• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.143-150
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• 2013

Recently, environmental issues such as climate warming, ozone layer depletion, reduction of tropical forests and desertification are emerging as global environmental problems beyond national problems. And international attention and effort have been carried out in many ways to solve these problems. In this study, the growth of green was calculated quantitatively using the technique of remote sensing and temperature change was figured out through temperature extraction in the city. The land-cover changes and thermal changes for research areas were analyzed using Landsat TM images on May 2002 and May 2009. Surface temperature distribution was calculated using spectral degree of brightness of Band 6 that was Landsat TM thermal infrared sensor to extract the ground surface temperature in the city. As a result of research, the area of urban green belt was increased by $2.87km^2$ and the ground surface temperature decreased by $0.6^{\circ}C{\sim}0.8^{\circ}C$ before and after tree planting projects. Henceforth, if the additional study about temperature of downtown is performed based on remote sensing and measurement data, it will contribute to solve the problems about the urban environment.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.165-167
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• 2003

To evaluate the thermal and water stress of vegetation canopy in Southern Qu$\'{e}$bec, leaf water status was evaluated from vegetation indices derived from SPOT VEGETATION images and surface temperature from NOAA AVHRR images. This study was conducted by investigating vegetation conditions for two different periods, from June to August, 1999 and 2000. The vegetation indices were integrated for the evaluating vegetation conditions as a new index, normalized moisture index (NMI). A trapezoid was defined by the NMI and surface temperature, and the thermal and water status of the vegetation canopy was determined according to separate small sections within the trapezoid.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.199-202
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• 2007

COMS (Communication, Ocean and Meteorological Satellite) is a geostationary satellite and has been developing by KARI for communication, ocean observation and meteorological observation. Conventional thermal control design, using MLI (Multi Layer Insulation), OSR (Optical Solar Reflector), heater and heat pipe, is utilized. Ka-band components are installed on South wall, while other equipment for sensors are installed on the opposite side, North wall. High dissipating communication units are located on external (surface) heat pipe and are covered by internal insulation blankets to decouple them from the rest of the satellite. External satellite walls are covered by MLI or OSR for insulation from space and for rejection internal heat to space. The ocean and meteorological sensors are installed on optical benches on the top floor to decouple thermally from the satellite. Single solar array wing is adopted in order to secure clear field of view of radiant cooler of IR meteorological sensor. This paper presents principles of thermal control design for the COMS.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.493-495
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• 2003

The establishment of the investigation and the maintenance technique is required for preservation of old structures in Japan. This study attempts to diagnose the deterioration status of the historic structures using the thermal infrared camera. In some structures, the difference of the spatio-temporal change was observed in the surface temperature. For example, the cold joint of concrete was examined using this method effectively. As a result of this study, we have found useful guidelines in developing methodology to conduct diagnosis of historic buildings by using thermal infrared camera.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.667-677
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• 2020

Crop water stress can be detected based on soil moisture content, crop physiological characteristics and remote-sensing technology. The detection of crop water stress is an important issue for the accurate assessment of yield decline. The crop water stress index (CWSI) has been introduced based on the difference between leaf and air temperature. In this paper, drone-based thermal infrared image was used to map of crop water stress in water control plot (WCP) and water deficit plot (WDP) over spring chinese cabbage fields. The spatial distribution map of CWSI was in strong agreement with the abnormal growth response factors (plant height, plant diameter, and measured value by chlorophyll meter). From these results, CWSI can be used as a good method for evaluation of crop abnormal growth monitoring.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Korean Journal of Remote Sensing
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• v.32 no.4
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• pp.353-364
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• 2016

