Korea Multi-Purpose Satellite Research Articles

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

AbstractThe Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Applicability of KOMPSAT Image Data in Open Data Cube

Abstract. Data cube terms a multi-dimensional stack of gridded datasets aligned for analysis. Open Data Cube (ODC) is an open-source based information processing and managing platform on the viewpoint from web-based infrastructure. This open platform is for a large volume of geo-spatial information with geo-rectified coordinates, and it has been applied by non-profit international organizations such as the Committee on Earth Observation Satellites (CEOS) for an international coordination and management of space-borne missions, Global Earth Observation System of Systems (GEOSS) in the Group on Earth Observations (GEO), as an intergovernmental organization to improve the applicability, accessibility and usability of Earth observations for benefit of human society.The building of Analysis Ready Data (ARD), which means the preparation of radiometric calibration and geo-rectification, is for the data cube utilization. The platform converts large-scale satellite image data into analytic information, providing functions for time series analysis. Internationally, there has been an ever-increasing number of country-based data cube deployments with freely available satellite images, including Australia Data Cube, Vietnam Data Cube, Swiss Data Cube, and Colombia Data Cube, as a computing environment for information distribution, sharing, and analysis.However, there is no program yet to register Korea Multi-purpose satellite (KOMPSAT) optical and radar images on this platform, so this study developed the registering and ingestion script codes for KOMPSAT optical and radar image sets into ODC. Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. Thus, an ingestion process is required to add satellite data to the ODC platform and the process can be divided into three main stages in Figure 1. First, it is to define the data type in the YAML format. Then, the indexing process of datasets for metadata registration is necessary. The next step is a data ingestion process that users can be used directly data sets collected in ODC.Figure 2 shows some of the Python module results for index datasets and the process of metadata generation. The metadata YAML, which is required for indexing, has many advantages in many respects in the creation of metadata through Python modules. This is why we added Python modules to create metadata YAML. In particular, the KOMPSAT data ingestion process was designed so that all of them were possible through one module.Using script modules for these steps, the functional accuracy was tested with actual satellite data. Color composite images using RGB bands of KOMPSAT optical images were generated in the ODC environment in Figure 3. In this process, image data formats of GeoTiff and netCDF are also supported.In this study, consideration points for implementation of ODC applications are also discussed. KOMPSAT data is basically commercial-based products, unlike other freely accessible satellite images in the ODC applications. For the practical contribution for ODC-GEOSS, careful considerations for data policy are needed, because it can be applied as a reference model for other commercial satellite data for GEOSS.

Quality Assessment of Four DEMs Generated Using In-Track KOMPSAT-3 Stereo Images

The purpose of this research was to analyze the quality and characteristics of four digital elevation models (DEMs) generated using in-track Korea Multi-Purpose Satellite (KOMPSAT)-3 stereo images. The sensor modeling methods were based on ground control points (GCPs), the initial rational polynomial coefficients (RPCs), relative adjustment, and the automatic bias-compensation method. The GCPs and check points (CPs) were extracted from the 0.25 m aerial orthoimage and the 5 m DEM provided by the National Geographic Information Institute (NGII). The DEMs had the same resolution as the reference DEM (5 m) and comparative analysis was carried out. The results indicate that when relative adjustment was applied alone (DEM 3), the percentage of matched points with a correlation of 0.8 or more was improved by at least 17% compared to the case where only initial RPCs were used (DEM 2). Although the absolute horizontal position error of DEM 3 could not be eliminated, the relative elevation error at the same position was reduced significantly. Therefore, if the relative positions of DEMs produced at different times can be corrected, they can be used for the detection of changes in altitude. When applying the automatic bias-compensation method (DEM 4) without GCPs, the percentage of matched points with a correlation of 0.8 or more was 70.1%. When GCPs were used (DEM 1), the value was 70.2%, i.e., almost identical to that of DEM 4. The mean difference in resolution among DEMs 1 and 4 was -1.8 ± 3.4 m (median, -1.0 m). The results show that DEMs of sufficient quality can be generated without GCPs. Furthermore, although discrepancies among the DEMs were noted in forest and shadow areas, it is possible to produce a 5~10 m resolution DEM by using additional image processing techniques, such as shadow removal.

Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery

Pansharpening algorithms are designed to enhance the spatial resolution of multispectral images using panchromatic images with high spatial resolutions. Panchromatic and multispectral images acquired from very high resolution (VHR) satellite sensors used as input data in the pansharpening process are characterized by spatial dissimilarities due to differences in their spectral/spatial characteristics and time lags between panchromatic and multispectral sensors. In this manuscript, a new pansharpening framework is proposed to improve the spatial clarity of VHR satellite imagery. This algorithm aims to remove the spatial dissimilarity between panchromatic and multispectral images using guided filtering (GF) and to generate the optimal local injection gains for pansharpening. First, we generate optimal multispectral images with spatial characteristics similar to those of panchromatic images using GF. Then, multiresolution analysis (MRA)-based pansharpening is applied using normalized difference vegetation index (NDVI)-based optimal injection gains and spatial details obtained through GF. The algorithm is applied to Korea multipurpose satellite (KOMPSAT)-3/3A satellite sensor data, and the experimental results show that the pansharpened images obtained with the proposed algorithm exhibit a superior spatial quality and preserve spectral information better than those based on existing algorithms.

Multi-objective Heuristic Design Approach for SAR Mission for Monitoring Local Target Area

The Korea first synthetic aperture radar (SAR) satellite with a 1-m resolution—Korea Multi-purpose Satellite-5 (KOMPSAT-5)—was successfully launched in Korea in 2013, and its successors will be launched continuously to monitor specific target areas. The major requirements of the orbit design for KOMPSAT-5 are that the mean average revisit time (ART) over the Korean peninsula is no longer than 24 h and that the repeat ground track orbit is guaranteed. For this type of problem, an iterative and tedious process may be used to derive the appropriate mission orbit to satisfy the mission requirements. During the design process, sophisticated coverage analysis software should be employed to evaluate the mean ART over a specific local target area. Moccia et al. (Acta Astronaut 47(11):819–829, 2000) presented a feasibility study of a new space-based observation technique using a bistatic SAR and performed a numerical simulation to estimate the measurement accuracy for the bright point target position and velocity. Kim and Chang (Aerosp Sci Technol 40:17–32, 2015) developed an optimal scheduling method employing a genetic algorithm (GA) to reduce the system response time for an SAR satellite constellation. Wei and Chunsheng (Adv Sp Res 50:272–281, 2012) proposed a new design method for a distributed satellite-borne SAR system with an error-propagation model and used a monostatic design to determine the key parameters of a single SAR satellite. Apart from the SAR mission, the orbit design for Earth-observation missions over a specific area or target has been studied by others. Abdelkhlik et al. (J Guid Control Dyn 29(5):1231–1235, 2006) utilized the RGT concept to design natural orbits for visiting a target area without the use of propulsion systems. Kim et al. (J Spacecr Rocket 46(3):725–728, 2009) proposed a new strategy employing a GA to search the current mission orbit for a temporary target orbit to achieve a temporary reconnaissance mission over a particular target site using a low-Earth-orbit satellite during a specific period. We propose an effective approach for the design of the SAR mission for a local target area. Here, the ART and average transmitted power are key parameters for the mission requirements and the bus system, respectively. To our knowledge, no previous studies have considered this kind of problem. To satisfy the mission requirements for the ART over a local target area and simultaneously consider the average transmitted power, multi-objective heuristic algorithms including GEs, particle swarm optimization, and differential evolution are used, and their performances are compared. The computational approach of the design strategy proposed by the authors is based on the optimization algorithms in Matlab® and the powerful coverage analysis tool STK®. Therefore, the proposed strategy is adaptable for various types of SAR mission designs having complex requirements regarding both the orbit and the bus system, particularly for monitoring a specific local target area.

