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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Remote Sensing
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Journal DOI :
The Korean Society of Remote Sensing
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Volume & Issues
Volume 31, Issue 6 - Dec 2015
Volume 31, Issue 5 - Oct 2015
Volume 31, Issue 4 - Aug 2015
Volume 31, Issue 3 - Jun 2015
Volume 31, Issue 2 - Apr 2015
Volume 31, Issue 1 - Feb 2015
Selecting the target year
Change Detection Using Spectral Unmixing and IEA(Iterative Error Analysis) for Hyperspectral Images
Song, Ahram ; Choi, Jaewan ; Chang, Anjin ; Kim, Yongil ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 361~370
DOI : 10.7780/kjrs.2015.31.5.1
Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.
Development of Cloud Detection Method with Geostationary Ocean Color Imagery for Land Applications
Lee, Hwa-Seon ; Lee, Kyu-Sung ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 371~384
DOI : 10.7780/kjrs.2015.31.5.2
Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.
Distribution Characteristics Analysis of Pine Wilt Disease Using Time Series Hyperspectral Aerial Imagery
Kim, So-Ra ; Kim, Eun-Sook ; Nam, Youngwoo ; Choi, Won Il ; Kim, Cheol-Min ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 385~394
DOI : 10.7780/kjrs.2015.31.5.3
Pine wilt disease has greatly damaged pine forests not only in East Asia including South Korea and China, but also in European region. The damage caused by pine wood nematode (Bursaphelenchus xylophilus) is expressed in bundles within stands and rapidly spreading, however, present field survey methods have limitations to detecting damaged trees at regional level. This study extracted the damaged trees by pine wilt disease using time series hyperspectral aerial photographs, and analyzed their distribution characteristics. Hyperspectral aerial photographs of 1 meter spatial resolution were obtained in June, September, and October. Damaged trees by pine wilt disease were extracted using Normalized Difference Vegetation Index (NDVI) and Vegetation Index green (VIgreen) of the September photograph. Among extracted damaged trees, dead trees with leaves and without leaves were classified, and the spectral reflectance values from the photographs obtained in June, September, and October were compared to extract new outbreaks in September and October. Based on the time series dispersion of extracted damaged trees, nearest neighbor analysis was conducted to analyze distribution characteristics of the damaged trees within the region where hyperspectral aerial photographs were acquired. As a result, 2,262 damaged trees were extracted in the study area, and 604 dead trees (dead trees in last year) with leaves in relation to the damaged time and 300 and 101 newly damaged trees in September and October were classified. The result of nearest neighbor analysis using the data shows that aggregated distribution was the dominant pattern both previous and current year in the study area. Also, 80% of the damaged trees in current year were found within 60 m of dead trees in previous year.
A Seamline Extraction Technique Considering the Characteristic of NDVI for High Resolution Satellite Image Mosaics
Kim, Jiyoung ; Chae, Taebyeong ; Byun, Younggi ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 395~408
DOI : 10.7780/kjrs.2015.31.5.4
High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.
Application of Seasonal AERI Reference Spectrum for the Improvement of Cloud data Filtering Method
Cho, Joon-Sik ; Goo, Tae-Young ; Shin, Jinho ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 409~419
DOI : 10.7780/kjrs.2015.31.5.5
The Atmospheric Emitted Radiance Interferometer (AERI) which is the Fourier Transform InfraRed (FTIR) spectrometer has been operated by the National Institute of Meteorological Research (NIMR) in Anmyeon island, South Korea since June 2010. The ground-based AERI with similar hyper-spectral infrared sensor to satellite could be an alternative way to validate satellite-based remote sensing. In this regard, the NIMR has focused on the improvement of Cloud data Filtering Method (CFM) which employed only one reference spectrum of clear sky in winter season. This study suggests Seasonal-Cloud data Filtering Method (S-CFM) which applied seasonal AERI reference spectra. For the comparison of applied S-CFM and CFM, the methane retrievals (surface volume mixing ratio) from AERI spectra are used. The quality of AERI methane retrieval applied S-CFM was significantly more improved than that of CFM. The positive result of S-CFM is similar pattern with the seasonal variation of methane from ground-based in-situ measurement, even if the summer season's methane is retrieved over-estimation. In addition, the comparison of vertical total column of methane from AERI and GOSAT shows good result except for the summer season.
