• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.5
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• pp.70-83
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• 2023

Recently, the Korea Forest Service has implemented a planning project about wind corridor forests as a response measure to climate change. Based on this, research on wind corridors has been underway. For the creation of wind corridor forests, a preliminary evaluation of the wind corridor function is necessary. However, currently, there is no evaluation index to directly evaluate and spatially distinguish the types of wind corridors, and analysis is being performed based on indirect indicators. Therefore, this study proposed a method to evaluate and classify wind corridors by utilizing heat deficit analysis as an evaluation index for cold air generation. Heat deficit was analyzed using a cold air analysis model called Kaltluftabflussmodell_21 (KLAM_21). According to the results of the simulation analysis, the wind path was functionally classified. The top 5% were classified as cold-air generating Areas (CGA), and the bottom 5% as cold-air vulnerable Areas (CVA). In addition, the cold-air flowing Areas (CFA) were classified by identifying the flow of cold air moving from the cold air generation area. It is expected that the methodology of this study can be utilized as an evaluation method for the effectiveness of wind corridors. It is also anticipated to be used as an evaluation index to be presented in the selection of wind corridor forest sites.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.48-52
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• 2008

지형지물은 각각의 특징적 요인을 내포하고 있다. 이 특징적 요인들은, 공간해상도에 따라 정도의 차이가 있겠지만, 수집된 위성영상에도 반영된다. 이러한 요인들 중에서는 영상분류에 활용될 경우 영상 분류의 정확도를 높혀 주고, 때로는 이것이 거의 물체인식의 수준까지 기여할 수 있는 것들이 있다. 이 연구에서는 텍스춰 및 지형지물의 배열에 있어서 특징적 현상을 보이는 비닐하우스를 대상으로 spatial auto-corelation 개념을 기반으로 자동적으로 이를 인지하는 방법을 개발하였다. 사용된 알고리즘은 디지타이징과 같은 사람의 직접적인 개입이 없이 자동화된 방법으로 비닐하우스의 특정한 패턴이 반복적으로 나타나는 것을 감지할 수 있도록 개발되었다. 패턴의 인식에 더하여 비닐하우스의 기하학적 모양을 고려하는 방법도 도입하였다. 그럼으로써 비닐하우스의 추출에 단순히 화소 단위의 분석이 아닌 보다 객체지향적인 방법으로 비닐하우스를 추출하도록 하였다. 개발된 방법을 제주지역의 IKONOS에 적용시켜 본 결과, 연구대상지역 내의 비닐하우스가 매우 정확하게 적출되었다.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.17-22
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• 2005

Generally, pixel-based classification, utilize the similarity of distances between the pixel values in feature space, is applied to land use mapping using satellite remote sensing data. But this method is Improper to be applied to the very high resolution satellite data (VHRS) due to complexity of the spatial structure and the variety of pixel values. In this paper, we performed the hierarchical classification of VHRS imagery by data fusion, which integrated LiDAR height and intensity information. MLC and ISODATA methods were applied to IKONOS-2 imagery with and without LiDAR data prior to the hierarchical classification, and then results was evaluated. In conclusion, the hierarchical method with LiDAR data was the superior than others in VHRS imagery and both MLC and ISODATA classification with LiDAR data were better than without.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.39-47
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• 2001

본 연구는 지리정보시스템 및 원격탐사기법을 응용하여 북한의 자연환경을 조사하기 위한 기초연구로서 수행되었으며, 과거 약 20년 동안의 평양 및 남포지역의 산림면적의 변화 및 경관구조 변화측면에서의 산림 파편화 양상을 조사하였다. 조사자료로는 Landsat MSS 및 TM 영상의 NDVI값을 이용하였으며, 보다 정확한 피복분류를 위하여 변형된 Cluster-Busting 알고리즘을 활용하여 산림과 비산림지역으로 단순화시켜 분석하였다. 경관구조의 변화를 살피기 위해서 조각밀도, 형태 및 핵심내부지역의 면적 등의 경관지수(Landscape Indices)를 활용하였다. 분석과정을 거쳐서 도출된 결론은 다음과 같다. 첫째, Cluster-busting 방법을 활용한 토지피복 분류결과 87.3%의 총 분류 정확도를 얻었으며, Binary Map을 이용한 변화감지(Change Detection)기법 또한 그 결과가 정확한 것으로 판단되었다. 둘째, '79년에서 '98년에 이르는 기간동안, 평양의 경우 '79년 산림면적의 15%, 그리고 남포지역의 경우 14%가 감소하였다. 셋째, 경관지수를 이용하여 북한 산림의 파편화 변화를 조사한 결과 산림조각의 개수는 늘어나고 조각의 평균면적 및 핵심내부면적은 감소하였으며 조각크기의 다양성 또한 낮아졌다. 산림조각 형태지수 또한 매우 증가하였는데 이러한 결과들은 평양 및 남포지역의 산림조각이 파편화되고 그 형태 또한 불규칙적이며 복잡하게 변화하였음을 보여주고 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.129-138
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• 2017

The aim of this paper is to suggest methodology for local governments when discovering locally customized future promising industries with regard to policies of central government, regional competencies, and industrial promising. Firstly, key industries by region specified in '5-years regional industrial development master plan(2014)' were utilized. Secondly, science and technology competency by region was calculated with analyzing patent data in each key industries. Thirdly, industrial promising was verified by calculating Knowledge Stock and Activity Index based on measuring industry-IPC linkage. Based on the methodology proposed above, case study(case of Busan city) was done. Finally, 7 core industries and 94 candidates of future promising industries were extracted on the basis of 5 digit of KSIC subdivision. The methodology is expected to contribute local governments to establish evidence-based, efficient, and future-oriented local R&D roadmapping.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.210-213
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• 2006