Selection and evaluation of optimal segmentation scale for high-resolution remote sensing images based on prior thematic maps and image features

Multiscale segmentation is the premise and key step of geographic object-based image analysis (GEOBIA), but scale selection remains a challenge in multiscale segmentation. Over the years, scale selection and evaluation in image segmentation has been extensively explored and many methods have been developed. In these methods, when a scale is chosen or evaluated, all the features are generally extracted from images. In addition, an optimal scale is generally selected based on the pre-estimation of the statistical variance in remote sensing images or determined based on the postsegmentation evaluation of segmented results. In this study, a method was proposed to identify the optimal scale of each segmented object during the segmentation through combining the a priori thematic map knowledge with image features. First, 25 image segmentations were obtained using multiresolution segmentation algorithm of Definiens Professional 9.0 with different scales. A global score (GS) value was assigned to each segmentation based on the calculation results of the weighted variance and global Moran’s I and the single-scale optimal segmentation result was determined according to each GS of 25 segmentation scales. Second, the image feature complexity information and the a priori thematic map complexity information of each segmentation object were extracted to calculate complexity values for each object. Third, the optimal scale of each segmentation object was determined through the iterative calculation with the multithreshold method. Finally, the segmentation results of the proposed method were evaluated. The proposed method was applied to process Gaofen-2 (GF-2), GF-1, Korea Multipurpose Satellite (KOMPSAT-2), IKONOS, QuickBird and WorldView-2 high resolution satellite images to obtain the segmentation results and classification results, compared with results obtained of the optimal single-scale segmentation and the unsupervised evaluation method. The experimental results of GEOBIA showed that the method was helpful for generating the segmentation object with the optimal scale.

Orbit Determination of Korean GEO Satellite Using Single SLR Sensor.

Geostationary Earth Orbit (GEO)-Korea Multi-Purpose Satellite (KOMPSAT)-2B (GK-2B) is a Korean geostationary Earth orbit (GEO) satellite that is scheduled to be launched in 2020 for meteorological and ocean monitoring. While the primary orbit determination (OD) for GK-2B is by ground-based radar observations and the expected orbit precision is less than 1 km, a satellite laser ranging (SLR) technique has been selected as a subsidiary OD method to verify/complement/enhance primary OD results. In general, the available time and equipment for observing GEO satellites with SLR are limited. Furthermore, because the optical sensors mounted on GK-2B may be defected by laser, only a domestic single SLR station would obtain the tracking data. This research presents the mitigation of these drawbacks to improve orbit precision. Observation data generation and the associated OD of GK-2B are performed by considering numerical SLR data analysis on Compass-G1, a Chinese GEO navigation satellite, and Chinese SLR station at Changchun. With the OD performed for two scenarios with the varying number of observations, the 3D position error is 24.01 m when 13 observations per day are obtained, while the error becomes 43.46 m when 9 observations per day are obtained. To verify these results, the OD of Compass-G1 using actual SLR data from Changchun station is performed to yield 31.89 m for 3D error, which is favorable compared with the external precise ephemeris by GeoForschungsZentrum (GFZ) analysis center. Therefore, the OD based on single SLR station is applicable to estimating the orbit within less than 100 m.

