• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.773-779
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• 2019

We developed the automatic ground deformation monitoring system using Sentinel-1 satellites which is operating by European Space Agency (ESA) for the Korea Peninsula's ground disaster monitoring. Ground deformation occurring over a long-term period are difficult to monitoring because it occurred in a wide area and required a large amount of satellite data for analysis. With the development of satellites, the methods to regularly observe large areas has been developed. These accumulated satellite data are used for time series ground displacement analysis. The National Disaster Management Research Institute (NDMI) established an automation system for all processes ranging from acquiring satellite observation data to analyzing ground displacement and expressing them. Based on the system developed in this research, ground displacement data on the Korean Peninsula can be updated periodically. In the future, more diverse ground displacement information could be provided if automated small regional analysis systems, multi-channel analysis method, and 3D analysis system techniques are developed with the existing system.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.447-449
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• 2003

Remotely sensed data can be used to estimate biomass production using methodologies relating vegetation indices to light absorption or to leaf photosynthetic capacity. The considerations of both light absorption and photosynthetic capacity in remote sensing-based modeling to estimate biomass production or NPP was introduced based upon Monteith model NPP is one of a evaluation of land degradation. NPP was estimated from annual maximum NDVI by MODIS data. It was known that NPP of the grassland that except the forest and the farming ground was distributed between 50-200g /m2.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.641-644
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• 2008

In order to improve a camera rotation calculation based on the bundle adjustment in Chon's camera motion (Chon and Shankar, 2007, 2008), this paper introduces a method calculating the camera rotation. It estimates a unit vector in the optical axis of a camera through the angles between the optical axis and vectors passing a camera position and ground control points (GCP). The camera position is estimated by using the inner product method proposed by Chon. The horizontal and vertical unit vectors of the camera are determined by using Yakimovsky and Cunningham's camera model (CAHV) (1978).

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.

• Korean Journal of Remote Sensing
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• v.18 no.2
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• pp.97-105
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• 2002

In this paper, we proposed a simplified strategy for the epipolarity of linear pushbroom imagery. The proposed strategy is verified on "Gupta and Hartly" sensor model and "Orun and Natarajan" sensor model. It is also compared with the precise epipolarity model of each sensor model on SPOT and KOMPSAT imagery. For the quantitative analysis, 20 ground control points are used as independent checking points. Based on the results, the accuracy of the proposed strategy is not different from that of the precise epipolarity model of each sensor model (below 0.1 pixels). Under the worst circumstance, the proposed strategy is robust. We can assure that the proposed strategy will show high accuracy on most of sensor models based on the co-linearity equations.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.131-140
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• 2011

In this study, temporal variations of the Advanced Microwave Scanning Radiometer E (AMSR-E) soil moisture products were evaluated using ground based measurements from the Haenam flux monitoring network site for two years (2004 and 2006). Even if there were major comparison issues including spatial resolutions, AMSR-E soil moisture production showed a great potential to replicate temporal variability patterns with ground based measurements. Additional intensive validation efforts should be conducted at a variety of field conditions including vegetation type for better utilization of remotely sensed soil moisture and understanding of the land surface-atmosphere interactions in the view of hydrometeorology.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.403-410
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• 2011

Consistent observation with high temporal and spatial resolution is required for an efficient monitoring of coastal environments. Remote monitoring system installed on the ground is capable of simultaneous observation of wide coastal area and consistent observation with high frequency, which a small number of in-situ measurements cannot manage. This paper studies two typical ground based coastal monitoring system, marine radar and camera system. Marine radar can produce time series of frequency spectrum by integrating wave number spectrum calculated from spatial and temporal variation of waves in the radar image. The time averaged radar images of waves can analyze wave breaking zone, rip currents and location of littoral bars. Camera system can observe temporal variation of foam generation originated from coastal contamination as well as shoreline changes. By extracting the part of foams from rectified images, quantitative analysis of temporal foam variation can be done. By using the two above systems of different characteristics, synergetic benefit can be achieved.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.415-417
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• 2005

In order to retrieve the latent and sensible heat fluxes, high-resolution airborne imageries with visible, near infrared, and thermal infrared bands and ground-base meteorology measurements are utilized in this paper. The retrieval scheme is based on the balance of surface energy budget and momentum equations. There are three basic surface parameters including surface albedo $(\alpha)$, normalized difference vegetation index (NOVI) and surface kinetic temperature (TO). Lowtran 7 code is used to correct the atmosphere effect. The imageries were taken on 28 April and 5 May 2003. From the scattering plot of data set, we observed the extreme dry and wet pixels to derive the fitting of dry and wet controlled lines, respectively. Then the sensible heat and latent heat fluxes are derived from through a partitioning factor A. The retrieved latent and sensible heat fluxes are compared with in situ measurements, including eddy correlation and porometer measurements. It is shown that the retrieved fluxes from our scheme match with the measurements better than those derived from the S-SEBI model.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.663-670
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• 2013

Total Vertical Column Density (VCD) of $NO_2$, a key component in air quality and tropospheric chemistry was measured using a ground-based instrument, Pandora, in Seoul from March 2012 to October 2013. The $NO_2$ measurements using Pandora were compared with those obtained by satellite remote sensing from Ozone Monitoring Instrument (OMI) where the intercomparison characteristics were analyzed as a function of measurement geometry, cloud amount and aerosol loading. The negative biases of the OMI $NO_2$ VCD were larger when cloud amount and Aerosol Optical Depth (AOD) were higher. The correlation coefficient between $NO_2$ VCDs from Pandora and OMI was 0.53 for the entire measurement period, whereas the correlation coefficient between the two was 0.74 when the cloud amount and AOD were low (cloud amount<3, AOD<0.4). The low bias of OMI data was associated with the shielding effect of the cloud and the aerosols.