상주시 남산 활엽수와 침엽수의 과거 분포 변화 분석 및 미래 분포 예측

학위논문 (석사)-- 서울대학교 대학원 : 생태조경·지역시스템공학부, 2016. 2. 이동근.Researches about analyzing and predicting distribution of forest are continuously proceeded in aspect of a forest management. To analyze the past distribution of forest, researchers record data about forest distribution and characteristics like forest inventory. However, these data couldnt provide long term data. Therefore, remote sensing was used to construct data. Landsat image which is remotely sensed data was used because Landsat image is appropriate to analyze the forest. Conifer and broad-leaved tree in Namsan, Sangju-si was analyzed for thirty years. As a result, distribution of broad-leaved tree was continuously increasing, on the other hand, conifer was decreased. Forest distribution was highly affected by human activity which is heating in the past. Therefore, broad-leaved tree which is more appropriate for fire wood was distributed in small area. Predicting future distribution was proceeded based on modelling method. Researches that based on niche model predict suitable habitat, and researches based on process-based models and demographic models predict real distribution. However, these methods have limitations in forest management because niche model cannot predict real distribution and process-based model and demographic model predict in large scale because of the scale of input data. This research aims to overcome this limitation by predicting future distribution of conifer and broad-leaved tree. Because conifer and broad-leaved tree are typical species which is in competition and conifer is weak competitor. In this research, present distribution of conifer and broad-leaved tree and replacement probability of conifer by broad-leaved tree was used to predict future distribution. Probability of conifer by broad-leaved tree was modelled based on logistic regression model using forest distributions from past to present. Remote sensing was used to construct data because forest distribution is changing slowly and satellite images provide long periodic data. Furthermore, Landsat images were selected because of fine spatial scale and long temporal extent. Past distribution map was constructed by using classification method. Comparing past distribution of conifer and broad-leaved tree maps for the periods 1984-1995, 1995-2005, and 2005-2014, classes that represented either a conifer to broad-leaved tree or conifer to conifer change were generated. For logistic regression, distribution changed maps were used for dependent variable and distance from broad-leaved forest edge, elevation, slope, topographic wetness index (TWI), annual solar radiation were used for independent variable. Compare the result of distribution changed map with previous researches, distance variable which used in this research seems suitable factor to predict distribution change. Most replacement of conifer by broad-leaved tree was occurred near broad-leaved forest edges and decreases sharply similar to seed dispersal and seed density pattern of other researches. However, distance was calculated based on 30m spatial resolution, therefore, the distance from broad-leaved forest edge has uncertainties. To overcome this uncertainty, Monte Carlo simulation was used. According to simulation result range of distance value was considered. As a result of logistic regression, annual solar radiation and distance from broad-leaved forest edge were turn out to be powerful factor to predict replacement probability of conifer by broad-leaved tree. In other words, replacement probability of conifer by broad-leaved tree was increased where close to broad-leaved forest edge and annual solar radiation is low. It reflects the seed dispersal and density of seed that density of seed is higher near the broad-leaved forest edge. In addition, it reflects the characteristic of conifer and broad-leaved tree that shade tolerance of conifer is weaker than broad-leaved tree. Future distribution of conifer and broad-leaved tree was predicted by using the result of logistic regression model. Distribution of conifer will decrease slowly than before. broad-leaved tree population curve seems similar to sigmoid curve which known as population growth model. It is considered that forest area comes to limited resource, therefore, competition was occurred between conifer and broad-leaved tree. As a result, distance was turn out to be an important variable to predict future distribution. Using the distance from broad-leaved forest edge, it is possible to predict future distribution of conifer and broad-leaved tree. Furthermore, to overcome the uncertainty due to spatial resolution, it is possible to use Monte Carlo simulation.1. Introduction 1 2. Literature Rewiews 4 2.1 Remote sensing 4 2.2 Forest distribution prediction 7 2.3 Seed dispersal 10 3. Research Methodology 13 3.1 Scope of the study 13 3.2 Materials and method 15 3.2.1 Study flow 15 3.2.2 Study material 16 3.2.3 Method 18 4. Result and Discussion 27 4.1 Remote sensing 27 4.1.1 Pre-processing 27 4.1.2 Image classification 27 4.2 Logistic regression 33 4.2.1 Constructing variable 33 4.2.2 Logistic regression model 37 4.2.3 Considering uncertainty 41 4.2.4 Distribution prediction 43 4.2.5 Distribution prediction considering uncertainty in distance 46 5. Conclusions 49 6. References 52 7. Appendix 59 국문초록 62Maste

