• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.567-577
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• 2016

SqueeSAR is a new technique to combine Persistent Scatterer (PS) and Distributed Scatterer (DS) for deformation monitoring. Although many PSs are available in urban areas, SqueeSAR analysis can be beneficial to increase the PS density in not only natural targets but also smooth surfaces in urban environment. The height of each targets is generally required to remove topographic phase in interferometric SAR processing. The result of PSInSAR analysis to use PS only is not affected by DEM resolution because the height error of initial input DEM at each PSs is precisely compensated in PS processing chain. On the contrary, SqueeSAR can be affected by DEM resolution and precision since it includes spatial average filtering for DS targets to increase a signal-to-noise ratio (SNR). In this study we observe the effect of DEM resolution on deformation measurement by PSInSAR and SqueeSAR. With ALOS-1 PALSAR L-band data, acquired over Daejeon city, Korea, two different DEM data are used in InSAR processing for comparison: 1 m LIDAR DEM and SRTM 1-arc (~30 m) DEM. As expected the results of PSInSAR analysis show almost same results independently of the kind of DEM, while the results of SqueeSAR analysis show the improvement in quality of the time-series in case of 1-m LIDAR DSM. The density of InSAR measurement points was also improved about five times more than the PSInSAR analysis.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.459-468
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• 2007

To estimate the groundwater recharge, the fully distributed parameter based model, MIKE SHE was applied to the Gyeongan-cheon watershed which is one of the tributaries of Han River Basin, and covers approximately $260km^2$ with about 49 km main stream length. To set up the model, spatial data such as topography, land use, soil, and meteorological data were compiled, and grid size of 200m was applied considering computer ability and reliability of the results. The model was calibrated and validated using a split sample procedure against 4-year daily stream flows at the outlet of the watershed. Statistical criteria for the calibration and validation results indicated a good agreement between the simulated and observed stream flows. The annual recharges calculated from the model were compared with the values from the conventional groundwater recession curve method, and the simulated groundwater levels were compared with the observed values. As a result, it was concluded that the model could reasonably simulate the groundwater level and recharge, and could be a useful tool for estimating spatially/temporally the groundwater recharges, and enhancing the analysis of the watershed water cycle.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.371-384
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• 2015

Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.471-482
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• 2000

Surface energy balance components were evaluated by Landsat TM data and GIS with meteorological data. Calibration and validation for the applicability of this methodology were made through the estimating of the large-scale evapotranspiration (ET). In addition, sensitivity and error analysis was conducted to see the effects of the surface energy balance components on ET and the accuracy of each components. Bochong-chon located on the upper part of Guem River basin was selected as the case study area. Spatial distribution map of ET were produced for five dates: Jan. 1, Apr. 3, May. 10, and Nov. 27, 1995. The study results showed tat ET was greatly varied with the aspect and theland use type on the surface. In the case of having northeast and southeast in the aspect, ET was linearly increased depending on growing net radiation. While surface temperature has a high value, NDVI(Normalized Difference Vegetation Index) has a low value in the vegetated area. Therefore, ground heat flux was increased but ET was relatively decreased. The results of sensitivity and error analysis showed that net radiation is most sensitive and effective, ranging from 12.5% to 23.6% of sensitivity. Furthermore, the surface temperature, air temperature, and wind speed have the significant effects on ET estimation using remotely sensed data.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.5-21
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• 2019

The purpose of this study is to investigate and analyze the cadastral transformation process of Dokdo-ri based on the institutional concept of the cadastre, while presenting the cadastral characteristics of Dokdo-ri therefrom. For this purpose, both the literature search method and the Internet search method were used. As for the analysis method, descriptive analysis method and comparative analysis method were used. The findings support that the cadastral characteristics of Dokdo-ri is drawn as follows: First, from the physical point of view, the cadastral characteristics of Dokdo-ri shows that the territory is given by the smallest lot numbers in the country and that the territory is also given by the land lot number consisting of small size lots without continuing the ground for which the cadastral resurvey project was carried out for the first time. Second, in terms of the rights, the cadastral characteristics of Dokdo-ri shows that the entire area given by the lots is owned by a single owner and no ownership has been changed. Third, in terms of its value, the cadastral characteristics of Dokdo-ri shows that it is the only area given by the lots of which the officially assessed individual land price has never been decreased in the entire lots. Fourth, when it comes to presenting the cadastral characteristics of Dokdo-ri in terms of the use control/restriction, the consciousness of preserving Dokdo-ri and the consciousness of enhancing real territory are confronted but due to the Cultural Heritage Protection Act etc, the cadastre turned out to be a very passive cadastral work.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.93-107
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• 2019

