• 한국지리정보학회지
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• 제11권4호
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• pp.10-21
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• 2008

본 연구는 국립산림과학원에서 개발된 GIS 기반 산림기능평가 프로그램을 활용하여 국유림 산림경영계획을 위한 임 소반단위 산림기능구분도 작성을 목적으로 연구를 수행하였다. 산림기능구분방법은 프로그램을 이용하여 Grid 단위 기능별 잠재력을 평가한 후 소반단위 기능 잠재력의 최대값을 찾아 기능평가도를 작성하고 기능우선순위에 따라 산림기능구분도를 작성하였다. 산림경영계획 수립에 필요한 최종 산림기능구분도는 우선적으로 지속가능한 산림자원관리지침의 법정림을 구분하고 다음으로 지방 산림청 자체기준에 따라 구분하였으며, 나머지 산림은 산림경영계획 담당자가 우선순위 산림기능구분도를 참고하여 구분한 결과들을 중첩하여 작성하였다. 연구결과 산림기능평가 프로그램을 이용한 Grid 단위의 기능별 잠재력평가를 통해 임 소반단위로 산림기능을 평가함으로서 국유림 산림경영계획 수립시 필요한 자료를 제공하여 합리적인 산림경영계획 수립을 가능하게 하였으며, 지속가능한 산림자원관리지침과 지방산림청 자체기준에 따라 기능을 구분할 수 있는 방법을 개발하였기 때문에 국유림 산림경영계획작성에 필요한 산림기능구분도를 보다 손쉽게 작성할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• 한국지리정보학회지
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• 제9권1호
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• pp.13-19
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• 2006

기존의 산림휴양기능구분에서는 모든 산림을 동일한 인자들로 평가함에 따라 각 산림형태에 따른 특성을 반영하지 못하고 있다. 본 연구의 목적은 산림형태에 따라 서로 다른 평가인자를 적용하여 휴양기능을 평가하는데 있다. 산림을 이용자 중심형과 자원 중심형으로 구분하고, 대구광역시와 지리산국립공원을 대상으로 산림휴양기능을 평가하였다. 평가인자별 카테고리 점수를 부여하고 등급화(고, 중, 저)하여 평가한 결과, 각 산림자원의 형태에 따라 도로분기점과 같은 접근성 인자와 유발성 인자를 적용하여 기능구분 하는 방법이 더 효과적임을 알 수 있었다.

• 한국산림과학회지
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• 제104권2호
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• pp.300-308
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• 2015

국내에서는 2007년 경관계획제도가 시작되었다. 산림관리의 장기성과 광범위성을 생각하면 산림경관 관리의 계획적 접근은 필수불가결한 일이나 산림경관계획은 거의 수립되지 못하고 있다. 계획 수립에 필요한 식견의 부족도 하나의 원인으로 판단된다. 산림의 특성이 최대한 발휘될 수 있는 계획 수립을 위해 산림경관요소의 도출과 산림경관계획 작성 시 고려해야 할 사항을 제안하였다. 산림경관요소(산림경관유형)는 4개의 중분류(지형자원, 수자원, 식생자원, 인문자원)와 12개의 소분류(지형자원:능선 파노라마, 기암봉우리와 암릉, 수자원:폭포, 계곡과 계류, 호수와 도경, 식생자원:수관층 풍경, 주능선의 철쭉, 능선사면의 단풍, 초본군락의 꽃밭, 임내풍경, 숲길풍경, 인문자원:시설물)로 구성되었다. 산림경관계획 작성 시 제안사항으로 유형별 계획 단계의 경관권역계획 작성 시 산림의 6개 기능의 검토 필요성과 요소별 계획단계에서는 주능선, 계절감, 산림수변, 암석자원 경관관리계획 수립 필요성을 제안하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1146-1148
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• 2003

This study purposes to present strategic forest planning that can support the identification of forest resources and the maximum uses of forest functions through FMIS combining GIS, RS and LP. Forest information such as compartment structure and forest road was entered into GIS to construct FMIS. Remotely sensed data were used to land cover classification, and then geographical attributes were identified. New grouping unit, so called 'arvest unit' was introduced as the forest management unit. Strategic forest planning based on the 'arvest unit' was proposed that considered together the economic, environmental aspect of forest.

