• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.587-598
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• 2017

Volcanic ash is a fine particle smaller than 2 mm in diameters. It falls after the volcanic eruption and causes various damages to transportation, manufacturing industry and respiration of living things. Therefore diffusion information of volcanic ash is highly significant for preventing the damages from it. It is advantageous to utilize satellites for observing the widely diffusing volcanic ash. In this study volcanic ash diffusion information about two eruptions of Mt. Sakurajima were calculated using the geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) and polar-orbiting satellite, Landsat-8 Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). The direction and velocity of volcanic ash diffusion were analyzed by extracting the volcanic ash pixels from COMS-MI images and the height was retrieved by adjusting the shadow method to Landsat-8 images. In comparison between the results of this study and those of Volcanic Ash Advisories center (VAAC), the volcanic ash tend to diffuse the same direction in both case. However, the diffusion velocity was about four times slower than VAAC information. Moreover, VAAC only provide an ash height while our study produced a variety of height information with respect to ash diffusion. The reason for different results is measured location. In case of VAAC, they produced approximate ash information around volcano crater to rapid response, while we conducted an analysis of the ash diffusion whole area using ash observed images. It is important to measure ash diffusion when large-scale eruption occurs around the Korean peninsula. In this study, it can be used to produce various ash information about the ash diffusion area using different characteristics satellite images.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.267-282
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• 2018

Despite the continuous development of phenology detection studies using satellite imagery, verification through comparison with the field observed data is insufficient. Especially, in the case of Korean forests patching in various forms, it is difficult to estimate the start of season (SOS) by using only satellite images due to resolution difference. To improve the accuracy of vegetation phenology estimation, this study reconstructed the large scaled forest type map (1:5,000) with MODIS pixel resolution and produced time series vegetation phenology curves from Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from MODIS images. Based on the field observed data, extraction methods for the vegetation indices and SOS for Korean forests were compared and evaluated. We also analyzed the correlation between the composition ratio of forest types in each pixel and phenology extraction from the vegetation indices. When we compared NDVI and EVI with the field observed SOS data from the Korea National Arboretum, EVI was more accurate for Korean forests, and the first derivative was most suitable for extracting SOS in the phenology curve from the vegetation index. When the eight pixels neighboring the pixels of 7 broadleaved trees with field SOS data (center pixel) were compared to field SOS, the forest types of the best pixels with the highest correlation with the field data were deciduous forest by 67.9%, coniferous forest by 14.3%, and mixed forest by 7.7%, and the mean coefficient of determination ($R^2$) was 0.64. The average national SOS extracted from MODIS EVI were DOY 112.9 in 2014 at the earliest and DOY 129.1 in 2010 at the latest, which is about 0.16 days faster since 2003. In future research, it is necessary to expand the analysis of deciduous and mixed forests' SOS into the extraction of coniferous forest's SOS in order to understand the various climate and geomorphic factors. As such, comprehensive study should be carried out considering the diversity of forest ecosystems in Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.35-45
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• 2018

Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.33-44
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• 2001

The paper presents several satellite models and satellite image decomposition methods for inaccessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in $1^{st}$, $2^{nd}$ and $3^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\Phi$(phi) correlated highly with positional parameters could be assigned to constant values. For inaccessible area, satellite images were decomposed, which means that two consecutive images were combined as one image, The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1st order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1177-1186
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• 2021

As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.

• 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.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.439-449
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• 2023

This study was conducted to verify the role of forest roads in the extinction of large forest fires in Korea. The study area was the forest fire-damaged area of Gangneung City, Gangwon Special Self-Governing Province, in April 2023, which is one of the areas with the highest road density among the major forest fires that have occurred so far. The scope of the forest fire damage area was confirmed through on-site survey, and the intensity of the fire was carried out through Sentinel-2 satellite imagery analysis. After that, the relationship between the damage range and intensity and the forest road was examined. About 59.6 km of roads were built within 50 m from the boundary of the forest fire damage area, which can easily access the entire 149.1 ha of forest fire damaged area. The road density is as high as 168.9 m/ha. All forests that were fragmented by roads were fragmented into 83 places, and all of these forests could be judged to have spread by spotting fire. As a result of analyzing the distribution of damage intensity by distance from the road to see the extent of damage according to the ease of access of fire extinguishing vehicles, it was confirmed that the proportion of areas with low-intensity damage has increased sharply even from 75 m or more away from the road. The results of analyzing the distribution of damage intensity by altitude to see the extent of damage according to the ease of access of fire extinguishing showed that the proportion of areas with low-intensity damage increased as the altitude increased, while the proportion of areas with damage of more than strong intensity decreased as the altitude increased. It was confirmed that there is no data that roads inside or adjacent to forests in the forest fire area of Gangneung City are effective in extinguishing forest fires. These results are contrary to the logic that increasing the road density in forests is effective in extinguishing forest fires. In the case of this fire area in Gangneung City, the road density is 43 times higher than the current road density in Korea claimed by the Korea Forest Service of 3.9 m/ha. This study suggests that roads can be a hindrance to extinguishing forest fires.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.