• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.997-1008
• /
• 2023

Ship detection at sea can be performed in various ways. In particular, satellites can provide wide-area surveillance, and Synthetic Aperture Radar (SAR) imagery can be utilized day and night and in all weather conditions. To propose an efficient ship detection method from SAR images, this study aimed to apply the You Only Look Once Version 5 (YOLOv5) model to Sentinel-1 images and to analyze the difference between individual vs. integrated models and the accuracy characteristics by polarization. YOLOv5s, which has fewer and lighter parameters, and YOLOv5x, which has more parameters but higher accuracy, were used for the performance tests (1) by dividing each polarization into HH, HV, VH, and VV, and (2) by using images from all polarizations. All four experiments showed very similar and high accuracy of 0.977 ≤ AP@0.5 ≤ 0.998. This result suggests that the polarization integration model using lightweight YOLO models can be the most effective in terms of real-time system deployment. 19,582 images were used in this experiment. However, if other SAR images,such as Capella and ICEYE, are included in addition to Sentinel-1 images, a more flexible and accurate model for ship detection can be built.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.991-993
• /
• 2003

The 4 sets of environmental variables dealing with meteorology, hydrology and physiography were analyzed to generate a spatial drought risk index of Phitsanulok province of Thailand. The analysis of K-mean and discriminant were applied to the set of the selective drought variables for grouping each of spatial variable set into 4 classes. The obtained 4 classes, based on group statistics, were thus recoded in the meaning of no risk, low risk, moderate risk, and high risk. The regression coefficient between recoded classes and a set of the selective environmental variables were then applied as spatial variable weighting on thematic dataset in GIS spatial analysis. The results showed that the weighting score of drought variable was highest in meteorological variable compared to other variables.

• Journal of Environmental Impact Assessment
• /
• v.11 no.4
• /
• pp.289-309
• /
• 2002

Solutions of many landscape problems depend on area-wide assessment and interpretation of spatial and physical characteristics over the study area. The authors argue that the public awareness for an area-wide urban landscape appears to be very low due to limited chance to the information. Acknowledging these constraints, an operational, user-friendly information system has been developed by combining internet technology with GIS. In particular, integration among satellite data and digital maps takes advantage of each component, and enables the landscape structure to be visualized, interacted with and deployed all on the Web. The 1m resolution IKONOS data realistically identified the major type of landscape by large scale spatial precision while TM data revealed successfully the major parameters that influence an area-wide spatial structure in the study area. This system would play a crucial role in improving the public awareness for area-wide landscape information if it is operationally introduced into the Government since the highly user-friendly interface based on image maps provides a completely new means for disseminating information for area-wide landscape in a visual and interactive manner to the general public.

• Journal of Korean Society of Forest Science
• /
• v.95 no.6
• /
• pp.663-671
• /
• 2006

This study was performed for exploring the spatial distribution pattern of Pinus densiflora and Quercus spp. in Korea. Firstly, the spatial distribution map of Pinus densiflora and Quercus spp. was prepared in grid of $100m{\times}100m$ at national level, using digital forest type map and actual vegetation map. And thematic maps for topography, climate, and soil were also prepared in the raster form of $100m{\times}100m$. Through GIS based spatial analysis of the digital distribution map of Pinus densiflora and Quercus spp. and thematic maps, the spatial characteristics of Pinus densiflora and Quercus spp. distribution was explored in relation to the environmental factors such as topography, climate, and soil. And the occurrence frequency models of Pinus densiflora and Quercus spp. were derived. Pinus densiflora occurs more often than Quercus spp. at low elevation, low slope gradient, and high temperature areas. In addition, Pinus densiflora is mainly distributed at shallow and well-drained loamy soil from igneous rocks. In contrast, Quercus spp. is more common at shallow and well-drained loamy soil from metamorphic rocks. As a result, the prediction model for the spatial distribution of Pinus densiflora and Quercus spp. by topographical variables has proven successful with high statistical significance. The result of this study can contribute to rational management of Pinus densiflora and Quercus spp. stand in Korea, considering environmental factors such as topography, climate, and soil.

• Journal of Korean Society on Water Environment
• /
• v.32 no.6
• /
• pp.511-519
• /
• 2016

National Institute of Environmental Research (NIER) has developed the Korean Reach File (KRF) for scientific and systematic analysis of variables related to water quality, pollutant sources and aquatic ecosystems in consideration of steam reach networks. The KRF provides a new framework for data production, storage, management and analysis for water related variables in relation to spatial characteristics, connections, and topologies of stream reaches. However, the current version of KRF (ver.2) has limited applicability because its nodes include not only the stream points based on topological characteristics but also those based on water quality monitoring stations, which may undermine its generality. In this study, a new version of KRF (ver.3) was designed and established to overcome the weak point of version 2. The version 3 is a generalization of the old KRF graphic data and it integrates the attribute data while separating it from the graphic data to minimize additional work that is needed for data association and search. We tested the KRF (ver.3) on actual cases and convenience and adaptability for each application was verified. Further research should focus on developing a database link model and real-world applications that are targeted to process event data.

