• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.1
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• pp.17-24
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• 2018

DEM (Digital Elevation Model) is a useful dataset which represents the earth surface. Beside many applications, production and frequent update of DEM is a costly task. Recently global satellite based DEMs are available which has huge potential for application. To check the accuracy, this study compares two global DEMs: AW3D30 (Advanced Land Observing Satellite World 3D 30m) and SRTM30 (Shuttle Radar Topography Mission Global 30m) with reference resampled LiDAR DEM 30m in a test area around Chuncheon, Korea. The comparison analysis was based on statistics of each DEM, their difference, profiles, slope, basin and stream orders. As a result, it is found that SRTM30 and AW3D30 were much similar but inconsistent in the test area compared to the LiDAR30 DEM. In addition, SRTM30 shows less difference with LiDAR30 compared to the AW3D30 DEM. But, DEMs should be very carefully examined for area which has temporal or season changes. For basin and stream analysis, global DEMs can be used only for regional scale analysis not local large scales.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.6
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• pp.563-571
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• 2010

The GPS satellite has three or four atomic clocks that consist of cesiums and rubidiums and the NANU messages can be used to identify the kind of the onboard atomic clock because they classify the clock type on a daily basis. In this study, for long-term analysis of the GPS satellite clock behavior, we extracted satellite clock errors for every PRN from years 2001 through 2009 using the SP3 files that are provided by the IGS. As a result, the cesium clock offsets usually have a linear trend of drifting. On the other hand, rubidium offsets show curvilinear variations in general, even though they cannot be represented as anyone specific polynomial function. For short-term analysis, we extracted satellite clock errors for each PRN for a week-long period using the CLK files that are also provided by the IGS and curve-fitted them with first-order and second-order polynomial functions. In cases of cesium clock errors, they were well-represented by first-order polynomial functions and rubidium clock errors were similar with second-order polynomials. However, some of rubidium clock errors could not be represented as any polynomial fitting function. To analyze the characteristic of GPS satellite by each block and atomic clock, we applied Modified Allan Deviation criterion to the dataset from years 2007 and 2010. We found that the Modified Allan Deviation characteristics changed significantly according the block and atomic clock type.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Journal of Korea Water Resources Association
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• v.49 no.11
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• pp.903-912
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• 2016

Accurate understanding and estimating Evapotranspiration (ET) is essential for understanding the mechanism of water cycle and water budget. ET has been analyzed by many researchers in worldwide while Ground-based ET has limiation in analyzing the spatio-temporal pattrens of ET. Thus, many researches have been conducted to represent the spatio-temporal variation of ET by using hydrometeorological variables estimated from remote sensing datasets. Previous remote sensing based ET algorithms, however, have disadvantage in that various hydrometeological input datasets were required. In this study, actual ET was estimated by MODIS-based Rn and MS-PT algorithm requiring relatively less input data than previous method. The result confirmed that the observed $R_N$ and latent heat flux from the eddy-covariance based fluxtowers located at CFK and SMK showed high correlation with the estimated $R_N$ and ET. The average determination coefficients ($R^2$) of ET estimated from satellite dataset over study periods were 0.77 (0.72-0.81) in Cheongmi (CFK) and 0.70 (0.67-0.78) in Sulma (SMK), respectively. Comparing with the actual ET of two flux tower sites, however, SMK showed more overestimated patterns than CFK due to the vegetation and radiation related errors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.297-304
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• 2015

The change detection algorithms, based on remotely sensed satellite imagery, can be applied to various applications, such as the hazard/disaster analysis and the land monitoring. However, unchanged areas sometimes detected as the changed areas due to various errors in relief displacements and noise pixels, included in the original multi-temporal dataset at the application of unsupervised change detection algorithm. In this research, the object-based changed detection for the high-spatial resolution satellite images is applied by using the IR-MAD (Iteratively Reweighted- Multivariate Alteration Detection), which is one of those representative change detection algorithms. In additionally, we tried to increase the accuracy of change detection results with using the additional information, based on the cross-sharpening method. In the experiment, we used the KOMPSAT-2 satellite sensor, and resulted in the object-based IR-MAD algorithm, representing higher changed detection accuracy than that by the pixel-based IR-MAD. Also, the object-based IR-MAD, focused on cross-sharpened images, increased in accuracy of changed detection, compared to the original object-based IR-MAD. Through these experiments, we could conclude that the land monitoring and the change detection with the high-spatial-resolution satellite imagery can be accomplished efficiency by using the object-based IR-MAD algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.451-457
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• 2014

