Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed “cohorts”, and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground based classifications and to extend structural cohort typing from plot to stand-levels. In both mixedwood and black spruce forest types, stand age and age related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

The native Eurasian red squirrel is considered endangered in the UK and is under strict legal protection. Long-term management of its habitat is a key goal of the UK conservation strategy. Current selection criteria of reserves and subsequent management mainly consider species composition and food availability. However, there exists a critical gap in understanding and quantifying the relationship between squirrel abundance, their habitat use and forest structural characteristics. This has partly resulted from the limited availability of structural data along with cost-efficient data collection methods. This study investigated the relationship between squirrel feeding activity and structural characteristics of Scots pine forests. Field data were collected from two study areas: Abernethy and Aberfoyle Forests. Canopy closure, diameter at breast height, height and number of trees were measured in 56 plots. Abundance of squirrel feeding signs was used as an index of habitat use. A GLM was used to model the response of cones stripped by squirrels in relation to the field collected structural variables. Results show that forest structural characteristics are significant predictors of feeding sign presence, with canopy closure, number of trees and tree height explaining 43% of the variation in stripped cones. The GLM was also implemented using LiDAR data to assess at wider scales the number of cones stripped by squirrels. The use of remote sensing -in particular Light Detection and Ranging (LiDAR) - enables cost efficient assessments of forest structure at large scales and can be used to retrieve the three variables explored in this study; canopy cover, tree height and number of trees, that relate to red squirrel feeding behaviour. Correlation between field-predicted and LiDAR-predicted number of stripped cones was performed to assess LiDAR-based model performance. LiDAR data acquired at Aberfoyle and Abernethy Forests had different characteristics (in particular pulse density), which influences the accuracy of LiDAR derived metrics. Therefore correlations between field predicted and LiDAR predicted number of cones (LSC) were assessed for each study area separately. Strong correlations (rs=0.59 for Abernethy and 0.54 for Aberfoyle) suggest that LiDAR-based model performed relatively well over the study areas. The LiDAR-based model was not expected to provide absolute numbers of cones stripped by squirrels but a relative measure of habitat use. This can be interpreted as different levels of habitat suitability for red squirrels. LiDAR-based GLM maps were classified into three levels of suitability: unsuitable (LSC = 0), Low (LSC < 10) and Medium to High Suitability (LSC >=10). These thresholds were defined based on expert knowledge. Such a classification of habitat suitability allows for further differentiation of habitat quality for red squirrels and therefore for a refined estimation of the carrying capacity that was used to inform population viability analysis (PVA) at Abernethy Forest. PVA assists the evaluation of the probability of a species population to become extinct over a specified period of time, given a set of data on environmental conditions and species characteristics. In this study, two scenarios were modelled in a PVA package (VORTEX). For the first scenario (Basic) carrying capacity was calculated for the whole forest, while for the second scenario (LiDAR) only Medium-to-High suitable patches were considered. Results suggest a higher probability of extinction for the LiDAR scenario (74%) than for the Basic scenario (55%). Overall the findings of this study highlight 1) the importance of considering forest structure when managing habitat for squirrel conservation and 2) the usefulness of LiDAR remote sensing as a tool to assist red squirrel, and potentially other species, habitat management.

Understanding how structure and function change across environmental gradients is a fundamental goal of ecology, with important applications in a changing world. In this dissertation, I explore how environmental variations in temperature and precipitation affect three-dimensional canopy structure, and how this, in turn, affects forest function. Characterising how climatic variations affect forest structure and function is particularly important in tropical forests, which are globally important carbon stores that have already shown vulnerability to climate change. The future of tropical forest carbon stocks is highly uncertain, with plant physiological responses representing the largest source of model uncertainties. As such, my dissertation research comprises empirical investigations into how tropical forests will respond to high temperatures and drought. Firstly, I examine tropical forest response to high temperature by conducting a comparison of natural forest sites and a tropical forest mesocosm using eddy-covariance data. I present evidence that high temperature declines in tropical forest photosynthesis are not due to direct temperature effects (i.e., that cause damage to the photosynthetic machinery), but instead are predominantly due to indirect temperature effects that result from concurrent increases in vapour pressure deficit (VPD). While both mechanisms reduce photosynthesis, the impact of increased VPD under future climate may be partly mitigated by enhanced water-use efficiency associated with rising atmospheric CO2 concentrations, suggesting that tropical forests may have opportunities for resilience in the face of global warming. The second part of my dissertation research examines how tropical forest canopy structure responds to seasonal dry periods and anomalous droughts on seasonal and interannual timescales, using data from ground-based LiDAR (Light Detection and Ranging). I show that total leaf area index (LAI) does not represent the seasonality of forest structure, since the upper and lower canopy levels exhibit divergent seasonal responses. The seasonal pattern of upper canopy LAI shows good agreement with the seasonal pattern of enhanced vegetation index (EVI) measured from satellites, suggesting that satellites are not capturing the response of the lower canopy. These results indicate that smaller trees are responding to seasonal water limitations and larger trees to light availability. I found that the response of canopy structure to anomalous (El Niño-induced) drought was similar to seasonal dry periods, but that the trends in LAI and vertical canopy structure were amplified. In particular, I document a delayed loss of LAI from the upper canopy following extreme drought, which supports the idea that while smaller trees may be more responsive to shorter, less severe dry periods, larger trees are more susceptible to prolonged or more severe droughts. Finally, I combine a long-term ground-based LiDAR dataset with tree inventory data in order to identify the mechanisms (i.e., changes in leaf area and/or woody biomass) of structural changes caused by droughts. I present evidence that loss of lower canopy LAI following an El Niño-induced drought was due to the mortality of small trees, not loss of leaf area, while an increase in LAI in the upper canopy predominantly resulted from plastic leaf area changes. If small trees are susceptible to drought-induced mortality and the incidence of droughts increases, this could prevent the recovery of tropical forests from drought-induced disturbances.

