• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.931-946
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• 2017

The purpose of this study is to observe and analyze soil moisture conditions with high resolution and to evaluate its application feasibility to agriculture. For this purpose, we used three Landsat-8 OLI (Operational Land Imager)/TIRS (Thermal Infrared Sensor) optical and thermal infrared satellite images taken from May to June 2015, 2016, and 2017, including the rural areas of Jeollabuk-do, where 46% of agricultural areas are located. The soil moisture conditions at each date in the study area can be effectively obtained through the SPI (Standardized Precipitation Index)3 drought index, and each image has near normal, moderately wet, and moderately dry soil moisture conditions. The temperature vegetation dryness index (TVDI) was calculated to observe the soil moisture status from the Landsat-8 OLI/TIRS images with different soil moisture conditions and to compare and analyze the soil moisture conditions obtained from the SPI3 drought index. TVDI is estimated from the relationship between LST (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) calculated from Landsat-8 OLI/TIRS satellite images. The maximum/minimum values of LST according to NDVI are extracted from the distribution of pixels in the feature space of LST-NDVI, and the Dry/Wet edges of LST according to NDVI can be determined by linear regression analysis. The TVDI value is obtained by calculating the ratio of the LST value between the two edges. We classified the relative soil moisture conditions from the TVDI values into five stages: very wet, wet, normal, dry, and very dry and compared to the soil moisture conditions obtained from SPI3. Due to the rice-planing season from May to June, 62% of the whole images were classified as wet and very wet due to paddy field areas which are the largest proportions in the image. Also, the pixels classified as normal were analyzed because of the influence of the field area in the image. The TVDI classification results for the whole image roughly corresponded to the SPI3 soil moisture condition, but they did not correspond to the subdivision results which are very dry, wet, and very wet. In addition, after extracting and classifying agricultural areas of paddy field and field, the paddy field area did not correspond to the SPI3 drought index in the very dry, normal and very wet classification results, and the field area did not correspond to the SPI3 drought index in the normal classification. This is considered to be a problem in Dry/Wet edge estimation due to outlier such as extremely dry bare soil and very wet paddy field area, water, cloud and mountain topography effects (shadow). However, in the agricultural area, especially the field area, in May to June, it was possible to effectively observe the soil moisture conditions as a subdivision. It is expected that the application of this method will be possible by observing the temporal and spatial changes of the soil moisture status in the agricultural area using the optical satellite with high spatial resolution and forecasting the agricultural production.

• Journal of the Korean Geographical Society
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• v.40
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• pp.1-13
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• 1989

The brightness temperature from NOAA AVHRR CH 4 images was examined for the metropolitan Seoul area, the capital city of Korea, to detect the characteristics of the urban heat island for this study. Surface data from 21 meteorological stations were compared with the brightness temperatures Through computer enhancement techniques, more than 20 heat islands could be recognized in South Korea, with 1 km spatii resolution at a scale of 1: 200, 00O(Fig. 3, 4 and 6). The result of the analysis of AVHRR CH 4 images over the metropolitan Seoul area can be summerized as follows (1) The pattern of brightness temperature distribution in the metropolitan Seoul area shows a relatively strong temperature contrast between urban and rural areas. There is some indication of the warm brightness temperature zone characterrizing built-up area including CBD, densely populated residential district and industrial zone. The cool brightness temperature is asociaed with the major hills such as Bukhan-san, Nam-san and Kwanak-san or with the major water bodies such as Han-gang, and reservoirs. Although the influence of the river and reservoirs is obvious in the brightness temperauture, that of small-scaled land use features such as parks in the cities is not features such as parks in the cities is not apperent. (2) One can find a linerar relationshop between the brightenss temperature and air temperature for 10 major cities, where the difference between two variables is larger in big cities. Though the coefficient value is 0.82, one can estimate that factors of the heat islands can not be explained only by the size of the cities. The magnitude of the horizontal brightness temperature differences between urban and rural area is found to be greater than that of horizontal air temperature difference in Korea. (3) Also one can find the high heat island intensity in some smaller cities such as Changwon(won(Tu-r=9.0$^{\circ}$C) and Po-hang(Tu-r==7.1$^{\circ}$~)T. he industrial location quotient of Chang-won is the second in the country and Po-hang the third. (4) A comparision of the enhanced thermal infrared imageries in 1986 and 1989, with the map at a scale of 1:200, 000 for the meotropolitan Seoul area showes the extent of possible urbanization changes. In the last three years, the heat islands have been extended in area. zone characterrizing built-up area including (5) Although the overall data base is small, the data in Fig. 3 suggest that brightness tempeautre could ge utilized for the study on the heat island characteristics. Satellite observations are required to study and monitor the impact of urban heat island on the climate and environment on global scale. This type of remote sensing provides a meams of monitoring the growth of urban and suburban aeas and its impact on the environment.

