• Journal of Korean Society of Rural Planning
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• v.15 no.4
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• pp.199-211
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• 2009

The aim of this paper is to investigate the applicability of high spatial resolution remote sensing images for conducting the rural amenity resources survey. There are a large number of rural amenity resources and field reconnaissance without a sufficient preliminary survey involves a big amount of cost and time even if the data quality cannot always be satisfied with the advanced study. Therefore, a new approach should be considered like the state-of-the-art remote sensing technology to support field survey of rural amenity resources as well as to identify the spatial attributes including the geographical location, pathway, area, and shape. Generally high-resolution satellite or aerial photo images are too expensive to cover a large area and not free of meteorological conditions, but recently rapidly-advanced internet-based image services, such as Google Earth, Microsoft Bing maps, Bluebirds, Daum maps, and so on, are expected to overcome the handicaps. The review of the different services shows that Google Earth would be the most feasible alternative for the survey of rural amenity resources in that it provides powerful tools to build spatial features and the attributes and the data format is completely compatible with other GIS(Geographic information system) software. Hence, this study tried to apply the Google Earth service to interpret the amenity resources and proposed the reformed work process conjugating the internet-based high-resolution images like satellite and aerial photo data.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.11-13
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• 2005

The Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that many collective observations gave a aspect of a global warming and other changes in the climate system. Future earth observation using satellite data should monitor global climate change, and should contribute to social benefits. Especially, human activities has given the big impacts to earth environment This is a very complex affair, and nature itself also impacts the clouds, namely the seasonal variations. JAXA (former NASDA) has the plan of the Global Change Observation Mission (GCOM) for monitoring of global environmental change. SGLI (Second Generation GLI) onboard GCOM-C (Climate) satellite, which is one of this mission, is an optical sensor from Near-UV to TIR. This sensor is the GLI follow-on sensor, which has the various new characteristics. Polarized/multi-directional channels and 250m resolution channels are the unique characteristics on this sensor. This sensor can be contributed to clarification of coastal change in sea surface. This paper shows the introduction of the unique aspects and characteristics of the next generation satellite sensor, SGLIIGCOM-C, and shows the preliminary research for this sensor.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.76-87
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• 2021

In this study, temporal trend of researches in earthquake with groundwater level, water quality, radon, remote sensing, electrical resistivity, gravity, and geomagnetism was searched from 2001 to 2020, using the journals indexed in Web of Science, and the number of articles published in international journals was counted in relation to the occurrences of earthquakes (≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0). The number of articles shows an increasing trend over the studied period. This is explained by that studies on earthquake precursor and seismic monitoring becomes active in various fields with integrated data analysis through the development of remote sensing technology, progress of measurement equipment, and big data. According to Mann-Kendall and Sen's tests, gravity-related articles exhibit an increasing trend of 1.30 articles/yr, radon-related articles (0.60 articles/yr), groundwater-related articles (0.70 articles/yr), electrical resistivity-related articles (0.25 articles/yr), and remote-sensing-related articles (0.67 articles/yr). By cross-correlation analysis of the number of articles in each field with removing trend effect and the number of earthquakes of ≥Mw 5.0, ≥Mw 6.0, ≥Mw 7.0, ≥Mw 8.0, and ≥Mw 9.0, radon and remote sensing fields exhibit a high cross-correlation with a delay time of one year. In addition, large-scale earthquakes such as the 2004 and 2005 Sumatra earthquake, the 2008 Sichuan earthquake, the 2010 Haiti earthquake, and the 2010 Chile earthquake are estimated to be related with the increase in the number of articles in the corresponding periods.

• Proceedings of the KSRS Conference
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• v.2
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• pp.946-949
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• 2006

With repeated coverage, spaceborne SAR (Synthetic Aperture Radar) instruments provide the most efficient means to monitor and study the changes in important elements of the marine environment. Due to highresolution of SAR data, the coverage of SAR sensor is always limited, especially for a repeat cycle. With more SAR sensors from various satellites, new data products such as ocean surface drift can be derived when two SARs' tracks overlap in a short time over coastal areas. Currently, there are two SAR sensors on different satellites with almost the exactly same path. That is, ERS-2 is following ENVISAT with a 30-minutes delay, which will be a good timing for ocean mesosclae feature tracking. For another application, a mystery ship near a big eddy with strong ship wake has been tracked between ERS-2 and ENVISAT SAR images to estimate its ship speed.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.64-69
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• 1998

The chlorophyll-a concentration measured by OCTS could be used for observing the physical phenomena such as eddies, fronts, and up welling in the oceans as well as for studying the ecology of phytoplankton. In this study, biological and physical features in the East Sea/Japan Sea (the East Sea) and the Yellow Sea observed by OCTS are analyzed in comparison with other satellite data. And in situ chlorophyll data were compared with OCTS Level 2 chlorophyll data. There was a striking correspondence between the satellite chlorophyll structure and other satellite data in the East Sea in the spring. Very complicated ring structures in the 557 are reflected in chlorophyll structure. In the Yellow Sea, the surface structure was rather simple. While the discrepancies between in situ and OCTS algorithm version 3 chlorophyll were small in the East Sea, those for the Yellow Sea were rather big. Comparison with CZCS data for similar time of the year (May-June) shows that OCTS chlorophyll is higher in general. Although the error is partly due to the fact that NASDA chlorophyll algorithm is an empirical algorithm for case 1 water, how much of this error is also due to the errors in sensor calibration or in atmospheric correction is not clear.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.253-266
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• 1999

ADEOS(Advanced Earth Observing Satellite)/IMG(Interferometric Monitor for Greenhouse Gases) measurements - temperature, water vapor($H_2O$), ozone($O_3$) have been compared with the radio sonde and ozone sonde observations at Osan and Pohang stations for the 4 cases on 10 Jan.(a), 28 Jan.(b), 2 Apr.(c), and 19 Jun.(d) 1997 to detect the error ranges of the IMG data. It showed that the IMG data of the cases (b), (d) when the ADEOS passed over the central part of Korea were quite stable with the good agreement with the sonde observations, however, that of (a),(c) when the ADEOS passed over south- east coastal area were unstable with the larger differences from the sonde-observations. The RMSE and bias analyses of temperature for the stable cases (b),(d) showed that the differences between the IMG data and the sonde observations were about 1~4 K at the 700~300 hPa level and about 4~5 K or more at the higher level, and the IMG measurements tended to be larger than the sonde observations at the higher level above 200 hPa, while no typical bias was seen at the lower level. The RMSE and bias analysis for the version of level 2 5_6_4_4 of ozone showed that the RMSE of ozone were quite small, in general, except at the higher level above 50~60 hPa in the all 4 cases, however the bias was generally big with the positive value in the troposphere and the negative in the stratosphere. An example of vertical profile of trace gases such as $CO_2, N_2O, CH_4, HNO_3$, CO measured by IMG was also presented and it showed that the IMG data had large differences between the 5 different observation points.