Data on rates of atmospheric deposition is limited in many montane ecosystems, where high spatial variability in meteorological, topographic, and vegetation factors contributes to elevated atmospheric inputs and to the creation of deposition hotspots. Addressing the ecological consequences of increasing deposition in these areas will require a better understanding of surface controls influencing atmospheric deposition rates at both large and small-scales. The overarching objective of this thesis research was to understand the influence of vegetation structure and canopy exposure on small-scale patterns of atmospheric sulfate, nitrate, and chloride deposition inputs to a conifer forest in the Santa Cruz Mountains, California. Throughfall ion fluxes (i.e., ions delivered in water that pass from the forest canopy to the forest floor), bulk deposition (i.e., primarily wet deposition), and rainfall data were collected during the rainy period from October 2012 to May 2013. Throughfall SO42-, Cl-, and NO3- fluxes were measured beneath eight clusters of Douglas fir (Pseudotsuga menziesii) trees (three trees per cluster) differing in tree size (i.e., diameter at breast height; DBH) and canopy exposure. In each cluster, a throughfall collector was placed 1-meter from the bole of an individual tree, for a total of 24 individual collectors. The position of each throughfall collector was recorded with a Trimble® GPS. In addition, tree height, tree diameter, and leaf area index, were measured for all trees. LiDAR data were obtained from GeoEarthScope’s Northern California Airborne LiDAR project and used to model the elevation (DEM), canopy surface height (DSM), tree height (CHM), slope, and curvature of the canopy surface across the entire study area. Over the rainy season, total throughfall flux of SO42--S, a conservative tracer of total deposition (wet + dry + fog), to Douglas fir clusters ranged from 1.44 - 3.84 kg S ha-1 wet season-1, while dry and fog deposition ranged from 0.13 -2.37 kg S ha-1 wet season-1. Total deposition to exposed mature tree clusters was 1.7-2.7 times higher than other clusters. Patterns of total Cl- fluxes (17.10 – 54.14 kg Cl- ha-1 wet season-1) resembled patterns of total SO42--S inputs. Overall, net throughfall fluxes (throughfall – bulk deposition) to Douglas fir trees clusters were more variable than total throughfall fluxes. Net SO42--S and Cl- fluxes to individual collectors increased with tree DBH and the convexity of the canopy surface. Compared to SO42--S and Cl- in throughfall, total NO3--N fluxes (0.17 - 4.03 kg N ha-1 wet season-1) were low and appeared to vary with small-scale changes in elevation. Geospatial technologies and remote sensing tools, such as LiDAR, are promising in the study of relationships between atmospheric deposition and topography (including vegetation), and in scaling-up estimates of atmospheric deposition to larger spatial scales. Understanding small-scale surface controls on atmospheric deposition has implications for different areas of research within geography, including modeling the spread of emerging infectious disease and assessing the effects of nitrogen cycling on native and invasive plant species composition.

