• Spatial Information Research
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• v.23 no.2
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• pp.11-20
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• 2015

Abstract Monitoring is the most critical element in implementing "forest carbon offset project" to enhance the visibility of CSR (Corporate Social Responsibility) in North Korea. This study is intended to explore monitoring potential in terms of forest carbon offset project using satellite image for Baekdu mountain of North Korea. The permanent record of standard satellite remote sensing system demonstrated its capability of presenting area-wide visual evidences of monitoring conditions in Mt. Baekdu mountain of North Korea (site suitability, carbon stock by forest biomass growth, carbon emission by forest biomass loss, deforestation and degradation, environmental, social and economic impact specified in the Carbon Sequestration Law). It doesn't seem very difficult to comply with monitoring requirements for "the forest carbon offset project" due to the probative value of satellite data. Therefore, it could be considerable or realistic approach to utilize CSR based forest carbon offset project as a point of reform and open-door in North Korea. It is anticipated that this research output could be used as a valuable reference for Korea-based enterprises to ensure monitoring potentials using satellite image in exploring forest carbon offset project sites in North Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.1
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• pp.12-31
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• 1999

Topex/Poseidon satellite altimetric data are used to estimate characteristics on the oceanic and atmospheric correction factors, and the mean sea level and its variations in the Yellow Sea, the East China Sea and the East Sea from September 1992 through August 1994(70cycles). For the atmospheric correction factors, the variations of dry troposphere, humid troposphere, ionosphere and inverted barometer were very small as a few centimeters, but the variations of electromagnetic bias were higher than other factors. For the oceanic correction factors, the variations of ocean tide(35cm in track 127 and 60cm in track 214) showed high ranges compared to elastic tide(5cm in track 127 and 1cm in track 214) and loading tide(1.8cm in track 127 and 1cm in track 214). It should be understood that the variations of ocean free surface is mainly under the influence of, firstly, ocean tide and, secondly, electromagnetic bias. Mean sea level in the Yellow Sea are higher than in the rest of Seas. Then its range generally comprised between -60cm and 210cm with mean value of about 100cm. Also its variations showed high values in the Yellow Sea and East China Sea, especially 5.689cm in Youngampo. This result is mainly due to the effects of local topography and tidal current.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.52-65
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• 2012

The net biomass accumulation (or net primary production, NPP) and gross primary production (GPP) have closely related with carbon accumulations(or carbon exchange) in vegetation. There are many approaches to estimate biomass using remote sensing techniques. The vegetation indices (VIs) can be a methodology to estimate biomass which assumes total chlorophyll contents. Various VIs were characterized with difference development conditions as vegetation species, input datasets. The hyperspectral data have also different spatial/spectral resolutions for aerial surveying. Additionally they need particular spectral bands selection difficulty to calculate the VIs. The objective of this study is to evaluate the correlations with airborne hyperspectral data (compact airborne spectrographic imager, CASI) and spectral unmixing model (or spectral mixture analysis, SMA) to characterize vegetation indices in forest area. The spectral mixture analysis was used to model the spectral purity of each pixel as an endmember. The endmembers are the fraction components derived from hyperspectral data through the SMA. In this study, we choose three endmembers represented vegetation pixels in the hyperspectral data. These endmembers were compared with 9 VIs by the Pearson's correlation coefficient. The results show MTVI1 and TVI have same correlation coefficient with 0.877. The MCARI, especially has very high relationship with vegetation endmembers as 0.9061 at less vegetation and soil distributed site. The MTVI1 and TVI have high correlations with the vegetation endmembers as 0.757 in whole test sites.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.973-983
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• 2020

Recently, SAR (Synthetic Aperture Radar) is being highlighted as a solution to the coarse spatial resolution of remote sensing data in water resources research field. Spatial resolution up to 10 m of SAR backscattering coefficient has facilitated more elaborate analyses of the spatial distribution of soil moisture, compared to existing satellite-based coarse resolution (>10 km) soil moisture data. It is essential, however, to multilaterally analyze how various hydrological and environmental factors affect the backscattering coefficient, to utilize the data. In this study, soil moisture estimated by WCM (Water Cloud Model) and linear regression is compared with in-situ soil moisture data at 5 soil moisture observatories in the Korean peninsula. WCM shows suitable estimates for observing instant changes in soil moisture. However, it needs to be adjusted in terms of errors. Soil moisture estimated from linear regression shows a stable error range, but it cannot capture instant changes. The result also shows that the effect of soil moisture on backscattering coefficients differs greatly by land cover, distribution of vegetation, and water content of vegetation, hence that there're still limitations to apply preexisting models directly. Therefore, it is crucial to analyze variable effects from different environments and establish suitable soil moisture model, to apply SAR to water resources fields in Korea.

• Journal of the Korean Geographical Society
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• v.50 no.6
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• pp.607-619
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• 2015

It is important to detect trails accurately for finding a proper management. We examined the applicability of KOMPSAT imagery to detect trails and found that it could be an efficient alternative to track trails correctly. We selected K2 and K3 imagery with different spatial resolution. Then, we processed each imagery to get NDVI, SAVI, and SC data. And then, we identified trails by object-based analysis and network analysis. Finally, we evaluated the potential trails with F-measurement and Jaccard coefficient which are based on correctness and completeness. The results show that the applicability is quite different in each case. Among them, especially the SC data with K3 shows the most highest value; correctness of detecting legal trails is 0.44 and completeness of that is 0.54. F-measurement and Jaccard coefficient are 0.49 and 0.32. In general, although there is a limit in detecting trails by using only KOMPSAT imagery, the usefulness of KOMPSAT imagery can be a higher considering its cost efficiency and availability of acquiring periodic data.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.5
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• pp.55-63
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• 2008

The characteristics of forest damage in the Geum-buk Mountains were analyzed by using satellite images and a field survey for landscape conservation purposes. A survey scope was fixed using DEM, and areas of damage in the mountain range were analyzed via ArcMap v. 9.2 using SPOT 5 images, a high resolution satellite image. All damaged areas were reviewed and corrected in a field survey. As a result, 75 roads were found to completely fragment forest patches. Of those roads, 26 have the width under 3m, which means that the fragmentation of the forest by these roads may have a minor effect on forest habitat and its ecosystems, while other roads such as two-lane roads may have broader detrimental influences on the ecosystem. Two thousand eighty-three sections of accounted for a total area of about 5,760.7ha. Orchard areas including chestnut tree plantations were ranked as the largest in the damaged area within the Geum-buk Mountains, followed by public facility areas and grassland areas. This means that man-made land usage has progressed in the area regardless of slope and elevation.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.2
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• pp.139-147
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• 2008

More precise estimation of the bed change, primary cause of flood damage, has been recognized significant for designs of levees and other river facilities. In this study, the long-term bed change was examined as the application of the relatively new Surface-water Modeling System (SMS) Model because there has not been broad verification of the model empirically on river of South Korea. This 2-dimensional model was used to examine the bed change of Pochon Stream Basin, a tributary of Imjin River, where heavy rain damages annually occur. First, in order to verify the model, the simulating period was set from 1986 to 1998 because of the existence of the field measurements. Cross sectional field measurements of 1986 were used for the initial condition and output were compared and analyzed with the observed cross sectional data in 1998. As the results of the verification, the comparison in lateral and streamwise bed level between results from the model and the field measurements showed a reasonable agreement except for the some cases of local scours. However, in terms of the quantitative comparison, the change of the bed elevations for each cross section for 1998 was rather underestimated than that of the field measurements.