• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.352-352
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• 2017

Recently, the South Korean government encourages the cultivation of upland crops in the paddy field to maintain an adequate level of rice production and then to balance the demand and supply of rice. This is mainly because the rice consumption per capita per year has continued to decline from 135 kg in 1979 to 61.9 kg in 2016, although the rice production was relatively stable. As a result, the rice overproduction became a big social problem. As a part of that, various upland crops such as soybean, maize, minor cereals and forage crops are planted in the paddy field 10 years ago. The cultivation of these crops may settle the problem of short supply and mass import of the crops to some extent. However, a systematic remote observation of upland crops in the paddy field is very scarce. This study investigated the cultivation status of upland crops and any changes of crop harvesting in the paddy field by using an unmanned aerial vehicle (UAV). Also, we analyzed the kind of upland crops and cultivation area in the paddy field by utilizing time series observation images. A fixed wing UAV is used for the investigation. This is because it is easy to use the flight operation and to control flight management software, and it can automatically cope with various emergency states such as a strong wind and battery discharge. The material of UAV is expanded polypropylene, which has an advantage of less equipment damage and risk during takeoff and landing. We acquired observed images in Buljeong-myeon, Goesan-gun, Chungcheongbuk-do, South Korea by using fixed wing UAV in 2015 and 2016. The total investigated area reaches 6,045 ha, and among them the agricultural area was 1,377 ha. For the next step, we created an orthoimage from all images taken using Pix 4D mapper program. According to the results of image analyses in 2015, the paddy field covered total 577 ha (75.9%) with crop plant. The cultivation area of beans, ginseng, maize, tobacco and peach was 256 ha (36.6%), 63 ha (9.2%), 37 ha (5.4%), 31 ha (4.5%) and 27 ha (3.8), respectively. And in 2016, the total covered area was 586 ha (77.1%), and it was comprised of 253 ha (35.5%), 88 ha (12.3%), 29 ha (4.1%), 22 ha (3.1%) and 32 ha (4.5%) in the same order. In this study, we focused on identifying the paddy field which was converted to the cultivation of upland crops by using UAV. And, it has been indicated that the cultivation area of rice decreased from 141 ha in 2015 to 127 ha in 2016, although that of ginseng increased by 25 ha. As a result, it is expected that a lot of paddy field could be replaced by high-income crops such as ginseng and fruit tree (peach) instead of relative low-income rice. More specific and widespread research on the remote sensing in the paddy field needs to be done.

• Spatial Information Research
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• v.7 no.2
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• pp.159-173
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• 1999

GIS has been regarded as one of important tools or methodologies for various geoscience applications. Recently, spatial data integration schemes for site-specific or field-specific thematic mapping are newly developed and utilized. However, these kinds of approaches are somewhat insufficient quantitative assessment of integrated layers towards known targets in-detailed . Moreover, GIS analysis scheme is rarely extended to scientific approaches. In this study, simple approach of Multi-Buffer Zone Analysis , related to GIS analystical aspect, is addressed and an actual application for predicting or favorable mapping of mineral occurrences, one of GIS-based geoscientific approaches, is performed, As for geo-processing in GIS itself, this scheme can be regarded as extension or adaptiation of cell-based buffering or proximity analysis to geoscientific data interpretation. This study is based on rationale that surface geological pattern around primitives such as a point, a line, or a polygon in GIS, representing significant geological features, can be efficiently utilized to delineate complex geological behaviors or events, especially handling multiple dta sets originated from multiple sources such as airborne geophysical/radiometric exploration, field survey, and even a classified image of remote sensing. Conclusively, this methodology associated wit GIS is though to be helpful to analyze the spatial pattern of multiple data, pointing given sources, and is expected to effectively utilize for exploratory analysis of cell-based resultant layer integrated with complex or different data sources.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.847-860
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• 2020

The 2-pass DInSAR (Differential Interferometric SAR) processing steps for DEM generation consist of the co-registration of SAR image pair, interferogram generation, phase unwrapping, calculation of DEM errors, and geocoding, etc. It requires complicated steps, and the accuracy of data processing at each step affects the performance of the finally generated DEM. In this study, we developed an improved method for enhancing the performance of the DEM generation method based on the 2-pass DInSAR technique of TanDEM-X bistatic SAR images was developed. The developed DEM generation method is a method that can significantly reduce both the DEM error in the unwrapped phase image and that may occur during geocoding step. The performance analysis of the developed algorithm was performed by comparing the vertical accuracy (Root Mean Square Error, RMSE) between the existing method and the newly proposed method using the ground control point (GCP) generated from GPS survey. The vertical accuracy of the DInSAR-based DEM generated without correction for the unwrapped phase error and geocoding error is 39.617 m. However, the vertical accuracy of the DEM generated through the proposed method is 2.346 m. It was confirmed that the DEM accuracy was improved through the proposed correction method. Through the proposed 2-pass DInSAR-based DEM generation method, the SRTM DEM error observed by DInSAR was compensated for the SRTM 30 m DEM (vertical accuracy 5.567 m) used as a reference. Through this, it was possible to finally create a DEM with improved spatial resolution of about 5 times and vertical accuracy of about 2.4 times. In addition, the spatial resolution of the DEM generated through the proposed method was matched with the SRTM 30 m DEM and the TanDEM-X 90m DEM, and the vertical accuracy was compared. As a result, it was confirmed that the vertical accuracy was improved by about 1.7 and 1.6 times, respectively, and more accurate DEM generation was possible with the proposed method. If the method derived in this study is used to continuously update the DEM for regions with frequent morphological changes, it will be possible to update the DEM effectively in a short time at low cost.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of the Korean Society of Marine Environment & Safety
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• v.14 no.2
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• pp.163-168
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• 2008

