• Journal of Korean Society of Rural Planning
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• v.16 no.4
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• pp.53-60
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• 2010

This study aims at suggesting the attributes and limitations of each methods through the evaluation of the verified analysis results, so that it will be possible to select an efficient method that may be applied to assess the green coverage ratio. Green coverage areas of each sites subject to this study were assessed utilizing the following four methods. First, assessment of green coverage area through direct planimetry of satellite images. Second, assessment of green coverage area using land cover map. Third, assessment of green coverage area utilizing the band value in satellite images. Forth, assessment of green coverage area using and land cover map and reference materials. For this study, four urban zones of the City of Seosan in Chungcheongnam-do. As a result, this study show that the best calculation method is the one that combines the merits of first and second methods. This method is expected to be suitable for application in research sites of middle size and above. It is also deemed that it will be possible to apply this method in researches of wide area, such as setting up master plans for parks and green zones established by each local self-government organizations.

• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.354-359
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• 2009

Land cover types of Hustai National Park (HNP) in Mongolia, a hotspot area with rare species, were classified and their temporal changes were evaluated using Landsat MSS TM/ETM data between 1994 and 2000. Maximum likelihood classification analysis showed an overall accuracy of 88.0% and 85.0% for the 1994 and 2000 images, respectively. Kappa coefficients associated with the classification were resulted to 0.85 for 1994 and 0.82 for 2000 image. Land cover types revealed significant temporal changes in the classification maps between 1994 and 2000. The area has increased considerably by $166.5km^2$ for mountain steppe. By contrast, agricultural areas and degraded areas affected by human being activity were decreased by $46.1km^2$ and $194.8km^2$ over the six year span, respectively. These areas were replaced by mountain steppe area. Specifically, forest area was noticeably fragmented, accompanied by the decrease of $\sim400$ ha. The forest area revealed a pattern with systematic gain and loss associated with the specific phenomenon called as forest free-south slope. We discussed the potential environmental conditions responsible for the systematic pattern and addressed other biological impacts by outbreaks of forest pests and ungulates.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.436-443
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• 2009

There have been rapid increases to the demands for modeling diverse and complex spatial phenomena and utilizing spatial data through the computer across all the aspects of society. As a result, the importance and utilization of remote sensing and GIS's(geographic information systems) have also increased. It can produce digital data of enormous accuracy and value by incorporating remote sensing images into GIS analysis technology and make various thematic maps by classifying and analyzing land cover. Once such a map is made for the target area, it can easily do modeling and constant monitoring based on the map, revise the database with ease, and thus efficiently update geo-spatial information. Under the goal of analyzing changes to land cover along the road by combining the remote sensing and GIS technology, this study classified land cover from the images of two periods, detected changes to the six classes over ten years, and obtained statistics about the study area's quantitative area changes in order to provide basic decision making data for urban planning and development. By analyzing land use along the road, one can set up plans for the area along the road and the downtown to supplement each other.

• Korean Journal of Remote Sensing
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• v.12 no.2
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• pp.155-168
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• 1996

Speckle noise has been a primary concern to many applications of synthetic aperture radar (SAR) imagery. In recent years, several satellites with radar imaging systems were launched and the use of SAR data are expected to be increased rapidly The objectives of this study are to provide introductory understanding on radar speckle filtering and to compare the effects of several filtering methods that are relatively unknown to user community. Two study sites were extracted from the RADARSAT SAR data obtained over the suburban areas near Seoul. The study sites include relatively homogeneous cover types, such as reservoir, parking lot, rice pad, and deciduous forest. Five filters (mean filter, median filter, sigma filter, local statistics filter, and autocorrelation filter) were applied to the SAR imagery and their effects were evaluated from the aspects of both image smoothing and edge preservation. In overall, the evaluation results indicate that the local statistics filter and autocorrelation filter, that are based on a speckle model, are more effective to suppress speckle within homogeneous cover type while maintaining the edge sharpness between cover types.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.7-12
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• 2019

Clouds in the atmosphere are important variables that affect the temperature change by reflecting the radiant energy of the earth surface as well as changing the amount of sunshine by reflecting the sun's radiation energy. Especially, the amount of sunshine on the surface is very important It is essential information. Therefore, eye-observations of the sky on the surface of the earth have been enhanced by satellite photographs or relatively narrowed observation equipments. Therefore, cloud automatic observing systems have been developed in order to replace the human observers, but depending on the seasons, the reliability of observations is not high enough to be applied in the field due to pollutants or fog in the atmosphere. Therefore, we have developed a cloud observation algorithm that is robust against smog and fog. It is based on the calculation of the degree of aerosol from the all-sky image, and is added to the developed cloud reader to develop season- and climate-insensitive algorithms to improve reliability. The result compared to existing cloud readers and the result of cloud cover is improved.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1567-1577
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• 2020

Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.52-67
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• 2020

Recently, the Arctic has been exposed to snow-covered land due to melting permafrost every year, and the Korea Geographic Information Institute(NGII) provides polar spatial information service by establishing spatial information of the polar region. However, there is a lack of spatial information on vegetation sensitive to climate change. This research used a multi-temporal Sentinel-2 image to perform land cover classification of the Ny-Ålesund in Arctic Svalbard. In the pre-processing step, 10 bands and 6 vegetation spectral index were generated from multi-temporal Sentinel-2 images. In image-classification step is consisted of extracting the vegetation area through 8-class land cover classification and performing the vegetation species classification. The image classification algorithm used Random Forest to evaluate the accuracy and calculate feature importance through Out-Of-Bag(OOB). To identify the advantages of multi- temporary Sentinel-2 for vegetation classification, the overall accuracy was compared according to the number of images stacked and vegetation spectral index. Overall accuracy was 77% when using single-time Sentinel-2 images, but improved to 81% when using multi-time Sentinel-2 images. In addition, the overall accuracy improved to about 83% in learning when the vegetation index was used additionally. The most important spectral variables to distinguish between vegetation classes are located in the Red, Green, and short wave infrared-1(SWIR1). This research can be used as a basic study that optimizes input characteristics in performing the classification of vegetation in the polar regions.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Journal of the Korean association of regional geographers
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• v.7 no.1
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• pp.35-50
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• 2001

Since remotely sensed images of coastal wetlands are very sensitive to spatial resolution, it is very important to select an optimum resolution for particular geographic phenomena needed to be represented. Scale is one of the most important factors in spatial analysis techniques, which is defined as a spatial and temporal interval for a measurement or observation and is determined by the spatial extent of study area or the measurement unit. In order to acquire the optimum scale for a particular subject (i.e., coastal wetlands), measuring and representing the characteristics of attribute information extracted from the remotely sensed images are required. This study aims to explore and analyze the scale effects of attribute information extracted from remotely sensed coastal wetlands images. Specifically, it is focused on identifying the effects of scale in response to spatial resolution changes and suggesting a methodology for exploring the optimum spatial resolution. The LANDSAT TM image of Sunchon Bay was classified by a supervised classification method, Six land cover types were classified and the Kappa index for this classification was 84.6%. In order to explore the effects of scale in the classification procedure, a set of images that have different spatial resolutions were created by a aggregation method. Coarser images were created with the original image by averaging the DN values of neighboring pixels. Sixteen images whose resolution range from 30 m to 480 m were generated and classified to obtain land cover information using the same training set applied to the initial classification. The values of Kappa index show a distinctive pattern according to the spatial resolution change. Up to 120m, the values of Kappa index changed little, but Kappa index decreased dramatically at the 150m. However, at the resolution of 240 m and 270m, the classification accuracy was increased. From this observation, the optimum resolution for the study area would be either at 240m or 270m with respect to the classification accuracy and the best quality of attribute information can be obtained from these resolutions. Procedures and methodologies developed from this study would be applied to similar kinds and be used as a methodology of identifying and defining an optimum spatial resolution for a given problem.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.144-158
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• 2012

A higher spatial resolution is preferable to support the accuracy of detailed climate analysis in urban areas. Airborne LiDAR (Light Detection And Ranging) and satellite (KOMPSAT-2, Korea Multi-Purpose Satellite-2) images at 1 to 4 m resolution were utilized to produce digital elevation and building surface models as well as land cover maps at very high(5m) resolution. The Climate Analysis Seoul(CAS) was used to calculate the fractional coverage of land cover classes in built-up areas and thermal capacity of the buildings from their areal volumes. It then produced analyzed maps of local-scale temperature based on the old and new input data. For the verification of the accuracy improvement by the precise input data, the analyzed maps were compared to the surface temperature derived from the ASTER satellite image and to the ground observation at our detailed study region. After the enhancement, the ASTER temperature was highly correlated with the analyzed temperature at building (BS) areas (R=0.76) whereas there observed no correlation with the old input data. The difference of the air temperature deviation was reduced from 1.27 to 0.70K by the enhancement. The enhanced precision of the input data yielded reasonable and more accurate local-scale temperature analysis based on realistic surface models in built-up areas. The improved analysis tools can help urban planners evaluating their design scenarios to be prepared for the urban climate.