• Bulletin of the Korean Chemical Society
• /
• v.35 no.12
• /
• pp.3427-3432
• /
• 2014

This study introduces a new method of combining Imaging Differential Optical Absorption Spectroscopy (Imaging-DOAS) data and plume dispersion formulas for power plant emissions to determine the three-dimensional structure of a dispersing pollution plume and the spatial distributions of trace gas volume mixing ratios (VMRs) under conditions of negligible water droplet and aerosol effects on radiative transfer within the plume. This novel remote-sensing method, applied to a power plant stack plume, was used to calculate the two-dimensional distributions of sulfur dioxide ($SO_2$) and nitrogen dioxide ($NO_2$) VMRs in stack emissions for the first time. High $SO_2$ VMRs were observed only near the emission source, whereas high $NO_2$ VMRs were observed at locations several hundreds of meters away from the initial emission. The results of this study demonstrate the capability of this new method as a tool for estimating plume dimensions and trace gas VMRs in power plant emissions.

• Journal of Korean Society for Geospatial Information Science
• /
• v.7 no.2 s.14
• /
• pp.93-100
• /
• 1999

Forest fires have been threats to natural resources, endangered species, properties and even to human lives. Efficient management of forest fires requires a complete understanding of the environmental and human related activities, as well as complicate spatial relationships among them. A geo-spatial information system(GSIS) is an appropriate method of being able to mapping and to analyze the spatial data for forest fires. Therefore, this study is to provide and classify the terrain, vegetation, life environment soil and geology factors, and to analyze spatial distribution for forest fire areas by applying the GSIS and the Remote Sensing technology. On the other hands, causes of increasing numbers of forest fires being occurred after In were assessed by comparing the normalized difference vegetation index((NDVI).

• Journal of the Korean GEO-environmental Society
• /
• v.15 no.6
• /
• pp.57-65
• /
• 2014

Recently, Construction business is changing from on the ground to underground space because of deficit of developing space, creation of green space and of incremental of land compensation expenses. Meanwhile, 3D Topographic, Marine and Cadastral maps need to have Spatial Interrelation. Also, understanding of the information is also needed. Spatial information object registration system is impossible to contact and understanding intelligence mutually because the former one is managed as automatic ID system. Therefore, 3D Object information ID System of underground space is managed based on Object Identifier. Construction of Spatial information integration ID System is required and it will offer Division Code (Ground, Index, Underground) and depth information. We are defined and classified Under Spatial Information in this paper. Moreover, we developed the integration ID System based on UFID for cadastral information Construction. We supposed underground spatial information DB Construction and a developed the way of exploiting 3D cadastral information system through the study. The research result will be the base data of Standard ID system, DB Construction and system Development of National spatial data which is considered together with spatial interrelation.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.8 no.1
• /
• pp.40-48
• /
• 2005

This paper will specify geo-objects and geo-fields of the geo-ecological contamination source and implement the system for evaluating an ocean Environmental contamination based on the spatial statistical analysis. In order to produce the grade of fishery that can evaluate the ocean effect, we will analysis the degree of the spatial correlation by semi-veriogram and predicate the elevation raster of spatial data using ordinary kriging method. This paper is to estimate the grade of fishery contamination region and produce the ratio of the area according to the fishery grade. Therefore, we can contribute to produce fishery grade that evaluates the ocean effect by means of deciding an efficient fishery environment.

• Journal of Environmental Science International
• /
• v.27 no.7
• /
• pp.499-507
• /
• 2018

Agricultural meteorological information is an important resource that affects farmers' income, food security, and agricultural conditions. Thus, such data are used in various fields that are responsible for planning, enforcing, and evaluating agricultural policies. The meteorological information obtained from automatic weather observation systems operated by rural development agencies contains missing values owing to temporary mechanical or communication deficiencies. It is known that missing values lead to reduction in the reliability and validity of the model. In this study, the hierarchical Bayesian spatio-temporal model suggests replacements for missing values because the meteorological information includes spatio-temporal correlation. The prior distribution is very important in the Bayesian approach. However, we found a problem where the spatial decay parameter was not converged through the trace plot. A suitable spatial decay parameter, estimated on the bias of root-mean-square error (RMSE), which was determined to be the difference between the predicted and observed values. The latitude, longitude, and altitude were considered as covariates. The estimated spatial decay parameters were 0.041 and 0.039, for the spatio-temporal model with latitude and longitude and for latitude, longitude, and altitude, respectively. The posterior distributions were stable after the spatial decay parameter was fixed. root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and bias were calculated for model validation. Finally, the missing values were generated using the independent Gaussian process model.