• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.158-174
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• 2018

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Korean Journal of Remote Sensing
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• v.27 no.1
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• pp.43-49
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• 2011

In the face of growing concern about global warming, increasing attention has been focused on the reduction of carbon dioxide emissions. One method to mitigating the release of carbon dioxide is Carbon Capture and Storage (CCS). CCS includes separation of carbon dioxide from industrial emission in plants, transport to a storage site, and long-term isolation in underground. It is necessary to conduct analyses on optimal site selection, surface monitoring, and additional effects by the construction of CCS facility in Gyeongsang basin, Korea. For the optimal site selection, necessary data; geological map, landcover map, digital elevation model, and slope map, were prepared, and a weighted overlay analysis was performed. Then, surface monitoring was performed using high resolution satellite image. As a result, the candidate region was selected inside Gyeongnam for carbon storage. Finally, the related regulations about CCS facility were collected and analyzed for legal question of selected site.

• Journal of Aerospace System Engineering
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• v.12 no.4
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• pp.64-74
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• 2018

Small high-resolution Earth observation satellites require precise optical alignment at the submicron level. However, misalignments can occur due to the influence of external factors during the launch and operation despite the sufficient alignment processes that take place before the launch. Thus, satellites need to realign their optical elements in orbit in what is known as a refocusing process to compensate for any misalignments. Refocusing algorithms developed for satellites have only considered de-space, which is the most sensitive factor with respect to image quality. However, the existing algorithms can cause correction error when inner and external forces generate tilt amount in an optical system. The present work suggests an improved online refocusing algorithm by considering the tilting effect for application in the case of a de-spaced and tilted optical system. In addition, the algorithm is considered to be efficient in terms of time and cost because it is designed to be used as an online method that does not require ground communication.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.393-398
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• 2004

It is transformed map data of different coordinate system into unique system and We triedto make topographic map on non-accessible area. We transformed Russian map coordinates(Krassovsky, G-K projection) intoWGS-84, TM projection and assessed accuracy. The RMSE(in East and West bearings : ${\pm}$13.67m, in North and South bearings ： ${\pm}$14.67m) using only SCP(Survey Control Point) is more accurate than that(in East and West bearings ： ${\pm}$24.26m, in North and South bearings : ${\pm}$25.32m) using SCP, intersection of road, bridge. Exterior orientation parameters are estimated using rigorous modelling and GCPs are classified with SCP, intersection of road, bridge. Rigorous modelling is performed with each classified GCP. The modelling result usingonly SCP(in East and West bearings : ${\pm}$13.53m, in North and South bearings ： ${\pm}$14.22m) is more accurate than that using intersection of road(in East and West bearings ： ${\pm}$16.l1m, in North and South bearings： ${\pm}$23.85m), bridge(in East and West bearings : ${\pm}$17.21m, in North and South bearings : ${\pm}$21.82m). The results means that SCP is more accurate than intersection of road, bridge because of edit to generate map. therefore, SCP is suitable for object of GCP in paper map(1:50,000). Geographic information on non-accessible area and analysis is performed. The results of stereoscopic plotting is well matched old map data on road, railroad but, many objects are generally editted. It is possible to update on new objects(building, tributary ‥‥etc). Ability of description using SPOT-5(stereo) is more than features and items included in 1:50,000 topographic map. Therefore, it is possible to make large scale map than 1:50,000 topographic map using SPOT-5 imagery. But, there are many problems(accurate GCPs, obtain of high resolution stereoscopic satellite imagery in a period ‥‥ etc) to make topographic map on non-accessible area. It is actually difficult to solve these problems. therefore, it is possible to update 1:50,000 topographic map in part of topographic map generation.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.5
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• pp.55-63
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• 2008

