• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1155-1164
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• 2023

Currently, the National Forest Inventory (NFI) collects tree information by human, so the range and time of the survey are limited. Research is actively being conducted to extract tree information from a large area using aerial Light Detection And Ranging (LiDAR) and aerial photographs, but it does not reflect the characteristics of forest areas in Korea because it is conducted in areas with wide tree spacing or evenly spaced trees. Therefore, this study proposed a methodology for generating Digital Surface Model (DSM), Digital Elevation Model (DEM), and Canopy Height Model (CHM) images using aerial LiDAR, extracting the tree height through the local Maxima, and calculating the Diameter at Breath Height (DBH) through the DBH-tree height formula. The detection accuracy of trees extracted through the proposed methodology was 88.46%, 86.14%, and 84.31%, respectively, and the Root Mean Squared Error (RMSE) of DBH calculated based on the tree height formula was around 5cm, confirming the possibility of using the proposed methodology. It is believed that if standardized research on various types of forests is conducted in the future, the scope of automation application of the manual national forest resource survey can be expanded.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.2
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• pp.78-87
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• 2024

The risk of global warming is increasing due to rapid climate change and increased greenhouse gas (GHG) emissions. Among the greenhouse gases, methane has a strong warming effect; in particular, 51.2% of the agricultural sector's methane emissions are from flooded rice fields. According to the current standard rice cultivation method, rice is grown during the maximum tillering stage with an intermittent drainage period of approximately 2 weeks. During the flooding period, methane-producing bacteria are active, but the activity of methane-producing bacteria and the amount of methane gas produced are reduced when the soil becomes oxidized through watering. Accordingly, this study used multiple-sensing technology to analyze the growth response according to the intermittent drainage period and to identify the extended intermittent drainage period with less impact on rice production. The equipment used for growth observations included NDVI, PRI, and IR sensors. The results confirmed that growth indices related to stress, such as NDVI and PRI, were not significantly different from those of the control when treated within 3 weeks of drainage, but drastically decreased when the drainage period was extended beyond 4 weeks. These results appear to result from the fact that soil water content (volumetric water content) also dropped to below 20% 4 weeks after irrigation, creating actual drought stress conditions. The 22^nd day after treatment, when the soil moisture content reached 20%, was considered the point in time when drought stress conditions were formed. The point at which the SPAD value decreased to 0.6% of normal was estimated to be 23.5 days after treatment by using the regression equation between NDVI and SPAD.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.83-107
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• 2014

This study presents spatial characteristics of cloud using satellite image in the extreme heavy snowfall of the Yeongdong region. 3 extreme heavy snowfall events in the Yeongdong region during the recent 12 years (2001 ~ 2012) are selected for which the fresh snow cover exceed 50 cm/day. Spatial characteristics (minimum brightness temperature; Tmin, cloud size, center of cloud-cell) of cloud are analyzed by tracking main cloud-cell related with these events. These characteristics are compared with radar precipitation in the Yeongdong region to investigate relationship between cloud and precipitation. The results are summarized as follows, selected extreme heavy snowfall events are associated with the isolated, well-developed, and small-scale convective cloud which is developing over the Yeongdong region or moving from over East Korea Bay to the Yeongdong region. During the period of main precipitation, cloud-cell Tmin is low ($-40{\sim}-50^{\circ}C$) and cloud area is small (17,000 ~ 40,000 $km^2$). Precipitation area (${\geq}$ 0.5 mm/hr) from radar also shows small and isolated shape (4,000 ~ 8,000 $km^2$). The locations of the cloud and precipitation are similar, but in there centers are located closely to the coast of the Yeongdong region. In all events the extreme heavy snowfall occur in the period a developed cloud-cell was moving into the coastal waters of the Yeongdong. However, it was found that developing stage of cloud and precipitation are not well matched each other in one of 3 events. Water vapor image shows that cloud-cell is developed on the northern edge of the dry(dark) region. Therefore, at the result analyzed from cloud and precipitation, selected extreme heavy snowfall events are associated with small-scale secondary cyclone or vortex, not explosive polar low. Detection and tracking small-scale cloud-cell in the real-time forecasting of the Yeongdong extreme heavy snowfall is important.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.721-732
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• 2016

The accuracy and error characteristics of microwave Sea Surface Temperature (SST) measurements in the Northwest Pacific were analyzed by utilizing 162,264 collocated matchup data between GCOM-W1/AMSR2 data and oceanic in-situ temperature measurements from July 2012 to August 2016. The AMSR2 SST measurements had a Root-Mean-Square (RMS) error of about $0.63^{\circ}C$ and a bias error of about $0.05^{\circ}C$. The SST differences between AMSR2 and in-situ measurements were caused by various factors, such as wind speed, SST, distance from the coast, and the thermal front. The AMSR2 SST data showed an error due to the diurnal effect, which was much higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. In addition, the RMS error tended to be large in the winter because the emissivity of the sea surface was increased by high wind speeds and it could induce positive deviation in the SST retrieval. Low sensitivity at colder temperature and land contamination also affected an increase in the error of AMSR2 SST. An analysis of the effect of the thermal front on satellite SST error indicated that SST error increased as the magnitude of the spatial gradient of the SST increased and the distance from the front decreased. The purpose of this study was to provide a basis for further research applying microwave SST in the Northwest Pacific. In addition, the results suggested that analyzing the errors related to the environmental factors in the study area must precede any further analysis in order to obtain more accurate satellite SST measurements.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.657-664
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• 2021

