Using Lidar Data to Analyse Sinkhole Characteristics Relevant for Understory Vegetation under Forest Cover\u2014Case Study of a High Karst Area in the Dinaric Mountains

In this article, we investigate the potential for detection and characterization of sinkholes under dense forest cover by using airborne laser scanning data. Laser pulse returns from the ground provide important data for the estimation of digital elevation model (DEM), which can be used for further processing. The main objectives of this study were to map and determine the geomorphometric characteristics of a large number of sinkholes and to investigate the correlations between geomorphology and vegetation in areas with such characteristics. The selected study area has very low anthropogenic influences and is particularly suitable for studying undisturbed karst sinkholes. The information extracted from this study regarding the shapes and depths of sinkholes show significant directionality for both orientation of sinkholes and their distribution over the area. Furthermore, significant differences in vegetation diversity and composition occur inside and outside the sinkholes, which indicates their presence has important ecological impacts

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation

On the use of rapid-scan, low point density terrestrial laser scanning (TLS) for structural assessment of complex forest environments

Forests fulfill an important role in natural ecosystems, e.g., they provide food, fiber, habitat, and biodiversity, all of which contribute to stable ecosystems. Assessing and modeling the structure and characteristics in forests can lead to a better understanding and management of these resources. Traditional methods for collecting forest traits, known as “forest inventory”, is achieved using rough proxies, such as stem diameter, tree height, and foliar coverage; such parameters are limited in their ability to capture fine-scale structural variation in forest environments. It is in this context that terrestrial laser scanning (TLS) has come to the fore as a tool for addressing the limitations of traditional forest structure evaluation methods. However, there is a need for improving TLS data processing methods. In this work, we developed algorithms to assess the structure of complex forest environments – defined by their stem density, intricate root and stem structures, uneven-aged nature, and variable understory - using data collected by a low-cost, portable TLS system, the Compact Biomass Lidar (CBL). The objectives of this work are listed as follow: 1. Assess the utility of terrestrial lidar scanning (TLS) to accurately map elevation changes (sediment accretion rates) in mangrove forest; 2. Evaluate forest structural attributes, e.g., stems and roots, in complex forest environments toward biophysical characterization of such forests; and 3. Assess canopy-level structural traits (leaf area index; leaf area density) in complex forest environments to estimate biomass in rapidly changing environments. The low-cost system used in this research provides lower-resolution data, in terms of scan angular resolution and resulting point density, when compared to higher-cost commercial systems. As a result, the algorithms developed for evaluating the data collected by such systems should be robust to issues caused by low-resolution 3D point cloud data. The data used in various parts of this work were collected from three mangrove forests on the western Pacific island of Pohnpei in the Federated States of Micronesia, as well as tropical forests in Hawai’i, USA. Mangrove forests underscore the economy of this region, where more than half of the annual household income is derived from these forests. However, these mangrove forests are endangered by sea level rise, which necessitates an evaluation of the resilience of mangrove forests to climate change in order to better protect and manage these ecosystems. This includes the preservation of positive sediment accretion rates, and stimulating the process of root growth, sedimentation, and peat development, all of which are influenced by the forest floor elevation, relative to sea level. Currently, accretion rates are measured using surface elevation tables (SETs), which are posts permanently placed in mangrove sediments. The forest floor is measured annually with respect to the height of the SETs to evaluate changes in elevation (Cahoon et al. 2002). In this work, we evaluated the ability of the CBL system for measuring such elevation changes, to address objective #1. Digital Elevation Models (DEMs) were produced for plots, based on the point cloud resulted from co-registering eight scans, spaced 45 degree, per plot. DEMs are refined and produced using Cloth Simulation Filtering (CSF) and kriging interpolation. CSF was used because it minimizes the user input parameters, and kriging was chosen for this study due its consideration of the overall spatial arrangement of the points using semivariogram analysis, which