• Economic and Environmental Geology
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• v.50 no.1
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• pp.1-14
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• 2017

The analytical conditions including surface state, moisture effect, and device condition were investigated for applying Short Wave Infrared(SWIR) spectroscopy to the field survey. Among the three surface state of samples (exposed surface, cutting face and powder), both spectra from the exposed surface and cutting face are almost identical whereas spectral variation was detected in powder sample. Over 24-hours-dryring of the wet sample at room temperature, the samples show a similar spectrum with that of dry condition. The result suggests that outcrop samples mighty be dried for 24 ~ 48 hours depending on the wetness of outcrop. The bright minerals could produce stable spectra with 10 times measurements as default value of the device under SWIR spectroscopy but the dark minerals would require about 10 seconds, which corresponds to 100 times measurements to get the reliable spectra. The position and shape 2,160 ~ 2,330 nm and/or other spectral features of hydrothermal alteration minerals by SWIR spectroscopy could be used for a classification of hydrothermal alteration zone in the field. Absorption peaks in 2,160 ~ 2180 nm are useful for identifying (advanced) argillic zone by spectral characteristics of kaoline, dickite, pyrophyllite, and alunite. Absorption peaks in 2,180 ~ 2,230 nm are able to define muscovite, sericite, and smectite, which are key alteration minerals in phyllic zone. Absorption peaks in 2,230 ~ 2,270 nm can be used to recognize prophylitic zone where chlorite and epidote occur. Absorption peaks of other principle minerals such as talc, serpentine, amphibole, and carbonate group are mainly detected within the wave length of 2,270 ~ 2,330 nm. This result indicates that the spectra of these minerals need to be carefully interpreted.

• Journal of Sensor Science and Technology
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• v.13 no.2
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• pp.128-132
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• 2004

Short wave infrared (SWIR) photovoltaic devices have been fabricated from metal organic vapour phase epitaxy (MOVPE) grown n- on p- HgCdTe films on GaAs substrates. The MOVPE grown films were processed into mesa type discrete devices with wet chemical etching employed for meas delineation and ZnS surface passivatlon. ZnS was thermally evaporated from effusion cell in an ultra high vacuum (UHV) chamber. The main features of the ZnS deposited from effusion cell in UHV chamber are low fixed surface charge density, and small hysteresis. It was found that a negative flat band voltage with -0.6 V has been obtained for Metal Insulator Semiconductor (MIS) capacitor which was evaporated at $910^{\circ}C$ for 90 min. Current-Voltage (I-V) and temperature dependence of the I-V characteristics were measured in the temperature range 80 - 300 K. The Zero bias dynamic resistance-area product ($R_{0}A$) was about $7500{\Omega}-cm^{2}$ at room temperature. The physical mechanisms that dominate dark current properties in the HgCdTe photodiodes are examined by the dependence of the $R_{0}A$ product upon reciprocal temperature. From theoretical considerations and known current expressions for thermal and tunnelling process, the device is shown to be diffusion limited up to 180 K and g-r limited at temperature below this.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.172-174
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• 2003

Using MODIS data and several different compositing algorithms utilizing the average cloud free days in a compositing period, maximum ndvi, or dual maximum NDVI/minimum blue, multi resolution composites (250m, 500m, 1km) have been produced for Southeast Asia, with spectral bands ranging from the visible to short-wave infrared with a single band in the thermal (for land and sea surface temperature). A total of nine composites have been produced for the months of May and August in 2003, including blue, green, red, NIR, three in the SWIR, and several to specifically monitor vegetation health.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1147-1154
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• 2022