Recently, the land degradation and pasture erosion in Tuv, located around Ulaanbaatar of Mongolia, have been increasing sharply due to escalating developments of mining sectors, well as the density of populations. Because of that, we have chosen the urban and mining area of Tuv for our study target. During the study, the temporal changes of land cover in Tuv, Mongolia were observed by the Landsat TM/+ETM satellite images from 2001 to 2009 that provided the fundamental dataset to apply NDVI and K-Mean algorithm of Unsupervised Classification and Maximum likelihood classification(MLC) of Supervised Classification in order to conclude in land cover change analyzation. The result of our study implies that the growth of mining area, the climate change, and the density of population led the land degradation to desertification.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.1 s.31
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• pp.55-61
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• 2005

Digital Elevation Model (DEM) generation from remotely sensed imagery is crucial for a variety of mapping applications such as ortho-photo generation, city modeling. High resolution imaging satellites such as SPOT-5, IKONOS, QUICK-BIRD, ORBVIEW constitute an excellent source for efficient and economic generation of DEM data. However, prerequisite knowledge in the areas of sensor modeling, epipolar resampling, and image matching is required to generate DEM from these high resolution satellite imagery. From the above requirements, epipolar resampling emerges as the most important factors. Research attempts in this area are still in high demand and short supply. Another cause that adds to the complication of the problem is that most studies of DEM generation from IKONOS scenes have been based on rational function model. In this paper, we proposed a new methodology for DEM generation from satellite scenes using parallel projection model which is sensor independent, makes it possible for sensor modeling and epipolar resampling by only few control points. The performance and feasibility of the developed methodology is evaluated through real dataset captured by IKONOS.

• Journal of the Korean Geophysical Society
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• v.8 no.2
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• pp.89-92
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• 2005

For the understanding the locus of the Quinling-Dabie-Sulu continental collision’s boundary and the underground structure of the sedimentray basin in the Yellow Sea, three dimensional density modelling is carrid out by using gravity dataset (Free Air Anomaly), which is measured by Tamhae 2, GIGAM in a period 2000-2002. The measured gravity anomaly in the investigations area is mainly responsed by depth distribution of the sedimentary basin. After comparing the sea-measured gravity data to CHAMP-GRACE satellite gravity data, I suggested that the high density model bodies extend mainly from the southern part of China to the middle-western part of the Korean Peninsula, which might be emplaced along the continental collision’s boundary. The total volume of very low density bodies modified by modelling might be about 20 000 km3.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.467-467
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• 2015

Sustainable water resource management requires the assessment of hydrological variability in response to climate fluctuations and anthropogenic activities. Determining quantitative estimates of water balance and total basin discharge are of utmost importance to understand the variations within a basin. Hard-to-reach areas with few infrastructures, coupled with lengthy administrative procedures makes in-situ data collection and water management processes very difficult and unreliable. In this study, the hydrological behavior of Lake Chad whose extent, extreme climatic and environmental conditions make it difficult to collect field observations was examined. During a 10 year period [January 2003 to December 2013], dataset from space-borne and global hydrological models observations were analyzed. Terrestial water storage (TWS) data retrieved from Gravity Recovery and Climate Experiment (GRACE), lake level variations from Satellite altimetry, water fluxes and soil moisture from Global Land Data Assimilation System (GLDAS) were used for this study. Furthermore, we combined altimetry lake volume with TWS over the lake drainage basin to estimate groundwater and soil moisture variations. This will be validated with groundwater estimates from WaterGAP Global Hydrology Model (WGHM) outputs. TWS showed similar variation patterns Lake water level as expected. The TWS in the basin area is governed by the lake's surface water. As expected, rainfall from GLDAS precedes GRACE TWS with a phase lag of about 1 month. Estimates of groundwater and soil moisture content volume changes derived by combining altimetric Lake Volume with TWS over the drainage basin are ongoing. Results obtained shall be compared with WaterGap Hydrology Model (WGHM) groundwater estimate outputs.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1591-1604
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• 2020

This study is a study on classifying land cover by applying high-resolution satellite images to deep learning algorithms and verifying the performance of algorithms for each spatial object. For this, the Fully Convolutional Network-based algorithm was selected, and a dataset was constructed using Kompasat-3 satellite images, land cover maps, and forest maps. By applying the constructed data set to the algorithm, each optimal hyperparameter was calculated. Final classification was performed after hyperparameter optimization, and the overall accuracy of DeeplabV3+ was calculated the highest at 81.7%. However, when looking at the accuracy of each category, SegNet showed the best performance in roads and buildings, and U-Net showed the highest accuracy in hardwood trees and discussion items. In the case of Deeplab V3+, it performed better than the other two models in fields, facility cultivation, and grassland. Through the results, the limitations of applying one algorithm for land cover classification were confirmed, and if an appropriate algorithm for each spatial object is applied in the future, it is expected that high quality land cover classification results can be produced.