results in a more robust model. The average consistency of the TLS-derived elevation change was 72%, with and RMSE value of 1.36 mm. However, what truly makes the TLS method more tenable, is the lower standard error (SE) values when compared to manual methods (10-70x lower). In order to achieve our second objective, we assessed structural characteristics of the above-mentioned mangrove forest and also for tropical forests in Hawaii, collected with the same CBL scanner. The same eight scans per plot (20 plots) were co-registered using pairwise registration and the Iterative Closest Point (ICP). We then removed the higher canopy using a normal change rate assessment algorithm. We used a combination of geometric classification techniques, based on the angular orientation of the planes fitted to points (facets), and machine learning 3D segmentation algorithms to detect tree stems and above-ground roots. Mangrove forests are complex forest environments, containing above-ground root mass, which can create confusion for both ground detection and structural assessment algorithms. As a result, we needed to train a supporting classifier on the roots to detect which root lidar returns were classified as stems. The accuracy and precision values for this classifier were assessed via manual investigation of the classification results in all 20 plots. The accuracy and precision for stem classification were found to be 82% and 77%, respectively. The same values for root detection were 76% and 68%, respectively. We simulated the stems using alpha shapes in order to assess their volume in the final step. The consistency of the volume evaluation was found to be 85%. This was obtained by comparing the mean stem volume (m3/ha) from field data and the TLS data in each plot. The reported accuracy is the average value for all 20 plots. Additionally, we compared the diameter-at-breast-height (DBH), recorded in the field, with the TLS-derived DBH to obtain a direct measure of the precision of our stem models. DBH evaluation resulted in an accuracy of 74% and RMSE equaled 7.52 cm. This approach can be used for automatic stem detection and structural assessment in a complex forest environment, and could contribute to biomass assessment in these rapidly changing environments. These stem and root structural assessment efforts were complemented by efforts to estimate canopy-level structural attributes of the tropical Hawai’i forest environment; we specifically estimated the leaf area index (LAI), by implementing a density-based approach. 242 scans were collected using the portable low-cost TLS (CBL), in a Hawaii Volcano National Park (HAVO) flux tower site. LAI was measured for all the plots in the site, using an AccuPAR LP-80 Instrument. The first step in this work involved detection of the higher canopy, using normal change rate assessment. After segmenting the higher canopy from the lidar point clouds, we needed to measure Leaf Area Density (LAD), using a voxel-based approach. We divided the canopy point cloud into five layers in the Z direction, after which each of these five layers were divided into voxels in the X direction. The sizes of these voxels were constrained based on interquartile analysis and the number of points in each voxel. We hypothesized that the power returned to the lidar system from woody materials, like branches, exceeds that from leaves, due to the liquid water absorption of the leaves and higher reflectivity for woody material at the 905 nm lidar wavelength. We evaluated leafy and woody materials using images from projected point clouds and determined the density of these regions to support our hypothesis. The density of points in a 3D grid size of 0.1 m, which was determined by investigating the size of the branches in the lower portion of the higher canopy, was calculated in each of the voxels. Note that “density” in this work is defined as the total number of points per grid cell, divided by the volume of that cell. Subsequently, we fitted a kernel density estimator to these values. The threshold was set based on half of the area under the curve in each of the distributions. The grid cells with a density below the threshold were labeled as leaves, while those cells with a density above the threshold were set as non-leaves. We then modeled the LAI using the point densities derived from TLS point clouds, achieving a R2 value of 0.88. We also estimated the LAI directly from lidar data by using the point densities and calculating leaf area density (LAD), which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was found to be 90%. Since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed a semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets, where each of the plots were 30 meter spaced for each subset. LAI model R2 values for these subsets ranged between 0.84 - 0.96. The results bode well for using this method for automatic estimation of LAI values in complex forest environments, using a low-cost, low point density, rapid-scan TLS