Development of the KARI Space Debris Collision Risk Management System (KARISMA)

Korea has been operating multi-purpose low-earth orbit (LEO) satellites such as the Korea multi-purpose satellite (KOMPSAT) since 1999 and the Communication, Ocean, and Meteorological Satellite (COMS), which was launched into geostationary orbit in 2006. The Korea Aerospace Research Institute (KARI) consequently became concerned about the deteriorating space debris environment. This led to the instigation, in 2011, of a project to develop the KARI space debris collision risk management system (KARISMA). In 2014, KARISMA was adopted as an official tool at the KARI ground station and is operated to mitigate collision risks while being continuously upgraded with input from satellite operators. The characteristics and architecture of KARISMA are described with detailed operational views. The user-friendly user interfaces including 2D and 3D displays of the results, conjunction geometries, and so on, are described in detail. The results of our analysis of the space collision risk faced by the KOMPSAT satellites as determined using KARISMA are presented, as well as optimized collision avoidance maneuver planning with maneuvering strategies for several conjunction events. Consequently, the development of KARISMA to provide detailed descriptions is expected to contribute significantly to satellite operators and owners who require tools with many useful functions to mitigate collision risk.

Surface Downward Longwave Radiation Retrieval Algorithm for GEO-KOMPSAT-2A/AMI

This study contributes to the development of an algorithm to retrieve the Earth’s surface downward longwave radiation (DLR) for 2nd Geostationary Earth Orbit KOrea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A)/Advanced Meteorological Imager (AMI). Regarding simulation data for algorithm development, we referred to Clouds and the Earth’s Radiant Energy System (CERES), and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-interim reanalysis data. The clear sky DLR calculations were in good agreement with the Gangneung-Wonju National University (GWNU) Line-By-Line (LBL) model. Compared with CERES data, the Root Mean Square Error (RMSE) was 10.14Wm−2. In the case of cloudy sky DLR, we estimated the cloud base temperature empirically by utilizing cloud liquid water content (LWC) according to the cloud type. As a result, the correlation coefficients with CERES all sky DLRs were greater than 0.99. However, the RMSE between calculated DLR and CERES data was about 16.67Wm−2, due to ice clouds and problems of mismatched spatial and temporal resolutions for input data. This error may be reduced when GK-2A is launched and its products can be used as input data. Accordingly, further study is needed to improve the accuracy of DLR calculation by using high-resolution input data. In addition, when compared with BSRN surface-based observational data and retrieved DLR for all sky, the correlation coefficient was 0.86 and the RMSE was 31.55 Wm−2, which indicates relatively high accuracy. It is expected that increasing the number of experimental Cases will reduce the error.

Coastal wetland change detection using high spatial resolution KOMPSAT-2 imagery

Coastal wetlands store terrestrial carbon and conserve biodiversity, thus playing an essential ecological role. Reliable regional-scale assessments of wetland dynamics, such as analysis of land use and land cover (LULC), and coastal monitoring, provide important hydrographical and socio-geographical information. Remote sensing images, such as Earth Observation satellite data, are useful for examining temporal LULC changes and providing environmental monitoring data. This study presents a method of mapping and monitoring changes in coastal LULC using classified multispectral images acquired by the Korea Multi-Purpose Satellite-2 (KOMPSAT-2). Wetland changes are monitored in three different protected tidal flats areas on the coastal boundary of the Korean Peninsula with the Yellow Sea, for the period of 2008 - 2015. High overall accuracy and Kappa coefficient values for the accuracy assessment indicate the suitability of LULC classification using high spatial resolution KOMPSAT imagery, even when an unsupervised classification approach is adopted. The LULC maps were analyzed and evaluated using post-classification change detection methods. Results showed spatial decreases of 6 and 20% for mixed forest and wetlands in the Gyeonggi area, respectively, but no significant changes over time for Jeonbuk and Jeonnam. There was a 12% increase in developed areas for Gyeonggi but only 1.9 and 6% for Jeonbuk and Jeonnam, respectively. LULC change is thus easily identified through a pixel-based analysis of multispectral KOMPSAT-2 images over time. Such data are useful for environmental and policy managers when developing advanced coastal management strategies.