• 한국환경복원기술학회지
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• 제10권1호
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• pp.57-71
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• 2007

While a concept on biotop or the urgency of its classification systems have been under discussion recently, this study aims to examine outdoor biotop classification systems for environment-friendly community. To this end, the feasibility of creating a biotop in the community and application elements were generated and biotops were classified and categorized. Then, elements that can be applied in consideration of traditional Korean techniques were generated and biotop classification systems and specific components in residential areas were reviewed. As for the result of this study, based on a preliminary draft prepared through literature review, considerations for biotop classification systems were taken into account. Then, based on classification criteria for biotop formats, biotop functions and biotop types, a second-tier classification system was developed. Criteria for biotop formats included surfaces, lines and points while criteria for biotop functions were large cores, small bases, corridors, stepping stones and ecological islands. Criteria for habitat types were divided to include natural forest, developed green areas, lacustrine wetland, palustrine wetland, shrubs, grasslands, linear habitats, vacant plots and practical green areas, which were sub-categorized. As for the biotop classification system, macro-classification divided biotops into three types-space, line and point-based on biotop formats. Meso-classification had five groups and micro-classification had 21 groups based on habitat types. Future studies should focus on the ecological features of each biotop categories generated in this study and their creation and management techniques to find many practical methods to create, protect and manage outdoor biotop for environment-friendly community.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권9호
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• pp.455-460
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• 2016

최근 강도, 성폭력과 같은 중범죄들의 수위가 높아짐에 따라 범죄 예측 및 예방에 대한 중요성이 강조되고 있다. 정확한 범죄예측을 위해서는 과거 범죄기록 데이터를 기반으로 정확도 높은 범죄분류모델을 만드는 작업이 필요하며, 신속한 범죄 대응을 위한 시스템 인터페이스가 요구된다. 그러나 기존의 범죄 요소 분석 연구는 데이터 전처리에 대한 난해함으로 인해 정확도 측면에서 한계를 보이며, 범죄 모니터링 시스템은 방대한 양의 범죄 사건기록 분석 결과를 단순 제공함으로써 사용자에게 효과적인 모니터링 기능을 제공하지 못하고 있다. 따라서 본 연구는 실시간 범죄 예측을 위한 랜덤 포레스트 알고리즘 기반의 범죄 유형 분류모델 및 시스템 인터페이스 디자인 요소를 제안한다. 실험을 통해 본 연구는 제안하는 모델이 단순히 범죄기록 데이터만으로 범죄유형을 분류하는 모델 보다 우수함을 입증하였고, 기존의 범죄 모니터링 시스템 분석을 통해 실시간 범죄 모니터링을 위한 시스템 인터페이스를 설계 및 구현하였다.

• 인간식물환경학회지
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• 제24권6호
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• pp.651-662
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• 2021

Background and objective: This study identifies whether children's planning-organizing executive function can be significantly classified and predicted by home environment quality and wealth factors. Methods: For empirical analysis, we used the data collected from the 10th Panel Study on Korean Children in 2017. Using machine learning tools such as support vector machine (SVM) and random forest (RF), we evaluated the accuracy of the model in which home environment factors classify and predict children's planning-organizing executive functions, and extract the relative importance of variables that determine these executive functions by income group. Results: First, SVM analysis shows that home environment quality and wealth factors show high accuracy in classification and prediction in all three groups. Second, RF analysis shows that estate had the highest predictive power in the high-income group, followed by income, asset, learning, reinforcement, and emotional environment. In the middle-income group, emotional environment showed the highest score, followed by estate, asset, reinforcement, and income. In the low-income group, estate showed the highest score, followed by income, asset, learning, reinforcement, and emotional environment. Conclusion: This study confirmed that home environment quality and wealth factors are significant factors in predicting children's planning-organizing executive functions.

• 농업과학연구
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• 제49권2호
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• pp.317-330
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• 2022

Recently, the policy regarding climate change in Korea and overseas has been to promote the utilization of forest biomass to achieve net zero emissions. In addition, with the implementation of the unused forest biomass system in 2018, the size of the Korean market for manufacturing wood pellets and wood chips using unused forest biomass is rapidly expanding. Therefore, it is necessary to estimate the total amount of unused forest biomass that can be used as an energy source and to identify the capacity that can be continuously produced annually. In this study, we estimated the actual forest area that can be produced of logging residue and the potential amount of unused forest biomass resources based on GT (green ton). Using a forest functions classification map (1 : 25,000), 5th digital forest type map (1 : 25,000), and digital elevation model (DEM), the forest area with a slope of 30° or less and mountain ridges of 70% or less was estimated based on production forest and IV age class or more. The total forest area where unused forest biomass can be produced was estimated to be 1,453,047 ha. Based on GT, the total amount of unused forest biomass potential resources in Korea was estimated to be 117,741,436 tons. By forest type, coniferous forests were estimated to be 48,513,580 tons (41.2%), broad-leaved forests 27,419,391 tons (23.3%), and mixed forests 41,808,465 tons (35.5%). Data from this research analysis can be used as basic data to estimate commercial use of unused forest biomass.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.