지상 라이다를 이용한 도시 가로 천공률과 녹시율 평가

학위논문(박사)--서울대학교 대학원 :환경대학원 협동과정 조경학,2020. 2. 이동근.Urban streets play an important role in improving biodiversity, storing carbon, mitigating the urban heat island effect, and on the physical and mental health of urban residents. Studies on quantifying the ecological structure of urban streets are an important research topic as they are important for urban planning. With the development of Light Detection and Ranging (LiDAR) systems, three-dimensional data acquisition is possible and an accurate ecological structure can be constructed for an urban street. Trees are one of the most essential ecological component of urban streets and the first step is to quantifying the ecological structure of urban streets. This research used ground based LiDAR to quantify three parameters of urban trees: their height, crown base height (CBH), and diameter at breast height (DBH). The tree height and CBH, estimated by LiDAR with high accuracy, showed a root mean square error of 0.359 m and 0.09 m, respectively, whereas the DBH was estimated with medium accuracy, showing a root mean square error of 0.0377 meters. Sky view factor (SVF) is a key indicator to evaluate the formation of urban buildings and trees, and also used as solar energy availability index in urban heat islands and renewable energy research. A method to compute SVF in complex urban areas using LiDAR has been developed recently; however, its accuracy in areas with trees and buildings is low because of limitations in resolution of aerial LiDAR. Hence, this study tried to improve the accuracy of the SVF by using the terrestrial LiDAR and proved that using the terrestrial LiDAR provided greater accuracy than the aerial LiDAR. The results of terrestrial LiDAR-based SVF were high, with an R^2 of 0.915, RMSE of 0.037, and a maximum error of 0.156. This is more accurate than the results obtained from aerial LiDAR. This work studied whether a higher accuracy is obtainable by increasing the spatial resolution of the data. With terrestrial LiDAR, a voxel size of 2.5 is sufficient to estimate SVF in a complex urban area, reducing maximum error by 60% in comparison with aerial LiDAR, thus providing an accurate estimate. This work explored the possibility of constructing data at a larger scale. For strategic urban planning urban-scale data that can be analyzed faster is ideal, even at lower resolutions, but for higher efficiency. Voxelized 3D point cloud data was applied to construct a virtual environment and help researchers take advantage of using objects actually scanned by LiDAR. The result shows that lanes where the experiment was conducted significantly affected the SVF and GVI values. Therefore, through multiple simulations and computations, ideal representative points were identified which can provide the most accurate average value of the research area. Hence, when constructing urban-scale data, researchers should select an appropriate lane that best represents the average SVF and GVI of the area and thereby reduce potential error.도시 가로 환경은 생물 다양성을 증진, 탄소를 저장, 도시열섬 효과를 완화, 도시 주민의 신체적, 정신적 건강 증진 등 다양한 기능을 수행한다. 이에 따라 도시 가로 환경 구조를 정량화 하기 위한 연구가 진행되어왔다. 특히 최근에는 LiDAR (Light Detection and Ranging) 시스템의 개발로 3차원 데이터 수집이 가능해졌고, 이로 인해 도시 구조의 정확한 측정이 가능해졌다. 이에 따라 기존 2차원 데이터의 한계로 직접 대상지에서 사진촬영을 통해 구축되던 천공률과 녹시율 등의 데이터 구축이 가능해졌다. 녹시율 분석을 위해 수목의 수관을 분류하는 등 LiDAR데이터의 전처리 과정이 필요하다. 특히 나무는 도시 가로 환경에서 가장 중요한 생태 요소이며, 수목 데이터의 정량화는 LiDAR데이터를 이용한 분석에서 중요한 기초자료가 된다. 