• 제목/요약/키워드: Forest Information Map

검색결과 366건 처리시간 0.038초

Flood Monitoring Using River Flow Forecasting Model with Special Reference to Luangwa River

  • Ngoma, Solomon
    • 한국농림기상학회:학술대회논문집
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    • 한국농림기상학회 2001년도 춘계 학술발표논문집
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    • pp.38-38
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    • 2001
  • The rainfall estimates give sufficiently accurate information to map areas which have received the minimum rainfall necessary for outbreaks of pests such as locusts, thus cutting down the cost of searching for likely outbreak sites. At the other end of the scale, satellite rainfall estimates can be used to give timely warnings of changes in river levels and the likelihood of floods in large river catchments.(omitted)

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산림 사업에 의한 산림 식생 및 토양 탄소 변화 (The Carbon Stock Change of Vegetation and Soil in the Forest Due to Forestry Projects)

  • 정헌모;장인영;한상학;조소연;최철현;이연지;강성룡
    • 생태와환경
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    • 제56권4호
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    • pp.330-338
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    • 2023
  • 산림 사업이 산림의 탄소저장량에 미치는 영향을 알아보기 위하여 임상도, 산림사업 정보 및 토양 정보 등을 활용하여 산림 사업 전후의 지상부 및 토양의 탄소저장량을 산정하고 그 변화를 분석하고자 하였다. 먼저 임상도 정보에 기초하여 면적이 넓고 영급이 감소하는 도엽 6곳을 선정하였다. 그리고 임상도와 생장계수, 산정 지역의 토양유기물함량, 토양용적밀도 등 데이터를 수집하여 산림 탄소저장량을 산정하였다. 그 결과 모든 곳에서 산림 탄소저장량은 산림사업 후 약 34.1~70.0%가 감소하였다. 그리고 기존 연구와 비교했을 때 국내 산림 토양은 지상부에 비해 더 적은 탄소를 저장하고 있어 우리나라의 산림 토양은 더 많은 탄소를 저장할 수 있는 잠재성이 큰 것으로 판단되며 탄소저장량 증대를 위한 전략이 필요할 것으로 판단되었다. 산림사업이 없을 때 있을 때보다 탄소저장량은 약 1.5배 많은 것으로 추정되었다. 그리고 본 연구에서 산림사업에 따라 간벌 전 산림 탄소저장량으로 회복되기까지 약 27년이 걸리는 것으로 추정되었다. 산림은 물리적 훼손에 의해 탄소저장량이 감소하면 원래의 탄소저장량으로 회복되기까지 오랜 시간이 걸리므로 특히 자연성이 높은 산림은 최대한 보전하는 계획을 수립하여 산림의 탄소저장 기능을 유지할 수 있도록 하여야 할 것이다.

WAVELET-BASED FOREST AREAS CLASSIFICATION BY USING HIGH RESOLUTION IMAGERY

  • Yoon Bo-Yeol;Kim Choen
    • 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.

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Forest Vertical Structure Mapping from Bi-Seasonal Sentinel-2 Images and UAV-Derived DSM Using Random Forest, Support Vector Machine, and XGBoost

  • Young-Woong Yoon;Hyung-Sup Jung
    • 대한원격탐사학회지
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    • 제40권2호
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    • pp.123-139
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    • 2024
  • Forest vertical structure is vital for comprehending ecosystems and biodiversity, in addition to fundamental forest information. Currently, the forest vertical structure is predominantly assessed via an in-situ method, which is not only difficult to apply to inaccessible locations or large areas but also costly and requires substantial human resources. Therefore, mapping systems based on remote sensing data have been actively explored. Recently, research on analyzing and classifying images using machine learning techniques has been actively conducted and applied to map the vertical structure of forests accurately. In this study, Sentinel-2 and digital surface model images were obtained on two different dates separated by approximately one month, and the spectral index and tree height maps were generated separately. Furthermore, according to the acquisition time, the input data were separated into cases 1 and 2, which were then combined to generate case 3. Using these data, forest vetical structure mapping models based on random forest, support vector machine, and extreme gradient boost(XGBoost)were generated. Consequently, nine models were generated, with the XGBoost model in Case 3 performing the best, with an average precision of 0.99 and an F1 score of 0.91. We confirmed that generating a forest vertical structure mapping model utilizing bi-seasonal data and an appropriate model can result in an accuracy of 90% or higher.

용도지역. 지구 자료간 불부합 해결을 위한 데이터모델링에 관한 연구 (A data modelling for the inconsistency resolving on zoning data)

  • 최병남;김대종;이권한
    • Spatial Information Research
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    • 제8권1호
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    • pp.1-14
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    • 2000
  • 국토이용계획도, 도시계획도, 농업진흥지역도, 산지이용구분도 등 용도지역·지구 자료는 법적·공간적 특성으로 자료간 연관성을 가지고 있다. 그러나 부처별·부서별 제도운용에 따른 자료공유의 어려움, 수작업체계에 의한 용도지역·지구 경계선의 저확성 한P 등으로 자료간의 관계가 일관성이 결여되어 토지 이용에 많은 문제가 발생하고 있다. 이 연구에서는 GIS의 중첩기법을 이용하여 용도지역·지구 자료간 불부합 실태와 원인을 분석하고, 이러한 문제를 해결할 수 있는 방안으로서 자료간 연관성을 고려한 데이터모델링 방안을 제시하였다.

