다중시기 항공 LiDAR와 초분광 영상을 활용한 도시림 수종 분류 및 바이오매스 추정

학위논문(석사) -- 서울대학교대학원 : 환경대학원 환경조경학과, 2023. 2. 송영근.기후변화가 전 세계적 관심사로 부각되고 도시에 거주하는 인구의 비율이 증가함에 따라 대기오염과 열섬 현상을 완화하고 바이오매스 생성, 생물다양성 보존, 탄소 저장 등 다양한 편익을 제공하는 공간으로서 도시림의 중요성이 증가하고 있다. 산림을 구성하는 수종에 따라서 바이오매스 산정량에 따른 탄소 흡수량과 축적량이 다르기 때문에 도시림이 제공하는 편익을 정량적으로 계산하고 기후변화 취약종을 관리하기 위해서는 정확한 수종 분류가 필요하다. 전통적인 산림 모니터링의 경우 산림청에서 항공 영상을 이용한 판독과 현장 조사를 통해 임상도를 제작하여 관리하고 있지만 많은 노동력과 시간이 필요하고 항공 사진으로는 도시림 식생의 수직구조를 파악할 수 없기 때문에 대상지에서 생장하는 수종을 분류하고 경계를 정확하게 구분하는 방법이 요구되고 있다. 선행연구에 따르면 항공 LiDAR에서 파생된 산림구조 특성과 초분광영상의 분광 반사율을 이용하는 효과적인 산림 모니터링 연구가 많이 진행되고 있다. 최근에는 측량 기술의 발달로 인해 고밀도의(10 point/m2) LiDAR 점군 데이터를 획득이 가능하게 되었고 오픈소스 소프트웨어서도 점군 데이터의 활용이 용이하게 되었으며 초분광 영상의 경우 다중 분광 영상에 비해 확대된 식생지수 목록과 전처리 및 보정 알고리즘 등이 개발되었다. 본 연구에서는 전통적인 산림조사 방법을 개선하기 위해 두시기의 초분광 영상과 항공 LiDAR 영상을 결합하여 각 자료가 가지는 특징과 식생의 계절적 특성 변화를 활용하여 수종 분류의 정확도와 효율성을 높이고 환경 계획에 활용 가능한 지도를 제작하여 도시림의 수종 분포를 파악하고자 하였으며 최종 분류 결과를 기반으로 대상지의 지상부 바이오매스를 산정하는 것을 목표로 하였다. 대상지는 북위 37° 23' ~ 37° 27', 동경 126° 57' ~ 127° 02'의 경기도 과천시 도시림으로 면적은 2,034 ha이고 10종의 주요 수종이 성립하고 있다. 분류를 위한 현장 조사 자료는 8월부터 10월에 취득된 산림조사결과 데이터를 사용하였고, 항공 LiDAR는 Leaf-on (11월), Leaf-off (4월) 시기에 취득되었고 항공 초분광의 경우 Leaf-on (9월), Leaf-off (11월)에 취득된 데이터셋을 사용하였다. 항공 LiDAR와 항공 초분광 데이터셋은 전처리 과정을 통해 보정되었으며 도시림의 식생 영역을 대상으로 항공 초분광 영상의 PC1 밴드, 잎의 색소 및 광합성 특성과 관련된 식생지수와 항공 LiDAR 영상의 높이, 반사강도 메트릭스 계산을 통해 수종 분류를 위한 독립변수 29개를 추출하였다. 대상지의 대표 수종 10종을 대상으로 16,522개의 랜덤 포인트를 생성하여 결측치를 제외한 총 165,216개의 데이터셋을 생성하였고 현장 조사에서 획득된 수종 정보를 기반으로 로지스틱 회귀 (LR), 서포트 벡터 머신 (SVM), 의사결정나무 (DT), 랜덤포레스트 (RF), Light Gradient Boosting Machine (LGBM)의 5개의 머신러닝 분류 모델을 학습하여 분류와 검증을 수행하였다. 머신러닝 학습을 통한 분류 결과 다중시기 다중 데이터셋의 5개 분류기 평균 정확도는 71%으로 단일시기 다중 데이터셋 (leaf-on: 57%; leaf-off: 61%)와 다중시기 단일 데이터셋 (항공 초분광: 64%; 항공 LiDAR: 55%)에 비해 높게 나타났다. 5개의 데이터셋의 머신러닝 분류기별 정확도 비교 결과 RF의 평균 정확도는 76%으로 LGBM (70%), DT (61%), SVM (60%), LR (39%)에 비해 높게 나타났다. 결과적으로 다중시기 다중 데이터셋을 RF 기법을 이용한 분류의 정확도가 83.3%로 (Kappa: 0.80) 가장 높은 것으로 나타났다. 수종 분류에 기여하는 주요 독립변수는 11월에 취득된 항공 초분광 데이터셋에서 추출된 Carotenoid 반사 지수 (Importance: 0.064)와 4월에 취득된 항공 LiDAR 영상의 엽면적 지수 (Importance: 0.062)로 추정되었다. 개별 수목 추출 알고리즘을 통해 추출한 928,015개의 수관 영역을 Modified Logistic 수고-흉고직경 관계식을 사용하여 개체목의 흉고직경을 도출하고 수고 및 흉고직경을 입목재적·바이오매스 및 임분수확표의 부위별 상대생장식에 대입하여 2 m 이상의 교목을 대상으로 지상부 바이오매스를 도출한 결과 총 45,351 t의 바이오매스를 산정하였다. 항공 LiDAR와 항공 초분광을 활용한 수종 분류는 시 단위의 도시림에서 80% 이상의 정확도로 수종 분류를 수행할 수 있음을 시사하였으며 단일 시기에 촬영된 영상에 비해 잎의 생장 시기, 갈변 시기, 낙엽 시기에 따라 촬영된 영상을 결합하여 실제 산림의 계절적 특징을 반영했을 때 분류 정확도가 증가하는 것이 뚜렷하게 나타났다. 분류 결과를 시각화한 수종 지도를 토대로 기후변화 취약종을 관리하고 지상부 바이오매스를 산정하여 탄소 흡수량과 저장량을 추정하는 연구에 기여할 수 있을 것으로 사료된다.As climate change has emerged as a global concern and the proportion of urban residents has increased, the importance of urban forest as a space that relieves air pollution and urban heat islands and provides various benefits such as biomass generation, biodiversity conservation, and carbon storage has increased. Given that the amount of carbon absorption and accumulation based on the biomass calculation differs by tree species that comprise a forest, accurate tree species classification is required to quantitatively calculate urban forest benefits and manage endangered species. Regarding