In this study, we investigated the relationships between the air quality (PM_2.5, PM₁₀, O₃) concentrations and local geographical characteristics (terrain heights, building area ratios, population density in 9 km × 9 km gridded subareas) in the Seoul metropolitan area. To analyze the terrain heights and building area ratios, we used the geographic information system data provided by the NGII (National Geographic Information Institute). Also, we used the administrative districts and population provided by KOSIS (Korean Statistical Information Service) to estimate population densities. We analyzed the PM_2.5, PM₁₀, and O₃ concentrations measured at the 146 AQMSs (air quality monitoring system) within the Seoul metropolitan area. The analysis period is from January 2010 to December 2020, and the monthly concentrations were calculated by averaging the hourly concentrations. The terrain is high in the northern and eastern parts of Gyeonggi-do and low near the west coastline. The distributions of building area ratios and population densities were similar to each other. During the analysis period, the monthly PM_2.5 and PM₁₀ concentrations at 146 AQMSs were high from January to March. The O₃ concentrations were high from April to June. The population densities were negatively correlated with PM_2.5, PM₁₀, and O₃ concentrations (weakly with PM_2.5 and PM₁₀ but strongly with O₃). On the other hand, the AQMS heights showed no significant correlation with the pollutant concentrations, implying that further studies on the relationship between terrain heights and pollutant concentrations should be accompanied.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.811-827
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• 2018

The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

• Korean Journal of Remote Sensing
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• v.28 no.1
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• pp.11-19
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• 2012

Air-borne pollen, biogenically created aerosol particle, influences Earth's radiative balance, visibility impairment, and human health. The importance of pollens has resulted in numerous experimental studies aimed at characterizing their dispersion and transport, as well as health effects. There is, however, limited scientific information concerning the optical properties of airborne pollen particles contributing to total ambient aerosols. In this study, for the first time, optical characteristics of pollen such as aerosol backscattering coefficient, aerosol extinction coefficient, and depolarization ratio at 532 nm and their effect to the atmospheric aerosol were studied by lidar remotes sensing technique. Dual-Lidar observations were carried out at the Gwangju Institute of Science & Technology (GIST) located in Gwagnju, Korea ($35.15^{\circ}E$, $126.53^{\circ}N$) for a spring pollen event from 5 to 7 May 2009. The pollen concentration was measured at the rooftop of Gwangju Bohoon hospital where the building is located 1.0 km apart from lidar site by using Burkard trap sampler. During intensive observation period, high pollen concentration was detected as 1360, 2696, and $1952m^{-3}$ in 5, 6, and 7 May, and increased lidar return signal below 1.5km altitude. Pollen optical depth retrieved from depolarization ratio was 0.036, 0.021, and 0.019 in 5, 6, and 7 May, respectively. Pollen particles mainly detected in daytime resulting increased aerosol optical depth and decrease of Angstrom exponent.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.439-450
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• 2018

The lineament is a linear or curved terrain element to discriminate adjacent geological structures in each other. It has been widely used for analysis of geology, mineral exploration, natural disasters, and earthquake, etc. In the past, the lineament has been extracted using cartographic map or field survey. However, it is possible to extract more efficiently the lineament for a very wide area thanks to development of remote sensing technique. Remotely sensed observation by aircraft, satellite, or digital elevation model (DEM) has been used for visual recognition for manual lineament extraction. Automatic approaches using computer science have been proposed to extract lineament more objectively. In this study, we evaluate the characteristics of lineament which is automatically extracted with respect to difference of spatial resolution of DEM. We utilized two types of DEM: one is Shuttle Radar Topography Mission (SRTM) with spatial resolution of about 90 m (3 arc sec), and the other is the latest world DEM of TerraSAR-X add-on for Global DEM with 12 m spatial resolution. In addition, a global DEM was resampled to produce a DEM with a spatial resolution of 30 m (1 arc sec). The shaded relief map was constructed considering various sun elevation and solar azimuth angle. In order to extract lineament automatically, we used the LINE module in PCI Geomatica software. We found that predominant direction of the extracted lineament is about $N15-25^{\circ}E$ (NNE), regardless of spatial resolution of DEM. However, more fine and detailed lineament were extracted using higher spatial resolution of DEM. The result shows that the lineament density is proportional to the spatial resolution of DEM. Thus, the DEM with appropriate spatial resolution should be selected according to the purpose of the study.