다중 규모 LiDAR 데이터를 활용한 도시생태계 구조 및 연결성 평가

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2021.8. 송영근.Integrated multiscale light detection and ranging (LiDAR) datasets are required for managing urban ecosystems because 1) LiDAR datasets can represent various spatial structures across the urban landscape and 2) the multitemporal LiDAR approach can derive the changes of urban landscape structures. This dissertation aimed to find the various spatiotemporal availabilities (i.e., from the tree-level spatial scale to the city-level regional scale with the multitemporal approach) of LiDAR or laser scanning (LS) datasets for monitoring urban ecosystems in the following three chapters. Chapter 2: Collecting tree inventory data in urban areas is important for managing green areas. Surveying using airborne laser scanning (ALS) is effective for collecting urban tree structures but less efficient regarding the economic costs and its operation. Terrestrial laser scanning (TLS), and mobile laser scanning (MLS) datasets could have the potential in complementing those of ALS in the respect to efficiency. However, to the best of my knowledge, there were limited studies for seeking the similarities and variations among the canopy metrics derived from various LiDAR platforms. In Chapter 2, I compared structural canopy metrics among ALS, TLS, and MLS datasets in the urban parks. The purpose of Chapter 2 was to test whether the estimates of tree metrics differed depending on single or clustered trees and to test whether the errors in LiDAR-derived metrics were related to the tree structures. Small, urban parks were selected for surveying trees using the three LiDAR platforms. The ALS datasets were acquired on 14 May, 2017. The TLS and MLS datasets were acquired from 10–11 May, 2017, and 21–25 April, 2020, respectively. The tree point clouds were classified into single and clustered trees. The structural metrics were compared in each pair (i.e., ALS and TLS, ALS and MLS, and TLS and MLS pairs). The heights related metrics (e.g., percentile heights and the distribution of the heights values), the complexity metric (e.g., the Rumple index) and area were calculated for comparisons. The root mean square error (RMSE), bias, and the Pearson’s correlation coefficient (r) were calculated to evaluate the difference in each metric among the LiDAR platforms. The results showed that ZMAX, max and mean CHM, and area showed good consistencies (RMSE% 0.900). Especially, the biases of CHM-derived metrics did not present significant differences (p > 0.05) regardless of single or clustered trees. Moreover, the biases from the comparisons in each pair showed linear relations with the tree heights and vertical canopy complexity (i.e., Pearson’s correlation coefficient showed significant; r >0.29, p < 0.05). My results could be references when combining multiple LiDAR systems to estimate the canopy structures of urban park areas. Chapter 3: Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional (3D) changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal ALS datasets enable me to quantify the vertical and lateral growth of trees across