of 18 national parks except for coastal-marine national parks, the larger the area, the higher the overall habitat quality. Comparing the value of habitat quality of each zone, the value of habitat quality was high in the order of the park nature preservation zone, the park nature environmental zone, the park cultural heritage zone, and the park village zone. Considering both the analysis of habitat quality and the legal regulations for each zone of use, it is judged that the more artificial acts are restricted, the higher the habitat quality. This study is meaningful in analyzing habitat quality of 21 National Parks by readjusting the parameters according to the situation of protected areas in Korea. It is expected to be easy to intuitively understand through accurate data and mapping, and will be useful in making policy decisions regarding the development and preservation of protected areas in the future.

• Economic and Environmental Geology
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• 제55권5호
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.