The open data portal collects data created by public institutions and opens and shares them according to related laws. With the activation of the Fourth Industrial Revolution, all sectors of our society are demanding high quality data, but the data required by the industry has not been greatly utilized due to the lack of quantity and quality. Numerous data collected in the real world can be implemented in cyber physical systems to simulate real-world problems, and alternatives to various social issues can be found. There is a limit to being provided. Location intelligence is a technology that enables existing data to be represented in space, enabling new value creation through convergence. In this study, to present location intelligence of open data, we surveyed the status of location information by data in open data portal. As a result, about 60% of the surveyed data had location information and the representative type was address. Appeared. Therefore, by suggesting location intelligence of open data based on address and how to use it, this study aimed to suggest a way that open data can play a role in creating future social data-based industry and policy establishment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.3
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• pp.183-196
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• 2010

Increasing human activity due to rapid economic growth and land use change alters the patterns of the Asian monsoon, which is key to crop yields in Asia. In this study, we tested the performance of regional climate model (RegCM3) by simulating important components of Indian summer monsoon, including land-ocean contrast, low level jet (LLJ), Tibetan high and upper level Easterly Jet. Three contrasting rain years (1994: excess year, 2001: normal year, 2002: deficient year) were selected and RegCM3 was integrated at 60 km horizontal resolution from April 1 to October 1 each year. The simulated fields of circulations and precipitation were validated against the observation from the NCEP/NCAR reanalysis products and Global Precipitation Climatology Centre (GPCC), respectively. The important results of RegCM3 simulations are (a) LLJ was slightly stronger and split into two branches during excess rain year over the Arabian Sea while there was no splitting during normal and deficient rain years, (b) huge anticyclone with single cell was noted during excess rain year while weak and broken into two cells in deficient rain year, (c) the simulated spatial distribution of precipitation was comparable to the corresponding observed precipitation of GPCC over large parts of India, and (d) the sensitivity experiment using NIMBUS-7 SMMR snow data indicated that precipitation was reduced mainly over the northeast and south Peninsular India with the introduction of 0.1 m of snow over the Tibetan region in April.

• Journal of the Korean association of regional geographers
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• v.17 no.4
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• pp.432-442
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• 2011

Climatic variables such as temperature and precipitation tend to vary both in space and in time simultaneously. Thus, it is necessary to include space-time autocorrelation into conventional spatial interpolation methods for reliable time-series mapping. This paper introduces and applies space-time variogram modeling and space-time kriging to generate time-series temperature maps using hourly Automatic Weather System(AWS) temperature observation data for a one-month period. First, temperature observation data are decomposed into deterministic trend and stochastic residual components. For trend component modeling, elevation data which have reasonable correlation with temperature are used as secondary information to generate trend component with topographic effects. Then, space-time variograms of residual components are estimated and modelled by using a product-sum space-time variogram model to account for not only autocorrelation both in space and in time, but also their interactions. From a case study, space-time kriging outperforms both conventional space only ordinary kriging and regression-kriging, which indicates the importance of using space-time autocorrelation information as well as elevation data. It is expected that space-time kriging would be a useful tool when a space-poor but time-rich dataset is analyzed.

• Korean Journal of Environmental Biology
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• v.37 no.4
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• pp.759-768
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• 2019

This study was conducted to estimate the mean annual extreme minimum temperature raster and predict the potential distribution of the invasive plant, Ipomoea triloba, on the Korean peninsula. We collected annual extreme minimum temperature and mean coldest month minimum temperature data from 129 weather stations on the Korean peninsula from 1990-2019 and used this data to create a linear regression model. The min temperature of the coldest month raster from Worldclim V2 were used to estimate a 30 second spatial resolution, mean annual extreme minimum temperature raster of the Korean peninsula using a regression model. We created three climatic rasters of the Korean peninsula for use with the Proto3 species distribution model and input the estimated mean annual extreme minimum temperature raster, a Köppen-Geiger climate class raster from Beck et al. (2018), and we also used the mean annual precipitation from Worldclim V2. The potential distribution of I. triloba was estimated using the Proto3 model with 117 occurrence points. As a result, the estimated area for a potential distribution of I. triloba was found to be 50.7% (111,969 ㎢) of the Korean peninsula.