The marbled murrelet (Brachyramphus marmoratus) is a Threatened seabird with very particular nesting requirements. They choose to nest almost exclusively on mossy platforms, provided by large branches or deformities, in the upper canopies of coniferous old-growth trees located within 50 km of the ocean. Due primarily to a loss of this nesting habitat, populations in B.C. have seen significant decline over the past several decades. As such, reliable spatial habitat data are required to facilitate efficient management of the species and its remaining habitats. Current habitat mapping methodologies are limited by their qualitative assessment of habitat attributes and the large, stand-based spatial scale at which they classify and map habitat. This research aimed to address these limitations by utilizing light detection and ranging (lidar) technologies to develop an object-based habitat mapping methodology capable of quantitatively mapping habitat suitability at the scale of an individual tree on Northern Vancouver Island, British Columbia (B.C.). Using a balanced random forest (BRF) classification algorithm and in-field habitat suitability data derived from low-level aerial surveys (LLAS), a series of lidar-derived terrain and canopy descriptors were used to predict the habitat suitability (Rank 1: Very High Suitability – Rank 6: Nil Suitability) of lidar-derived individual tree objects. The classification model reported an overall classification accuracy of 71%, with Rank 1 – Rank 5 reporting individual class accuracies of 90%, 86%, 74%, 67%, and 98%, respectively. Evaluation of the object-based predictive habitat suitability maps provided evidence that this new methodology is capable of identifying and quantifying within-stand habitat variability at the scale of an individual tree. This improved quantification provides a superior level of habitat differentiation currently unattainable using existing habitat mapping methods. As the total amount of suitable nesting habitat in B.C. is expected to continue to decline, this improved quantification is a critical advancement for strategic managers, facilitating improved habitat and species management. / Graduate / 2018-04-07 / 0329 / 0368 / 0478 / gclyde@uvic.ca

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The importance of single-tree-based information for forest management and related industries in countries like Sweden, which is covered in approximately 65% by forest, is the motivation for developing algorithms for tree detection and species identification in this study. Most of the previous studies in this field are carried out based on aerial and spectral images and less attention has been paid on detecting trees and identifying their species using laser points and clustering methods. In the first part of this study, two main approaches of clustering (hierarchical and K-means) are compared qualitatively in detecting 3-D ALS points that pertain to individual tree clusters. Further tests are performed on test sites using the supervised k-means algorithm in which the initial clustering points are defined as seed points. These points, which represent the top point of each tree are detected from the cross section analysis of the test area. Comparing those three methods (hierarchical, ordinary K-means and supervised K-means), the supervised K-means approach shows the best result for clustering single tree points. An average accuracy of 90% is achieved in detecting trees. Comparing the result of the thesis algorithms with results from the DPM software, developed by the Visimind Company for analysing LiDAR data, shows more than 85% match in detecting trees. Identification of trees is the second issue of this thesis work. For this analysis, 118 trees are extracted as reference trees with three species of spruce, pine and birch, which are the dominating species in Swedish forests. Totally six methods, including best fitted 3-D shapes (cone, sphere and cylinder) based on least squares method, point density, hull ratio and slope changes of tree outer surface are developed for identifying those species. The methods are applied on all extracted reference trees individually. For aggregating the results of all those methods, a fuzzy logic system is used because of its good reputation in combining fuzzy sets with no distinct boundaries. The best-obtained model from the fuzzy system provides 73%, 87% and 71% accuracies in identifying the birch, spruce and pine trees, respectively. The overall obtained accuracy in species categorization of trees is 77%, and this percentage is increased dealing with only coniferous and deciduous types classification. Classifying spruce and pine as coniferous versus birch as deciduous species, yielded to 84% accuracy.

