• Geophysics and Geophysical Exploration
• /
• v.8 no.2
• /
• pp.145-155
• /
• 2005

A two-dimensional (2-D) interpretation of MT data has been performed for the purpose of fracture detection for geothermal development. Remote stations have been operated in Kyushu, Japan (480 km apart) as well as in Korea (60 km and 165 km apart in 2002 and 2003 data set, respectively). Apparent resistivity and phase curves calculated by remote processing with the Japan remote data showed enough quality for 2-D inversion for the whole frequency range. Remote reference processing with Korea remote reference data also showed quite good continuity in apparent resistivity and phase curves except some noisy frequency bands; around the power frequency, 60 Hz, and around the dead band $10^{-1}Hz\;Hz\;\~1\;Hz$, where the natural EM signal is known to be very weak. Even though the subsurface showed severe three-dimensional (3-D) characteristics in the survey area so that 2-D inversion by itself could not give enough information for deep geological structures, the 2-D inversion for the 5 survey lines showed several common features. The conductive semi-consolidate mudstone layer is dipping from north to south (about 500 m depth on the south and 200 m on the north most part of the survey area). The boundary between the low (L-2) and high (H-2) resistivity anomalies can be thought as a major fault with strike $N15^{\circ}E$, passing through the sites 206, 112 and 414. The shallow (< 1 km) conductive anomalies (L-4) seem to be fracture zones having strike E-W (at site 105) and $N60^{\circ}W$ (at site 434). And there exists a conductive layer in the western and west-southern part of the survey area in the depth below $2\~3\;km$, for which further investigation is to be needed.

• Horticultural Science & Technology
• /
• v.31 no.6
• /
• pp.720-725
• /
• 2013

When highly shrinkable materials such as coir dust are major component of root media, the degrees of compaction during container filling of root media severely influences the physical properties of root media. It results in the changes in total porosity (TP), container capacity (CC) and air-filled porosity (AFP). This research was conducted to secure the fundamental information in changes of soil physical properties as influenced by the compaction of root media during container filling. To achieve this, three root media were formulated by blending coir dust (CD) with expanded rice hull (CD + ERH, 8:2, v/v), carbonized rice hull (CD + CRH, 6:4) and ground and raw pine bark (CD + GRPB, 8:2). Based on the optimum bulk density, the amount of root media filled into 6.0, 7.5, 8.5, 10.5 and 12.5 cm were adjusted to 90, 100, 110, 120 and 130% based on the weight of root media. Then the changes in TP, CC, and AFP were measured. Elevation of the packing amount of root media in all sizes of pot resulted in the decrease of TP. But the decrease was more severe in CD + ERH and CD + CRH than those in CD + GRPB. The CC also decreased gradually as the packing amounts were elevated in three root media, but the decreases were severe as the container sizes became larger. The AFP decreased drastically by the elevation of the packing amount of root media in all sizes of pot. The AFP was the highest in CD + CRH medium when pot sizes were smaller than 7 cm, but that was the highest in CD + ERH when the pot sizes were larger than 8.5 cm among the 3 root media tested. In this research, the elevation of packing amount of three root media influenced more severely the AFP rather than CC. This result indicates that the packing amount should be controlled to maintain appropriate level of AFP because AFP rather than CC influence severely crop growth. The results obtained through this study can be used to predict the changes in physical properties of root media as influenced by packing amount in various sizes of pots.

• Korean Journal of Soil Science and Fertilizer
• /
• v.42 no.2
• /
• pp.79-87
• /
• 2009

