• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1145-1153
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• 2023

Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.471-475
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• 2004

LiDAR의 표고점 데이터에서 건물, 수목 등과 같이 주위보다 높은 고도 값을 가지는 대상물을 제거하여 DEM을 생성하기 위한 여러 가지 필터링 기법들이 개발되고 있으며 대표적인 필터링 방법으로는 분산을 이용한 linear prediction 기법, 주변 점들과의 경사관계를 이용한 slope-based 기법, morphology 필터, dual rank 필터 등이 있다. 이러한 기법들은 커널(kernel)의 크기를 대상 지역에 맞도록 사용자가 직접 지정해주어야 하고, 건물의 크기가 다양한 지역에 적용하기 위해서는 가변 크기(variable size)의 커널을 필요로 한다. 본 연구에서는 다양한 크기의 건물이 존재하는 지역에 대하여 커널의 크기를 변화시키지 않고 필터링을 수행하는 새로운 커널 연산 기법을 제안하였다. 또한 기존 필터링 기법에서는 커널에 의해 갱신된 연산값이 다음 연산에 반영되지 않으나 본 연구에서는 갱신된 값이 바로 다음 연산에 반영되도록 하였다. 건물과 수목 등을 제거하기 위하여 주변 화소와의 높이 차를 이용하였으며 대상물이 제거된 부분은 주변 화소를 이용하여 보간하였다.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• 한국공간정보시스템학회:학술대회논문집
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• 2004.12a
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• pp.125-131
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• 2004

LiDAR 데이터의 필터링은 원 데이터로부터 건물, 수목 등과 같은 비지면점을 제거하는 과정이며, 이러한 필터링을 통해 DEM을 생성할 수 있다. 대표적인 필터링 방법들로는 분산을 이용한 linear prediction 기법, 주변 점들과의 경사관계를 이용한 slope-based 기법, morphology 필터, local maxima 필터 등이 있으며 이러한 기존의 기법들의 단점을 보완하기 위한 연구가 활발히 진행되고 있다. 대부분의 필터링 기법들은 필터의 크기(윈도우의 크기)와 같은 인자를 대상 지역에 적합하게 사용자가 직접 설정해주어야 한다. 더욱이 복잡한 지형, 지물이 존재하는 지역에 적용하기 위해서는 인자를 변형시켜줘야 하며 특히, 다양한 크기의 건물이 존재하는 지역에 대하여 적용하기 위해서는 가변적인 크기의 필터가 필요하다. 이에 본 논문에서는 다양한 크기의 건물이 존재하는 지역에 대하여 필터의 크기를 변화시키지 않고 필터링을 수행할 수 있는 연산기법을 제안하였다. 본 연구에서는 수목이나 자동차 등과 같은 작은 개체의 제거를 위해 고정된 작은 크기의 윈도우를 가지는 모폴로지 필터를 우선 적용한다. 그 후 건물과 같은 큰 개체의 포인트는 이웃 포인트와의 고도차이를 이용하여 인식하고 이웃에 위치하는 지면 포인트로 대체하며, 갱신된 값이 바로 다음 연산에 반영 되도록 한다. 또한 상, 하, 좌, 우 네 방향에 대하여 라인별로 독립된 연산을 수행한 후에 이들을 비교함으로써 오차를 보정한다.

• Journal of Forest and Environmental Science
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• v.18 no.1
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• pp.45-52
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• 2001

This paper investigated external factors (height. crown width. diameter breath height, clear length) of tree and electric resistance ($k{\Omega}$) in damaged forest by Thecodiplosis japonensis Uchida et Inouye. The height. crown width and diameter breath height of tree external factors have high a coefficient of correlation. but clear length has not a coefficient of correlation. In relationship of electric resistance and external factor. big tree that height. crown width. diameter breath height has lower electric resistance value than that of small tree. (low electric resistance value is high tree vigor, high electric resistance value is low tree vigor)) Dead tree have smaller diameter breath height. crown width. higher clear length than survival tree in damaged forest by Thecodiplosis japonensis Uchida et Inouye. To investigation of relationship external factors according to electric resistance value. electric resistance value was divided three class (< $l4k{\Omega}$, possible of survival. $14{\sim}20k{\Omega}$, > $20k{\Omega}$, possible of dead). In lower class(< $l4k{\Omega}$), external factors have bigger which was height. crown width. diameter breath height and lower which was clear length than them of higher class ($14{\sim}20k{\Omega}$, > $20k{\Omega}$). Linear regression solutions of electric resistance and external factors were Y = -0.572 ${\times}$ Height - 1.163 ${\times}$ crown width - 0.242 ${\times}$ diameter breath height + 0.757 ${\times}$ clear length + 25.765. Regression solutions were significant in 5%.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.05a
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• pp.267-272
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• 2005