Comparison of Single-Sensor Stereo Model and Dual-Sensor Stereo Model with High-Resolution Satellite Imagery
Jeong, Jaehoon ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 421~432
DOI : 10.7780/kjrs.2015.31.5.6
There are significant differences in geometric property and stereo model accuracy between single-sensor stereo that uses two images taken by stereo acquisition mechanism within identical sensor and dual-sensor stereo that randomly combines two images taken from two different sensors. This paper compares the two types of stereo pairs thoroughly. For experiment, two single-sensor stereo pairs and four dual-sensor stereo pairs were constituted using SPOT-5 stereo and KOMPSAT-2 stereo covering same area. While the two single-sensor stereos have stable geometry, the dual-sensor stereos produced two stable and two unstable geometries. In particular, the unstable geometry led to a decrease in stereo model accuracy of the dual-sensor stereos. The two types of stereo pairs were also compared under the stable geometry. Overall, single-sensor stereos performed better than dual-sensor stereos for vertical mapping, but dual-sensor stereos was more accurate for horizontal mapping. This paper has revealed the differences of two types of stereos with their geometric properties and positioning accuracies, suggesting important considerations for handling satellite stereo images, particularly for dual-satellite stereo images.
Study on Improving Hyperspectral Target Detection by Target Signal Exclusion in Matched Filtering
Kim, Kwang-Eun ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 433~440
DOI : 10.7780/kjrs.2015.31.5.7
In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.
A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data
Hong, Suk Young ; Na, Sang-Il ; Lee, Kyung-Do ; Kim, Yong-Seok ; Baek, Shin-Chul ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 441~448
DOI : 10.7780/kjrs.2015.31.5.8
Lack of agricultural information for food supply and demand in Democratic People's republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS
in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.
Comparison of Forest Carbon Stocks Estimation Methods Using Forest Type Map and Landsat TM Satellite Imagery
Kim, Kyoung-Min ; Lee, Jung-Bin ; Jung, Jaehoon ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 449~459
DOI : 10.7780/kjrs.2015.31.5.9
The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the
NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.
Investigating Applicability of Unmanned Aerial Vehicle to the Tidal Flat Zone
Kim, Bum-Jun ; Lee, Yoon-Kyung ; Choi, Jong-Kuk ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 461~471
DOI : 10.7780/kjrs.2015.31.5.10
In this study, we generated orthoimages and Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the accuracy of possibility of geospatial information system generation, then compared the DEM with the topographic height values measured from Real Time Kinematic-GPS (RTK-GPS). The DEMs were generated from aerial triangulation method using fixed-wing UAV and rotary-wing UAV, and DEM based on the waterline method also generated. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up the 30 Ground Control Points (GPCs) in order to correct of the UAVs position error. Therefore, the mosaic images and DEM were obtained with geometric error less than 30 cm. The height of generated DEMs by UAVs were compared with the levelled elevation by RTK-GPS. The value of R-square is closely 1. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAVs and these detailed spatial information about tidal flat will be widely used for tidal flat management.
An Analysis of the Relationship between Inherent Optical Properties and Ocean Color Algorithms Around the Korean Waters
Min, Jee-Eun ; Ryu, Joo-Hyung ; Park, Young-Je ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 473~490
DOI : 10.7780/kjrs.2015.31.5.11
There are diverse sea areas within the coverage of GOCI which is observed around the Korea at one-hour intervals. It includes not only very clear ocean of East Sea, but also extremely turbid waters of the Yangtze River estuary. In this study, we analyzed the different optical characteristics of various sea areas using absorption coefficients of phytoplankton, Suspended Particulate Matter(SPM), Dissolved Organic Matter(DOM). Totally 959 sets of bio-optical and marine environmental data were obtained from 2009 to 2014 around the sea area of Korea. The East Sea, South Sea, East China Sea and offshore part of Yellow Sea showed similar pattern having high levels of contribution of phytoplankton and DOM. On the other hands, the coastal part of Mokpo and Gyeonggi Bay showed opposite pattern having high levels of contribution of SPM and DOM. As a result of the algorithm performance for chlorophyll-a(Chl-a) and SPM, Chl-a is mostly overestimated and SPM is mainly tended to be underestimated. Large amount of errors are induced by the SPM rather than the chl-a and DOM. These errors are primarily founded in the coastal waters having relatively high levels of
contribution of more than 60%.
Publishing a Web Based Crop Monitoring System and Performance Test
Lee, Jung-Bin ; Kim, Jeong-Hyun ; Park, Yong-Nam ; Hong, Suk-Young ; Heo, Joon ;
Korean Journal of Remote Sensing, volume 31, issue 5, 2015, Pages 491~499
DOI : 10.7780/kjrs.2015.31.5.12
In developed countries such as USA and Europe, agricultural monitoring system is developed and utilized in various fields in order to predict crop yield, observe weather conditions and anomaly, categorize crop fields, and calculate areas for each crop. These system is Web Map Service(WMS) which utilizes open source and commercial softwares, and various information collected from remote sensing data are provided. This study will utilize tools such as GeoServer, ArcGIS Server, which are widely used to monitor agricultural production, to publish Map Server and Web Application Server. This enables performance test study for future agricultural production monitoring system by making use of response time and data transfer test. When tested in identical condition GeoServer showed a better result in response time and data transfer for performance test.