• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.167-175
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• 2019

Water color analysis allows non-destructive estimation of abundance of optically active water constituents in the water body. Recently, there have been increasing needs for light-weighted multispectral cameras that can be integrated with low altitude unmanned platforms such as drones, autonomous vehicles, and heli-kites, for the water color analysis by spectroradiometers. This study performs the preprocessing of the Micasense Rededge-M camera which recently receives a growing attention from the earth observation community for its handiness and applicability for local environment monitoring, and investigates the applicability of Rededge-M data for water color analysis. The Vignette correction and the band alignment were conducted for the radiometric image data from Rededge-M, and the sky, water, and solar radiation essential for the water color analysis, and the resultant remote sensing reflectance were validated with an independent hyperspectral instrument, TriOS RAMSES. The experiment shows that Rededge-M generally satisfies the basic performance criteria for water color analysis, although noticeable differences are observed in the blue (475 nm) and the near-infrared (840 nm) band compared with RAMSES.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1273-1282
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• 2018

Recently, active research on the Arctic Ocean has been conducted due to the influence of global warming and new Arctic ship route. Although previous studies already calculated quantitative extent of sea ice using passive microwave radiometers, melting at the edge of sea ice and surface roughness were hardly considered due to low spatial resolution. Since Sentienl-1A/B data in Extended Wide (EW) mode are being distributed as free of charge and bulk data for Arctic sea can be generated during a short period, the entire Arctic sea ice data can be covered in high spatial resolution by mosaicking bulk data. However, Sentinel-1A/B data in EW mode, especially in HV polarization, needs significant radiometric correction for further classification. Thus, in this study, we developed algorithms that can correct thermal noise and scalloping effects, and confirmed that Arctic sea ice and open-water were well classified using the corrected dual-polarization SAR data.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1437-1449
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• 2023

The purpose of this study is to analyze whether pervious and impervious areas in urban areas affect tree growth. In order to determine the differences in the growth of six species of trees planted simultaneously, the effects of pervious and impervious surfaces on tree growth were analyzed using the Normalized Difference Vegetation Index (NDVI) produced using Sentinel-2 and sub-divided land cover map from the Ministry of Environment. For this purpose, the Geospatial eXplainable Artificial Intelligence(GeoXAI) concept was applied. As a result of the analysis, the explanatory power of the model was found to be the best when considering the area of land cover included in the 10m range for Pinus densiflora, the 20 m range for Zelkova Serrata, Metasequoia glyptostroboides, and Ginkgo biloba, the 30 m range for Platanus occidentalis, and the 40 m range for Yoshino cherry trees. In addition, the wider the pervious area, the more active the growth of trees,showing a positive correlation, and the wider the impervious area, such as nearby artificial ground, showed a negative correlation with tree growth. This shows that surrounding pervious and impervious areas affect the growth of trees and that the scope of influence varies depending on the tree species.

• Korean Journal of Environmental Biology
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• v.42 no.2
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• pp.193-206
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• 2024

This study aimed to quantify changes in forest cover and carbon storage of Korean Peninsular during the last two decades by integrating field measurement, satellite remote sensing, and modeling approaches. Our analysis based on 30-m Landsat data revealed that the forested area in Korean Peninsular had diminished significantly by 478,334 ha during the period of 2000-2019, with South Korea and North Korea contributing 51.3% (245,725 ha) and 48.6% (232,610 ha) of the total change, respectively. This comparable pattern of forest loss in both South Korea and North Korea was likely due to reduced forest deforestation and degradation in North Korea and active forest management activity in South Korea. Time series of above ground biomass (AGB) in the Korean Peninsula showed that South and North Korean forests increased their total AGB by 146.4Tg C (AGB at 2020=357.9Tg C) and 140.3Tg C (AGB at 2020=417.4Tg C), respectively, during the last two decades. This could be translated into net AGB increases in South and North Korean forests from 34.8 and 29.4 Mg C ha^-1 C to 58.9(+24.1) and 44.2(+14.8) Mg C ha^-1, respectively. It indicates that South Korean forests are more productive during the study period. Thus, they have sequestered more carbon. Our approaches and results can provide useful information for quantifying national scale forest cover and carbon dynamics. Our results can be utilized for supporting forest restoration planning in North Korea

• 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.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.31-39
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• 2011

In this paper, we propose a signal readout system with small area and low power consumption for CNT sensor arrays. The proposed system consists of signal readout circuitry, a digital controller, and UART I/O. The key components of the signal readout circuitry are 64 transimpedance amplifiers (TIA) and SAR-ADC with 11-bit resolution. The TIA adopts an active input current mirror (AICM) for voltage biasing and current amplification of a sensor. The proposed architecture can reduce area and power without sampling rate degradation because the 64 TIAs share a variable gain amplifier (VGA) which needs large area and high power due to resistive feedback. In addition, the SAR-ADC is designed for low power with modified algorithm where the operation of the lower bits can be skipped according to an input voltage level. The operation of ADC is controlled by a digital controller based on UART protocol. The data of ADC can be monitored on a computer terminal. The signal readout circuitry was designed with 0.13${\mu}m$ CMOS technology. It occupies the area of 0.173 $mm^2$ and consumes 77.06${\mu}W$ at the conversion rate of 640 samples/s. According to measurement, the linearity error is under 5.3% in the input sensing current range of 10nA - 10${\mu}A$. The UART I/O and the digital controller were designed with 0.18${\mu}m$ CMOS technology and their area is 0.251 $mm^2$.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.6
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• pp.10-21
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• 2010

In the cluster-tree network which covers wide area network and has many nodes for monitoring purpose traffic is concentrated around the sink. There are long transmit delay and high data loss due to the intensive traffic when IEEE 802.15.4 is adapted to the cluster-tree network. In this paper we propose Dynamic Channel Allocation algorithm which dynamically allocates channels to increase the channel usage and the transmission success rate. To evaluate the performance of DCA, we assumed the monitoring network that consists of a cluster-tree in which sensing data is transmitted to the sink. Analysis uses the traffic data which is generated around the sink. As a result, DCA is superior when much traffic is generated. During the experiment assuming the least amount of traffic, IEEE 802.15.4, has the minimum length of active period and 90% data transmission success rate. However DCA maintains 11.8ms of active period length and results in 98.9% data transmission success rate.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.13-21
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• 2009

The sensor nodes that form a wireless sensor network must perform both routing and sensing roles, since each sensor node always has a regular energy drain. The majority of sensors being used in wireless sensor networks are either unmanned or operated in environments that make them difficult for humans to approach. Furthermore, since many wireless sensor networks contain large numbers of sensors, thus requiring the sensor nodes to be small in size and cheap in price, the amount of power that can be supplied to the nodes and their data processing capacity are both limited. In this paper, we proposes the WSN(Wireless Sensor Network) algorithm which is applied sensor node that has low power consumption and efficiency measurement. Moreover, the efficiency routing protocol is proposed in this paper. The proposed algorithm reduces power consumption of sensor node data communication. It has not researched in LEACH(Low-Energy Adaptive Clustering Hierarchy) routing protocol. As controlling the active/sleep mode based on the measured data by sensor node, the energy consumption is able to be managed. In the event, the data is transferred to the local cluster head already set. The other side, this algorithm send the data as dependent on the information such as initial and present energy, and the number of rounds that are transformed into cluster header and then transferred. In this situation, the assignment of each node to cluster head evenly is very important. We selected cluster head efficiently and uniformly distributed the energy to each cluster node through the proposed algorithm. Consequently, this caused the extension of the WSN life time.