• Title/Summary/Keyword: GIS-based Spatial Analysis

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Groundwater Recharge Estimation for the Gyeongan-cheon Watershed with MIKE SHE Modeling System (MIKE SHE 모형을 이용한 경안천 유역의 지하수 함양량 산정)

  • Kim, Chul-Gyum;Kim, Hyeon-Jun;Jang, Cheol-Hee;Im, Sang-Jun
    • 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.

Rate of Shoreline Changes for Barrier Islands in Nakdong Estuary (낙동강 하구역 울타리 섬의 해안선 변화율)

  • Kim, Baeck-Oon;Khim, Boo-Keun;Lee, Sang-Ryong
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.19 no.4
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    • pp.361-374
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    • 2007
  • This study presents long-term shoreline changes of barrier islands in Nakdong Estuary using aerial photographs. Digital photogrammetry is used for constructing mosaic aerial photographs, which yield six sets of shoreline data ranging from 1975 to 2001. Three kinds of rate of shoreline changes such as EPR (End Point Rate), JKR(Jackknife Rate) and LRR (Linear Regression Rate) are computed by a GIS-based Digital Shoreline Analysis Systems. There have been remarkable changes both in Sinja Island and Doyodeung. Western part of Sinja Island advanced seaward, whereas eastern part retreated landward, giving appearance that the island rotated counterclockwise. Rate of shoreline changes at both ends reach 20 m/yr. Doyodeung occurred newly in front of Baekhapdeung in 1993, resulting in shoreline advance in a rate of 40 m/yr. Rate of shoreline changes differ both within and between barrier islands and have a tendency to increase eastward. To understand this spatial variability of rate of shoreline changes, it is suggested to make a detailed investigation into the impact of coastal development on hydrodynamic and sedimentary processes.

Automatic Construction of Deep Learning Training Data for High-Definition Road Maps Using Mobile Mapping System (정밀도로지도 제작을 위한 모바일매핑시스템 기반 딥러닝 학습데이터의 자동 구축)

  • Choi, In Ha;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.3
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    • pp.133-139
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    • 2021
  • Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

Actions to Expand the Use of Geospatial Data and Satellite Imagery for Improved Estimation of Carbon Sinks in the LULUCF Sector

  • Ji-Ae Jung;Yoonrang Cho;Sunmin Lee;Moung-Jin Lee
    • Korean Journal of Remote Sensing
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    • v.40 no.2
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    • pp.203-217
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    • 2024
  • The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.