• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.107-115
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• 2003

This research investigated the 2001 spring drought on croplands in South Korea using satellite imagery. South Korea has suffered from spring droughts almost every year. Meteorological indices have been used for monitoring droughts, however they don't tell the local severity of drought. Therefore, this research aimed at detecting the local, spatial pattern of drought severity at a cropland level. This research analyzed the agricultural drought using the wetness of remotely sensed pixels that affects the growth of early crops significantly in the spring. This research, specifically, analyzed the spatial distribution and severity of drought using the tasseled cap transformation and topographical factors. The wetness index from the tasseled cap transformation of Landsat 7 ETM/sub ＋/ imagery was very useful for detecting the 2001 spring drought susceptibility in agricultural croplands. Especially, the wetness values smaller than -0.2 were identified as the croplands that were suffering from serious water deficit. Using the water deficit pixels, drought severity was modeled finally.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.114-114
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• 2021

본 연구에서는 Sentinel-1 SAR 센서 기반 이미지자료와 딥러닝기법을 이용하여 고해상도 토양수분을 산정하였다. 입력자료는 지표특성(모래함량, 점토함량, 경사도), 인공위성 기반의 강우와 LANDSAT 기반의 이미지자료(NDVI, LST, 공간분포 토양수분)를 사용하였다. 강우자료의 경우 GPM(Global Precipitation Measurement) 일강우 자료를 사용하였으며, 관측일 기준으로 5일전까지의 강우자료와 5일평균강우를 구분하여 사용하였다. LANDSAT 기반의 토양수분 이미지자료와 지점관측 토양수분을 이용하여 검·보정 이후 딥러닝 모형의 입력자료로 사용하였다. 입력자료는 30m × 30m 해상도로 Resample 하여 딥러닝 모형의 학습을 진행하였으며, 학습에 사용된 모형을 이용하여 Sentinel-1 기반의 고해상도(10m × 10m) 토양수분이미지를 산정하였다. 검증지점은 거창군 거창읍, 계룡시 두마면, 장수군 장수읍 및 무주군 무주읍 토양수분 관측지점을 선정하였다. 거창군 거창읍의 산정결과, LANDSAT 기반의 토양수분 이미지와 DNN 기반의 토양수분 이미지가 매우 유사하게 나타났으며, 모의값(DNN 기반 토양수분)이 실측값(LANDSAT 기반의 토양수분)을 잘 반영한 것(R: 0.875 ; RMSE: 0.013)으로 나타났다. 또한 학습모형을 토지피복이 유사한 지역에 적용하여 토양수분을 산정한 결과 검증지점 계룡시(R: 0.897 ; RMSE: 0.014), 장수군(R: 0.770 ; RMSE: 0.024) 및 무주군(R: 0.909 ; RMSE: 0.012)의 모의값이 실측값과 매우 유사한 것으로 나타났다. 이를 바탕으로 Seninel-1 SAR센서 이미지자료와 딥러닝기법을 연계한 고해상도 토양수분자료가 농업, 수문, 환경 등 다양한 분야에서 활용될 수 있을 것으로 판단된다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.915-922
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• 2008

Impervious surface is an important index for the estimation of urbanization and the assessment of environmental change. In addition, impervious surface influences on short-term rainfall-runoff model during rainy season in hydrology. Recently, the necessity of impervious surface estimation is increased because the effect of impervious surface is increased by rapid urbanization. In this study, impervious surface estimation is performed by using remote sensing image such as Landsat-7 ETM+image with $30m{\times}30m$ spatial resolution and satellite image with $1m{\times}1m$ spatial resolution based on Jungnangcheon basin. A tasseled cap transformation and NDVI(normalized difference vegetation index) transformation are applied to Landsat-7 ETM+ image to collect various predict variables. Moreover, the training data sets are collected by overlaying between Landsat-7 ETM+ image and satellite image, and CART(classification and regression tree) is applied to the training data sets. As a result, impervious surface prediction model is consisted and the impervious surface map is generated for Jungnangcheon basin.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.1
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• pp.5-15
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• 2001