conventional forest monitoring, the Korea Forest Service produces and manages forest type maps by aerial image analysis and field surveys, a labor-intensive and time-consuming approach. In addition, because aerial imaging cannot identify the vertical structure of urban forest vegetation, a method for classifying the species of the research sites and accurately distinguishing boundaries is required. Notably, many effective forest monitoring studies are being conducted using forest structure characteristics derived from airborne LiDAR and the spectral reflectance of hyperspectral images. With survey technology advancements, LiDAR point data with high density (10 point/m2) can be obtained, point data can be easily used in open-source software, and hyperspectral images can be developed with expanded vegetation index lists and preprocessing and correction algorithms. In this research, the traditional forest survey method was improved by combining airborne hyperspectral images (AHI) with airborne LiDAR data from two periods, thereby leveraging the characteristics of each data set and seasonal characteristics of vegetation. The goal was to increase the accuracy and efficiency of tree classification, understand species distribution in urban forests by creating an environmental planning map, and calculate the research site Aboveground biomass (AGB) based on the classification results. The research site is an urban forest in Gwacheon, Gyeonggi-do, located at 37° 23'–37° 27' north latitude and 126° 57'–127° 02' east longitude, with an area of 2,034 ha and 10 major species. Forest surveys were conducted from August to October to gather field survey data for classification; the airborne LiDAR dataset was acquired during the leaf-on (November) and leaf-off (April) periods, whereas the AHI dataset was acquired during the leaf-on (September) and leaf-off (November) periods. The airborne LiDAR and AHI datasets were calibrated through preprocessing, and 29 independent variables for tree classification were extracted by calculating the PC1 band of AHI, the vegetation index related to the pigment and photosynthesis properties of leaves, and the height of airborne LiDAR images. In addition, 165,216 points were obtained by generating 16,522 random points for 10 major species of the research site, excluding missing values. Classification and verification were performed by learning five machine learning classification models of logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and light gradient boosting machine (LGBM) based on tree species information obtained from field surveys. The tree classification results indicated that the average accuracy of the five classifiers for the multitemporal multidataset was 71%, exceeding those of the single temporal multidataset (leaf-on: 57%; leaf-off: 61%) and multitemporal single dataset (AHI: 64%; airborne LiDAR: 55%). Comparing the accuracy of each machine learning classifier on five datasets revealed that RF had the highest average accuracy (76%), followed by LGBM (70%), DT (61%), SVM (60%), and LR (39%). Consequently, the classification accuracy of multitemporal multidatasets using