a landscape scale. The goal of Chapter 3 is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, I generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests. Chapter 4: Although many studies have considered urban structure when investigating urban ecological networks, few have considered the 3D structure of buildings as well as urban green spaces. In Chapter 4, I examined an urban ecological network using the 3D structure of both green spaces and buildings. Using breeding-season bird species observations and ALS data collected, I assessed the influence of 3D structural variables on species diversity. I used correlation analyses to determine if vertical distribution, volume, area, and height of both buildings and vegetation were related to bird species diversity. Then I conducted circuit theory-based current flow betweenness centrality (CFBC) analysis using the LiDAR-derived structural variables. I found that the volumes of buildings and 8–10 m vegetation heights were both highly correlated with species richness per unit area. There were significant differences between 2D and 3D connectivity analysis using LiDAR-derived variables among urban forest patches, boulevards, and apartment complexes. Within urban forest patches and parks, 3D CFBC represented canopy structural characteristics well, by showing high variance in spatial distributions. The 3D CFBC results indicated that adjacent high-rise buildings, dense apartment complexes, and densely urbanized areas were isolated, as characterized by low centrality values, but that vegetation planted in open spaces between buildings could improve connectivity by linking isolated areas to core areas. My research highlights the importance of considering 3D structure in planning and managing urban ecological connectivity. In this dissertation, the availability of integrated multiscale LiDAR datasets was found via three standalone studies. It was revealed that 3D information could enhance the quality of urban landscape monitoring and ecological connectivity analysis by elaborately explaining spatial structures. However, the spatiotemporal scales of each standalone study were limited to the city scale and to five years. The recently launched Global Ecosystem Dynamics Investigation (GEDI) would help to solve these limitations. Furthermore, the GEDI dataset could help researchers understand the relationship between ecosystem structures and their functions.본 학위논문은 다양한 시공간 스케일에서 도시생태계 모니터링을 위한 LiDAR 데이터의 활용과 생태적 의미 도출에 관한 내용을 다룬다. LiDAR란 Light Detection and Ranging의 약어로, LiDAR 센서에서 발사된 레이저가 대상에 도달한 뒤 반사되어 돌아오는 레이저의 세기와 시간을 계산하여 대상의 위치 정보를 3차원 점군 데이터로 변환해주는 능동형 원격탐사 도구이다. LiDAR 원격탐사 도구의 등장으로 자연과 도시의 3차원 공간정보의 취득이 기능해짐에 따라, 서식지의 3차원 공간정보와 생물 종 사이의 관계 도출, 시계열 LiDAR 데이터를 활용한 녹지 모니터링 연구 등이 이뤄지고 있다. 또한 항공 LiDAR(ALS), 지상 LiDAR(TLS), 이동형 LiDAR(MLS) 등 다양한 LiDAR 시스템의 개발로 연구 목적에 알맞은 시공간 해상도의 3차원 공간정보를 취득할 수 있게 되었다. 본 학위논문에서는 도시녹지를 대상으로 LiDAR 원격탐사 도구의 다양한 시공간 스케일 적용 측면에서, Chapter 2 항공, 지상, 이동형 LiDAR 시스템 사이 수목구조관련 변수들의 일치성 평가, Chapter 3 시계열 분석을 통한 도시녹지 동태 모니터링, Chapter 4 도시의 생태적 연결성 분석을 진행하였다. Chapter 2: 도시의 수목정보를 취득하는 것은 도시녹지 관리에 있어 필수적이다. LiDAR 기술의 발달로 도시수목의 3차원 정보를 취득할 수 있게 되었으며, 이를 통해 수목높이와 수목구조, 지상 바이오매스 등을 높은 정확도로 산출할 수 있게 되었다. 