Mountains are vital to ecosystems and human society given their influence on global carbon and water cycles. Yet the extent to which topography regulates montane forest carbon uptake and storage remains poorly understood. To address this knowledge gap, we compared forest aboveground carbon loading to topographic metrics describing energy balance and water availability across three headwater catchments of the Boulder Creek Watershed, Colorado, USA. The catchments range from 1800 to 3500 m above mean sea level with 46-102 cm/yr mean annual precipitation and -1.2 degrees to 12.3 degrees C mean annual temperature. In all three catchments, we found mean forest carbon loading consistently increased from ridges (27 +/- 19 Mg C ha) to valley bottoms (60 +/- 28 Mg C ha). Low topographic positions held up to 185 +/- 76 Mg C ha, more than twice the peak value of upper positions. Toe slopes fostered disproportionately high net carbon uptake relative to other topographic positions. Carbon storage was on average 20-40 Mg C ha greater on north to northeast aspects than on south to southwest aspects, a pattern most pronounced in the highest elevation, coldest and wettest catchment. Both the peak and mean aboveground carbon storage of the three catchments, crossing an 11 degrees C range in temperature and doubling of local precipitation, defied the expectation of an optimal elevation-gradient climatic zone for net primary production. These results have important implications for models of forest sensitivity to climate change, as well as to predicted estimates of continental carbon reservoirs.

This paper describes approaches to retrieve important aerosol results from the strong lidar return signals that are received by the space-borne CALIPSO lidar system after reflecting off-ocean surfaces. Relations, from which the theoretically expected values of area under ocean surface returns can be computed, are presented. A detailed description of the lidar system response to the ocean surface returns and the processes of sampling and averaging of lidar return signals are provided. An effective technique that reconstructs the lidar response to surface returnsstarting from down-linked samplesand calculates the area under it, has been developed and described. The calculated area values are validated after comparing them to their theoretically predicted counterpart values. Methods to retrieve aerosol optical depths (AODs) from these calculated areas are described and retrieval results are presented, including retrieval comparison with independent AOD measurements made by an airborne High Spectral Resolution Lidar (HSRL) that yielded quite good agreement. Techniques and results are also presented on using the spectral ratios of the surface response areas to determine spectral ratios of aerosol round-trip transmission and AOD spectral difference, without need of a specific/accurate ocean-surface reflectance model.

A new approach to improve the determination of building boundaries through automatic processing of light detection and ranging (LIDAR) data is presented. The LIDAR data is processed and interpolated into a grayscale image of intensity values corresponding to height measurements. Ground measurements are separated from non-ground measurements by using a progressive morphological filter. With these measurements now distinct, further separation of non-ground measurements into building and non-building measurements is performed by growing regions with similar characteristics. These building areas are then refined, resulting in a ground plan representation of building boundaries, known as building footprints. Several algorithms are then implemented to clean these footprints. A new method is developed to analyze actual known satellite imagery in order to confirm identified building footprints.

A utilização do sistema LiDAR na obtenção de dados da superfície da Terra vem se disseminando cada vez mais pelo alto desempenho na aquisição da informação e pela efetiva utilização dos produtos e subprodutos. Diversos segmentos passaram a adotar os produtos LiDAR como insumo básico e fundamental em suas rotinas de trabalho e estudos. O sucesso em um projeto que envolve aquisição e utilização desse tipo de dado está atrelado diretamente à definição e seu planejamento. O usuário deve ser capaz de definir o escopo básico e as diretrizes técnicas fundamentais que irão garantir que a demanda final seja alcançada. Diante disto se faz necessário elaborar um planejamento estabelecendo a melhor configuração para a aquisição dos dados e também os tipos de informações que serão extraídas destes produtos. Referente ao primeiro aspecto pode-se dizer que este é a base para a todo o projeto. Por meio dele é garantido a obtenção de produtos conforme a necessidade do usuário (resoluções espaciais dos produtos, o nível de detalhamento dos objetos, representação da topografia e outros). Muitos dos usuários que contratam serviços de perfilamento LiDAR não possuem embasamento técnico suficiente para definir a melhor especificação a ser adotada. Isto faz com que a maioria deles opte por adquirir uma alta densidade de pontos que, muitas vezes é desnecessária e ainda que atendam à demanda final tornam o projeto financeiramente oneroso. Esta pesquisa mostra que para um ambiente urbano regular, nuvens de pontos com baixas densidades (4 pts/m² e 8 pts/m²) apresentam uma equivalência na qualidade geométrica para produtos e subprodutos obtidos que serão utilizados para determinadas aplicações, não havendo a necessidade de utilizar nuvens com uma alta densidade (16 pts/m²) em projetos que utilizam estes dados em estudos altimétricos: geração do Modelo Digital de Terreno, curvas de nível, pontos cotados e também planimétricos: Modelo Digital de Elevação (Superfície e Normalizada) e seus derivados (altura e contorno de objetos, imagem de intensidade, cobertura vegetal e outros). / The use of the LiDAR system in obtaining data from the Earth\'s surface has been increasingly disseminated by the high performance in the acquisition of information and the effective use of products and by-products. Several segments started to adopt LiDAR products as basic and fundamental input in their works and studies. Success in a project that involves the acquisition and use of this type of data is directly linked to the definition and its planning. The user must be able to define the basic scope and the fundamental technical guidelines that will guarantee that the final demand is reached . In view of this, it is necessary to prepare a planning establishing the best configuration for the data acquisition and also the types of information that will be extracted from these products. Regarding the first aspect can be said that this is the basis for the whole project. Through it is guaranteed to obtain products according to the user is needs (spatial resolutions of the products, the level of detail of the objects, representation of the topography and others). Many of the users who hire LiDAR profiling services do not have sufficient technical background to define the best specification to adopt. This makes the majority of them choose to acquire a high density of points that is often unnecessary and even if they meet the final demand make the project financially costly. This research shows that for a regular urban environment, low point density clouds (4 pts/m² and 8 pts/m²) present an equivalence in the geometric quality for products and by-products obtained that will be used for certain applications. The need to use clouds with a high density (16 pts/m²) in projects that use this data in altimetric studies: generation of Digital Terrain Model, contourlines, quoted points and also planimetric: Digital Elevation Model (Surface and Normalized) and its derivatives (height and contour of objects, intensity image, vegetation cover and others).

