• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.76-79
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• 2008

This study presents the use of multi-temporal JERS-1 SAR images to the land cover classification. So far, land cover classified by high resolution aerial photo and field survey and so on. The study site was located in Non-san area. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then classified land cover. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.475-478
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• 2007

This study presents the use of multi-temporal JERS-1 SAR images to extract the land cover information and possibility. So far, land cover information extracted by high resolution aerial photo and field survey. The study site was located in Non-san area. This study developed on multi-temporal land cover status monitoring and coherence information mapping can be processing by L band SAR image. From July, 1997 to October, 1998 JERS SAR images (9 scenes) coherence values are analyzed and then extracted land cover information factors, so on. This technique which forms the basis of what is called SAR Interferometry or InSAR for short has also been employed in spaceborne systems. In such systems the separation of the antennas, called the baseline is obtained by utilizing a single antenna in a repeat pass

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.261-265
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• 2004

The purpose of this study is to present a standardized scheme for providing agriculture-related information at various spatial resolutions of satellite images including Landsat＋ETM, KOMPSAT-1 EOC, ASTER VNIR, and IKONOS panchromatic and multi-spectral images. The satellite images were interpreted especially for identifying agricultural areas, crop types, agricultural facilities and structures. The results were compared with the land cover/land use classification system suggested by Ministry of Construction & Transportation based on NGIS (National Geographic Information System) and Ministry of Environment based on satellite remote sensing data. As a result, high-resolution agricultural land cover map from IKONOS imageries was made out. The results by IKONOS image will be provided to KOMPSAT-2 project for agricultural application.

• Spatial Information Research
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• v.1 no.1
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• pp.55-61
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• 1993

The need for unified graphic base with high accuracy for nation¬wide is emerging since many facility management organizations and local governments have been taking steps for introducing facility management systems from the late of 1980's. A major finding of the above efforts was the importance of establishment of National Land Information System supporting all land management and use, administration purpose and effective decision-making. So to meet the recent need and to prepare future information society, a study is going on progress for in t roduc i ng Parce I-Based Land In forma t ion Sys tern based on I arge-sca I e cadastral graphic data and parcel information, capable to link with admini¬stration, communication, statistics etc.

• Environmental Engineering Research
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• v.25 no.1
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• pp.123-128
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• 2020

Non-point source pollutant load reductions were calculated using the Hydrologic Simulation Program-Fortran (HSPF) model under the assumption that landuse pattern was changed according to land purchases. Upon the simulation of non-point pollutant and areas with high land purchase ratios to select a buffer zone, the Namgang dam Reach 11, Imha dam Reach 10, and the Reach 136 watershed of the main river were found to rank high for the construction of buffer zones. Assuming that the forms of the purchased lands were changed to wetlands, biological oxygen demand (BOD) loads were changed through the HSPF model. No changes of BOD were present in the Namgang dam and the Imha dam watersheds. BOD loads in Reach 136 according to landuse change were analyzed through a flow duration analysis based on the total maximum daily loads of the United States. The flow duration analyses undertaken to examine changes in BOD of main river Reach 136 watershed indicated a shift of 0.64 kg/d from 3.16 to 2.52 during high flow. The change of BOD under the conditions of moist, mid-range and dry were 11.9%, 9% and 4.5%. At the low flow condition, the variation range in the BOD load was from 0.58 kg/d to 0.41 kg/d.

• Journal of Korean Society on Water Environment
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• v.24 no.2
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• pp.214-220
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• 2008

Nitrifying bacteria were detected by fluorescent in situ hybridization (FISH) method at 6 sampling sites with different eutrophication degree in the Nak-Dong River and their tributaries. And conventional physico-chemical parameters including $NH_4-N$, $NO_3-N$, and TN were determined concurrently. In rainy period (July), there was no noticeable difference between the number of ammonia/nitrite-oxidizing bacteria detected at each site except Sang-Ju and the ratio of nitrifying bacteria to total counts stained by DAPI varied in 6~33%. By contrast, in the dry period (October), both of bacterial population was increased differently and the ratio of nitrifying bacteria to total counts ranged more widely from 6% in heavily polluted water zone, Hwa-Won to 60% in upper tributary with high agricultural land use. Byung-Sung-Chun. In January, the numbers of ammonia-oxidizing bacteria was reduced up to one tenth, while those of nitrite-oxidizing bacteria was apparently increased maybe due to high DO and low DOC.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.237-241
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• 2009

Yangyang Gangwon-do has begun the clearing of upland forested areas for development. This process has caused great damage from natural hazards such as landslides and flooding for many years. Moreover, proper hazard prevention strategies have not Yet been prepared. To provide useful information for developing hazard prevention strategies this study attempted to detect areas vulnerable to flooding in Yangyang using data such as topology, meteorology, history, land use, soil, hydrology, and society. It was found that roughly 30% of the study area was vulnerable to flooding. Also it was discovered that where the vulnerability index was high, there was increased amounts of flooding. The most vulnerable areas were where forests were cut and near livers. In addition, areas where frequent hazard events were reported had a high index of vulnerability. The results of this study will provide useful information in developing hazard prevention strategies.

• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.