항공 LiDAR는 넓은 범위의 공간정보를 높은 정확도로 측정하는 특성을 지녀 산림 모니터링 분야에서 활발히 활용되고 있다. 하지만 항공 LiDAR 데이터의 취득은 항공기 운용비, 장비관련 막대한 비용이 발생하고 운용에 있어 전문성을 요구하며 대상의 점군밀도가 상대적으로 낮다는 단점을 지닌다. 반면 지상 LiDAR와 이동형 LiDAR는 운용하기 편리하고 높은 점군밀도를 출력한다는 점에서 항공 LiDAR의 단점을 극복할 수 있다. 이처럼 다양한 LiDAR 시스템의 등장과 이를 활용한 생태계 모니터링 연구 시도가 증가하면서 LiDAR 시스템간 효율적인 운용과 데이터의 보완 방법들이 요구되고 있다. 하지만 현재까지 ALS, TLS, MLS의 3개의 시스템을 통해 취득된 수목 정보를 서로 비교하고, 서로 대체가능한 수목정보를 도출한 연구는 많이 진행된 바 없다. 따라서 본 학위논문의 Chapter 2에서는 ALS, TLS, MLS 통해 취득된 도시의 수목정보를 서로 비교하여 일치성을 평가하고, 어떠한 수목구조관련 지표가 세 LiDAR 시스템 사이에서 대체가능한지 다루고 있다. 세부적으로 Chapter 2는 수목구조관련 지표가 단목차원과 군집차원, 수목구조에 따라 ALS, TLS, MLS 시스템에서 차이가 발생하는지에 대한 내용을 담고 있다. 천안시 도시공원 9개소에서 ALS 데이터는 2017년 5월 14일, TLS 데이터는 2017년 5월 10일과 11일, MLS 데이터는 2020년 4월 21에서 25일 취득되었다. 취득된 데이터셋은 수관의 겹침 여부에 따라 단목과 군집으로 분류되었으며, 3개의 페어(ALS-TLS, MLS-TLS, ALS-MLS)로 수관의 퍼센타일 높이, 수관복잡성, 면적 등의 수목구조관련 변수들을 1:1 비교하였다. 항공 LiDAR 데이터를 통해 도출된 수목구조관련 변수들을 참조로 하여 평균제곱근오차(RMSE), 편향(bias), 피어슨 상관계수(r) 등을 계산하고 세 LiDAR 시스템 사이의 일치성을 평가하였다. 평가 결과 ZMAX, CHM관련 수관높이 관련 변수들, 그리고 수관면적이 높은 일치성을 보였다(RMSE% 0.900). 특히 CHM을 통해 도출된 수관높이 관련 변수들은 단목과 군집에서 세개의 LiDAR 시스템간 통계적인 차이를 보이지 않았다(p > 0.05). 반면 퍼센타일 수관높이와 평균 수관높이 등은 매우 낮은 일치성을 나타냈으며, 세 페어에서 도출된 편향은 수고, 수관복잡성과 약한 선형관계를 나타냈다(r >, p < 0.05). Chapter 3: 수관동태는 숲의 건강성을 반영한다. 특히, 자연적·인위적인 교란에 의해 발생한 숲틈은 숲 내부에 빛의 투과율, 온도, 습도 등에 영향을 끼쳐 주변 환경의 변화를 야기한다. 따라서 숲틈을 탐지하고 모니터링하는 것은 숲의 동태를 이해하는데 있어 매우 중요하다. 항공 LiDAR 센서를 활용할 경우 위성영상이나 항공사진 등 2차원 데이터로 탐지하기 어려운 숲틈 또는 개방공간의 탐지와 수관의 3차원 형상의 취득이 가능하다. Chapter 3에서는 2012년도부터 2015년도 4개년의 항공 LiDAR를 활용하여 자연형 도시공원(봉서산)의 수관과 숲틈의 수평적 수직적 변화양상을 추정하였다. 수관은 높이 5m를 기준으로 상층부와 하층부 수관으로 분류되었으며, 수관높이모델(canopy height model, CHM)을 생성하여 연간변화를 탐지하였다. 연구결과 상층부 및 하층부 수관의 수직생장량과 엽면적지수는 일정한 연간 변화양상을 보인 반면, 수평적 변화와 엽면적밀도는 불규칙적인 연간 변화양상을 보였다. 전반적으로 상층부 수관은 높이 12m에서 측방향 생장을 하는 것으로 나타났으며, 하층부 수관 중 숲틈에서는 높이 5m에서 측방향 생장이 활발하게 나타났다. LiDAR 데이터의 연간 변화 탐지를 통해 자연적으로 형성된 숲틈의 경우 생장과 교란 측면에서 매우 활발한 동태가 발생하고 있으며, 인위적으로 형성된 개방공간의 경우 수관의 동태가 다소 침체됨을 도출하였다. Chapter 4는 도시 내 건물과 녹지의 3차원 구조를 입력자료로 활용하여 도시의 생태적 연결성을 평가하는 연구를 다룬다. 도시 내 생태적 연결성 도출과 관련한 연구는 도시와 녹지의 형태 등을 주요 변수로 하여 진행이 되고 있다. 그러나 3차원적인 특성인 도시 건물의 부피, 수목의 수직적인 구조 등을 고려한 연결성 분석은 많이 진행된 바 없다. 연구 대상지는 천안시 시청을 중심으로한 4 km × 4 km 지역으로, 2015년에 취득된 항공 LiDAR와 같은 해 취득된 조류 종 조사 데이터를 활용하여 1)도시 내 건물과 녹지의 3차원 구조와 조류 종 다양성 사이 관계를 살피고, 2)조류 종 다양성과 상관관계를 가지는 3차원 구조변수를 활용하여 전류흐름기반 매개중심성 연결성 분석(CFBC)을 진행하였다. 연구결과 건축물의 부피와 수목높이 8-10m의 녹지 부피비가 면적당 조류 종 풍부도와 스피어만 순위상관관계에서 높은 상관관계(ρ> 0.6)를 나타냈다. 연결성 분석의 결과는 입력변수의 공간차원(2D 및 3D)에 따라 다르게 나타났다. 특히 도시숲, 대로변, 아파트단지내 녹지 등에서 2D 기반 CFBC와 3D기반 CFBC는 통계적으로 유의미한 차이를 보였다. 또한 도시녹지의 3D 기반 CFBC의 경우 같은 녹지 면적임에도 수관의 구조적인 특성에 따라 높은 차이가 나타남을 확인하였다. 3D CFBC 분석결과를 통해 고층 건물 주변부, 고밀도 아파트단지, 고밀 시가화지역 등이 낮은 중심성을 보여 고립지역으로 나타났으며, 건물 사이 공지 내 식생은 연결성이 고립된 지역과 핵심지역을 연결하는 기능을 나타냈다. 이 학위논문은 서로 다른 LiDAR 시스템을 활용하여 단목, 경관 지역단위 등 다양한 공간 스케일에서의 도시경관구조 분석, 도시녹지구조와 토지이용 등에 따른 시간적 변화양상, 도시경관구조가 가지는 생태적 의미 등과 관련된 내용을 다루고 있다. 