• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• The Journal of Engineering Geology
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• v.29 no.3
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• pp.251-264
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• 2019

The displacement monitoring of unstable block at the rock slope located in the Cheonbuldong valley of Seoraksan National Park was carried out using Terrestrial LiDAR. The rock slopes around Guimyeonam and Oryeon waterfall where rockfall has occurred or is expected to occur are selected as the monitoring section. The displacement monitoring of unstable block at the rock slope in the selected area was performed 5 times for about 7 months using Terrestrial LiDAR. As a result of analyzing the displacement based on the Terrestrial LiDAR scanning, the error of displacement was highly influenced by the interpolation of the obstruction section and the difference of plants growth. To minimize the external influences causing the error, the displacement of unstable block should be detected at the real scanning point. As the result of analyzing the displacement of unstable rock at the rock slope using the Terrestrial LiDAR data, the amount of displacement was very small. Because the amount of displacement was less than the range of error, it was difficult to judge the actual displacement occurred. Meanwhile, it is important to select a section without vegetation to monitor the precise displacement of unstable rock at the rock slope using Terrestrial LiDAR. Also, the PointCloud removal and the mesh model analysis in a vegetation section were the most important work to secure reliability of data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.143-156
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• 2019

The purpose of this study is to examine whether the Sinseo Innovation City of Daegu has been eco-friendly developed by analyzing changes in NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) and conducting field surveys. Using Landsat satellite images, it compares NDVI and LST changes between the years of 2008 and 2018. The results of the study are as follows. First, the NDVI has decreased by 0.07 and the zLST has increased by $0.85^{\circ}C$ over the past 10 years. Second, districts with lower NDVI and higher zLST were concentrated with infrastructure with impermeable materials. Districts with higher NDVI and lower zLST were utilized urban design techniques such as permeable parking lot, green roof, and permeable pavement. Third, districts with higher NDVI and lower zLST were applied eco-friendly planning items properly by district unit plan guideline. It is meaningful to suggest planing directions and urban planning elements considering the environmental friendly development.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1467-1475
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• 2023

The public-interest direct payment program involves providing direct payments to agricultural producers and rural residents through public funds, premised on performing public functions such as environmental conservation, stable food supply, and maintaining rural communities via agricultural activities. Scientific estimation of crop cultivation areas and production levels is crucial for formulating agricultural policies linked to regulating food supply, which increasingly impacts the national economy. Conducting comprehensive on-site inspections for compliance monitoring of direct payment programs has shown very low efficiency in relation to budget and time. The expansion of areas subject to compliance monitoring and various challenges in on-site inspections necessitate streamlining current monitoring methods and devising effective strategies. As a solution, the application of Remote Sensing technology and spatial information utilization, allowing swift acquisition of necessary information for policies without overall on-site visits, is being discussed as an efficient compliance monitoring method. Therefore, this study evaluated the potential use of remote sensing for improving operational efficiency in monitoring compliance with public-interest direct payment programs. Using satellite images during farming seasons in Gimje and Hapcheon, vegetation indices and spatial variations were utilized to identify cultivated areas, presence of mixed crops, validated against on-site inspection data.

• Journal of The Korean Society of Grassland and Forage Science
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• v.6 no.1
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• pp.24-30
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• 1986

This experiment was carried out in order to study the effect of fertilizer application and seasonal changes on the productivity of the native reed (Phragmites communis Trinius) on the reclaimed tidal flate in Chonnam province. The samples of reed were taken at about 30-days interval from May to October, 1982 and evaluated plant height, blade & sheath-stem ratio, grass yield, the feed compositions and in vitro dry matter digestibility (IVD). The results are summarized as follows: 1. Plant height, grass yield, crude protein content and in vitro dry matter digestibility of the reed were significantly increased by fertilizer application, and ADF content was significantly decreased. 2. According to the significance test of coefficience, there was a significant negative correlation (P<0.05) between in vitro dry matter digestibility and plant height, grass yield, and the content of crude fiber and ADF, but a positive correlation (P<0.05) between in vitro dry matter digestibility and blade & sheath-stem ratio, and the content of crude protein and crude fat.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.598-607
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• 2016