For estimating paddy field area with Landsat TM images, two dates, May 31, 1991 (transplanting stage) and August 19, 1991 (heading stage) were selected by the data analysis of digital numbers considering rice cropping calendar. Four different estimating methods (1) rule-based classification method, (2) supervised classification(maximum likelihood), (3) unsupervised classification (ISODATA, No. of class:15), (4) unsupervised classification (ISODATA, No. of class:20) were examined. Paddy field area was estimated to 7291.19 ha by non-classification method. In comparison with topographical map (1:25,000), accuracy far paddy field area was 92%. A new image stacked by 10 layers, Landsat TM band 3,4,5, RVI, and wetness in May 31,1991 and August 19,1991 was made to estimate paddy field area by both supervised and unsupervised classification method. Paddy field was classified to 9100.98 ha by supervised classification. Error matrix showed 97.2% overall accuracy far training samples. Accuracy compared with topographical map was 95%. Unsupervised classifications by ISODATA using principal axis. Paddy field area by two different classification number of criteria were 6663.60 ha and 5704.56 ha and accuracy compared with topographical map was 87% and 82%. Irrespective of the estimating methods, paddy fields were discriminated very well by using two-date Landsat TM images in May 31,1991 (transplanting stage) and August 19,1991 (heading stage). Among estimation methods, rule-based classification method was the easiest to analyze and fast to process.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.251-263
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• 2019

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.1-8
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• 2001

Detection of red tides by satellite remote sensing can be done either by detecting enhanced level of chlorophyll pigment or by detecting changes in the spectral composition of pixels. Using chlorophyll concentration, however, is not effective currently due to the facts: 1) Chlorophyll-a is a universal pigment of phytoplankton, and 2) no accurate algorithm for chlorophyll in case 2 water is available yet. Here, red band algorithm, classification and PCA (Principal Component Analysis) techniques were applied for detecting patches of Cochlodinium polykrikoides red tides which occurred in Korean waters in 1995. This dinoflagellate species appears dark red due to the characteristic pigments absorbing lights in the blue and green wavelength most effectively. In the satellite image, the brightness of red tide pixels in all the three visible bands were low making the detection difficult. Red band algorithm is not good for detecting the red tide because of reflectance of suspended sediments. For supervised classification, selecting training area was difficult, while unsupervised classification was not effective in delineating the patches from surrounding pixels. On the other hand, PCA gave a good qualitative discrimination on the distribution compared with actual observation.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.

• Journal of the Korean earth science society
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• v.26 no.2
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• pp.109-113
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• 2005

In this study, the estimation of the temperature distribution of Jeju Island with coastal ocean derived from the thermal band of Landsat 7/ETM+ of January 6, 2003 was carried out. For the computation of the temperature of the island and the coastal ocean based on the thermal band, we used NASA method wiich is the 8 bit Digital Number(DN) converted into spectral radiance. The computed results showed that the land temperature variations were from 0 to 12 Celsius degrees, and a good agreement with the observation ones based on the method. However, the ocean surface temperature was not much changed ground 15 degree since the water was well mixed between the coastal and the offshore ocean. The interesting results were that the temperature distributions of the southern part(Seogwipo City) of Jeju Island were higher than those of the north one(Jeju City) by more than 2 Celsius degree at the same height although the distance between the Jeju and the Seogwipo is only about 35km in winter season. The reason was found that the solar irradiance intensity of the south part was stronger than the north one by Halla mountain in winter season only. From the results, we found that the seasonal variations of solar irradiation and the height of Mt. Halla were an important role of temperature distribution of Jeju Island.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.1-4
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• 2001

The classification performance of Artificial Neural Network (ANN) and RBF-NN was compared for Landsat TM image. The RBF-NN was validated for three unique landuse types (e.g. Mixed landuse area, Cultivated area, Urban area), different input band combinations and classification class. The bootstrap resampling technique was employed to estimate the confidence intervals and distribution for unit load, The pollutant generation was varied significantly according to the classification accuracy and percentile unit load applied. Especially in urban area, where mixed landuse is dominant, the difference of estimated pollutant load is largely varied.

• Proceedings of the KSRS Conference
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• v.2
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• pp.539-542
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• 2006

To investigate the streamflow impact of land cover changes by a typhoon, WMS HEC-1 storm runoff model was applied by using land cover information before and after the typhoon. The model was calibrated with three storm events of 1985 to 1988 based on 1985 land cover condition for a 192.7 $km^2$ watershed in northeast coast of South Korea. After the model was tested, it was run to estimate impacts of land cover change by the typhoon RUSA occurred in 2002 (31 August - 1 September) with 897.5 mm rainfall. The land covers before and after the typhoon were prepared using Landsat 7 ETM+ of September 11 of 2000 and Landsat 5 TM of September 29 of 2002 respectively. For the 6.9 $km^2$ damaged area (3.6 % of the watershed), the peak runoff and total runoff by the changed land cover condition increased 12.5 % and 12.7 % for 50 years rainfall frequency and 1.4 % and 1.8 % for 500 years rainfall frequency respectively based on AMC (Antecedent Moisture Condition)-I condition.