• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.402-402
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• 2005

The purpose of this study is to analyze the land cover environment changes of tidal flat in the Hampyeong Bay. Especially, it centers on the changes in the sedimentary environment using remote sensing data. Multi-temporal Landsat data (Path-Row: 116-034) were used in this study. Remote sensing data can be effectively applied for quantitative analysis of geological environment changes in the Hampyeong-bay.

• 대한원격탐사학회지
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• 제2권2호
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• pp.117-131
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• 1986

The purpose of this study was to develop techniques acquiring hydrologic parameters that affect runoff conditions from Landsat imagery. Runoff conditions in a study area were analyzed by employing the U.S. Soil Conservation Service(SCS) Method. SCS runoff curve numbers(CN) were estimated by the computer analysis of Landsat imagery and digiral terrain model(DTM) data. The SCS Method requires land use/cover and soil conditions of the area as input parameters. A land use/cover map of 5 hydrological classes was produced from the Landsat multi-spectral scannerr imagery. Slope-gradient and contour and contour maps were also made using the DTM topographic data. Inundation areas depending on reservoir levels were able to be mapped on the Landsat scene by combining the contour data.

• Journal of Forest and Environmental Science
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• 제36권3호
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• pp.187-198
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• 2020

This study extracted deforestation area and degraded forestland area, which are potential REDD+ (Reducing Emissions from Deforestation and Forest Degradation) project candidate areas in Paraguay using Land Cover Map (LCM) and Tree Cover Map (TCM). The REDD+ project objectives scenarios were set three stages: 'afforestation and economic efficiency scenario', 'local capacity reinforcement scenario', and 'Infrastructure-oriented scenario'. And then, we evaluated the project unit suitable area of the REDD+ project. All scenarios selected the evaluation factors for each scenario in addition to the area ratio factors for deforestation area and degraded forestland area and weighted values were extracted by assigning category scores. As a result of the three scenarios comparison analysis, Concepcion state score was the highest. Within Concepcion state, the Belon district had the highest score, making it appropriate as a project unit REDD+ project candidate area in Paraguay, while the San Carlos district had the lowest score. This study can be used as basic data for selecting REDD+ project candidate area in Paraguay, and it is expected to contribute sufficiently to REDD+ project if additional data or information of social, cultural and economic sectors are secured.

• 대기
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• 제21권4호
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• pp.415-427
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• 2011

In this study, the impacts of land surface conditions, land cover (LC) map and leaf area index (LAI), on the short-range weather forecast over the East-Asian region were examined using Unified Model (UM) coupled with the MOSES 2.2 (Met-Office Surface Exchange Scheme). Four types of experiments were performed at 12-km horizontal resolution with 38 vertical layers for two months, July and August 2009 through consecutive reruns of 72-hour every 12 hours, 00 and 12 UTC. The control experiment (CTRL) uses the original IGBP (International Geosphere-Biosphere Programme) LC map and old MODIS (MODerate resolution Imaging Spectroradiometer) LAI, the new LAI experiment (NLAI) uses improved monthly MODIS LAI. The new LC experiment (NLCE) uses KLC_v2 (Kongju National Univ. land cover), and the new land surface experiment (NLSE) uses KLC_v2 and new LAI. The reduced albedo and increased roughness length over southern part of China caused by the increased broadleaf fraction resulted in increase of land surface temperature (LST), air temperature, and sensible heat flux (SHF). Whereas, the LST and SHF over south-eastern part of Russia is decreased by the decreased needleleaf fraction and increased albedo. The changed wind speed induced by the LC and LAI changes also contribute the LST distribution through the change of vertical mixing and advection. The improvement of LC and LAI data clearly reduced the systematic underestimation of air temperature over South Korea. Whereas, the impacts of LC and LAI conditions on the simulation skills of precipitation are not systematic. In general, the impacts of LC changes on the short range forecast are more significant than that of LAI changes.

• 한국물환경학회지
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• 제25권4호
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• pp.539-546
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• 2009

We examined the effects of land cover types on water quality based on data surveyed during April 2007-February 2008 from 178 sites of 111 streams in Paldang watershed. BOD, COD, DO, SS, T-N, and T-P concentrations of spring and summer were strongly and significantly associated with the first principal component of the proportions of eight land cover types, and differences between all parameter's concentration except SS and T-N of spring and summer were insignificantly related with them. SS and T-N concentration of summer were significantly correlated with increase and decrease of stream flow. T-P concentration of spring was the most significantly related with the second principal component which was positively correlated with the proportions of residential and forest land covers and was negatively correlated with the proportions of paddy and grass land covers. It is necessary to manage land use of the upper watershed and stream flow for improvement in water quality because seasonal variations of each water quality parameter are dependent upon land cover and flow variations.

• 한국조경학회지
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• 제26권3호
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• pp.34-44
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• 1998

The purpose of this study is to provide the basic data for land use in the future, which result from analyzing land use, obtained after studying on the natural environment by Geographic Information System and Remote Sensing. The results of this study are as follows : ·According to the classification of land-cover, agricultural land use is relatively prominent except for overall natural covering. According to the average value of Green Vegetation Index class, the average value of GVI is 3.0, and 45% of the regions have relatively good condition of floral state. ·With a view to natural environment, the survey shows that the altitude of 90% of the total areas is below 400m, and most of them are flattened or moderately-inclined area. Therefore, this region has a good condition to be used for development. · The area for the first class in preservation degree of natural scenery of Namcheon-Myun is 2.3% of the total areas. According to the results about unstable areas on all sides, unstable districs are distributed in so small-scale units that they will be safe from some damages drawn by developing activity. But we have to consider every aspects for the future development of them. In this study, the natural environment-variables are regarded firstly, and effective designation of the land with natural environment is researched too. However, to establish more practical developing plan, ecological and human variables should be regarded.

• 한국측량학회지
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• 제24권1호
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• pp.139-145
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• 2006

라이다 데이터는 데이터 취득시간과 처리시간이 짧으며 높은 점밀도와 정확도를 가지고 있다. 그러나 광학영상과는 달리 3차원 형태의 비정규 점군의 형태이기 때문에 지표면에 대한 정확한 분류가 어렵다. 본 연구에서는 라이다 데이터와 광학영상을 동시에 이용해서 감독분류 기법을 통해 토지피복분류를 수행하였다. 먼저 라이다 데이터로부터 격자 크기가 1m인 DSM 영상과 DEM 영상을 제작하고 이를 이용하여 nDSM 영상을 제작하였다. 또한 라이다 데이터의 인텐서티(intensity) 정보를 이용해서 인텐서티 영상을 제작하였다. 광학영상의 입력데이터는 CCD 영상의 적색, 청색, 녹색 파장영역과 IKONOS 영상의 근적외선 파장영역이다. 그리고 CCD 영상의 적생광 파장영역을 이용해서 제작한 식생지수 영상이다. 광학영상과 라이다 데이터를 동시에 이용해서 토지피복 분류를 수행한 결과 74%의 분류 정확도를 얻을 수 있었다. 추가적으로 그림자 지역의 재분류, 수계지역의 처리 그리고 숲과 건물의 오분류 수정 과정을 수행하여 최종적으로 81.8%의 분류 정확도를 얻을 수 있었다.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.214-222
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• 2022

Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• 천문학회보
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• 제25권2호
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• pp.110-110
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• 2000