• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.179-187
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• 2008

In this paper, we proposed the qualify metric based on SVC and the subjective quality. The proposed quality metric is for a general purpose. It means we can use it for any video sequences regardless of its temporal and spatial characteristics. The Quality of Service(QoS) is one of the important issues in heterogeneous environment which has diverse restrictions such as limited network bandwidth and limited display resolution. Scalable Video Coding(SVC) is the efficient video coding skill in heterogeneous environment. Because SVC can be adapted to various quality bitstreams using three scalabilities(spatial, temporal, and SNR) from one bitstream which has full scalability. To maximize the QoS in this environment, we should consider the subjective quality which is the viewer response. And also we should consider temporal and spatial characteristics of video sequence because the subjective quality is affected by temporal and spatial characteristics of video sequence. To verify the efficiency of the proposed method, we perform subjective assessments. The experimental results show that the proposed method has high correlation with subjective quality. The proposed method can be a decision tool of SVC birstream extraction.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.999-1011
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• 2021

In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutionsfrom multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used asinput. In particular, the improvement in prediction performance wassubstantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.

• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.165-177
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• 2018

Particulate matter (PM) measurements are important in air quality, public health, epidemiological studies and decision making for short and long-term policies implementation. However, only few cities in the word have advance air quality-monitoring networks able to provide reliable information of PM leaves in the ambient air, trends and extent of the pollution. In Colombia, only major cities measure PM concentrations. Available measurements from Bogota, Medellin and Bucaramanga show that PM concentration are well above World Health Organization guidelines, but up to now levels and trends of PM in other cities and regions of the country are not well known. Satellite measurements serve as an alternative approach to study air quality in regions were surface measurements are not available. The aim of this study is to perform a spatial and temporal assessment of PM in the ambient air of Colombia. We used Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite of NASA and surface measurements from the air quality networks of Bogota, Medellin and Bucaramanga. In a first step, we estimated the correlation between MODIS-AOD and monthly average surface measurements (2000 to 2015) from these three cities, obtaining correlation coefficient R values over 0.4 for the cities under study. After, we used AOD and $PM_{10}$ measurements to study the temporal evolution of PM in different cities and regions. Finally, we used AOD measurements to identify cities and regions with the highest AOD levels in Colombia. All the methods presented in this paper may serve as an example for other countries or regions to identify and prioritize locations that require the implementation of more accurate air quality measurements.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.149-158
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• 2017

Fine temporal and spatial resolution of image data are necessary to monitor the phenology of vegetation. However, there is no single sensor provides fine temporal and spatial resolution. For solve this limitation, researches on spatiotemporal data fusion methods are being conducted. Among them, FSDAF (Flexible spatiotemporal data fusion) can fuse each band in high accuracy.In thisstudy, we applied MODIS NDVI and Landsat NDVI to enhance time resolution of NDVI based on FSDAF algorithm. Then we proposed the possibility of utilization in vegetation phenology monitoring. As a result of FSDAF method, the predicted NDVI from January to December well reflect the seasonal characteristics of broadleaf forest, evergreen forest and farmland. The RMSE values between predicted NDVI and actual NDVI (Landsat NDVI) of August and October were 0.049 and 0.085, and the correlation coefficients were 0.765 and 0.642 respectively. Spatiotemporal data fusion method is a pixel-based fusion technique that can be applied to variousspatial resolution images, and expected to be applied to various vegetation-related studies.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.775-784
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• 2020

Recent global warming and anthropogenic activities have caused more frequent and massive wildfires with longer durations and more significant damages. MODIS has been monitoring global wildfires for almost 20 years, and GK2A and Himawari-8 are observing the wildfires in East Asia 144 times a day. However, the spatial resolution of 1 to 2 km is not sufficient for the detection of small and medium-size active fires, and therefore the studies on the active fire detection using high-resolution images are essential. However, there is no official product for the high-resolution active fire detection. Hence, we implemented the active fire detection algorithm of Landsat 8 and carried out a high-resolution-based detection of active fires in Australia in 2019, followed by the comparisons with the products of Himawari-8 and MODIS. Regarding the intense fires, the three satellites showed similar results, whereas the weak igniting and extinguishing fires or the fires in narrow areas were detected by only Landsat 8 with a 30m resolution. Small-sized fires, which are the majority in Korea, can be detected by the high-resolution satellites such as Landsat 8, Sentinel-2, Kompsat-3A, and the forthcoming Kompsat-7. Also, a comprehensive analysis together with the geostationary satellites in East Asia such as GK2A, Himawari-8, and Fengyun-3 will help the interoperability and the improvement of spatial and temporal resolutions.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.223-230
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• 2010