Optimal reliability design of a system with k-out-of-n subsystems considering redundancy strategies

This study presents new reliability models for k-out-of-n systems using a structured continuous-time Markov chain. The approach makes it comfortable to identify the characteristics of the lifetime of the system, such as reliability and expected lifetime. To demonstrate the advantage, the analysis results of them for the multi-spectral camera system, which is the only payload of Korea multi-purpose satellite-2 as an example of a real system. Furthermore, existing studies on a k-out-of-n system with standby redundancy has provided the approximated reliability for it. In this paper, it is confirmed that the approximation error could have an effect on the reliability design for a system. Moreover, new versions of reliability optimization problems, redundancy allocation problem (RAP) and reliability-redundancy allocation problem(RRAP), are proposed. In order to maximize system reliability, they further determine the redundancy strategy, either active or standby redundancy for each k-out-of-n subsystem from the traditional problems. A parallel genetic algorithm is proposed for an RRAP modeled by nonlinear mixed integer programming.

A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer

ABSTRACTIn this study, a sea ice mapping model based on Random Forest (RF), a rule-based machine learning approach, has been developed for the Korea Multi-Purpose Satellite-5 (KOMPSAT-5) Synthetic Aperture Radar (SAR) data in Enhanced Wide swath mode obtained from 6 August to 9 September 2015 in the Chukchi Sea. A total of 12 texture features derived from backscattering intensity and the gray-level co-occurrence matrix were used as input variables for sea ice mapping. The RF model produced a sea ice map with a grid spacing of 125 m, demonstrating excellent performance in the classification of sea ice and open water with an overall accuracy of 99.2% and a kappa coefficient of 98.5%. Sea ice concentration (SIC) retrieved from the RF-derived sea ice maps was compared with that from ice charts. The mean and median values of the differences between the SICs derived from the RF model and the ice charts were −8.85% and −8.38%, respectively. Such difference was attributed to both the uncertainty in the ice charts and classification error of the RF model.

Orthorectification of KOMPSAT Optical Images Using Various Ground Reference Data and Accuracy Assessment

Along with the appearance of high resolution satellite images, image correction using Rational Polynomial Coefficients (RPCs) has become common. Location accuracy of Korea Multipurpose Satellite (KOMPSAT) standard images is still not adequate, so, in order to leverage the KOMPSAT images for applications such as mapping and change detection, it is necessary to orthorectify the images. In this study, using updated RPCs, we performed orthorectification of KOMPSAT-2, KOMPSAT-3, and KOMPSAT-3A images using various data. Through this study, we discovered that the orthorectification result using precise Ground Control Points (GCPs) and Digital Elevation Model (DEM) is the best, but it was found that the correction results through image matching are also excellent. In particular, it was confirmed that orthoimages with a planimetric accuracy around 3 m (Root Mean Square Error (RMSE)) can be generated by using well-known matching algorithms with open data such as OpenStreetMap (OSM) and Shuttle Radar Topography Mission (SRTM) DEM, which can be acquired by anyone. Although the accuracy was low in some mountainous terrain, it was confirmed that it could be used for generating KOMPSAT orthoimages using open data. This paper describes the results for orthorectifying high resolution KOMPSAT optical images using various reference data.

Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image

New sets of satellite sensors are frequently being added to the constellation of remote sensing satellites. These new sets offer improved specification to collect imagery on-demand over specific locations and for specific purposes. The Korea Multi-Purpose Satellite (KOMPSAT) series of satellites is a multi-purposed satellite system developed by Korea Aerospace Research Institute (KARI). The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution multi-spectral imagery with infrared and high resolution electro-optical bands for geographical information systems applications in environmental, agricultural and oceanographic sciences as well as natural disasters. In this study, land cover classification of multispectral data was performed using four supervised classification methods: Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML) and Support Vector Machine (SVM), using a KOMPSAT-3A multi-spectral imagery with 2.2 m spatial resolution. The study area for this study was selected from southwestern region of South Korea, around Buan city. The training data for supervised classification was carefully selected by visual interpretation of KOMPSAT-3A imagery and field investigation. After classification, the results were then analyzed for the validation of classification accuracy by comparison with those of field investigation. For the validation, we calculated the User’s Accuracy (UA), Producer’s Accuracy (PA), Overall Accuracy (OA) and Kappa statistics from the error matrix to check the classification accuracy for each class obtained individually from different methods. Finally, the comparative analysis was done for the study area for various results of land cover classification using a KOMPSAT-3A multi-spectral imagery.

