• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.389-401
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• 2011

Most nations around the world have expressed significant concern in the climate change due to a rapid increase in green-house gases and thus reach an international agreement to control total amount of these gases for the mitigation of global warming. As the most important absorber of carbon dioxide, one of major green-house gases, forest resources should be more tightly managed with a means to measure their total amount, forest biomass, efficiently and accurately. Forest biomass has close relations with forest areas and tree height. Airborne LiDAR data helps extract biophysical properties on forest resources such as tree height more efficiently by providing detailed spatial information about the wide-range ground surface. Many researchers have thus developed various methods to estimate tree height using LiDAR data, which retain different performance and characteristics depending on forest environment and data characteristics. In this study, we attempted to investigate such various techniques to estimate tree height, elaborate their advantages and limitations, and suggest future research directions. We first examined the characteristics of LiDAR data applied to forest studies and then analyzed methods on filtering, a precedent procedure for tree height estimation. Regarding the methods for tree height estimation, we classified them into two categories: individual tree-based and regression-based method and described the representative methods under each category with a summary of their analysis results. Finally, we reviewed techniques regarding data fusion between LiDAR and other remote sensing data for future work.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.232-232
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• 2020

하천기본계획 수립이나 생태하천 조성사업 등 다양한 하천사업에서 하천측량은 대상 하천의 지형 현황과 과거 사업이후의 변화량을 확인할 수 있는 중요한 요소이다. 국내 측량 기준인 공공측량작업규정(국토지리정보원)에서 하천 측량은 육지부에서는 횡단측량을 수부에서는 수심측량을 실시하고 수심측량은 음향측심기 사용을 원칙으로 한다. 국내 대부분의 수심측량은 단빔 음향측심기를 사용하고 있는 실정이며 일부 수심 확보 구간 또는 연구목적으로 멀티빔 음향측심기를 적용한 사례가 일부 보고된 바가 있다. 최근 수심측정이 가능한 항공수심측량(Airbone LiDAR Bathymetry) 장비 중 핵심계측기기인 Green LiDAR 센서 국산화 및 경량화에 관한 연구가 진행중이다. 이에 본 연구는 국내 하천 여건에서 개발 센서가 어느 정도의 활용성을 확보할 수 있는지를 검토하였다. 우선 환경부가 운영중인 수질자동측정망 71개 지점의 정기측정성과 중 탁도에 관한 일자료를 확보가 가능한 45개 지점을 대상으로 G-LiDAR 센서의 SD(Secchi Depth)를 기준으로 가용계측일을 산정해 보았다. 분석기간은 '12. 7.부터 '19.12.까지이며 분석기간중 SD 1.5m(1.94 NTU 추정) 기준을 만족하는 기간은 한강 2.07년, 낙동강 0.64년, 금강 2.21년, 영산강 2.71년으로 나타났다. 또한 지점별 가용기간 분석결과 분석기간인 7.33년 동안 탁도 기준이하인 운영 가능 기간은 연중 평균 80여일(2.74개월)로 나타나 제한적이나마 활용이 가능할 것으로 확인되었다. 향후 현장조사를 통해 공공측량 성과와 대상수계의 탁도 실측자료와의 연계분석을 통해 정확한 활용성 검토를 수행할 예정이다. 향후 적용 센서의 개발 성능목표를 달성한다면 하천내의 다양한 분야에서 활용이 가능할 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.661-670
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• 2012

Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.533-536
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• 2007

The LiDAR data structure has the potential for modeling in three dimensions because the LiDAR data can represent voxels with z value under certain defined conditions. Therefore, it is possible to classify the physical damaged degree of vegetation by forest fire as using the LiDAR data because the physical loss of canopy height and width by forest fire can be relative to an amount of points reached to the ground through the canopy of damaged forest. On the other hand, biological damage of vegetation by forest fire can be explained using the NDVI (Normalized Difference Vegetation Index) which show vegetation vitality. In this study, we graded the damaged degree of vegetation by forest fire in Yangyang-Gun of South Korea using the LiDAR data for physical grading and digital aerial photograph including Red, Green, Blue and Near Infra-Red bands for biological grading. The LiDAR data was classified into 2 classes, of which one was Serious Physical Damaged (SPD) and the other was Light Physical Damaged (LPD) area. The NDVI was also classified into 2 classes which are Serious Biological Damaged (SBD) and Light Biological Damaged (LBD) area respectively. With each 2 classes ofthe LiDAR data and NDVI, the damaged area by forest fire was graded into 4 degrees like damaged class 1,2,3 and 4 grade. As a result of this study, 1 graded area was the broadest and next was the 3 grade. With this result, we could know that the burned area by forest fire in Yangyang-Gun was damaged rather biologically because the NDVI in 1 and 3 grade appeared low value whereas the LiDAR data in 1 and 3 grade included light physical damage like the LPD.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.53-63
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• 2012

As a result of the economical loss caused by unusual climate changes resulting from emission of excessive green house gases such as carbon dioxide which is expected to account for 5~20% of the world GDP by 2100, researching technologies regarding the reduction of carbon dioxide emission is being favored worldwide as a part of the high value-added industry. As one of the Annex II countries of Kyoto Protocol of 1997 that should keep the average $CO_2$ emission rate of 5% by 2013, South Korea is also dedicated to the researches and industries of $CO_2$ emission reduction. In this study, Application of LiDAR data & KOMPSAT-2 satellite image for calculated forest Biomass. Raw LiDAR data's tree numbers and tree-high with field survey data resulted in 90% similarity of objects and an average of 0.3m difference in tree-high. Calculating the forest biomass through forest type information categorized as KOMPSAT-2 image and LiDAR data's tree-high data of tree enabled the estimation of $CO_2$ absorption and forest biomass of forest type, The similarity between the field survey average of 90% or higher were analyzed.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.111-112
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• 2022

