• Journal of Korea Water Resources Association
• /
• v.49 no.4
• /
• pp.305-314
• /
• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Journal of KIISE:Software and Applications
• /
• v.32 no.11
• /
• pp.1121-1135
• /
• 2005

This paper proposes a moving object extraction method using the contrast map and salient points. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and directional map and extract salient points from an image. By using these features, we can decide the Attention Window(AW) location easily The purpose of the AW is to remove the useless regions in the image such as background as well as to reduce the amount of image processing. To create the exact location and flexible size of the AW, we use motion feature instead of pre-assumptions or heuristic parameters. After determining of the AW, we find the difference of edge to inner area from the AW. Then, we can extract horizontal candidate region and vortical candidate region. After finding both horizontal and vertical candidates, intersection regions through logical AND operation are further processed by morphological operations. The proposed algorithm has been applied to many video sequences which have static background like surveillance type of video sequences. The moving object was quite well segmented with accurate boundaries.

• Journal of Korea Water Resources Association
• /
• v.52 no.4
• /
• pp.279-289
• /
• 2019

Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.6
• /
• pp.208-221
• /
• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Korean Journal of Remote Sensing
• /
• v.23 no.6
• /
• pp.507-520
• /
• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.

• Korean Journal of Remote Sensing
• /
• v.36 no.3
• /
• pp.487-501
• /
• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.6
• /
• pp.110-123
• /
• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.3
• /
• pp.129-136
• /
• 2014

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

• Journal of Environmental Impact Assessment
• /
• v.27 no.3
• /
• pp.279-290
• /
• 2018

The importance of monitoring and assessing the status of urban forests in the aspect of urban forest management is emerging as urban forest edges increase due to urbanization and human impacts. The purpose of this study was to investigate the status of vegetation condition of urban forest edge that is affected by different land cover types using $NDVI_{max}$ images derived from FSDAF (Flexible Spatio-temporal DAta Fusion). Among 4 land cover types,roads had the greatest effect on the forest edge, especially up to 30m, and it was found to affect up to 90m in Seoul urban forest. It was also found that $NDVI_{max}$ increased with distance away from the forest edge. The results of this study are expected to be useful for assessing the effects of land cover types and land cover change on forest edges in terms of urban forest monitoring and urban forest management.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.6
• /
• pp.471-476
• /
• 2016

In this paper, recognition system for continuous human action is explained by using motion history image and histogram of oriented gradient with spotter model based on depth information, and the spotter model which performs action spotting is proposed to improve recognition performance in the recognition system. The steps of this system are composed of pre-processing, human action and spotter modeling and continuous human action recognition. In pre-processing process, Depth-MHI-HOG is used to extract space-time template-based features after image segmentation, and human action and spotter modeling generates sequence by using the extracted feature. Human action models which are appropriate for each of defined action and a proposed spotter model are created by using these generated sequences and the hidden markov model. Continuous human action recognition performs action spotting to segment meaningful action and meaningless action by the spotter model in continuous action sequence, and continuously recognizes human action comparing probability values of model for meaningful action sequence. Experimental results demonstrate that the proposed model efficiently improves recognition performance in continuous action recognition system.