The characteristics of forest damage in the Geum-buk Mountains were analyzed by using satellite images and a field survey for landscape conservation purposes. A survey scope was fixed using DEM, and areas of damage in the mountain range were analyzed via ArcMap v. 9.2 using SPOT 5 images, a high resolution satellite image. All damaged areas were reviewed and corrected in a field survey. As a result, 75 roads were found to completely fragment forest patches. Of those roads, 26 have the width under 3m, which means that the fragmentation of the forest by these roads may have a minor effect on forest habitat and its ecosystems, while other roads such as two-lane roads may have broader detrimental influences on the ecosystem. Two thousand eighty-three sections of accounted for a total area of about 5,760.7ha. Orchard areas including chestnut tree plantations were ranked as the largest in the damaged area within the Geum-buk Mountains, followed by public facility areas and grassland areas. This means that man-made land usage has progressed in the area regardless of slope and elevation.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.3
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• pp.265-272
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• 2002

Spatial distribution of Phytoplankton Pigment Concentration (PPC) and Sea Surface Temperature (SST) around the East Korean Warm Current (EKWC) was described, using both Coastal Zone Color Scanner (CZCS) images and Advanced Very High Resolution Radiometer (AVHRR) images in May, 1980. Water mass in this region can be classified into five categories in the horizontal profile of PPC and SST, nLw (normalized water-leaving radiance) images: (1) coastal cold water region associated with concentrations of dissolved organic material or yellow colored substances and suspended sediments, (2) cold water region of thermal frontal occurred by a combination of phytoplankton absorption and suspended materials, (3) warm water overlay region by the phytoplankton absorption than the suspended materials; (4) warm water region occurred by the low phytoplankton absorption, and (5) offshore region occurred by the high phytoplankton absorption. In particular, the highest PPC (>2.0 mg/m^3) area appeared in the CZCS and AVHRR images with a band shaped distribution of the thermal front and ocean color front region, which is located the coastal cold waters alonB western thermal front of the warm streamer of the EKWC. In this region, the highest PPC occurred by a combination of the high absorption of the phytoplankton (443 nm) and highest reflectance of suspended materials (550 nm). Another high PPC ($\simeq$$6\;mg/m^3$) appeared in the warm water overlay region inside warm streamer. High phytoplankton pigment concentration of this region was corresponding to the short wavelength of 443 nm, which represented phytoplankton absorption of the CZCS image.

• Journal of the Korean earth science society
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• v.31 no.6
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• pp.600-607
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• 2010

In 2008, multiple episodes of large-scale transport of natural airborne particles and anthropogenically affected particles from different sources in the East Asian continent were identified in the National Oceanic and Atmospheric Administration (NOAA) satellite RGB-composite images and the mass concentrations of ground level particulate matters. To analyze the aerosol size distribution during the large-scale transport of atmospheric aerosols, both aerosol optical depth (AOD; proportional to the aerosol total loading in the vertical column) and fine aerosol weighting (FW; fractional contribution of fine aerosol to the total AOD) of Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products were used over the East Asian region. The six episodes of massive natural airborne particles were observed at Cheongwon, originating from sandstorms in northern China, Mongolia and the loess plateau of China. The $PM_{10}$ and $PM_{2.5}$ stood at 70% and 16% of the total mass concentration of TSP, respectively. However, the mass concentration of $PM_{2.5}$ among TSP increased as high as 23% in the episode in which they were flowing in by way f the industrial area in east China. In the other five episodes of anthropogenically affected particles that flowed into the Korean Peninsula from east China, the mass concentrations of $PM_{10}$ and $PM_{2.5}$ among TSP reached 82% and 65%, respectively. The average AOD for the large-scale transport of anthropogenically affected particle episodes in the East Asian region was measured at $0.42{\pm}0.17$ compared with AOD ($0.36{\pm}0.13$) for the natural airborne particle episodes. Particularly, the regions covering east China, the Yellow Sea, the Korean Peninsula, and the east Korean sea were characterized by high levels of AOD. The average FW values observed during the event of anthropogenically affected aerosols ($0.63{\pm}0.16$) were moderately higher than those of natural airborne particles ($0.52{\pm}0.13$). This observation suggests that anthropogenically affected particles contribute greatly to the atmospheric aerosols in East Asia.