• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.105-113
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• 2016

Fire radiative power(FRP), which means the power radiated from wildfire, is used to estimate fire emissions. Currently, the geostationary satellites of East Asia do not provide official FRP products yet, whereas the American and European geostationary satellites are providing near-real-time FRP products for Europe, Africa and America. This paper describes the first retrieval of Himawari-8 FRP using the mid-infrared radiance method and shows the comparisons with MODIS FRP for Sumatra, Indonesia. Land surface emissivity, an essential parameter for mid-infrared radiance method, was calculated using NDVI(normalized difference vegetation index) and FVC(fraction of vegetation coverage) according to land cover types. Also, the sensor coefficient for Himawari-8(a = 3.11) was derived through optimization experiments. The mean absolute percentage difference was about 20%, which can be interpreted as a favourable performance similar to the validation statistics of the American and European satellites. The retrieval accuracies of Himawari FRP were rarely influenced by land cover types or solar zenith angle, but parts of the pixels showed somewhat low accuracies according to the fire size and viewing zenith angle. This study will contribute to estimation of wildfire emissions and can be a reference for the FRP retrieval of current and forthcoming geostationary satellites in East Asia.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1055-1066
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• 2018

Vegetation indices on the basis of optical characteristics of vegetation can represent various conditions such as canopy biomass and physiological activity. Those have been mostly developed with the large-scaled applications of multi-band optical sensors on-board satellites. However, the sensitivity of vegetation indices for detecting vegetation features will be different depending on the spatial scales. Therefore, in this study, the investigation of photochemical reflectance index (PRI), known as one of useful vegetation indices for detecting photosynthetic ability and vegetation stress, under the three spatial scales was conducted using multi-spectral camera installed in unmanned aerial vehicle (UAV),field spectrometer, and leaf reflectometer. In the leaf scale, diurnal PRI had minimum values at different local-time according to the compass direction of leaf face. It meant that each leaf in some moment had the different degree of light use efficiency (LUE). In early growth stage of crop, $PRI_{leaf}$ was higher than $PRI_{stands}$ and $PRI_{canopy}$ because the leaf scale is completely not governed by the vegetation cover fraction.In the stands and canopy scales, PRI showed a large spatial variability unlike normalized difference vegetation index (NDVI). However, the bias for the relationship between $PRI_{stands}$ and $PRI_{canopy}$ is lower than that in $NDVI_{stands}$ and $NDVI_{canopy}$. Our results will help to understand and utilize PRIs observed at different spatial scales.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.457-468
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• 2005

Increasing of impervious surface resulting from urban development has negative impacts on urban environment. Therefore, it is absolutely necessary to estimate and quantify the temporal and spatial aspects of impervious area for study of urban environment. In many cases, conventional image classification methods have been used for analysis of impervious surface fraction. However, the conventional classification methods have shortcoming in estimating impervious surface. The DN value of the each pixel in imagery is mixed result of spectral character of various objects which exist in surface. But conventional image classification methods force each pixel to be allocated only one class. And also after land cover classification, it is requisite to additional work of calculating impervious percentage value in each class item. This study used the spectral mixture analysis to overcome this weakness of the conventional classification methods. Four endmembers, vegetation, soil, low albedo and high albedo were selected to compose pure land cover objects. Impervious surface fraction was estimated by adding low albedo and high albedo. The study area is the Tanchon watershed which has been rapidly changed by the intensive development of housing. Landsat imagery from 1988, 1994 to 2001 was used to estimate impervious surface fraction. The results of this study show that impervious surface fraction increased from $15.6\%$ in 1988, $20.1\%$ in 1994 to $24\%$ in 2001. Results indicate that impervious surface fraction can be estimated by spectral mixture analysis with promising accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.9-17
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• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.228-233
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• 2007

지표면 온도(Land Surface Temperature, LST)는 지표와 대기간의 수증기 교환을 조절하는 중요한 기상변수중의 하나이다. 그 외에도 지표면 온도는 토양의 상태나 식생의 성장에도 밀접한 관계가 있어 임업과 농업에도 널리 활용되고 있다. 본 연구의 목적은 위성 지표면 자료를 이용하여 지상관측점의 열적 공간 대표성을 알아내는 것이다. 전국에총 76개의 관측소가 있으며 그중에서 선정된 6곳 의 관측소(서울，부산，대전，대구，광주，춘천)를 MODIS LST product와 비교를 하였다. 비교 방법은 위성 자료의 pixel size를 $3{\time}\;3$, $5{\time}\;5$, $7{\time}\;7$, $9{\time}\;9$, $11{\time}\;11$, $15{\time}\;15$, $19{\time}\;19$, $25{\time}\;25$로 변환하여 각 pixel size별 평균값을 계산하여 MODIS product와 비교하여 선형분석을 하였다. 분석의 요소로 Fraction Vegetation Cover(FVC)와 Digital Elevation Model(DEM)을 사용하였으며 분석 결과 FVC의 상관관계과 DEM보다 높은 상관성을 보여주었다. 선형분석으로 도출한 식으로 지표면 온도를 재산출한 뒤 지상관측값과의 RMSE를 산출하였다. 대표성 규명을 위한 RMSE는 일 최고 기온 산출 모델에 관한 연구를 참고하여 $^{\circ}C$로 결정하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.1
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• pp.158-167
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• 2006