• Economic and Environmental Geology
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• v.44 no.6
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• pp.463-474
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• 2011

The Haenam epithermal mineralized zone is located in the southwestern part of South Korea, and hosts low sulfidation epithermal Au-Ag deposit (Eunsan-Moisan) and clay quarries (Okmaesan, Seongsan, and Chunsan). Epithermal deposits and accompanying hydrothermal alteration related to Cretaceous volcanism caused large zoned assemblages of hydrothermal alteration minerals. Advanced argillic-altered rocks with mineral assemblages of alunite-quartz, alunite-dickite-quartz, and dickite-kaolinite-quartz exposed on the Okmaesan, Seongsan, and Chunsan area. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with three visible and near infrared bands, six shortwave infrared bands, and five thermal infrared bands, was used to identify advanced argillic-altered rocks within the Haenam epithermal mineralized zone. The distinct spectral features of hydrothermal minerals allow discrimination of advanced argillic-altered rocks from non-altered rocks within the study area. Because alunite, dickite, and kaolinite, consisting of advanced argillic-altered rocks within the study area are characterized by Al-O-H-bearing minerals, these acid hydrothermal minerals have a strong absorption feature at $2.20{\mu}m$. The band combination and band ratio transformation cause increasing differences of DN values between advanced argillic-altered rock and non-altered rock. The alunite and dickite-kaolinite of advanced argillic-altered rocks from the Okmaesan, Seongsan, and Chunsan have average DN values of 1.523 and 1.737, respectively. These values are much higher than those (1.211 and 1.308, respectively) of non-altered area. ASTER images can remotely provide the distribution of hydrothermal minerals on the surface. In this way good relation between ASTER spectra analysis and field data suggests that ASTER spectral analysis can be useful tool in the initial steps of mineral exploration.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1985-1999
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• 2021

The purpose of this study is to apply urban heat island reduction techniques(green roof, cool roof, and cool pavements using heat insulation paint or blocks) recommended by the Environmental Protection Agency (EPA) to our study area and determine their actual effects through a comparative analysis between land cover objects. To this end, the area of Mugye-ri, Jangyu-myeon, Gimhae, Gyeongsangnam-do was selected as a study area, and measurements were taken using a drone DJI Matrice 300 RTK, which was equipped with a thermal infrared sensor FLIR Vue Pro R and a visible spectrum sensor H20T 1/2.3" CMOS, 12 MP. A total of nine heat maps, land cover objects (711) as a control group, and heat island reduction technique-applied land covering objects (180) were extracted every 1 hour and 30 minutes from 7:15 am to 7:15 pm on July 27. After calculating the effect values for each of the 180 objects extracted, the effects of each technique were integrated. Through the analysis based on daytime hours, the effect of reducing heat islands was found to be 4.71℃ for cool roof; 3.40℃ for green roof; and 0.43℃ and -0.85℃ for cool pavements using heat insulation paint and blocks, respectively. Comparing the effect by time period, it was found that the heat island reduction effect of the techniques was highest at 13:00, which is near the culmination hour, on the imaging date. Between 13:00 and 14:30, the efficiency of temperature reduction changed, with -8.19℃ for cool roof, -5.56℃ for green roof, and -1.78℃ and -1.57℃ for cool pavements using heat insulation paint and blocks, respectively. This study was a case study that verified the effects of urban heat island reduction techniques through the use of high-resolution images taken with drones. In the future, it is considered that it will be possible to present case studies that directly utilize micro-satellites with high-precision spatial resolution.