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Korean Journal of Remote Sensing

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수록범위 : 1권1호(1985)~36권1호(2020) |수록논문 수 : 1,453
대한원격탐사학회지
36권1호(2020년 02월) 수록논문
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KCI등재

1Seasonal Water Change Assessment at Mahanadi River, India using Multi-temporal Data in Google Earth Engine

저자 : Ratiranjan Jena , Biswajeet Pradhan , Hyung-sup Jung , Abhishek Kumar Rai , Hossein Mojaddadi Rizeei

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 1-13 (13 pages)

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Seasonal changes in river water vary seasonally as well as locationally, and the assessment is essential. In this study, we used the recent technique of post-classification by using the Google earth engine (GEE) to map the seasonal changes in Mahanadi river of Odisha. However, some fixed problems results during the rainy season that affects the livelihood system of Cuttack such as flooding, drowning of children and waste material deposit. Therefore, this study conducted 1) to map and analyse the water density changes and 2) to analyse the seasonal variation of river water to resolve and prevent problem shortcomings. Our results showed that nine types of variation can be found in the Mahanadi River each year. The increase and decrease of intensity of surface water analysed, and it varies in between -130 to 70 m3/nf. The highest frequency change is 2900 Hz near Cuttack city. The pi diagram provides the percentage of seasonal variation that can be observed as permanent water (30%), new seasonal (28%), ephemeral (12%), permanent to seasonal (7%) and seasonal (10%). The analysis is helpful and effective to assess the seasonal variation that can provide a platform for the development of Cuttack city that lies in Mahanadi delta.

KCI등재

2Land Use Feature Extraction and Sprawl Development Prediction from Quickbird Satellite Imagery Using Dempster-Shafer and Land Transformation Model

저자 : Maryam Adel Saharkhiz , Biswajeet Pradhan , Hossein Mojaddadi Rizeei , Hyung-sup Jung

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 15-27 (13 pages)

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Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster- Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

KCI등재

3Detection of Wildfire-Damaged Areas Using Kompsat-3 Image: A Case of the 2019 Unbong Mountain Fire in Busan, South Korea

저자 : Soo-jin Lee , Yang-won Lee

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 29-39 (11 pages)

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Forest fire is a critical disaster that causes massive destruction of forest ecosystem and economic loss. Hence, accurate estimation of the burned area is important for evaluation of the degree of damage and for preparing baseline data for recovery. Since most of the area size damaged by wildfires in Korea is less than 1 ha, it is necessary to use satellite or drone images with a resolution of less than 10m for detecting the damage area. This paper aims to detect wildfire-damaged area from a Kompsat-3 image using the indices such as NDVI (normalized difference vegetation index) and FBI (fire burn index) and to examine the classification characteristics according to the methods such as Otsu thresholding and ISODATA (iterative self-organizing data analysis technique). To mitigate the salt-and-pepper phenomenon of the pixel-based classification, a gaussian filter was applied to the images of NDVI and FBI. Otsu thresholding and ISODATA could distinguish the burned forest from normal forest appropriately, and the salt-and-pepper phenomenon at the boundaries of burned forest was reduced by the gaussian filter. The result from ISODATA with gaussian filter using NDVI was closest to the official record of damage area (56.9 ha) published by the Korea Forest Service. Unlike Otsu thresholding for binary classification, since the ISODATA categorizes the images into multiple classes such as (1) severely burned area, (2) moderately burned area, (3) mixture of burned and unburned areas, and (4) unburned area, the characteristics of the boundaries consisting of burned and normal forests can be better expressed. It is expected that our approach can be utilized for the high-resolution images obtained from other satellites and drones.

KCI등재

4An Artificial Intelligence Method for the Prediction of Near- and Off-Shore Fish Catch Using Satellite and Numerical Model Data

저자 : You-jeong Yoon , Subin Cho , Seoyeon Kim , Nari Kim , Soo-jin Lee , Jihye Ahn , Eunjeong Lee , Seongeok Joh , Yang-won Lee

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 41-53 (13 pages)

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The production of near- and off-shore fisheries in South Korea is decreasing due to rapid changes in the fishing environment, particularly including higher sea temperature in recent years. To improve the competitiveness of the fisheries, it is necessary to provide fish catch information that changes spatiotemporally according to the sea state. In this study, artificial intelligence models that predict the CPUE (catch per unit effort) of mackerel, anchovies, and squid (Todarodes pacificus), which are three major fish species in the nearand off-shore areas of South Korea, on a 15-km grid and daily basis were developed. The models were trained and validated using the sea surface temperature, rainfall, relative humidity, pressure, sea surface wind velocity, significant wave height, and salinity as input data, and the fish catch statistics of Suhyup (National Federation of Fisheries Cooperatives) as observed data. The 10-fold blind test results showed that the developed artificial intelligence models exhibited accuracy with a corresponding correlation coefficient of 0.86. It is expected that the fish catch models can be actually operated with high accuracy under various sea conditions if high-quality large-volume data are available.

KCI등재

5Evaluating the Contribution of Spectral Features to Image Classification Using Class Separability

저자 : Chul-soo Ye

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 55-65 (11 pages)

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Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.

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The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

KCI등재

7Evaluation of the Population Distribution Using GIS-Based Geostatistical Analysis in Mosul City

저자 : Sabah Hussein Ali , Faten Azeez Mustafa

발행기관 : 대한원격탐사학회 간행물 : 대한원격탐사학회지 36권 1호 발행 연도 : 2020 페이지 : pp. 83-92 (10 pages)

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The purpose of this work was to apply geographical information system (GIS) for geostatistical analyzing by selecting a semi-variogram model to quantify the spatial correlation of the population distribution with residential neighborhoods in the both sides of Mosul city. Two hundred and sixty-eight sample sites in 240 km2 are adopted. After determining the population distribution with respect to neighborhoods, data were inserted to ArcGIS10.3 software. Afterward, the datasets was subjected to the semi-variogram model using ordinary kriging interpolation. The results obtained from interpolation method showed that among the various models, Spherical model gives best fit of the data by cross-validation. The kriging prediction map obtained by this study, shows a particular spatial dependence of the population distribution with the neighborhoods. The results obtained from interpolation method also indicates an unbalanced population distribution, as there is no balance between the size of the population neighborhoods and their share of the size of the population, where the results showed that the right side is more densely populated because of the small area of residential homes which occupied by more than one family, as well as the right side is concentrated in economic and social activities.

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한국과학기술원(KAIST) 부경대학교 경북대학교 한국방송통신대학교 한국외국어대학교
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  • 1 한국과학기술원(KAIST) (154건)
  • 2 부경대학교 (150건)
  • 3 경북대학교 (65건)
  • 4 한국방송통신대학교 (50건)
  • 5 한국외국어대학교 (49건)
  • 6 강원대학교 (40건)
  • 7 홍익대학교 (32건)
  • 8 충남대학교 (24건)
  • 9 성균관대학교 (23건)
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