Now that the development of autonomous driving is progressing, LiDAR has become an indispensable element. However, LiDAR is a device that uses lasers, and laser side effects may occur. One of them is the much-talked-about eye-safety, and developers have been satisfying this through laser characteristics and operation methods. But eye-safety is just one of the problems lasers pose. For example, irradiating a laser with a specific energy level or higher in a dusty environment can cause deterioration of the dust particles, leading to a sudden explosion. For this reason, the dust ignition proof regulations clearly state that "a source with a pulse period of less than 5 seconds is considered a continuous light source, and the average energy does not exceed 5 mJ/mm 2 or 35 mW" [2]. Energy of output optical power is limited by the law. In this way, the manufacturer cannot define the usage environment of the LiDAR, and the development of a LiDAR that can be used in such an environment can increase the ripple effect in terms of use in application fields using the LiDAR. In this paper, we develop a LiDAR with low optical power that can be used in environments where high power lasers can cause problems, evaluate its performance. Also, we discuss and present one of the directions for the development of LiDAR with laser power limited by dust ignition proof regulations.

• Journal of Bio-Environment Control
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• v.23 no.1
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• pp.50-55
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• 2014

This study was conducted to investigate the responses of red pepper (Hongjinju) leaves under water stress. Hyperspectral short wave infrared (SWIR, 1000~1800 nm) reflectance imaging techniques were used to acquire the spectral images for the red pepper leaves with and without water stress. The acquired spectral data were analyzed with a multivariate analysis method of ANOVA (analysis of variance). The ANOVA model suggested that 1449 nm wavebands was the most effective to determine the stress responses of the red pepper leaves exposed to the water deficiency. The waveband of 1449 nm was closely related to the water absorption band. The processed spectral image of 1449 nm could separate the non-stress, moderate stress (-20 kPa), and severe stress (-50 kPa) groups of red pepper leaves distinctively. Results demonstrated that hyperspectral imaging technique can be applied to monitoring the stress responses of red pepper leaves which are an indicator of physiological and biochemical changes under water deficiency.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.94-100
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• 2008

Recently, global warming for climate system is a crucial issue over the world and it brings about severe climate change, abnormal temperature, a downpour, a drought, and so on. Especially, a drought over the earth surface accelerates desertification which has been advanced over the several years mainly originated from a climatic change. The objective of this study is to detect variation of vegetation water condition around China and Mongolia desert by using satellite data having advantage in observing surface biological system. In this study, we use SPOT/VEGETATION satellite image to calculate NDWI (Normalized Difference Water Index) around study area desert for monitoring of status of vegetation characteristics. The vegetation water status index from remotely sensing data is related to desertification since dry vegetation is apt to desertify. We can infer vegetation water status using NDWI acquired by NIR (Near infrared) and SWIR (Short wave infrared) bands from SPOT/VGT. The consequence is that NDWI decreased around desert from 1999 to 2006. The areas that NDWI was decreased are located in the northeast of Mongolian Gobi desert and the southeast of China Taklamakan desert.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.52-67
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• 2020

Recently, the Arctic has been exposed to snow-covered land due to melting permafrost every year, and the Korea Geographic Information Institute(NGII) provides polar spatial information service by establishing spatial information of the polar region. However, there is a lack of spatial information on vegetation sensitive to climate change. This research used a multi-temporal Sentinel-2 image to perform land cover classification of the Ny-Ålesund in Arctic Svalbard. In the pre-processing step, 10 bands and 6 vegetation spectral index were generated from multi-temporal Sentinel-2 images. In image-classification step is consisted of extracting the vegetation area through 8-class land cover classification and performing the vegetation species classification. The image classification algorithm used Random Forest to evaluate the accuracy and calculate feature importance through Out-Of-Bag(OOB). To identify the advantages of multi- temporary Sentinel-2 for vegetation classification, the overall accuracy was compared according to the number of images stacked and vegetation spectral index. Overall accuracy was 77% when using single-time Sentinel-2 images, but improved to 81% when using multi-time Sentinel-2 images. In addition, the overall accuracy improved to about 83% in learning when the vegetation index was used additionally. The most important spectral variables to distinguish between vegetation classes are located in the Red, Green, and short wave infrared-1(SWIR1). This research can be used as a basic study that optimizes input characteristics in performing the classification of vegetation in the polar regions.