• KCID journal
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• v.21 no.1
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• pp.45-54
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• 2014

In this study, Agricultural Infrastructures of Shincheon-gun in North Korea are investigated using Kompsat-2 and RapidEye satellite imagery. Target agricultural infrastructures are agricultural landuse, irrigation and drainage canals, dammed pools for irrigation and pumping stations. KOMPSAT-2 satellite imagery are use to investigate agricultural hydraulic structures and agricultural landuse are investigated by RapidEye Imagery. Geometric correction are performed using 28 GCP and QUAC method are used for atmospherical correction in all imagery. ISODATA clustering and naked-eye classification method are used for extracting agricultural hydraulic structures and Object-based analysis is applied to classifying the agricultural landuse. Extraction results of agricultural hydraulic structures and agricultural are presented and we suggest the applicability of satellite imagery to investigate agricultural infrastructures in North Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.71-79
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• 2014

Most researches have created the impervious surface map by using low-spatial-resolution satellite imagery and are inefficient to generate the object-based impervious map with a broad area. In this study, segment-based impervious surface mapping algorithm is proposed using the RapidEye satellite imagery in order to map impervious area. At first, additional bands are generated by using TOA reflectance conversion RapidEye data. And then, shadow and water class are extracted using training data of converted reflectance image. Object-based impervious surface can be generated by spectral mixture analysis based on land cover map of Ministry of Environment with medium scale, in the case of other classes except shadow and water classes. The experiment shows that result by our method represents high classification accuracy compared to reference data, quantitatively.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.149-155
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• 2011

A fast-changing agriculture environment induced by global warming and abnormal climate conditions demands scientific systems for monitoring and predicting crop conditions as well as crop yields at national level. Remote sensing opens up a new application field for precision agriculture with the help of commercial use of high resolution optical as well as radar satellite data. In this study, we investigated the multi-temporal spectral characteristics relative to different agricultural land use types in Korea using RapidEye satellite imagery. There were explicit differences between vegetation and non-vegetation land use types. Also, within the vegetation group spectral vegetation indices represented differences in temporal changing trends as to plant species and paddy types.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.4
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• pp.245-252
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• 2013

Leaf area index (LAI) is important in explaining the ability of crops to intercept solar energy for biomass production, amount of plant transpiration, and in understanding the impact of crop management practices on crop growth. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of RapidEye imagery obtained from 2010 to 2012 using empirical models in a rice plain in Seosan, Chungcheongnam-do. Rice plants were sampled every two weeks to investigate LAI, fresh and dry biomass from late May to early October. RapidEye images were taken from June to September every year and corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). Linear, exponential, and expolinear models were developed to relate temporal satellite NDVIs to measured LAI. The expolinear model provided more accurate results to predict LAI than linear or exponential models based on root mean square error. The LAI distribution was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when RapidEye imagery was applied to expolinear model. The spatial trend of LAI corresponded with the variation in the vegetation growth condition.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.407-411
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• 2012

Space-borne remote sensing is an effective and inexpensive way to identify crop fields and detect the crop condition. We examined the multi-temporal spectral characteristics of rice fields in South Korea to detect their phenological development and condition. These rice fields are compact, small-scale parcels of land. For the analysis, moderate resolution imaging spectroradiometer (MODIS) and RapidEye images acquired in 2011 were used. The annual spectral tendencies of different crop types could be detected using MODIS data because of its high temporal resolution, despite its relatively low spatial resolution. A comparison between MODIS and RapidEye showed that the spectral characteristics changed with the spatial resolution. The vegetation index (VI) derived from MODIS revealed more moderate values among different land-cover types than the index derived from RapidEye. Additionally, an analysis of various VIs using RapidEye satellite data showed that the VI adopting the red edge band reflected crop conditions better than the traditionally used normalized difference VI.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.485-496
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• 2022

This study is for the software implementation to generate atmospheric and surface reflectance products from RapidEye satellite imagery. The software is an extension based on Orfeo Toolbox (OTB) and an open-source remote sensing software including calibration modules which use an absolute atmospheric correction algorithm. In order to verify the performance of the program, the accuracy of the product was validated by a test image on the Radiometric Calibration Network (RadCalNet) site. In addition, the accuracy of the surface reflectance product generated from the KOMPSAT-3A image, the surface reflectance of Landsat Analysis Ready Data (ARD) of the same site, and near acquisition date were compared with RapidEye-based one. At the same time, a comparative study was carried out with the processing results using QUick Atmospheric Correction (QUAC) and Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) tool supported by a commercial tool for the same image. Similar to the KOMPSAT-3A-based surface reflectance product, the results obtained from RapidEye Extension showed accuracy of agreement level within 5%, compared with RadCalNet data. They also showed better accuracy in all band images than the results using QUAC or FLAASH tool. As the importance of the Red-Edge band in agriculture, forests, and the environment applications is being emphasized, it is expected that the utilization of the surface reflectance products of RapidEye images produced using this program will also increase.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.581-588
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• 2018

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.505-513
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• 2014

The RapidEye can acquire the 6.5m spatial resolution satellite imagery with the high temporal resolution on each day, based on its constellation of five satellites. The image products are available in two processing levels of Basic 1B and Ortho 3A. The Basic 1B image have radiometric and sensor corrections and include RPCs (Rational Polynomial Coefficients) data. In Korea, the geometric accuracy of RapidEye imagery can be improved, based on the scaled national digital maps that had been built. In this paper, we present the fully automated procedures to georegister the 1B data using 1:25,000 digital maps. Those layers of map are selected if the layers appear well in the RapidEye image, and then the selected layers are RPCs-projected into the RapidEye 1B space for generating vector images. The automated edge-based matching between the vector image and RapidEye improves the accuracy of RPCs. The experimental results showed the accuracy improvement from 2.8 to 0.8 pixels in RMSE when compared to the maps.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.