Pot experiments using sand culture were conducted in 2004 under greenhouse conditions to evaluate the effect of nitrogen deficiency on red pepper biomass. Nitrogen stress was imposed by implementing 6 levels (40% to 140%) of N in Hoagland's nutrient solution for red pepper. Canopy reflectance measurements were made with hand held spectral sensors including $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$, and $Field\;Scout^{TM}$ Chlorophyll meter, and a spectroradiometer as well as Minolta SPAD-502 chlorophyll meter. Canopy reflectance and dry weight of red pepper were measured at five growth stages, the 30th, 40th, 50th, 80th and 120th day after planting(DAT). Dry weight of red pepper affected by nitrogen stress showed large differences between maximum and minimum values at the 120th DAT ranged from 48.2 to $196.6g\;plant^{-1}$, respectively. Several reflectance indices obtained from $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$ and Spectroradiometer including chlorophyll readings were compared for evaluation of red pepper biomass. The reflectance indices such as rNDVI, aNDVI and gNDVI by the $Crop\;Circle^{TM}$ sensor showed the highest correlation coefficient with dry weight of red pepper at the 40th, 50th, and 80th DAT, respectively. Also these reflectance indices at the same growth station was closely correlated with dry weight, yield, and nitrogen uptake of red pepper at the 120th DAT, especially showing the best correlation coefficient at the 80th DAT. From these result, the aNDVI at the 80th DAT can significantly explain for dry weight of red pepper at the 120th DAT as well as for application level of nitrogen fertilizer. Consequently ground remote sensing as a non-destructive real-time assessment of plant nitrogen status was thought to be a useful tool for in season nitrogen management for red pepper providing both spatial and temporal information.

• Korean Journal of Remote Sensing
• /
• v.24 no.6
• /
• pp.551-558
• /
• 2008

It is well known that the protein content of rice grain is an indicator of taste of cooked rice in the countries where people as the staple food. Ground-based optical sensing over the crop canopy would provide information not only on the mass of plant body which reflects the light, but also on the crop nitrogen content which is closely related to the greenness of plant leaves. The vegetation index has been related to crop variables such as biomass, leaf nitrogen, plant cover, and chlorophyll in cereals. The objective of this study was to investigate the correlation between GNDVI and NDVI values, and grain protein content at different dates and to estimate the grain protein content using G(NDVI) values. We measured Green normalized difference vegetation index [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$] and [$GNDVI=({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$] by using two different active sensors. The study was conducted during the rice growing season for three years from 2005 through 2007 at the experimental plots of National Institute of Agricultural Science and Technology. The experiments were carried out by randomized complete block design with the application of four levels of nitrogen fertilizers(0, 70, 100, 130kg N/ha) and the same amount of phosphorous and potassium content of the fertilizers. After heading stage, relationships between GNDVI of rice canopy and grain protein content showed the highly positive correlation at different dates for three years. GNDVI values showed higher correlation coefficients than that of NDVI during growing season in 2005-07. The correlation between GNDVI values at different dates and grain protein contents was highly correlated at early July. We attempted to estimate the grain protein content at harvesting stage using GNDVI values from early July for three years. The determination coefficients of the linear model by GNDVI values were 0.9l and the measured and estimated grain protein content at harvesting stage using GNDVI values highly correlated($R^2=0.96^{***}$). Results from this study show that GNDVI appeared very effective to estimate leaf nitrogen and grain protein content of rice canopy.

• Korean Journal of Soil Science and Fertilizer
• /
• v.40 no.6
• /
• pp.435-441
• /
• 2007

Ground-based optical sensing over the crop canopy provides information on the mass of plant body which reflects the light, as well as crop nitrogen content which is closely related to the greenness of plant leaves. This method has the merits of being non-destructive real-time based, and thus can be conveniently used for decision making on application of nitrogen fertilizers for crops standing in fields. In the present study relationships among leaf nitrogen content of rice canopy, crop growth status, and Normalized Difference Vegetation Index (NDVI) values were investigated. We measured Green normalized difference vegetation index($gNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$) and NDVI($({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$) were measured by using two different active sensors (Greenseeker, NTech Inc. USA). The study was conducted in the years 2005-06 during the rice growing season at the experimental plots of National Institute of Agricultural Science and Technology located at Suwon, Korea. The experiments carried out with randomized complete block design with the application of four levels of nitrogen fertilizers (0, 70, 100, 130kg N/ha) and same amount of phosphorous and potassium content of the fertilizers. gNDVI and rNDVI increased as growth advanced and reached to maximum values at around early August, G(NDVI) were a decrease in values of observed with the crop maturation. gNDVI values and leaf nitrogen content were highly correlated at early July in 2005 and 2006. On the basis of this finding we attempted to estimate the leaf N contents using gNDVI data obtained in 2005 and 2006. The determination coefficients of the linear model by gNDVI in the years 2005 and 2006 were 0.88 and 0.94, respectively. The measured and estimated leaf N contents using gNDVI values showed good agreement ($R^2=0.86^{***}$). Results from this study show that gNDVI values represent a significant positive correlation with leaf N contents and can be used to estimate leaf N before the panicle formation stage. gNDVI appeared to be a very effective parameter to estimate leaf N content the rice canopy.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1029-1042
• /
• 2021