이에 본 연구에서는 첫 번째로, mobile LiDAR을 사용하여 도시 수목의 수고, 지하고, 흉고직경을 분석하고 정확도를 검증하였다. 결과적으로 수고와 지하고는 각각 RMSE가 0.359m와 0.09m로 나타나 높은 정확도를 나타내었다. 반면 흉고직경은 RMSE 0.0377m로 중간 정도의 정확도를 나타냈다. 본 연구에서 두 번째로 분석한 천공률은 도시 인프라와 관련된 주요 지표이며 도시 열섬 및 재생 가능 에너지 연구에서 태양복사에너지양을 산정하기 위해 주로 사용된다. 그러나 기존 방법은 정확도 측면에서 한계가 있거나, 현장조사를 통해 데이터를 구축하여 한계가 있었다. 최근 LiDAR를 사용하여 복잡한 도시 지역에서도 천공률 계산이 가능해졌다. 그러나 항공 LiDAR의 해상도 한계로 인해 나무가 있는 도시지역에서의 정확도가 떨어졌다. 따라서 본 연구는 지상 LiDAR을 이용한 천공률 계산의 정확성을 검증하고자 하였으며, 항공 LiDAR보다 높은 정확도로 천공률을 계산하였다. 지상 LiDAR 기반 SVF의 결과는 R^2 0.915, RMSE 0.037, 최대 오차 0.156으로 높은 정확도를 나타냈다. 본 연구에 따르면 지상 LiDAR의 경우 복셀 크기 2.5cm가 복잡한 도시 지역에서 SVF를 추정하기에 적합하며, 항공 LiDAR과 비교하여 최대 오차를 60 % 감소시킬 수 있는 것으로 나타났다. 마지막으로 본 연구에서는 천공률과 녹시율 데이터의 도시 규모 구축 가능성에 대해 분석하고 구축 방법을 제시하였다. 연구 결과에 따르면 LiDAR 데이터의 해상도가 높을수록 결과의 정확도가 높아진다. 그러나 높은 해상도의 LiDAR 데이터로 높은 해상도의 데이터를 구축하기 위해서는 분석량이 많아 시간 효율이 떨어진다. 이에 대해 본 연구에서는 가상의 복셀화된 3차원 환경에서 시뮬레이션 분석을 수행하여 효율적인 데이터 구축 방법을 도출하고자 하였다. 연구 결과에 따르면 도로 위 어떤 차선에서 분석하는지에 따라 천공률과 녹시율 값의 변동이 크게 나타나며, 도로의 폭에 따라 연구 대상지를 대표할 수 있는 위치를 특정지을 수 있음을 도출하였다. 천공률과 녹시율 모두 대상지의 평균과 가장 가까운 위치가 도로 폭에 따라 왕복 8차선 도로에서는 3차선, 왕복 6차선 도로에서는 2차선에서 나타나며, 왕복 4차선과 2차선에서는 각각 2차선과 1차선에서 나타난다. 따라서 연구 대상지 모든 지점에서 분석하지 않고 도출된 지점에서 분석한다면 연구 대상지를 대표하는 천공률과 녹시율을 쉽게 도출할 수 있다. 본 연구를 통해 LiDAR 데이터를 이용한 가로환경 중 천공률과 녹시율 데이터 구축을 더 효율적으로 할 수 있을 것으로 기대된다. 이는 기존 사진촬영을 통해 높은 정확도의 데이터를 낮은 효율로 취득하는 방식이나 2차원, 2.5차원 데이터를 통해 중간 정확도의 데이터를 높은 효율로 취득하는 방식에 비해 상당부분 개선된 것으로 판단된다. 또한 LiDAR 데이터를 이용함에 있어서도 분석 효율을 높여, 넓은 대상지에서도 결과물을 도출하는 시간을 앞당길 수 있을 것으로 기대할 수 있다.LIST OF FIGURES 4 LIST OF TABLES 6 INTRODUCTION 11 LITERATURE REVIEW 16 1. SVF estimation 16 2. GVI estimation 20 3. Summary 24 EVALUATING THE ACCURACY OF LIDAR DERIVED TREE CHARACTERISTICS: TREE HEIGHT, CBH, DBH 27 1. Scope of study 27 2. Method 28 2.1. Mobile LiDAR and field data collection 28 2.2. Mobile LiDAR data preprocessing 30 2.3. Tree height estimation 31 2.4. CBH estimation 31 2.5. DBH estimation 32 2.6. Statistical analysis 34 3. Result 35 3.1. LiDAR data construction 35 3.2. Tree height estimation 36 3.3. CBH estimation 37 3.4. DBH estimation 40 3.5. Statistical analysis 41 4. Discussion 43 EVALUATING LIDAR BASED SVF ACCURACY ON COMPLEX URBAN STREET 49 1. Scope of study 49 2. Method 51 2.1. Terrestrial LiDAR data collection 51 2.2. SVF calculation with terrestrial LiDAR (SVFt) 53 2.3. SVF calculation with a fisheye lens (SVFf) 56 2.4. Accuracy analysis 57 2.5. Tree effect on SVF calculation 58 2.6. Sensitivity analysis 58 3. Result 60 3.1. LiDAR data construction 60 3.2. Testbed A 61 3.3. Testbed B 62 3.4. Testbed C 63 3.5. Testbed D 66 3.6. Accuracy 68 3.7. Sensitivity analysis 71 4. Discussion 73 DEVELOPING A METHOD TO CONSTRUCT URBAN SCALE DATA WITH HIGH ACCURACY USING LIDAR: SVF AND GVI ON THE STREET 81 1. Scope of study 81 2. Research area setting 84 3. Method 86 3.1. SVF estimation 86 3.2. GVI estimation 86 3.3. Observation points 90 3.4. Statistical analysis 92 4. Result 93 4.1. SVF 93 4.1.1. SVF values as measured from various positions 93 4.1.2. Effects of horizontal and vertical road positions on SVF values 95 4.1.3. What position best represents the research setting? 98 4.2. GVI 101 4.2.1. GVI values as measured from various positions 101 4.2.2. Effects of horizontal and vertical road positions on GVI values 103 4.2.3. What position best represents the research setting? 105 5. Discussion 107 CONCLUSION 111 BIBLIOGRAPHY 114 APPENDIX 129Docto