A study of a target identification method for an active debris removal system

In a preparatory study conducted prior to the development of an active space debris removal system, a method for selecting target debris based on information such as the cumulative collision probability, the operational condition of objects, and their sizes and launch dates was developed for use in the protection of four Korea Multi-Purpose Satellite constellation satellites. This method can be used to select candidate removal targets. Two-line element data are used to identify threatening objects with high cumulative collision probability. Using information in the Satellite Catalog database, objects smaller than a certain size or objects that are currently operational were excluded from the selection range. The results of an analysis of the cumulative collision probability, object size, object type, and primary mission information showed that the COSMOS 1328, COSMOS 1862, COSMOS 375, and COSMOS 1606 satellites were suitable targets for an active debris removal mission.

Development and Validation of Fire Damage-Severity Indices in the Framework of the PREFER Project

PREFER (space-based information support for prevention and recovery of forest fires emergency in the Mediterranean area) is one of the Copernicus FP7 Emergency projects funded in 2012. It is uniquely devoted to forest pre- and post-fire management. The overall goal of the project is to develop and demonstrate a preoperational portfolio of products, based on Earth observation data for helping fire management on a Mediterranean scale. Samples of the PREFER information products are available to stakeholders through the project Geoserver (prefer.cgspace.it). The project foresees the utilization of satellite images’ optical and SAR at medium (MODIS—moderate resolution imaging spectroradiometer), high (Landsat, Spot-Satellite Pour l’Observation de la Terre), and very high (KOMPSAT—Korea Multi-Purpose Satellite, RapidEye, Pleiades, COSMO-SkyMed—constellation of small satellites for Mediterranean basin observation, and TanDEM-X—TerraSAR-X add on for digital elevation measurement) spatial resolution, and a refresh rate of the products varying from high (days) to low (twice a month) to very low (once a year). The present paper is devoted to introducing the methodology developed for computing one of the project product, i.e., the damage-severity map. These maps provide the level of damage caused in vegetated areas by fires. Further, the paper aims at presenting the results of the validation of such product carried out during the first semester of 2015. The methodology is based on the utilization of Landsat8/OLI images.

서해연안 토지이용 및 토지피복 변화탐지를 위한 KOMPSAT-2 영상의 활용

토지이용 및 토지피복변화에 대한 신뢰성 높은 평가는 수로학 및 지리학적 연구에서 침식 및 퇴적, 해안 모니터링, 생태영향평가와 같은 다양한 실질적인 사안들을 발전시켰다. 원격탐사 이미지는 시간 변화에 따른 자연 및 토지변화를 살펴보는데 있어 뛰어난 잠재력을 지니고 있다. 따라서 최근에서는 환경 모니터링을 위해 고해상도의 원격탐사 영상 이미지를 활용한 보다 정확한 연구가 요구되고 있다. 본 논문에서는 갯벌보호지역이 위치한 한반도의 전라남도, 전라북도 일부지역의 토지이용 및 토지피복 변화에 대한 맵핑 및 변화탐지 방법을 실시하였다. 이를 위하여 2008년부터 2015년에 촬영된 KOMPSAT-2 위성의 다중분광 이미지를 사용하였다. 토지이용 및 토지피복변화 맵핑은 무감독 토지분류방법으로 분석하였으며, postclassification 변화탐지 방법으로 평가하였다. 전라북도와 전라남도의 연안지역에 대한 토지이이용 및 토지 피복변화에 대한 평가결과는 시간변화에 따라 큰 차이가 나타나지는 않았으나 각각 약 1.97%, 4.34% 정도의 변화를 보였다. 본 연구결과는 연구지역의 토지피복 변화 양상을 정량화 하였으며, 특히, 화소기반 분석을 통해 연안지역에 대한 KOMPSAT-2 다중분광 이미지의 효율적이고 경제적인 활용 가능성을 확인하였다. 이러한 토지이용 및 토지피복변화 정보는 연안환경 관리 및 정책결정을 위해서 환경 및 정책관리자들에게 유용할 것으로 기대된다. Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.