향후 Global Ecosystem Dynamics Investigation(GEDI) 미션의 데이터를 활용하여 본 학위논문에서 다루는 지역규모의 연구를 국가단위, 대륙단위 등으로 확장할 수 있을 것이며 이를 통해 도시생태계 구조와 그 기능 사의 관계를 이해하는데 도움을 줄 수 있을 것이다.Chapter 1. Introduction 1 1. Background 1 1.1. Urbanization and the importance of urban green spaces 1 1.2. Urban landscape and Light detection and ranging application 1 2. Purpose 6 Chapter 2. Comparing tree structures derived among multiple LiDAR systems in urban parks 10 1. Introduction 10 2. Methods and materials 12 2.1. Study site and tree classification 12 2.2. LiDAR survey and processing 14 2.3. Deriving the structural variables of the parks 17 2.4. Assessing the accuracy of the LiDAR-derived indices 18 3. Results 19 3.1. Comparing height metrics among the three LiDAR systems 19 3.2. Comparing CHM-derived canopy height metrics from each LiDAR systems 22 3.3. Comparing the area and the Rumple index determined using the LiDAR systems 23 4. Discussion 25 4.1. LiDAR configurations and data acquisition time intervals 25 4.2. Uncertainty of the structural indices derived from the three LiDAR systems 28 Chapter 3. Urban forest growth and gap dynamics detected by yearly repeated airborne LiDAR 31 1. Introduction 31 2. Methods and Materials 33 2.1. Field survey 33 2.2. Canopy opening detection 34 2.3. Airborne LiDAR dataset acquisition and registration 35 2.4. Generation of height models and change detection 36 2.5. Gap detection and classification 38 2.6. Estimating changes of vertical canopy distribution and canopy complexity 38 3. Results 39 3.1. Pixel and hexagon height model-based change detection 39 3.2. Continuous one-year vertical growth area 41 3.3. Open canopy change detection 42 3.4. Changes in vertical canopy structures in High Canopy and Open Canopy 43 4. Discussion 45 4.1. What are the differences between the canopy structural changes derived from annual change detections and three-year interval change detection? 45 4.2. What are the characteristics of the structural changes according to the different canopy classes (e.g., high canopies and low canopies) in the urban forest? 46 Chapter 4. LiDAR-derived three-dimensional ecological connectivity mapping 49 1. Introduction 49 2. Materials and Methods 51 2.1. Study area and avian species observation 52 2.2. Airborne LiDAR acquisition, preprocessing and classification and deriving structural variables 53 2.3. Correlation analysis and selection of structural variables 55 2.4. 2D and 3D ecological networks 55 3. Results 56 3.1. Avian species survey 56 3.2. Correlation analyses and variable selection 56 3.3. Connectivity analysis results 58 3.4. Correlation between connectivity results with bird species diversity 59 3.5. Differences between 2D- and 3D-based CFBCs 60 4. Discussion 61 4.1. Vegetation and building structures and bird species diversity 61 4.2. 3D-based connectivity results 62 4.3. Differences between 2D and 3D network analyses 64 4.3.1. Forest and artificial green area 65 4.3.2. Roads and residential areas 66 Appendix 67 Chapter 5. Conclusion 70 1. Combination with multiple LiDAR data for surveying structures of urban green spaces 70 2. Multi-temporal urban forest gap monitoring 71 3. Ecological connectivity analysis using LiDAR 71 4. LiDAR application to Urban ecosystem monitoring 72 5. Expanding spatiotemporal scale and further works 73 Acknowledgments 75 Reference 76 Abstract in Korean 85박

