• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1003-1017
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• 2017

Multispectral image data of high spatial resolution is required to obtain correct information on the ground surface. The multispectral image data has lower resolution compared to panchromatic data. PAN-sharpening fusion technique produces the multispectral data with higher resolution of panchromatic image. Recently the object-based approach is more applied to the high spatial resolution data than the conventional pixel-based one. For the object-based image analysis, it is necessary to perform image segmentation that produces the objects of pixel group. Image segmentation can be effectively achieved by the process merging step-by-step two neighboring regions in RAG (Regional Adjacency Graph). In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. This degradation increases variation of pixel values in same area, and results in deteriorating the accuracy of image segmentation. An iterative approach that reduces the difference of pixel values in two neighboring pixels of same area is employed to alleviate variation of pixel values in same area. The size of segmented regions is associated with the quality of image segmentation and is decided by a stopping rue in the merging process. In this study, the image restoration and segmentation was quantitatively evaluated using simulation data and was also applied to the three PAN-sharpened multispectral images of high resolution: Dubaisat-2 data of 1m panchromatic resolution from LA, USA and KOMPSAT3 data of 0.7m panchromatic resolution from Daejeon and Chungcheongnam-do in the Korean peninsula. The experimental results imply that the proposed method can improve analytical accuracy in the application of remote sensing high resolution PAN-sharpened multispectral imagery.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Journal of Ecology and Environment
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• v.41 no.7
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• pp.182-190
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• 2017

Background: Baekdudaegan was designated in 2005 as a protected area to prevent destruction and conserve. However, there are many disturbed and destroyed areas. The total disturbed area amounts to $25.9km^2$ (0.94%), including $13.4km^2$ (0.49%) in the core area and $12.5km^2$ (0.45%) in the buffer area. This study aims to classify the vegetation types established in the disturbed areas and diagnose the current conditions for ecological restoration in the forest ecosystem. Methods: We surveyed the vegetation in the disturbed areas of Daegwallyeong and Chupungryeong and the surrounding natural areas. The survey conducted from July to September 2015 targeted a total of 54 quadrats by Braun-Blanquet method (Daegwallyeong, 22; Chupungryeong, 32). We also investigated the height and coverage of each layer. We classified the vegetation types based on the field data and analyzed the ratio of life form and the exotic plants, species richness, and vegetation index (Hcl). The Normalized Difference Vegetation Index (NDVI) was calculated from rapideye satellite imagery in 2014 and 2015. Results: Vegetation types were classified into 11 groups according to the criteria that included successional sere or plantation at first, followed by developmental stage and origins. As a result of the analysis of the survey data, species richness, vegetation index (Hcl), ratio of tree plants, and the NDVI tended to increase, while the ratio of the exotic plants tended to decrease with the time since disturbance. These indicators had the classified values according to the vegetation types with time since the disturbance. Conclusions: These indicators can be effectively used to diagnose the conditions of the present vegetation in the disturbed area of the Baekdudaegan area. In addition, the NDVI might be effective for the diagnosis of the disturbed status instead of the human efforts based on the higher spatial resolution of satellite imagery. Appropriate diagnosis of the disturbed forests in the Baekdudaegan area considering the established vegetation types is essential for the elaboration of restoration plans. In addition, restoration target and level should be different according to the disturbed status of restoration site.

• 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 Association of Geographic Information Studies
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• v.21 no.4
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• pp.91-107
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• 2018

It is required to monitor and assess the desertification in Tunisia, where the Sahara Desert, which is located in the southern part of Tunisia, is recently expanding northward. In this study, by using remote sensed data, land cover changes were examined, and the Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI) and Albedo are used to monitor and assess desertification in Tunisia. Decision Tree was constructed, and the frequencies and trends of each assessment indicator, desertification degree and land cover were identified. In addition, we analyzed the correlation between assessment indicators and precipitation. As a result, desertification is generally intensifying northward, especially in areas with high levels of desertification. Also, bivariate correlation analysis showed that Albedo, NDVI and TGSI were all highly correlated with precipitation. It indicates that changes in precipitation have also been shown to affect Tunisian desertification. In conclusion, this study has improved the usability of various methodologies considering the assessment indicators based on satellite imagery, Decision Tree, which is a method of evaluating them complexly, and trends of land cover change.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1371-1388
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• 2023