RF techniques was also highest (83.3%; Kappa: 0.80). The main independent variables contributing to tree classification were the CRI (Importance: 0.064) extracted from the AHI dataset acquired in November and the leaf area index (Importance: 0.062) of the airborne LiDAR images acquired in April. The diameter at breast height (DBH) of the independent tree was derived using the modified logistic tree height (TH) - DBH relational expression of 928,015 tree crown areas, which were extracted using the independent tree segmentation algorithm. By substituting TH and DBH into the allometric equations for each part of the tree volume, biomass, and stand yield table, AGB was derived for trees with at least 2 m height, and a total biomass of 45,351 t was calculated. Tree classification using airborne LiDAR and AHI could result in over 80% accuracy when employed in urban forests, and the forest's actual seasonal characteristics were clearly increased by combining images acquired based on leaf growth, fall foliage season, and leaf fall season compared to single temporally acquired images. Overall, research into the estimation of carbon absorption and storage through the management of climate change-vulnerable species and AGB computation can benefit from tree species maps visualized in the classification results.Chapter 1. Introduction 1 Section 1.1. Research Background and Purpose 1 1.1.1. Background of the Research 1 1.1.2. Research Purpose and Significance 7 1.1.3. Research Scope 8 Chapter 2. Literature Review 10 Section 2.1. Tree Species Classification and Biomass Estimation 10 2.1.1. Tree Species Classification and Biomass Estimation 10 Chapter 3. Methods 16 Section 3.1. Research Progress 16 3.1.1. Research Progress 16 Section 3.2. Research Area 17 3.2.1. Overview of Research area 17 Section 3.3. Data Acquisition 22 3.3.1. Airborne LiDAR and Hyperspectral Imaging 22 3.3.2. Ground Truth Data 24 Section 3.4. Data Processing 25 3.4.1. Pre-Processing of Airborne LiDAR and Hyperspectral Imaging 25 3.4.2. Dataset Composition 30 3.4.3. Selection of Major Tree Species 31 3.4.4. Tree Crown Segmentation 32 Section 3.5. Feature Extraction 33 3.5.1. LiDAR Feature Extraction 34 3.5.2. Hyperspectral Imaging Feature Extraction 34 3.5.3. Principal Component Analysis 35 3.5.4. Feature Selection 36 Section 3.6. Classification Technique 39 3.6.1. Classification Technique 39 Section 3.7. Estimating Aboveground Biomass 43 3.7.1. Estimating Aboveground Biomass 43 Chapter 4. Results 47 Section 4.1. Tree Species Classification 47 4.1.1. Tree Species Classification 47 Section 4.2. Important Independent Variables 50 4.2.1. Important Independent Variables 50 Section 4.3. Estimating Aboveground Biomass 53 4.3.1. Estimating Individual Tree Height and DBH 53 4.3.2. Estimating Aboveground Biomass 55 Chapter 5. Discussion 58 Section 5.1. Discussion 58 5.1.1. Tree Species Classification 58 5.1.2. Important Independent Variables 59 Chapter 6. Conclusion 62 Section 6.1. Overall Summary 62 6.1.1. Overall Summary 62 Section 6.2. Implications and Limitations 62 6.2.1. Implications of Research 62 6.2.2. Limitations of Research 64 References 66 초록 75석

