Characterizing the Impacts of the Invasive Hemlock Woolly Adelgid on the Forest Structure of New England

Climate change is raising winter temperatures in the Northeastern United States, both expanding the range of an invasive pest, the hemlock woolly adelgid (HWA; Adelges tsugae), and threatening the survival of its host species, eastern hemlock (Tsuga canadensis). As a foundation species, hemlock trees underlie a distinct network of ecological, biogeochemical, and structural systems that will likely disappear as the HWA infestation spreads northward. Remote sensing can offer new perspectives on this regional transition, recording the progressive loss of an ecological foundation species and the transition of evergreen hemlock forest to mixed deciduous forest over the course of the infestation. Lidar remote sensing, unlike other remote sensing tools, has the potential to penetrate dense hemlock canopies and record HWA’s distinct impacts on lower canopy structure. Working with a series of lidar data from the Harvard Forest experimental site, these studies identify the unique signals of HWA impacts on vertical canopy structure and use them to predict forest condition. Methods for detecting the initial impacts of HWA are explored and a workflow for monitoring changes in forest structure at the regional scale is outlined. Finally, by applying terrestrial, airborne, and spaceborne lidar data to characterize the structural variation and dynamics of a disturbed forest ecosystem, this research illustrates the potential of lidar as a tool for forest management and ecological research

Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance

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

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Assessing wood properties in standing timber with laser scanning

Managed forests play crucial roles in ongoing climatic and environmental changes. Among other things, wood is capable of sinking and storing carbon in both standing timber and wood products. To promote these positive effects, more precise planning is required that will ensure sustainable forest management and maximal deposition of harvested wood for long-term applications. Information on wood properties plays a key role; i.e. the wood properties can impact the carbon stocks in forests and the suitability of wood for structural timber. With respect to the theoretical background of wood formation, stem, crown, and branching constitute potential inputs (i.e. wood quality indicators) to allometric wood property, tree biomass, and wood quality models. Due to the complex nature of wood formation, measurements of wood quality indicators that could predict wood properties along the relevant directions of variation have previously been elusive in forest inventories. However, developments in laser scanning from aerial and terrestrial platforms support more complex mapping and modeling regimes based on dense three-dimensional point clouds. The aim here was to determine how wood properties could be estimated in remote-sensing-aided forest inventories. For this purpose, methods for characterizing select wood quality indicators in standing timber, using airborne and terrestrial laser scanning (ALS and TLS, respectively) were developed and evaluated in managed boreal Scots pine (Pinus sylvestris L.) forests. Firstly, the accuracies of wood quality indicators resolved from TLS point clouds were assessed. Secondly, the results were compared with x-ray tomographic references from sawmills. Thirdly, the accuracies of tree-specific crown features delineated from the ALS data in predictive modeling of the wood quality indicators were evaluated. The results showed that the quality and density of point clouds significantly impacted the accuracies of the extracted wood quality indicators. In the assessment of wood properties, TLS should be considered as a tool for retrieving as dense stem and branching data as possible from carefully selected sample trees. Accurately retrieved morphological data could be applied to allometric wood property models. The models should use tree traits predictable with aerial remote sensing (e.g. tree height, crown dimensions) to enable extrapolations. As an outlook, terrestrial and aerial remote sensing can play an important role in filling in the knowledge gaps regarding the behavior of wood properties over different spatial and temporal extents. Further interdisciplinary cooperation will be needed to fully facilitate the use of remote sensing and spatially transferable wood property models that could become useful in tackling the challenges associated with changing climate, silviculture, and demand for wood.Hoidetuilla metsillä on useita tärkeitä rooleja muuttuvassa ilmastossa ja ympäristössä. Puu sitoo ja varastoi hiiltä niin kasvaessaan, kuin pitkäikäisiksi puutuotteiksi jalostettuna. Näiden vaikutusten huomioiminen metsänhoidossa vaatii tarkkaa suunnittelua, jolla varmistetaan metsänhoidon ja puunkäytön kestävyys. Tieto puuaineen ominaisuuksista on keskeisessä osassa, sillä ne vaikuttavat hiilivarastojen suuruuteen metsissä, sekä puun käytettävyyteen pitkäikäisenä rakennesahatavarana. Puunmuodostuksen teoreettisen taustan mukaisesti, runko, latvus ja oksarakenne ovat potentiaalisia selittäviä muuttujia (eli puun laatuindikaattoreita), kun mallinnetaan puuaineen ominaisuuksia, puubiomassaa ja puun laatua. Puunmuodostuksen monimutkaisuudesta ja moniulotteisesta vaihtelusta johtuen, tarvittavien laatuidikaattorien mittaaminen osana metsävarojen inventointia ja riittävällä yksityiskohtaisuudella on ollut aiemmin mahdotonta. Monialustaisen laserkeilauksen kehittyminen kuitenkin tukee aiempaa monipuolisempien kartoitus- ja mallinnusjärjestelmien rakentamista, jotka perustuvat tiheisiin kolmiulotteisiin pistepilviin. Tämän työn tavoitteena oli määritellä, kuinka puuaineen ominaisuuksia voidaan arvioida kaukokartoitusta hyödyntävässä metsävarojen inventoinnissa. Tätä tarkoitusta varten kehitettiin menetelmiä puun laatuindikaattorien mittaamiseksi hoidetuissa männiköissä (Pinus sylvestris L.) lento- ja maastolaserkeilauksen avulla, ja arvioitiin niiden toimivuutta. Ensin arvioitiin laatuindikaattorien mittatarkkuus pistepilvissä. Toiseksi verrattiin pistepilvimittauksia röntgentomografiamittauksiin teollisilla sahoilla. Kolmanneksi arvioitiin lentolaserkeilauksella tuotettujen latvuspiirteiden tarkkuutta laatuindikaattorien ennustamisessa. Tuloksien perusteella pistepilvien laatu ja pistetiheys vaikuttivat merkittävästi mitattujen laatuindikaattorien tarkkuuteen. Puuaineen ominaisuuksien arvioimisessa, maastolaserkeilausta tulisi käyttää työkaluna mahdollisimman yksityiskohtaisten runko- ja oksikkuustietojen keräämiseen tarkkaan valikoiduista näytepuista. Tarkasti mitatut laatuindikaattorit voivat selittää puuaineen ominaisuuksia mallinnuksessa. Käytettyjen mallien tulisi perustua laatuindikaattoreille, jotka voidaan ennustaa lentolaserkeilausaineistosta (esim. puun pituus ja latvuksen mittasuhteet), jotta ennusteet ovat yleistettävissä laajoille alueille. Tulevaisuudessa, maasta ja ilmasta tehtävällä kaukokartoituksella voi olla tärkeä rooli puuaineen ominaisuuksien aikaan ja paikkaan sidotun vaihtelun tutkimuksessa. Lisää poikkitieteellistä työtä tarvitaan, jotta kaukokartoitusta ja puuaineen ominaisuuksia ennustavia spatiaalisia malleja voidaan täysimittaisesti hyödyntää kiihtyvän ilmastonmuutoksen, muuttuvan metsänhoidon ja lisääntyvän puunkäytön tuomien haasteiden kohtaamisessa