Trajectory dispersion models are used for the dispersion calculations in air quality assessments, Yellow-sand modeling, environmental planning and the emergency response. Meso-scale forcing and coastal circulations are calculated by trajectory model in the East Asia region. In this study the meteorological fields (GDAS and MM5) coupled to the trajectory model (HY-SPLIT) are applied to simulate the transport and the dispersion. Seoul is selected as a starting point of the HY-SPLIT. The sensitivity studies are performed by conducting an ensemble of simulations using the GDAS and the MM5 model for the same dispersion cases. The results in this study show a significant difference depending on the resolution of meteorological models. Additionally, in most cases of the compared tionally,results from MM5 and GDAS, the absolute and relative distance, shows significant difference and the difference increased with the increasing distance of HY-SPLIT. Therefore, for the case of small domai for twi d field distefbution over complex terrai, should be used only high model temporal or spatial resolution to improve the HY-SPLIT model results.

• Korean Journal of Agricultural Science
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• v.42 no.3
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• pp.269-276
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• 2015

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 discussed the state-of-the-art of the domestic and international use of UAV in agricultural sector as well as assessed its utilization and applicability for agricultural environment in Korea. Association of robotic, computer vision and geomatic technologies have established a new paradigm of low-altitude aerial remote sensing that has now been receiving attention from researchers all over the world. In a field study, it has been found that use of UAV imagery in an agricultural subsidy program can reduce the farmers' complain and provide objective evidence. UAV high resolution photography can also be helpful in monitoring the disposal zone for animal carcasses. Due to its expeditiousness and accuracy, UAV imagery can be a very useful tool to evaluate the damage in case of an agricultural disaster for both parties insurance companies and the farmers. Also high spatial and temporal resolution in UAV system can increase the prediction accuracy which in turn help to maintain the agricultural supply and demand chain.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.6
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• pp.571-581
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• 2015

Soil moisture content is generally accepted as an important factor to understand the process of crop growth and is the basis of earth system models for analysis and prediction of the crop condition. To continuously monitor soil moisture changes at kilometer scale, it is demanded to create high resolution data from the current, several tens of kilometers. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) from 10 km to 30 m resolution using a soil texture and field measurements that have a high correlation with the SMC. As a result, the soil moisture variations of both data (before and after downscaling) were identical, and the Root Mean Square Error (RMSE) of SMC exhibited the low values. Also, time series analyses showed that three kinds of SMC data (field measurement, original AMSR2, and downscaled AMSR2) had very similar temporal variations. Our method can be applied to downscaling of other soil variables and can contribute to monitoring small-scale changes of soil moisture by providing high resolution data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.295-303
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• 2010

The variability of vegetation is strongly related to the variability of hydrometeorological factors such as precipitation, temperature, runoff and so on. Analysis of the variability of vegetation will aid to understand the regional impact of climate change. Thus we analyzed the spatial-temporal variability of NOAA(National Oceanic and Atmospheric Administration)/AVHRR(Advanced Very High Resolution Radiometer) NDVI(Normalized Difference Vegetation Index). In the results from Mann-Kendall test, there is no significant linear trend of annual NDVI from 1982 to 2006 in the most area except the downward trend on the significance level 90% in the Guem-river basin area. In addition, using EOF(Empirical Orthogonal Function) analysis, the variability of NDVI in the region of higher latitude and altitude is higher than that in the other region since the spatial variability of NDVI follows the latitudinal gradient. Also we could get higher NDVI in June, July, August and September. We had the highest NDVI in Han-river basin area and the lowest in Je-Ju island.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.883-896
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• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.