Ship detection from satellite remote sensing is a crucial application for global monitoring for the purpose of protecting the marine environment and ensuring marine security. It permits to monitor sea traffic including fisheries, and to associate ships with oil discharge. An automatic ship detection approach for RADARSAT Fine Synthetic Aperture Radar (SAR) image is described and assessed using in situ ship validation information collected during field experiments conducted on August 6, 2004. Ship detection algorithms developed here consist of five stages: calibration, land masking, prescreening, point positioning, and discrimination. The fine image was acquired of Ulsan Port, located in southeast Korea, and during the acquisition, wind speeds between 0 m/s and 0.4 m/s were reported. The detection approach is applied to anchoring ships in the anchorage area of the port and its results are compared with validation data based on Vessel Traffic Service (VTS) radar. Our analysis for anchoring ships, above 68 m in length (LOA), indicates a 100% ship detection rate for the RADARSAT single beam mode. It is shown that the ship detection performance of SAR for smaller ships like barge could be higher than the land-based radar. The proposed method is also applied to estimate the ship's dimensions of length and breadth from SAR radar cross section(RCS), but those values were comparatively higher than the actual sizes because of layover and shadow effects of SAR.

• Journal of the Korean association of regional geographers
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• v.7 no.1
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• pp.35-50
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• 2001

Since remotely sensed images of coastal wetlands are very sensitive to spatial resolution, it is very important to select an optimum resolution for particular geographic phenomena needed to be represented. Scale is one of the most important factors in spatial analysis techniques, which is defined as a spatial and temporal interval for a measurement or observation and is determined by the spatial extent of study area or the measurement unit. In order to acquire the optimum scale for a particular subject (i.e., coastal wetlands), measuring and representing the characteristics of attribute information extracted from the remotely sensed images are required. This study aims to explore and analyze the scale effects of attribute information extracted from remotely sensed coastal wetlands images. Specifically, it is focused on identifying the effects of scale in response to spatial resolution changes and suggesting a methodology for exploring the optimum spatial resolution. The LANDSAT TM image of Sunchon Bay was classified by a supervised classification method, Six land cover types were classified and the Kappa index for this classification was 84.6%. In order to explore the effects of scale in the classification procedure, a set of images that have different spatial resolutions were created by a aggregation method. Coarser images were created with the original image by averaging the DN values of neighboring pixels. Sixteen images whose resolution range from 30 m to 480 m were generated and classified to obtain land cover information using the same training set applied to the initial classification. The values of Kappa index show a distinctive pattern according to the spatial resolution change. Up to 120m, the values of Kappa index changed little, but Kappa index decreased dramatically at the 150m. However, at the resolution of 240 m and 270m, the classification accuracy was increased. From this observation, the optimum resolution for the study area would be either at 240m or 270m with respect to the classification accuracy and the best quality of attribute information can be obtained from these resolutions. Procedures and methodologies developed from this study would be applied to similar kinds and be used as a methodology of identifying and defining an optimum spatial resolution for a given problem.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.361-370
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• 2020

Long-term observation of the life cycle of plants allows the identification of critical signals of the effects of climate change on plants. Indeed, plant phenology is the simplest approach to detect climate change. Observation of seasonal changes in plants using digital repeat imaging helps in overcoming the limitations of both traditional methods and satellite remote sensing. In this study, we demonstrate the utility of camera-based repeat digital imaging in this context. We observed the biological events of plants and quantified their phenophases in the northern temperate type deciduous broadleaf forest of Jeombong Mountain. This study aimed to identify trends in seasonal characteristics of Quercus mongolica (deciduous broadleaf forest) and Pinus densiflora (evergreen coniferous forest). The vegetation index, green chromatic coordinate (GCC), was calculated from the RGB channel image data. The magnitude of the GCC amplitude was smaller in the evergreen coniferous forest than in the deciduous forest. The slope of the GCC (increased in spring and decreased in autumn) was moderate in the evergreen coniferous forest compared with that in the deciduous forest. In the pine forest, the beginning of growth occurred earlier than that in the red oak forest, whereas the end of growth was later. Verification of the accuracy of the phenophases showed high accuracy with root-mean-square error (RMSE) values of 0.008 (region of interest [ROI]1) and 0.006 (ROI3). These results reflect the tendency of the GCC trajectory in a northern temperate type deciduous broadleaf forest. Based on the results, we propose that repeat imaging using digital cameras will be useful for the observation of phenophases.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.2
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• pp.96-109
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• 2021

Since terrestrial input plays a major role in coastal primary production, an understanding of land-ocean coupling (LOC) is key to understand coastal ecological changes. In this study, the LOC has been classified into three stages (i.e., the baseflow, plume event and residual flow). In order to characterize its pattern in Nakdong River estuary, multi-platform data were obtained from remote sensing (geostationary ocean color image (GOCI)), in-situ measurement (marine environment information system (MEIS)), on-site measurement (discharge data and meteorological data). The MEIS data were grouped into three stages of LOC using principal component analysis (PCA), and the LOC (2013 ~ 2018) was examined at each stage using multi-platform data. In the Nakdong River estuary, the maximum value of chlorophyll-a (chl-a) was unexpectedly appeared during the plume event. It is assumed that there was no significant increase in turbidity, expected during the typical plume event, together with the weak flushing effect, caused the enhanced phytoplankton growth. Compared with other estuaries, LOC is common in estuaries affected by freshwater inflow, but LOC has different pattern depending on the size of the plume. While estuaries that form small plumes of about 10 km (low freshwater discharge and weak flushing effect) observed high chl-a in the plume event because the phytoplankton can response to the increased nutrient more rapidly. estuaries that form large plumes of more than 100 km est (high freshwater discharge and strong flushing effect) follow the typical LOC pattern conceptualized in this study (high chl-a in the residual flow).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.