Submitted by Lucas Jamiro Barbosa (eng.lucasjb@gmail.com) on 2018-02-21T18:50:52Z No. of bitstreams: 1 barbosa_lj_me_prud.pdf: 5180173 bytes, checksum: 561a235397d5860ed83f8776cc40f6a4 (MD5) / Approved for entry into archive by ALESSANDRA KUBA OSHIRO ASSUNÇÃO (alessandra@fct.unesp.br) on 2018-02-21T19:37:41Z (GMT) No. of bitstreams: 1 barbosa_lj_me_prud.pdf: 5180173 bytes, checksum: 561a235397d5860ed83f8776cc40f6a4 (MD5) / Made available in DSpace on 2018-02-21T19:37:41Z (GMT). No. of bitstreams: 1 barbosa_lj_me_prud.pdf: 5180173 bytes, checksum: 561a235397d5860ed83f8776cc40f6a4 (MD5) Previous issue date: 2017-04-28 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / O Sensoriamento Remoto tem-se mostrado, nos últimos anos, uma excelente ferramenta na aquisição de informações da cobertura da Terra. Dentre os diversos sensores remotos, o sistema de aquisição de dados por varredura LASER (Light Amplification by Stimulated Emission of Radiation) se apresenta como uma ferramenta poderosa na coleta de informações tridimensionais. A tecnologia lidar (Light Detection And Ranging), quando a bordo de aeronaves, pode ser denominada como Airborne Laser Scanning (ALS), diferente dos sistemas aerofotogramétricos de imageamento tradicionais, permite coletar, simultaneamente, pontos com coordenadas 3D sobre copas de árvores, bem como o terreno abaixo dela, em função da capacidade de registro de múltiplos retornos e da divergência do pulso. Por esta razão, esta tecnologia tem sido utilizada em diversas aplicações florestais, como manejo e recuperação florestal, silvicultura, exploração madeireira, dentre outras. Diversas pesquisas têm mostrado a possibilidade de utilização dos dados lidar na extração e delimitação de indivíduos arbóreos e, portanto, na estimativa de variáveis dendrométricas. Desta forma, o desenvolvimento de técnicas que proporcionem a automatização no delineamento das copas das árvores e na estimativa destas informações é de grande interesse. Contudo, grande parte das pesquisas relacionadas à detecção de árvores, delimitação de copa e estimativa de algumas variáveis são desenvolvidas considerando cenários homogêneos e específicos, onde a vegetação é caracterizada pela presença de árvores coníferas e/ou deciduais, ou florestas de exploração madeireira. Portanto, o objetivo desta pesquisa foi a implementação e avaliação de uma técnica que permita detectar indivíduos arbóreos e aglomerados, considerando um cenário urbano heterogêneo e complexo; e, destes indivíduos, estimar variáveis dendrométricas como área da copa; altura da árvore; e raio médio da copa. A metodologia proposta é realizada em três etapas e se baseia no uso do método de crescimento de regiões, aplicado à nuvem de pontos originais, ordenados quanto à altura. Além disso, são usados polígonos convexos visando a extração de indivíduos arbóreos e aglomerados. Para isso, são utilizados três parâmetros: distância mínima, buffer e perímetro comum. Foram realizados experimentos considerando dados reais e cenários diferentes em uma área urbana, para diferentes conjuntos de parâmetros utilizados no processo de delimitação das copas. Os mesmos foram avaliados quanto a acurácia temática, completeza e F-Score, calculados em função de referências obtidas de forma manual. Na delimitação de indivíduos arbóreos e aglomerados, simultaneamente, o maior valor de F-Score foi de 54% e na delimitação de indivíduos e aglomerados, em separado, o melhor resultado obtido foi 74% e 39%, respectivamente. Embora melhorias possam ser feitas visando aumentar estes indicadores, principalmente para aglomerados, pode-se considerar que o método proposto tem potencial de aplicação, sobretudo quando se tem por objetivo a extração de árvores individuais em ambiente urbano. / Remote Sensing has shown to be, in the last years, an excellent way of acquiring Earth’s surface data. Among all remote sensors, the system of data acquisition by LASER (Light Amplification by Stimulated Emission of Radiation) scanning has been presented as a powerful tool for three-dimensional information collection. lidar (Light Detection And Ranging) technology, when onboard of airplanes can be named Airborne Laser Scanning (ALS) and, differently from traditional photogrammetric techniques, allows the collection of simultaneously 3D points over tree crowns, as well as the ground underneath it, due to the recording capacity of multiple echoes arising from the divergence of the pulse. For this reason, this technology has been used in many different forest applications, as management and forest recovery, forestry, logging and others. Some researchers have shown the possibility of using lidar data on tree extraction and crown delineation and, therefore, on the estimation of their dendrometric variables. In this way, the development of techniques that can provide the automation of tree crowns delineation and estimation of this information has increased. However, most of the researches performed ever related to tree detection, canopy delineation and estimation of some dendrometric variables are developed considering homogeneous and specific scenarios where the vegetation is characterized by the presence of coniferous and/or deciduous trees. For this purpose, the objective of this research was the implementation and evaluation of a technique capable of detecting individual trees and clumps, considering a heterogeneous urban scenario. Additionally, from those individual trees some dendrometric variables such as crown area; tree height and average crown radius were estimated. Experiments were conducted considering different study areas in an urban environment varying the parameters used in the crown delineation process. Those experiments were evaluated in terms of thematic accuracy, completeness and F-Score, computed based on reference values obtained manually. When the simultaneous delimitation of arboreal individuals and agglomerates was performed the best F-Score was 54%. For independent processing, the best result was 74% and 39%, respectively, for individuals and agglomerates. Although improvements can be performed aiming to improve those indicators, mainly to clumps, it is possible to consider that the proposed method has potential, especially when the objective is the extraction of individual trees in an urban environment.