Floods are becoming more severe and frequent due to global warming-induced climate change. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventive climate change measures and efficient water catastrophe responses crucial, and synthetic aperture radar satellite imagery can help. This research created 1,423 water body learning datasets for individual water body regions along the Han and Nakdong waterways to reflect domestic water body properties discovered by Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for many situations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detection results. The results from applying the learned model to water body locations not involved in the learning process were studied to validate soil water body monitoring on a national scale. The analysis showed that the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection over Union [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showed similar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accurately spotted water bodies in most areas, however tiny streams and gloomy areas had problems. This work should improve water resource change and disaster damage surveillance. Future studies will likely include more water body attribute datasets. Such databases could help manage and monitor water bodies nationwide and shed light on misclassified regions.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1731-1745
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• 2023

Within the framework of the post-2020 climate regime, the Paris Agreement's emphasis on Nationally Determined Contributions and Biennial Transparency Reporting is paramount in addressing its long-term temperature goal. A salient issue is the treatment of wetland ecosystems within the context of Land Use, Land-Use Change, and Forestry, as defined by the Intergovernmental Panel on Climate Change. In the 2019 National Inventory Report, wetlands were recategorized as emission sources due to their designation as inundated areas. This study employs C-band radar imagery to discriminate between inundated and non-inundated regions of wetlands, enabling the quantification of their spatial dynamics. The research capitalizes on 24-period Sentinel-1 satellite data to cover both the inundation and desiccation phases while centering its attention on Ungok Wetland, a Ramsar-designated inland wetland conservation area in Korea. The inundated area is quantitatively assessed through the integration of multi-temporal Sentinel-1 Single-Look Complex (SLC) data, aerial orthophotography, and inland wetland spatial information. Furthermore, the study scrutinizes fluctuations in the maximum and minimum inundated areas, with substantial changes corroborated via drone aerial reconnaissance. The outcomes of this investigation hold the potential to make substantive contributions to the refinement of national greenhouse gas absorption and emission factors, thereby informing the development of comprehensive greenhouse gas inventories. These efforts align directly with the overarching objectives of the Paris Agreement.

• 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.40 no.3
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• pp.295-305
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• 2024

This study aims to analyze the micro-sedimentary structures of the Hwangdo tidal flats using ultra-high resolution unmanned aerial vehicle (UAV) data. Tidal flats, located in the transitional area between land and sea, constantly change due to tidal activities and provide a unique environment important for understanding sedimentary processes and environmental conditions. Traditional field observation methods are limited in spatial and temporal coverage, and existing satellite imagery does not provide sufficient resolution to study micro-sedimentary structures. To overcome these limitations, high-resolution images of the Hwangdo tidal flats in Chungcheongnam-do were acquired using UAVs. This area has experienced significant changes in its sedimentary environment due to coastal development projects such as sea wall construction. From May 17 to 18, 2022, sediment samples were collected from 91 points during field surveys and 25 in-situ points were intensively analyzed. UAV data with a spatial resolution of approximately 0.9 mm allowed identifying and extracting parameters related to micro-sedimentary structures. For mud cracks, the length of the major axis of the polygons was extracted, and the wavelength and ripple symmetry index were extracted for ripple marks. The results of the study showed that in areas with mud content above 80%, mud cracks formed at an average major axis length of 37.3 cm. In regions with sand content above 60%, ripples with an average wavelength of 8 cm and a ripple symmetry index of 2.0 were formed. This study demonstrated that micro-sedimentary structures of tidal flats can be effectively analyzed using ultra-high resolution UAV data without field surveys. This highlights the potential of UAV technology as an important tool in environmental monitoring and coastal management and shows its usefulness in the study of sedimentary structures. In addition, the results of this study are expected to serve as baseline data for more accurate sedimentary facies classification.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.251-263
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• 2019

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.