이 연구에서는 다중시기 Landsat 영상을 이용하여 강원도 일대 임상의 변화를 살펴보고 상록수의 영급을 구분하는 알고리즘을 개발하여 적용하였다. 1980년대에서 현재까지 축적된 Landsat-5와 Landsat-7영상 중에서, 대부분 지역에 활잡목 및 활엽수가 낙엽이 지고 눈이 아직 쌓이지 않을 시기인 11월에 촬영된 영상만을 이용하였다. 각 영상에서 양지바른 상록수, 활엽수, 그늘진 지역, 도시 및 바다 등을 클래스로 지정하여 감돌분류를 하였다. 분류 결과에서 양지바른 상록수만 추출하여 5개의 영상을 이진 분류체계로 조합한 후 임상의 시기적 변화 양상을 관찰한 결과, 강원대 연습림의 조림 기록 및 현황도와 상당히 일치함을 확인하였으며, Path 115, Row 34에 해당하는 강원도 일대로 연구지역을 확대하였다. 향후 Kompsat-2를 비롯한 고해상도 11월 영상이 지속적으로 촬영된다면, 이 연구에서 개발된 이진 분류체계 방법을 통하여 산림변화의 모니터링을 보다 용이하고 효율적으로 할 수 있을 것으로 기대된다.

• Korean Journal of Environment and Ecology
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• v.13 no.2
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• pp.143-152
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• 1999

전북 장안산의 신갈나무림을 분류법과 서열법에 의하여 분석하였다. 분류법(식물사회학적 방법)에 의한 신갈나무림의 분석결과 신갈나무-철쭉꽃군락, 신갈나무-노린재나무군락, 신갈나무-조릿대군락의 3개 군락으로 분류되었다. CCA에 의하여 분석된 결과에 의하면 신갈나무, 철쭉꽃, 노린재나무는 고도가 높은 지역에서 분포하고, 고로쇠나무, 들메나무, 고광나무, 함박꽃나무, 까치박달은 습하고 유기물 함량 및 전질소, C.E.C등의 양료가 양호한 지역에 소나무, 굴참나무, 붉나무, 병꽃나무는 고도가 낮은 지역에서 분포하고 있다. 신갈나무림의 분포와 환경과의 상호관계를 분석한 결과 고도, 토양습도, 유기물함량, 지형, pH등이 높은 상관관계를 나타냈다.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.171-183
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• 2018

Despite the rapid expansion of satellite images supply, the application of imagery is often restricted due to unautomated image processing. This paper presents the automated process for the selection of training areas which are essential to conducting supervised image classification. The training areas were selected based on the prior and cover information. After the selection, the training data were used to classify land cover in an urban area with the latest image and the classification accuracy was valuated. The automatic selection of training area was processed with following steps, 1) to redraw inner areas of prior land cover polygon with negative buffer (-15m) 2) to select the polygons with proper size of area ($2,000{\sim}200,000m^2$) 3) to calculate the mean and standard deviation of reflectance and NDVI of the polygons 4) to select the polygons having characteristic mean value of each land cover type with minimum standard deviation. The supervised image classification was conducted using the automatically selected training data with Sentinel-2 images in 2017. The accuracy of land cover classification was 86.9% ($\hat{K}=0.81$). The result shows that the process of automatic selection is effective in image processing and able to contribute to solving the bottleneck in the application of imagery.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.121-135
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• 2014

Object-based classification methods provide a valid alternative to traditional pixel-based methods. This study reports the results of an object-based classification to examine land cover in the demilitarized zones(DMZs) of Inje-gun. We used land cover classes(7 classes for main category and 13 classes for sub-category) selected from the criteria by Korea Ministry of Environment. The average and standard deviation of the spectrum values, and homogeneity of GLCM were chosen to map land cover types in an hierarchical approach using the nearest neighborhood method. We then identified the distributional characteristics of land cover by considering 3 topographic characteristics (altitude, slope gradient, distance from the Southern Limited Line(SLL)) within the DMZs. The results showed that scale 72, shape 0.2, color 0.8, compactness 0.5 and smoothness 0.5 were the optimum weight values while scale, shape and color were most influenced parameters in image segmentation. The forests (92%) were main land cover type in the DMZs; the grassland(5%), the urban area (2%) and the forests (broadleaf forest: 44%, mixed forest: 42%, coniferous forest: 6%) also occupied mostly in land cover classes for sub-category. The results also showed that facilities and roads had higher density within 2 km from the SLL, while paddy, field and bare land were distributed largely outside 6 km from the SLL. In addition, there was apparent distinction in land cover by topographic characteristics. The forest had higher density at above altitude 600m and above slope gradient $30^{\circ}$ while agriculture, bare land and grass land were distributed mainly at below altitude 600m and below slope gradient $30^{\circ}$.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.47-55
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• 2018

In this paper, we propose a novel face recognition(FR) method that takes advantage of combining weighted deep local features extracted from multiple Deep Convolutional Neural Networks(DCNNs) learned with a set of facial local regions. In the proposed method, the so-called weighed deep local features are generated from multiple DCNNs each trained with a particular face local region and the corresponding weight represents the importance of local region in terms of improving FR performance. Our weighted deep local features are applied to Joint Bayesian metric learning in conjunction with Nearest Neighbor(NN) Classifier for the purpose of FR. Systematic and comparative experiments show that our proposed method is robust to variations in pose, illumination, and expression. Also, experimental results demonstrate that our method is feasible for improving face recognition performance.