As part of the next-generation Compact Advanced Satellite 500 (CAS500) project, CAS500-4 is scheduled to be launched in 2025 focusing on the remote sensing of agriculture and forestry. To obtain quantitative information on vegetation from satellite images, it is necessary to acquire surface reflectance through atmospheric correction. Thus, it is essential to develop an atmospheric correction method suitable for CAS500-4. Since the absorption and scattering characteristics in the atmosphere vary depending on the wavelength, it is needed to analyze the sensitivity of atmospheric correction parameters such as aerosol optical depth (AOD) and water vapor (WV) considering the wavelengths of CAS500-4. In addition, as CAS500-4 has only five channels (blue, green, red, red edge, and near-infrared), making it difficult to directly calculate key parameters for atmospheric correction, external parameter data should be used. Therefore, thisstudy performed a sensitivity analysis of the key parameters (AOD, WV, and O₃) using the simulated images based on Sentinel-2 satellite data, which has similar wavelength specifications to CAS500-4, and examined the possibility of using the products of GEO-KOMPSAT-2A (GK2A) as atmospheric parameters. The sensitivity analysisshowed that AOD wasthe most important parameter with greater sensitivity in visible channels than in the near-infrared region. In particular, since AOD change of 20% causes about a 100% error rate in the blue channel surface reflectance in forests, a highly reliable AOD is needed to obtain accurate surface reflectance. The atmospherically corrected surface reflectance based on the GK2A AOD and WV was compared with the Sentinel-2 L2A reflectance data through the separability index of the known land cover pixels. The result showed that two corrected surface reflectance had similar Seperability index (SI) values, the atmospheric corrected surface reflectance based on the GK2A AOD showed higher SI than the Sentinel-2 L2A reflectance data in short-wavelength channels. Thus, it is judged that the parameters provided by GK2A can be fully utilized for atmospheric correction of the CAS500-4. The research findings will provide a basis for atmospheric correction of the CAS500-4 in the future.

• Economic and Environmental Geology
• /
• v.52 no.2
• /
• pp.169-188
• /
• 2019

This study was carried out unmanned aerial photography and terrestrial laser scanning to establish digital database on original wall of Gyejoksanseong fortress, and measured ground control points for continuity of the monitoring. It also performed precise examination with the naked eye, unmanned aerial photogrammetry, endoscopy, total station and handy measurement to examine the structural stability of the original walls. The ground control points were considered as a point where visual field can be secured, 3 points were selected around each of the south and north walls. For the right side of the south original wall, aerial photogrammetry was conducted using drones and a deviation analysis of 3-dimensional digital models was performed for short-term monitoring. As a result, the two original walls were almost matched in range within 5mm, and no difference indicating displacement of stones was found, except for partial deviation. Regular monitoring of the areas with structural deformation such as bulging, weak and fracture zone by precisely examining with the naked eye and using high-resolution photo data revealed no distinct change. The inner foundation observed through endoscopy found out that filling stones of the original walls were still remained, while most filling soil was lost. As a result of measuring the total station focusing around the points with structural deformation on the original walls, the maximum displacements of the north and south walls were somewhat high with 6.6mm and 3.8mm, respectively, while the final displacements were relatively stable at below 2.9mm and 1.4mm, respectively. Handy measurement also did not reveal clear structural deformation with displacements below 0.82mm at all points. Even though the results of displacement monitoring on the original walls are stable, it is hard to secure structural stability due to the characteristics of ramparts where sudden brittle fracture occurs. Therefore, it is necessary to conduct conservational scientific diagnosis, precise monitoring, and structural analysis based on the 3-dimensional figuration information obtained in this research.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.6-7
• /
• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.207-221
• /
• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.13 no.4
• /
• pp.111-124
• /
• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.