Estimation Carbon Storage of Urban Street trees Using UAV Imagery and SfM Technique

Carbon storage is one of the regulating ecosystem services provided by urban street trees. It is im- portant that evaluating the economic value of ecosystem services accurately. The carbon storage of street trees was calculated by measuring the morphological parameter on the field. As the method is labor-intensive and time-consuming for the macro-scale research, remote sensing has been more widely used. The airborne Light Detection And Ranging (LiDAR) is used in obtaining the point clouds data of a densely planted area and extracting individual trees for the carbon storage estimation. However, the LiDAR has limitations such as high cost and complicated operations. In addition, trees change over time they need to be frequently. Therefore, Structure from Motion (SfM) photogrammetry with un- manned Aerial Vehicle (UAV) is a more suitable method for obtaining point clouds data. In this paper, a UAV loaded with a digital camera was employed to take oblique aerial images for generating point cloud of street trees. We extracted the diameter of breast height (DBH) from generated point cloud data to calculate the carbon storage. We compared DBH calculated from UAV data and measured data from the field in the selected area. The calculated DBH was used to estimate the carbon storage of street trees in the study area using a regression model. The results demonstrate the feasibility and ef- fectiveness of applying UAV imagery and SfM technique to the carbon storage estimation of street trees. The technique can contribute to efficiently building inventories of the carbon storage of street trees in urban areas.N

Suggestion for spatialization of environmental planning using spatial optimization model

Environmental planning includes resource allocation and spatial planning process for the conservation and management of environment. Because the spatialization of the environmental planning is not specifically addressed in the relevant statutes, it actually depends on the qualitative methodology such as expert judgement. The results of the qualitative methodology have the advantage that the accumulated knowledge and intuition of the experts can be utilized. However, it is difficult to objectively judge whether it is enough to solve the original problem or whether it is the best of the possible scenarios. Therefore, this study proposed a methodology to quantitatively and objectively spatialize various environmental planning. At first, we suggested a quantitative spatial planning model based on an optimization algorithm. Secondly, we applied this model to two kinds of environmental planning and discussed about the model performance to present the applicability. Since the models were developed based on conceptual study site, there was a limitation in showing possibility of practical use. However, we expected that this study can contribute to the fields related to environmental planning by suggesting flexible and novel methodology.OAIID:RECH_ACHV_DSTSH_NO:T201806607RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075721CITE_RATE:0DEPT_NM:조경·지역시스템공학부EMAIL:[email protected]_YN:NN

A study on assessment indicators for integrated management on Korea national planning and environmental planning

Both the national land plan and the environmental plan reflect the need for sustainable land use and management. However, the linkage between the plans is reduced due to the lack of integrated management. Therefore, this study developed indicators to achieve integrated management. A total of 59 environmental plans were reviewed for the development of indicators, and a total of 74 integrated management indicators were derived through a three-stage process. In this process, the relevance of the integrated management indicators of this study to the UN 's sustainable development goals (SDGs) is presented in order to derive indicators that meet the level of international consultation. In order to facilitate the utilization of the indicators, the final indicators are divided into seven areas: natural ecology, water resource and quality, urban and green space, atmospheric, energy, landscape, resource circulation and waste. Furthermore, the indicators were classified into national, regional, and city level. Accordingly, the final indicator can be adapted to the field of influence of the planned to be established, and the indicator can be selected and applied to the level of the plan. The final indicators can be used to examine the extent to which the national plan reflects the contents of the environmental plan and can be used as an aid to confirm the contents to be included in the plan when establishing a new national plan.OAIID:RECH_ACHV_DSTSH_NO:T201807479RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075721CITE_RATE:0DEPT_NM:조경·지역시스템공학부EMAIL:[email protected]_YN:NN

Assessment of National and Regional Plans Using Integrated Management Index of Korea National Planning and Environmental Planning for Present Status Evaluation

Integrated management of Korea national and environmental planing for sustainable development is suggested, and basic research is needed. In this study, national and regional plans were assessed using integrated management index of Korea national planning and environmental planning' to grasp the current status of integrated management on Korea national planning and environmental planning. As a result of the assessment, it was found that both national and regional plans need to improve considering the natural ecology part and water resource and quality part. In addition, it was derived that the detailed contents of the indicator can not be reflected according to the characteristics in the high- er-level plan. Therefore, it has been found necessary to include proclamatory contents so as to be able to establish a detailed plan that reflects environmental goals in the lower-level plan.OAIID:RECH_ACHV_DSTSH_NO:T201912536RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075721CITE_RATE:0DEPT_NM:조경·지역시스템공학부EMAIL:[email protected]_YN:NN