Laser scanning applications in fluvial studies

Peer reviewe

The global tree carrying capacity (keynote)

editorial reviewe

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

Vegetation Dynamics in Ecuador

Global forest cover has suffered a dramatic reduction during recent decades, especially in tropical regions, which is mainly due to human activities caused by enhanced population pressures. Nevertheless, forest ecosystems, especially tropical forests, play an important role in the carbon cycle functioning as carbon stocks and sinks, which is why conservation strategies are of utmost importance respective to ongoing global warming. In South America the highest deforestation rates are observed in Ecuador, but an operational surveillance system for continuous forest monitoring, along with the determination of deforestation rates and the estimation of actual carbon socks is still missing. Therefore, the present investigation provides a functional tool based on remote sensing data to monitor forest stands at local, regional and national scales. To evaluate forest cover and deforestation rates at country level satellite data was used, whereas LiDAR data was utilized to accurately estimate the Above Ground Biomass (AGB; carbon stocks) at catchment level. Furthermore, to provide a cost-effective tool for continuous forest monitoring of the most vulnerable parts, an Unmanned Aerial Vehicle (UAV) was deployed and equipped with various sensors (RBG and multispectral camera). The results showed that in Ecuador total forest cover was reduced by about 24% during the last three decades. Moreover, deforestation rates have increased with the beginning of the new century, especially in the Andean Highland and the Amazon Basin, due to enhanced population pressures and the government supported oil and mining industries, besides illegal timber extractions. The AGB stock estimations at catchment level indicated that most of the carbon is stored in natural ecosystems (forest and páramo; AGB ~98%), whereas areas affected by anthropogenic land use changes (mostly pastureland) lost nearly all their storage capacities (AGB ~2%). Furthermore, the LiDAR data permitted the detection of the forest structure, and therefore the identification of the most vulnerable parts. To monitor these areas, it could be shown that UAVs are useful, particularly when equipped with an RGB camera (AGB correlation: R² > 0.9), because multispectral images suffer saturation of the spectral bands over dense natural forest stands, which results in high overestimations. In summary, the developed operational surveillance systems respective to forest cover at different spatial scales can be implemented in Ecuador to promote conservation/ restoration strategies and to reduce the high deforestation rates. This may also mitigate future greenhouse gas emissions and guarantee functional ecosystem services for local and regional populations