Yangon city is experienced a rapid urban expansion over the last two decades due to accelerate with the socioeconomic development. This research work studied an investigation into the application of the integration of the Remote Sensing (RS) and Geographic Information System (GIS) for observing Land Use and Land Cover (LULC) patterns and evaluate its impact on Land Surface Temperature (LST) of the downtown, suburban 1 and suburban 2 of Yangon city. The main purpose of this paper was to examine and analyze the variation of the spatial distribution property of the LULC of urban spatial information related with the LST and Normalized Difference Vegetation Index (NDVI) using RS and GIS. This paper was observed on image processing of LULC classification, LST and NDVI were extracted from Landsat 8 Operational Land Imager (OLI) image data. Then, LULC pattern was linked with the variation of LST data of the Yangon area for the further connection of the correlation between surface temperature and urban structure. As a result, NDVI values were used to examine the relation between thermal behavior and condition of land cover categories. The spatial distribution of LST has been found mixed pattern and higher LST was located with the scatter pattern, which was related to certain LULC types within downtown, suburban 1 and 2. The result of this paper, LST and NDVI analysis exhibited a strong negative correlation without water bodies for all three portions of Yangon area. The strongest coefficient correlation was found downtown area (-0.8707) and followed suburban 1 (-0.7526) and suburban 2(-0.6923).

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.721-727
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• 2002

Precipitation evapotranspiration and runoff are three key parameters of regional water balance. Problems exist in the traditional methods for calculating such factors , such as explaining of the geographic rationality of spatial interpolating methods and lacking of enough observation stations in many important area for bad natural conditions. With the development of modern spatial info-techniques, new efficient shifts arose for traditional studies. Guided by theories on energy flow and materials exchange within Soil-Atmosphere-Plant Continuant (SPAC), retrieval models of key hydrological parameters were established in the Yellow River basin using CMS-5 and FengYun-2 meteorological satellite data. Precipitation and evapotranspiration were then estimated: (1) Estimating tile amount of solar energy that is absorbed by the ground with surface reflectivity, which is measured in the visible wavelength band (VIS): (2) Assessing the partitioning of the absorbed energy between sensible and latent heat with the surface temperature, which was measured in the thermal infrared band (TIR), the latent heat representing the evapotranspiration of water; (3) Clouds are identified and cloud top levels are classified using both VIS and TIR data. Hereafter precipitation will be calculated pixel by pixel with retrieval model. Daily results are first obtained, which are then processed to decade, monthly and yearly products. Precipitation model has been has been and tested with ground truth data; meanwhile, the evapotranspiration result has been verified with Large Aperture Scintillometry (LAS) presented by Wageningen University of the Netherlands. Further studies may concentrate on the application of models, i.e., establish a hydrological model of the Yellow river basin to make the accurate estimation of river volume and even monitor the whole hydrological progress.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1469-1478
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• 2018

Earthquakes and volcanic eruptions are disaster that causes billions of dollars in property damage and the loss of human life. Therefore, it is required to effectively monitor earthquakes and volcanoes. With the increase of satellite data, researches on earthquake and volcano using satellite imagery has been improved. Satellite images can be divided into three types i.e. optical, thermal, Synthetic Aperture Radar (SAR) and each image has different characteristics. In this article, we summarized its advantages and disadvantages of each type of satellite image. Moreover, we investigated the previous researches about earthquake and volcano using satellite images. Finally, we suggest application method to respond earthquake and volcano disaster using satellite images.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.921-932
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• 2023

Land cover change due to urban population concentration and urban expansion can cause various environmental problems such as urban heat islands. In particular, New towns are considered an appropriate study site to analyze changes in urban climate due to rapid urbanization in a short period. This study used Landsat satellite imagery to compare and analyze the land cover changes before and after the development of two new towns with different plans, and the resulting changes in surface urban heat island (SUHI) phenomena. Correlation analysis was also conducted between urban structural features that may affect the SUHI intensity. The results of the analysis confirm the rapid change in land cover as new town development progresses and the direct intensification of the SUHI phenomenon. This study confirms the differences in SUHI caused by different urban plans and suggests the need for three-dimensional urban planning to improve the thermal environment.