• Journal of Environmental Policy
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• v.11 no.2
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• pp.3-16
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• 2012

Objectives of the Korean Environmental Conservation Value Assessment Map (ECVAM) is to evaluate environmental value used in comprehensive environmental information in order to encourage eco-friendly land use and management. The first research was conducted in 2001 to establish the evaluation items and the criteria of the ECVAM, and the first nationwide map was established in the period of 2003 to 2005. The maps are updated annually to reflect environmental changes of land. The evaluation items and the criteria have been modified based on feasibility studies to improve the accuracy of the maps. This study re-evaluated the ECVAMs from 2005 to 2010 with criteria used in current environment and analyzed the changes in the area of the maps in 6 years. This is also an investigation on the maps whether they are appropriate as an index for sustainable environmental monitoring. The result shows that the 1st grade level of the ECVAM area with the highest conservation value had been expanding since 2005. These changes were analyzed in terms of updating the 4th Forest Map (2008) produced once every 10 years, reflecting the new legal protected areas such as Baekdudaegan Protected Area(2010), and the environmental/ecological assessment items such as the National Ecological Network (2009). This mean the ECVAM are a monitoring index that integrates individual environmental indexes including the increase of forest age and diameter due to sustainable management of forest areas, and the change of conservation areas. Therefore, ECVAM can be used as a new index integrating national environmental indicators for monitoring changes of national environment and policy. In order to utilize the ECVAM, improving accuracy and reducing renewal cycle time of thematic maps are required.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.484-491
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• 2015

Aerial scene is greatly increased by the introduction and supply of the image due to the growth of digital optical imaging technology and development of the UAV. It has been used as the extraction of ground properties, classification, change detection, image fusion and mapping based on the aerial image. In particular, in the image analysis and utilization of deep learning algorithm it has shown a new paradigm to overcome the limitation of the field of pattern recognition. This paper presents the possibility to apply a more wide range and various fields through the segmentation and classification of aerial scene based on the Deep learning(ConvNet). We build 4-classes image database consists of Road, Building, Yard, Forest total 3000. Each of the classes has a certain pattern, the results with feature vector map come out differently. Our system consists of feature extraction, classification and training. Feature extraction is built up of two layers based on ConvNet. And then, it is classified by using the Multilayer perceptron and Logistic regression, the algorithm as a classification process.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.56-70
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• 2017

In this study, multi-image information for the central Taewha River basin was used to develop and analyze a distribution map of non-point pollution sources. The data were collected using a hyper-sensor (image), aerial photography, and a field spectro-radiometer. An image correction process was performed for each image to develop an ortho-image. In addition, the spectra from the field spectro-radiometer measurements were analyzed for each classification to create land cover and distribution maps of non-point pollutant sources. In the western region of the Taewha River basin, where most of the forest and agricultural land is distributed, the distribution map showed generated loads for BOD($kg/km^2{\times}day$) of 1.0 - 2.3, for TN($kg/km^2{\times}day$) of 0.06 - 9.44, and for TP($kg/km^2{\times}day$) of 0.03 - 0.24, which were low load distributions. In the eastern region where urbanization is in progress, the BOD, TN, and TP were 85.9, 13.69, and 2.76, respectively and these showed relatively high load distributions when the land use was classified by plot.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.141-166
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• 2018

Information technology improves the efficiency of humanities research. In humanities research, information technology can be used to analyze a given topic or document automatically, facilitate connections to other ideas, and increase our understanding of intellectual history. We suggest a method to identify and automatically analyze the relationships between arguments contained in unstructured data collected from humanities writings such as books, papers, and articles. Our method, which is called history mining, reveals influential relationships between arguments and the philosophers who present them. We utilize several classification algorithms, including a deep learning method. To verify the performance of the methodology proposed in this paper, empiricists and rationalism - related philosophers were collected from among the philosophical specimens and collected related writings or articles accessible on the internet. The performance of the classification algorithm was measured by Recall, Precision, F-Score and Elapsed Time. DNN, Random Forest, and Ensemble showed better performance than other algorithms. Using the selected classification algorithm, we classified rationalism or empiricism into the writings of specific philosophers, and generated the history map considering the philosopher's year of activity.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.16-28
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• 2013