Spaceborne Lidar in the Study of Marine Systems

Satellite passive ocean color instruments have provided an unbroken ~20-year record of global ocean plankton properties, but this measurement approach has inherent limitations in terms of spatial-temporal sampling and ability to resolve vertical structure within the water column. These limitations can be addressed by coupling ocean color data with measurements from a spaceborne lidar. Airborne lidars have been used for decades to study ocean subsurface properties, but recent breakthroughs have now demonstrated that plankton properties can be measured with a satellite lidar. The satellite lidar era in oceanography has arrived. Here we present a review of the lidar technique, its applications in marine systems, a prospective on what can be accomplished in the near future with an ocean- and atmosphere-optimized satellite lidar, and a vision for a multi-platform virtual constellation of observational assets enabling a 3-dimensional reconstruction of global ocean ecosystems

A backpack-mounted omnidirectional camera with off-the-shelf navigation sensors for mobile terrestrial mapping: Development and forest application

The use of Personal Mobile Terrestrial System (PMTS) has increased considerably for mobile mapping applications because these systems offer dynamic data acquisition with ground perspective in places where the use of wheeled platforms is unfeasible, such as forests and indoor buildings. PMTS has become more popular with emerging technologies, such as miniaturized navigation sensors and off-the-shelf omnidirectional cameras, which enable low-cost mobile mapping approaches. However, most of these sensors have not been developed for high-accuracy metric purposes and therefore require rigorous methods of data acquisition and data processing to obtain satisfactory results for some mapping applications. To contribute to the development of light, low-cost PMTS and potential applications of these off-the-shelf sensors for forest mapping, this paper presents a low-cost PMTS approach comprising an omnidirectional camera with off-the-shelf navigation systems and its evaluation in a forest environment. Experimental assessments showed that the integrated sensor orientation approach using navigation data as the initial information can increase the trajectory accuracy, especially in covered areas. The point cloud generated with the PMTS data had accuracy consistent with the Ground Sample Distance (GSD) range of omnidirectional images (3.5–7 cm). These results are consistent with those obtained for other PMTS approaches. View Full-Text Keywords: personal mobile terrestrial system; omnidirectional cameras; low-cost sensors; forest mapping; PMTS data quality </div