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국토환경성평가지도 평가항목 구성의 적정성 검토 (Review of Compositional Evaluation Items for Environmental Conservation Value Assessment Map(ECVAM) of National Land in Korea)

  • 전성우;이명진;송원경;성현찬;박욱
    • 한국환경복원기술학회지
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    • 제11권1호
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    • pp.1-13
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    • 2008
  • This study review of Compositional Evaluation Items for Environmental Conservation Value Assessment Map (ECVAM) in Korea. The ECVAM is composed of legal assessment and environmental/ecological assessment items. ECVAM basically adapts an overlay method for environmental/ecological assessment items. The objective of this study is to suggest supplementary items for the ECVAM with the following process : Overlapping rates of the assessment items in the ECVAM are calculated to understand the grade distribution of the environmental conservation value assessment and to analyze the overlapping rates among the assessment items, as a result it is found that various items are overlapped each other. In order to reflect effectively each assessment item to the ECVAM, Analyzed the overlapping degree among assessment items to be applied to this map. On the concrete we gripped results to be assessed by various items, which were overlapped each other. In order to reflect effectively each assessment item to the environmental conservation value assessment map of national land, we analyzed the overlapping degree on environmental/ecological items, and investigated the grade distribution by field survey. In this study we assessed the ECVAM by 5 kinds of method. Method 1 is Grade 1 areas of each administrative district, Method 2 is Comparing overlapping areas of each assessment items Grade 1, 2 and Permission of each assessment items' duplication, Method 3 is Grade 1, 2 areas by only singular assessment items, Method 4 is Only Grade 1 areas of Method 2 and Method 5 is Only Grade 2 areas of Method 2. As results, Method 1 showed Seoul and other metropolitan cities reveal a high proportion of Grade I regions by the legal assessment items. Kangwon-Do, show a high proportion of Grade I regions by the environmental/ecological assessment item. Method 2 showed 93.4% of diameter Grade II(standard for stability), forest diameter item was accounted for 99.9% by Method 3, Method 4 showed 95.7% of forest diameter and forest density was accounted for 66.4% by Method 5. From now on, this study will contribute to reduce the complexity in the process of manufacturing ECVAM of National Land, and to raise the pliability in the process of managing and updating this map.

Object Classification based on Weakly Supervised E2LSH and Saliency map Weighting

  • Zhao, Yongwei;Li, Bicheng;Liu, Xin;Ke, Shengcai
    • 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.

수치임상도 제작을 위한 영상탑재 현장조사 시스템 개발 (Development of the Field Investigation System (FIS) loading Image Data for Digital Forest Type Mapping)

  • 유병오;권수덕;김성호
    • 한국산림과학회지
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    • 제97권4호
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    • pp.445-451
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    • 2008
  • 본 연구는 Tablet PC를 기반으로 정밀수치임상도 제작을 위한 현장조사용 맞춤형 시스템을 개발하는데 그 목적이 있다. 본 연구에서 개발한 FIS의 주요 개발내용 및 특징은 다음과 같다. FIS는 GPS에서 수신한 정보를 바탕으로 다양한 공간 Data에서 위치정보를 쉽게 파악할 수 있어 정보의 접근성이 용이한 장점이 있다. 또한 임상편집 뿐만 아니라 메모 및 야장도구를 통해 여러 가지 정보들을 현장에서 손쉽게 기록하고 관리할 수 있으며, 간단한 측정도구로 거리와 면적을 쉽게 산출할 수 있다. 본 연구에서 개발한 시스템을 활용하여 효율적인 현장조사가 가능하므로 현장에서 시간 및 비용을 절감할 수 있으며, 현장작업의 생산력이 향상될 것이다.

토지부문 온실가스 통계 산정을 위한 토지이용변화 평가방법 비교 (Comparison of Land-use Change Assessment Methods for Greenhouse Gas Inventory in Land Sector)

  • 박진우;나현섭;임종수
    • 한국기후변화학회지
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    • 제8권4호
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    • pp.329-337
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    • 2017
  • In this study, land-use changes from 1990 to 2010 in Jeju Island by different approaches were produced and compared to suggest a more efficient approach. In a sample-based method, land-use changes were analyzed with different sampling intensities of 2 km and 4 km grids, which were distributed by the fifth National Forest Inventory (NFI5), and their uncertainty was assessed. When comparing the uncertainty for different sampling intensities, the one with the grid of 2 km provided more precise information; ranged from 6.6 to 31.3% of the relative standard error for remaining land-use categories for 20 years. On the other hand, land-cover maps by a wall-to-wall approach were produced by using time-series Landsat imageries. Forest land increased from 34,194 ha to 44,154 ha for 20 years, where about 69% of total forest land were remained as forest land and 19% and 8% within forest lands were converted to grassland and cropland, respectively. In the case of grassland, only about 40% of which were remained as grassland and most of the area were converted to forest land and cropland. When comparing land-cover area by land-use categories with land-use statistics, forest areas were underestimated while areas of cropland and grassland were overestimated. In order to analyze land use change, it is necessary to establish a clear and consistent definition on the six land use classification.