The object of this study is to detect of land-cover change in western DMZ and vicinity. This was performed as a basic study to construct a decision support system for the conservation or a sustainable development of the DMZ and Vicinity near future. DMZ is an is 4km wide and 250km long and it's one of the most highly fortified boundaries in the world and also a unique thin green line. Environmentalists want to declare the DMZ as a natural reserve and a biodiversity zone, but nowadays through the strengthening of the inter-Korean economic cooperation, some developers are trying to construct a new-town or an industrial complex inside of the DMZ. This study investigates the current environmental conditions, especially deforestation of the western DMZ adopting remote sensing and GIS techniques. The Land-covers were identified through the linear spectvral mixture analysis(LSMA) which was used to handle the spectral mixture problem of low spatial resolution imagery of Landsat TM and ETM+ imagery. To analyze quantitative and spatial change of vegetation-cover in western DMZ, GIS overlay method was used. In LSMA, to develop high-quality fraction images, three endmembers of green vegetation(GV), soil, water were driven from pure features in the imagery. Through 15 years, from 1987 to 2002, forest of western DMZ and vicinity was devastated and changed to urban, farmland or barren land. Northern part of western DMZ and vicinity was more deforested than that of southern part. ($52.37km^2$ of North Korean forest and $39.04km^2$ of South Korean were change to other land-covers.) In case of North Korean part, forest changed to barren land and farmland and in South Korean part, forest changed to farmland and urban area. Especially, In North Korean part of DMZ and vicinity, $56.15km^2$ of farmland changed to barren land through 15 years, which showed the failure of the 'Darakbat' (terrace filed) project which is one of food increase projects in North Korea.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.25-43
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• 2018

The effects of hydro-meteorological and surface variables on the frequency of Asian dust events (FAE) were investigated using ground station and satellite-based data. Present weather codes 7, 8, and 9 derived from surface synoptic observations (SYNOP)were used for counting FAE. Surface wind speed (SWS), air temperature (Ta), relative humidity (RH), and precipitation were analyzed as hydro-meteorological variables for FAE. The Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and snow cover fraction (SCF) were used to consider the effects of surface variables on FAE. The relationships between FAE and hydro-meteorological variables were analyzed using Z-score and empirical orthogonal function (EOF) analysis. Although all variables expressed the change of FAE, the degrees of expression were different. SWS, LST, and Ta (indices applicable when Z-score was < 0) explained about 63.01, 58.00, and 56.17% of the FAE,respectively. For NDVI, precipitation, and RH, Asian dust events occurred with a frequency of about 55.38, 67.37, and 62.87% when the Z-scores were > 0. EOF analysis for the FAE showed the seasonal cycle, change pattern, and surface influences related to dryness condition for the FAE. The intensity of SWS was the main cause for change of FAE, but surface variables such as LST, SCF, and NDVI also were expressed because wet surface conditions suppress FAE. These results demonstrate that not only SWS and precipitation, but also surface variables, are important and useful precursors for monitoring Asian dust events.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.123-133
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• 2007

Thermal spatial representativities of meteorological stations over Korea have been investigated using land surface temperature (LST) based on MODerate resolution Imaging Spectroradiometer (MODIS) satellite observation. The linear regression method was used to estimate air temperatures from MODIS LST product. To compare MODIS LST with observed air temperatures at six meteorological stations, the mean values of MODIS LST with nine given window sizes were calculated. In this case, the position of centered pixel in each given window size is correspond to that of each meteorological station. We also applied $4^{\circ}C$ threshold for RMSE comparison, which is based on a analogous study on daily maximum air temperature model using satellite data. In this study, the results showed that each station has a different representativity; Deajeon $15km{\times}15km$, Chuncheon $11km{\times}11km$, Seoul $7km{\times}7km$, Deagu $5km{\times}5km$, Kwangju $3km{\times}3km$, and Busan $3km{\times}3km$.