This study was conducted to evaluate the amount of nitrogen (N) top dressing based on the normalized difference vegetation indices (NDVI) by ground based sensors for leaf perilla under the polyethylene house. Experimental design was the randomized complete block design for five N fertilization levels and conventional fertilization with 3 and 4 replications in Gumsan-gun and Milyang-si field, respectively. Dry weight (DW), concentration of N, and amount of N uptake by leaf perilla as well as NDVIs from sensors were measured monthly. Difference of growth characteristics among treatments in Gumsan field was wider than Milyang. SPAD-502 chlorophyll meter reading explained 43.4% of the variability in N content of leaves in Gumsan field at $150^{th}$ day after seedling (DAS) and 45.9% in Milyang at $239^{th}$ DAS. Indexes of red sensor (RNDVI) and amber sensor (ANDVI) at $172^{th}$ day after seedling (DAS) in Gumsan explained 50% and 57% of the variability in N content of leaves. RNDVI and ANDVI at $31^{th}$ DAS in Milyang explained 60% and 65% of the variability in DW of leaves. Based on the relationship between ANDVI and N application rate, ANDVI at $172^{th}$ DAS in Gumsan explained 57% of the variability in N application rate but non significant relationship in Milyang field. Average sufficiency index (SI) calculated from ratio of each measurement index per maximum index of ANDVI at $172^{th}$ DAS in Gumsan explained 73% of the variability in N application rate. Although the relationship between NDVIs and growth characteristics was various upon growing season, SI by NDVIs of ground based remote sensors at top dressing season was thought to be useful index for recommendation of N top dressing rate of leaf perilla.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1951-1963
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• 2021

Remote sensing data using earth observation satellites in agricultural environment monitoring has many advantages over other methods in terms of time, space, and efficiency. Since the sensor mounted on the satellite measures the energy that sunlight is reflected back to the ground, noise is generated in the process of being scattered, absorbed, and reflected by the Earth's atmosphere. Therefore, in order to accurately measure the energy reflected on the ground (radiance), atmospheric correction, which must remove noise caused by the effect of the atmosphere, should be preceded. In this study, atmospheric correction sensitivity analysis, inter-satellite cross-analysis, and comparative analysis with ground observation data were performed to evaluate the application of KOMPSAT-3 satellite's atmospheric correction for agricultural application. As a result, in all cases, the surface reflectance after atmospheric correction showed a higher mutual agreement than the TOA reflectance before atmospheric correction, and it is possible to produce the time series vegetation index of the same standard. However, additional research is needed for quantitative analysis of the sensitivity of atmospheric input parameters and the tilt angle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.195-202
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• 2006

The infiltration route analysis is a military application using geospatial information technology. The result of the analysis would present vulnerable routes for potential enemy infiltration. In order to find the susceptible routes, optimal path search algorithms (Dijkstra's and $A^*$) were used to minimize the cost function, summation of detection probability. The cost function was produced by capability of TOD (Thermal Observation Device), results of viewshed analysis using DEM (Digital Elevation Model) and two related geospatial information coverages (obstacle and vegetation) extracted from VITD (Vector product Interim Terrain Data). With respect to 50m by 50m cells, the individual cost was computed and recorded, and then the optimal infiltration routes was found while minimizing summation of the costs on the routes. The proposed algorithm was experimented in Daejeon region in South Korea. The test results show that Dijkstra's and $A^*$ algorithms do not present significant differences, but A* algorithm shows a better efficiency. This application can be used for both infiltration and surveillance. Using simulation of moving TOD, the most vulnerable routes can be detected for infiltration purpose. On the other hands, it can be inversely used for selection of the best locations of TOD. This is an example of powerful geospatial solution for military application.