Recently the various applications and spectral indices development of airborne hyperspectral imagery(A-HSI) has been increased. Especially the vegetation indices (VIs) were used to verify stress and vigor of vegetation. The VIs needs two or more spectral bands selectively to calculate as NIR(near infrared) and red wavelength. The A-HIS has specific band characteristics as narrow, continues and many. The A-HIS has narrow, continues and many specific band characteristics. That could be make it confuse which of bands could be explained for appropriate vegetation characteristics. If the A-HIS bands is not the same the wavelength with VIs' development band setting, then it need a selection adequate for spectral characteristics of target vegetation. Therefore we set 4 substitute bands for NIR and red wavelength respectively and calculated two VIs combined with substitute bands such as NDVI(normalized difference vegetation index) and MSRI(modified simple ratio index). To consider the variation of each VIs, we adapted the image differencing method of change detection technique. Also, we used spectral derivative to identify appropriate bands for spectral characteristics of digital forest cover type map. The result of adequate bands for two VIs selected red #3 as 680.2nm and NIR #2 as 801.7nm. This wavelength was good for any forest type in low variations.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.155-160
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• 2014

This paper deals with comparison of classification accuracy between three land cover classification results having difference in resolution and they were classified with eight classes including building, road, forest, etc. Airborne hyperspectral image used in this study was acquired at 1000m, 2000m, 3000m elevation and had 24 bands(0.5m spatial resolution), 48 bands(1.0m), 96 bands(1.5m). Assessment of classification accuracy showed that the classification using 48 bands hyperspectral image had outstanding result as compared with other images. For using hyperspectral image, it was verified that 1m spatial resolution image having 48 bands was appropriate to classify land cover and qualitative improvement is expected in thematic map creation using airborne hyperspectral image.

• Korean Journal of Environmental Biology
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• v.36 no.1
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• pp.21-32
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• 2018

This study was carried out to define the characteristics of the identified ground-dwelling invertebrate communities at Nari basin and Tonggumi area in Ulleungdo Island, designated as a nationally important agricultural heritage. The habitat types were divided into the following categories: crop land, forest, and ecotone, and the soil-dwelling invertebrates were collected according to habitat type. The ground-dwelling invertebrates were collected using a pitfall trap, and a self-organizing map (SOM) was applied to the invertebrates dataset to define the characteristics in invertebrates distribution. The SOM clearly classified the relevant information into four clusters, and extracted ecological information from the invertebrates dataset. The cluster II was composed of invertebrate communities which are collected in the Tonggumi area. The Tonggumi area is where mountainous areas were developed for agricultural purposes, which has geographical features commonly observed in Ulleungdo Island. It is noted that the cluster II has different characteristics as compared other clusters. The results of this study are expected to be used for the preservation of agricultural environment and maintenance of biodiversity by providing basic data, on the biotope of Ulleungdo Island designated as a nationally important agricultural heritage and information on the characteristics of the applicable ground-dwelling invertebrate communities.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.3
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• pp.54-67
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• 2013

This study deals with the characteristics of view landscape in modern Daegu city which were analysed employing geographic information system(GIS). The view landscape analysis was performed by using GIS that enables to overlap land use map with the map of range of visibility, and the 3-D simulation. The results are as follows; First of all, the ratio of forest is enormously high in the range of visibility. The distribution of landscape components allows the dwellers to obtain a clear view towards forests from anywhere. The landscape components include west eroded lowlands, east open rolling lands, east eroded lowlands, and high mountain areas: Apsan(Mt.) in the south; Waryoungsan(Mt.) in the west; and Hamjisan(Mt.) and Hakbong(Mt.) in the north. On the tops of those, people are able to secure a clear vision from the viewpoint towards the surrounding mountains because of the rural areas continuing from the viewpoint to the mountains. A continuous view landscape has been formed by these natural environmental factors. Finally, there are multiple view targets with relatively high altitude that are covered with forests in the space between the urban area and the outer mountains that are higher than the view targets, which provides a scenery of mountains overlapped by higher mountains.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.