Kinetic study on sulfur transfer in condition of liquid iron drops falling through the Al2O3-CaO-MgO slag layer

Maste

Pharmacological management of muscle spasticity

Background: Muscle spasticity is a neurologic disorder, which is considered one of the positive signs of upper motor neuron diseases. Spasticity is common after brain or spinal cord injury. Since spasticity results in tendon retraction, muscle weakness, pain, ankylosis, and disability in activities of daily living, treatment is warranted. Current Concepts: Spasticity is usually assessed using the Modified Ashworth Scale or Modified Tardieu Scale. It is treated with various methods, including physical therapy, occupational therapy, orthosis, medication, and surgery. Pharmacological management should be selected according to the location and severity of the symptom and includes oral medications, chemical nerve block, and intrathecal baclofen pump insertion. Oral medications include baclofen, benzodiazepine, dantrolene, and tizanidine. Chemoneurolysis of spasticity is done with botulinum toxin or a mixture of phenol and alcohol. Discussion and Conclusion: Since muscle spasticity affects motor function and activities of daily living, understanding of this symptom and choosing an optimal treatment are necessary. Pharmacologic treatments should be administered with caution especially with the side effects. Optimal treatment of spasticity will bring the best neurological outcome for the patients

THE NUMBER OF POINTS ON ELLIPTIC CURVES y2 = x3 + Ax AND y2 = x3 + B3 MOD 24

In this paper, we calculate the number of points on elliptic curves $y^2 =x^3 +Ax$ over $F_{p^r}$ modulo $24$. This is a generalization of \cite{PDE}, \cite{Soonho} and \cite{SHH}.

Central inhibitory effect of botulinum toxin type A in humans : a paired pulse transcranial magnetic stimulation study

Thesis (master`s)--서울대학교 대학원 :의학과 재활의학 전공,2003.Maste

Influence of exercise intensity in early rehabilitation on neurological recovery after focal ischemia in rats

학위논문(박사)--서울대학교 대학원 :의학과 재활의학과전공,2006.Docto

Roles of preoperative and early postoperative electrodiagnosis in brachial plexus injury patients undergoing nerve transfer operations: retrospective feasibility study

Objective The purpose of this retrospective observational study was to assess the feasibility of electrodiagnostic parameters, perioperatively, and to discover optimal values as prognostic factors for patients with brachial plexus injury undergoing nerve transfer operations. Methods We retrospectively reviewed the records of 11 patients who underwent nerve transfer surgery. The patients underwent perioperative electrodiagnosis (EDX) before and approximately 6 months after surgery. We evaluated the compound muscle action potential (CMAP) ratio, motor unit recruitment, and their interval changes. To evaluate motor strength, we used the Medical Research Council (MRC) grade, 6 and 12 months after surgery. We evaluated the relationships between improved CMAP ratio, and motor unit recruitment and MRC grade changes 6 and 12 months postoperatively. Results All parameters increased significantly after surgery. The CMAP ratio improvement 6 months after surgery correlated with the MRC grade change from baseline to 12 months, with a correlation coefficient of 0.813. Conclusion EDX parameters improved significantly postoperatively, and the CMAP ratio improvement 6 months after surgery correlated with the clinical outcomes at 1 year. The results of perioperative EDX might help establish long-term treatment plans for patients who undergo nerve transfer surgery