For a little over the past decade since the DARPA Grand Challenge in 2004 and the more successful Urban Challenge in 2007 autonomous vehicles have seen a surge in popularity with car manufacturers, and companies such as Google and Uber. Light Detection And Ranging (LiDAR) has been one of the major sensors in use to sense for acting on the surrounding environment instead of the classic radar which has a much narrower field of vision. However the cost of the higher end 3D LiDAR systems which started seeing use during the DARPA challenges still have the high cost of $70,000 a piece which is an issue when trying to design a consumer friendly system on a family car. This work aims to investigate alternate 2D LiDAR systems to the costly systems currently in use in many prototypes to find a cost efficient alternative that can detect and track obstacles in front of a vehicle. The introduction begins by summarizing some related prior works, particularly papers from after the Grand Challenge as well as some about the competition itself. Detection and tracking methods for point clouds generated by the LiDAR are explored including ways to search through the data in an efficient manner to meet real-time constraints. Some of the trade-offs in going from a 3D system to a 2D system and examined along with how some of the drawbacks can be mitigated.

Creating 3D models of our surrounding world has seen a rapid increase in research and development over the last few years. A common method is to use laser scanners. Mapping is done either by ground based systems or airborne systems. With stationary ground-based laser scanning, or terrestrial laser scanning (TLS), it is possible to obtain high accuracy point clouds. But stationary TLS can often be a cumbersome and time-demanding task due to its lack of mobility. Because of this, much research has gone into mobilised TLS systems, referred commonly to as mobile laser scanning (MLS). Georeferencing point clouds to a world coordinate system is a difficult task in environments where global navigation satellite systems (GNSS) is unreliable. One such environment is forests, where the GNSS signal can be blocked, absorbed or reflected from the trees and canopy. Accurate georeference of points clouds for MLS systems in forests is difficult task that can be solved by using additional measurement instruments and post-processing algorithms to reduce the accumulation of errors, also known as drift. In this thesis a backpack-based MLS system to be used in forests was tested. The MLS system was composed of a GNSS, an inertial navigation unit (INS) and a laser scanner. The collected data was post-processed and analyzed to reduce the effects of detecting multiple ground layers and multiples of the same tree due to drift. The post-processing algorithm calculated tree and ground features to be used for adjusting the point cloud in the horizontal and vertical planes. The forest survey was done for an area roughly 40 meters in diameter. The MLS data was compared against TLS data as well as manual caliper data - where the caliper data was only measured in an area roughly 24 meters in diameter. The results indicated that the effects of multiple ground layers and multiple tree copies were removed after post-processing. Out of the total 214 TLS trees, 185 managed to be co-registered to MLS trees. The root mean square error (RMSE) and bias of the diameter at breast height (DBH) between the MLS andTLS data were 27.00 mm and -9.33 mm respectively. Co-registrationof the MLS and manual caliper data set gave 36 successful matches out of the total 43 manually measured DBH. The DBH RMSE and bias were 16.95 mm and -10.58 mm respectively. A Swedish TLS forest study obtained a DBH RMSE and bias (between TLS and caliper) of approximately 10 mm and +0.06 mm respectively. A Finnish backpack MLS forest study obtained a DBH RMSE and bias (between MLS and TLS) of 50.6 mm and +11.1 mm respectively. Evaluating the difference in radius at different heights along the tree stems between the MLS and TLS revealed a slight dependence on height, as the radius difference increased slightly closer to the stem base. The results indicated that backpack-based MLS systems has the potential for accurate lidar mapping in forests, and future development is of great interest to improve this system further.