Improvement of KOMPSAT Imagery Locational Accuracy Using Value-Added Processing System

To increase the utilization of the KOrea Multi-Purpose SATellite(KOMPSAT) series imagery being developed pursuant to the national space development program, high quality images with enhanced locational accuracy should be created through standardized post-processing processes. In the present study, using the Value-Added Processing System(VAPS) constructed for the post-processing of KOMPSAT imagery, location correction experiments were conducted using KOMPSAT-2 and -3 imagery from domestic and overseas regions. First, 50 pieces from each of KOMPSAT-2 imagery were selected from South Korean and North Korean regions, and modeling was conducted using GCP Chips. According to the results, the Root Mean Square Errors(RMSE) for South Korea and North Korea were 1.59 pixels and 2.04 pixels, respectively, and the locational accuracy of ortho mosaic imagery using check points were 1.33m(RMSE) and 1.90m(RMSE), respectively. Meanwhile, in the case of overseas regions for which GCP could not be easily obtained, the improvement of locational accuracy could be identified through image corrections using Open Street Map(OSM). The VAPS and reference materials used in the present study are expected to be very useful in constructing a precise image DB for entire global regions.

Fuel-Optimal Altitude Maintenance of Low-Earth-Orbit Spacecrafts by Combined Direct/Indirect Optimization

This work presents fuel-optimal altitude maintenance of Low-Earth-Orbit (LEO) spacecrafts experiencing non-negligible air drag and J2 perturbation. A pseudospectral (direct) method is first applied to roughly estimate an optimal fuel consumption strategy, which is employed as an initial guess to precisely determine itself. Based on the physical specifications of KOrea Multi-Purpose SATellite-2 (KOMPSAT-2), a Korean artificial satellite, numerical simulations show that a satellite ascends with full thrust at the early stage of the maneuver period and then descends with null thrust. While the thrust profile is presumably bang-off, it is difficult to precisely determine the switching time by using a pseudospectral method only. This is expected, since the optimal switching epoch does not coincide with one of the collocation points prescribed by the pseudospectral method, in general. As an attempt to precisely determine the switching time and the associated optimal thrust history, a shooting (indirect) method is then employed with the initial guess being obtained through the pseudospectral method. This hybrid process allows the determination of the optimal fuel consumption for LEO spacecrafts and their thrust profiles efficiently and precisely.

Volcanic Ash Retrieval Using a New Geostationary Satellite

Abstract. The paper presents currently developing method of volcanic ash detection and retrieval for the Geostationary Korea Multi-Purpose Satellite (GK-2A). With the launch of GK-2A, aerosol remote sensing including dust, smoke, will begin a new era of geostationary remote sensing. The Advanced Meteorological Imager (AMI) onboard GK-2A will offer capabilities for volcanic ash remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Based on the physical principles for the current polar and geostationary imagers are modified in the algorithm. Volcanic ash is estimated in detection processing from visible and infrared channel radiances, and the comparison of satellite-observed radiances with those calculated from radiative transfer model. The retrievals are performed operationally every 15 min for volcanic ash for pixel sizes of 2 km. The algorithm currently under development uses a multichannel approach to estimate the effective radius, aerosol optical depth (AOD) simultaneously, both over water and land. The algorithm has been tested with proxy data generated from existing satellite observations and forward radiative transfer simulations. Operational assessment of the algorithm will be made after the launch of GK-2A scheduled in 2018.