최근 항공레이저스캐닝(ALS)은 높은 정확도와 경제성을 이유로 지형정보를 획득하는 탁월한 수단으로 주목받고 있다. ALS에 의해 수집되는 고도자료는 DSM, DEM 제작에 유용하게 이용된다. ALS는 고도자료 이외에 지표면의 물질적 특성을 나타내는 반사강도를 획득한다. 그러나 반사강도는 노이즈로 인해 널리 이용되지 못하고 있으며, 노이즈의 주원인은 반사각으로 알려져 있다. 따라서 본 연구는 센서 위치정보와 ALS 고도자료를 이용하여 반사각을 이용하여 반사강도를 보정하는 방법을 제안하였다 여기에는 ${\theta}$의 각도로 입사한 레이저의 강도는 수직으로 입사한 레이저의 강도보다 $sin{\theta}$만큼 감소한다는 물리학적 원리가 이용되었다 반사각은 지표면과 레이저가 이루는 각으로, 센서와 측정점 사이의 각과 지표면의 경사각의 두 단계로 나누었다. 방법의 적합 여부를 확인하기 위해 적외선 영역에서 분리도가 잘 이루어지는 아스팔트, 휴경지(토양), 콘크리트, 수목의 네 가지 검증영역을 선정하여 보정된 반사강도와 보정 전의 반사강도를 비교하였다. 모든 영역에서 반사강도가 증가하였으며 특히 콘크리트와 수목에서의 증가가 두드러졌다. 보정을 통해 네 영역에서 반사강도의 분리도가 향상됨을 물론 그 크기가 '아스팔트<토양<콘크리트<수목'으로 나타나는 이론적인 경향과 유사함을 확인할 수 있다.

• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.302-306
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• 2016

There are various methods to evaluate tree vigor. Cambial electrical resistance represents tree vigor using the method of electrophysiological diagnosis. This study investigated the vigor of several tree species using Shigometer, and compared the differences among the species. The factors, such as foliation, trunk orientation and bark temperature, which affect electrical resistance were also investigated. The needle penetration into cambium was controlled to keep the depth consistent in order to minimize measurement error. Each of three trees were selected from Zelkova serrata, Ginkgo biloba, Metasequoia glyptostroboides, Pinus koraiensis, and Liriodendron tulipifera. The electrical resistances were measured at 60 and 120 cm height of the stem in 4 directions from March until May 2011. The soil conditions in surrounding areas and tree stress responses were also measured. The results were analyzed for the relationship between electrical resistance and the affecting factors. The electrical resistance showed a relatively higher level before foliation until mid-March. The values started to decline from April and recorded a minimal level on May 11. The changes of soil moisture, soil electric conductivity, and tree stress responses during the measurement period showed a similar trend to that of electrical resistance. The Pinus koraiensis, an evergreen conifer, showed few changes on the electrical resistance values during the measurement period. Zelkova serrata, Ginkgo biloba, and Metasequoia glyptostroboides showed the highest bark temperatures and lowest electrical resistances at their south-facing stem. Shigometer can provide measures simple to assess tree vigor in the fields, and to the management of trees.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Journal of Korean Society of Forest Science
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• v.103 no.2
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• pp.248-257
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• 2014

The objectives of this study were to identify the root plate dimension of wind-uprooted trees and to analyze the relationship among wind direction, aboveground and belowground properties of the trees. The root plates of 77 Japanese larches (Larix kaempferi) and 24 Korean pines (Pinus koraiensis), which were uprooted by a typhoon in 2012, in the Taehwa Experimental Forest of Seoul National University, Korea, were investigated. The results showed the root plate shape could be assumed to be an oval or a circle in above view, and half an ellipse in side view, respectively. Also, the number and surface area of individual roots in root plates were greater in uprooting direction than in non-uprooting direction. The results of correlation analyses between aboveground and belowground properties indicated DBH had more significant correlation with belowground properties than tree height. Finally, simple linear relationships were derived for significantly correlated tree aboveground and belowground properties.

• Journal of the Korean Institute of Landscape Architecture
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• v.16 no.1
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• pp.27-35
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• 1988

Size characteristics of three widely used landscape trees were analized to establish a methodology of size prediction as time Passes. Tree height, tree width, stem diameter(breast or surface), canopy length and tree age were measured directly and indirectly(by using photograph), and the data were analized by using regression analysis through PC-SAS. The results are summarized as follows : 1. Zelkova serrata MAKINO showed relatively slow growth rate and the tree form was changed as aged. Size predictions were available by using the regression equations listed below : Surface diameter = 0.8293 x AGE Tree height = 0.4109(0.8293 x AGE) - 0.0039(0.7273 x AGE)$^2$Tree width = 0.3240(0.8293 x AGE) - 0.0024(0.1293 x AGE)$^2$Canopy length = 0.1337(0.8293 x AGE) - 0.0020(0.7293 x AGE)$^2$2. Pinus strobus L. showed relatively fast growth rate and the tree form did not change much as aged. Size predictions were available by using the regression equations listed below. Breast diameter = 0.756 x AGE Tree height = 0.7695(0.756 x AGE) - 0.0164(0.75\ulcorner x AGE)$^2$Tree width = 0.4331(0.756 x AGE) - 0.0079(0.75\ulcorner x AGE)$^2$Canopy length = 0.1365(0.756 x AGE) - 0.0032(0.75f x AGE)$^2$ 3. In case of Magnolia denudata DESROUX, tree form was determined relatively earlier than the other two species. Si2e predictions were available by using the regression equations listed below : Surface diameter = 0.88 x AGE Tree height = 0.5412(0.88 x AGE) - 0.0110(0.88 x AGE)$^2$ Tree width = 0.3752(0.88 x AGE) - 7.0061(0.88 x AGE)$^2$Canopy length = 0.1110(0.88 x AGE) - 0.0022(0.88 x AGE)$^2$ This study aimed to find a way to predict size change of landscaping plants. This methodology will be applied to a wide range of landscape plants to provide practical data to landscape designers.