모든 도시가 발전하고 번창하기 위해서는 핵심기반시설의 재난 및 안전이 선제적으로 확보되어야 한다. 본 논문에서는 환경핵심기반시설을 중심으로 지역사회가 지속 가능한 녹색도시로 거듭나기 위해 재난준비태세 증진에 실제 활용 가능한 위험지도를 드론에 장착한 LiDAR 센서를 통해 수집한 고도 데이터를 바탕으로 제작하였다. 나아가 지진과 같은 재난 발생 시 시설에서부터 확산하는 관리 오염물의 경로 및 범위를 시범 모의하여, 기능 연속성 계획 및 재난대응 가이드와 연계를 하는 방안을 제시함으로 지자체 중심의 통합적 지역사회의 노력이 발현될 수 있도록 기초적 연구를 진행하고, 전략적 활성화 방안을 제시하였다. 본 연구는 끊임없는 성장과 거듭되는 개발로 인해 변화하는 도시의 형상에 따라 리스크를 최신화하여 대응력을 높이고, 이해관계자들에게 시각화된 재난 범위 모의를 제시함으로써 지역사회와 지자체 역량에 따른 협력적 재난대응태세에 필요한 프레임워크 도출 및 계획수립을 가능하게 한다는 점에서 큰 의의를 지닌다. 또한, 각 영역별 전문가들의 자문을 통하여 본 논문에서 제시된 확산 모의의 방법론이 타당함을 확인하였다. 무엇보다 모호한 "가능한 신속한 자원관리"와 같은 추상적인 대응계획이 아닌, 객관적인 재난자원관리계획을 수립할 수 있게 함으로써 추후 국가적 재난 및 안전역량을 계량화시킬 수 있을 것으로 사료된다.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.5
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• pp.1-12
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• 2019

It is important to measure the height of trees as an essential element for assessing the forest health in urban areas. Therefore, an automated method that can measure the height of individual tree as a three-dimensional forest information is needed in an extensive and dense forest. Since airborne LiDAR dataset is easy to analyze the tree height(z-coordinate) of forests, studies on individual tree height measurement could be performed as an assessment forest health. Especially in urban forests, that adversely affected by habitat fragmentation and isolation. So this study was analyzed to measure the height of individual trees for assessing the urban forests health, Furthermore to identify environmental factors that affect forest growth. The survey was conducted in the Mt. Bongseo located in Seobuk-gu. Cheonan-si(Middle Chungcheong Province). We segment the individual trees on coniferous by automatic method using the airborne LiDAR dataset of the two periods (year of 2016 and 2017) and to find out individual tree growth. Segmentation of individual trees was performed by using the watershed algorithm and the local maximum, and the tree growth was determined by the difference of the tree height according to the two periods. After we clarify the relationship between the environmental factors affecting the tree growth. The tree growth of Mt. Bongseo was about 20cm for a year, and it was analyzed to be lower than 23.9cm/year of the growth of the dominant species, Pinus rigida. This may have an adverse effect on the growth of isolated urban forests. It also determined different trees growth according to age, diameter and density class in the stock map, effective soil depth and drainage grade in the soil map. There was a statistically significant positive correlation between the distance to the road and the solar radiation as an environmental factor affecting the tree growth. Since there is less correlation, it is necessary to determine other influencing factors affecting tree growth in urban forests besides anthropogenic influences. This study is the first data for the analysis of segmentation and the growth of the individual tree, and it can be used as a scientific data of the urban forest health assessment and management.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.148-157
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• 2012

Recently, people are getting more interested in green energy resource and environment friendly energy resource due to the lack of energy and global warming. This study produced a solar energy resource map using LiDAR(Light Detection And Ranging) data to check if it is utilized for spatial information technology and solar energy sectors that people pay more attentions to as new recycling energy. This study assigned Ulleungdo(Island) located in Gyeongsangbuk-do as a target area. This study created the contour line with 1 meter by newly photographing LiDAR and data processing. And using this contour line, this study built DEM(Digital Elevation Model) data with 1 meter. The incidence range depending on the altitude and azimuth of sun using DEM data is used to evaluate solar energy resource. This is expected to suggest an accurate method to evaluate more reliable and more precise information of new recycling energy resource by producing solar energy resource map based on accurate and precise spatial resolution data with 1 meter level.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.1-9
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• 2020

ABL (Airborne Bathymetric LiDAR) is an advanced survey technology that uses green lasers to simultaneously measure the water depths and oceanic topography in coastal and river areas. Seabed point cloud extraction is an essential prerequisite to further utilizing the ABL data for various geographic data processing and applications. Conventional seabed detection approaches often use return waveforms. However, their limited accessibility often limits the broad use of the bathymetric LiDAR (Light Detection And Ranging) data. Further, it is often questioned if the waveform-based seabed extraction is reliable enough to extract seabed. Therefore, there is a high demand to extract seabed from the point cloud using other sources of information, such as geometric information. This study aimed to assess the feasibility of a ground filtering method to seabed extraction from geo-referenced point cloud data by using CSF (Cloth Simulation Filtering) method. We conducted a preliminary experiment with the RIGEL VQ 880 bathymetric data, and the results show that the CSF algorithm can be effectively applied to the seabed point segmentation.