Orientador: Mauricio Galo / Resumo: Um Levantamento Hidrográfico (LH) tem como principal meta a obtenção de dados para a edição e atualização de documentos náuticos, estes, voltados à segurança das atividades de navegação. Objetivando padronizar parâmetros de incerteza das cartas náuticas, a Organização Hidrográfica Internacional (OHI) define níveis mínimos de confiança para diferentes ordens. A sugestão dessas especificações foi internalizada pela Marinha do Brasil, responsável pela produção das cartas náuticas brasileiras, na NORMAM-25. Um desses parâmetros é a Incerteza Vertical Total máxima permitida, um indicador de qualidade da medição da profundidade. A informação de profundidade influencia no calado máximo permitido a uma embarcação para transitar em uma região com segurança, o que pode impactar inclusive nas limitações de transações comerciais em terminais portuários, uma vez que as profundidades estimadas com acurácia potencializam os parâmetros de operação dos portos. Por se tratar de um ambiente dinâmico, seja por ação da própria natureza ou devido a atividades antrópicas, a atualização de uma carta náutica deve ser uma preocupação constante. Como complemento à tradicional técnica de levantamento por meio de um ecobatímetro acoplado a embarcações, há a opção de se realizar um LH com o emprego da tecnologia LiDAR (Light Detection And Ranging) a partir de aeronaves, por meio de um aerolevantamento batimétrico por LiDAR (ALB – Airborne LASER Bathymetry), que operam com pulsos LASER na região verde do e... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: A Hydrographic Survey (HS) has as main goal to obtain data for editing and updating nautical documents, these, focused on the safety of navigation. In order to establish a standard of uncertainty parameters for nautical charts, the International Hydrographic Organization (IHO) defines minimum levels of confidence for different orders. The suggestion of these specifications was acknowledged by the Brazilian Navy, institution responsible to produce Brazilian nautical charts, as described in NORMAM-25. One such parameter is the maximum allowed Total Vertical Uncertainty, a quality indicator of the depth measurement. Depth information influences the maximum operational draft for a vessel to safely travel in a region, causing impact on port operations and limiting the commercial transactions. Accurately estimated depths enhance the operational parameters of the ports. Due to the aim of representing a dynamic environment, whether as consequence of the action of nature itself or because of anthropic activities, updating a nautical chart must be a constant concern. As a complement to the traditional survey technique conducted with a boat-coupled echosounder, there is the option of performing a HS using LiDAR (Light Detection And Ranging) technology from aircraft, through LiDAR aerial bathymetry (ALB - Airborne LASER Bathymetry), which operate with LASER pulses in the green region of the electromagnetic spectrum. Considering these points, this work analyzed the differences between the... (Complete abstract click electronic access below) / Mestre

There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications. LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory. The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang¡¦s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.

Submitted by Fernanda Rodrigues de Lima (fernanda.rlima@ufpe.br) on 2018-09-26T21:08:19Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Rafael Lopes Mendonça.pdf: 5746497 bytes, checksum: 6427f5355ad301a77ff31c0401d54da5 (MD5) / Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-10-01T15:23:00Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Rafael Lopes Mendonça.pdf: 5746497 bytes, checksum: 6427f5355ad301a77ff31c0401d54da5 (MD5) / Made available in DSpace on 2018-10-01T15:23:00Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Rafael Lopes Mendonça.pdf: 5746497 bytes, checksum: 6427f5355ad301a77ff31c0401d54da5 (MD5) Previous issue date: 2017-08-31 / CNPq / Com o crescente uso do escaneamento a LASER aerotransportado para aquisição de Modelo Digital, tem-se observado um maior interesse em desenvolver técnicas de filtragens capazes de gerar um Modelo Digital do Terreno (MDT) de forma automática. Os métodos existentes na literatura consideram regiões mais uniformes, com características diferentes das encontradas nas cidades brasileiras, principalmente em áreas onde há uma maior variação do relevo e diversidade de elementos, tanto naturais quanto artificiais, sobre o solo exposto. Diante disto, o presente trabalho tem como objetivo analisar algumas técnicas existentes, e propor uma abordagem para a filtragem de dados Light Detection And Ranging (LIDAR) para geração de MDT. A área de estudo corresponde a uma região do bairro do Vasco da Gama, na cidade de Recife, capital Pernambucana. Esta região apresenta um relevo acidentado com grande concentração de imóveis, além da presença de vegetação arbórea e rasteira. Através de alguns métodos de filtragens foram analisados os resultados obtidos para essa área de estudo. A combinação dos métodos de identificação de bordas e da classificação supervisionada apresentou resultado mais satisfatório, em comparação aos outros utilizados nesta dissertação. / With the increasing use of the airborne LASER scanning to acquire Digital Model, it has been observed a greater interest in developing filtering techniques capable of generating a Digital Terrain Model (DTM) automatically. The existing methods in the literature consider more uniform regions, with characteristics different from those found in Brazilian cities, especially in areas where there is a greater variation of the relief and diversity of elements, both natural and artificial, above the ground. Therefore, the present work aims to analyze some existing techniques, and to propose an approach to Light Detection and Ranging (LIDAR) data filtering for MDT generation. The study area corresponds to a region of the neighborhood of Vasco da Gama, in the city of Recife, capital of Pernambuco. This region presents a rough relief with great urban agglomeration, besides the presence of arboreal and low vegetation. Through some filtration methods the results obtained for this area of study were analyzed. The combination of the methods of edge identification and supervised classification presented a more satisfactory result, compared to the others used in this dissertation.

Flood inundation modelling is highly dependent on an accurate representation of floodplain topography. These  remotely  sensed  accurate  data  are  often  not  available  or  expensive,  especially  in  developing countries. As an alternative, freely available Digital Elevation Models (DEMs), such as the near-global Shuttle Radar Topography Mission (SRTM) data, have come into the focus of flood modellers. To what extent  these  low-resolution  data  can  be  exploited  for  hydraulic  modelling  is  still  an  open  research question. This benchmarking study investigated the potentials and limitations of the SRTM data set for flood inundation  modelling  on  the  example  of  the  Papaloapan  River,  Mexico.  Furthermore  the  effects  of vegetation signal removal from the SRTM DEM as in Baugh et al. (2010) were tested. A reference model based on a light detection and ranging (LiDAR) DEM was set up with the model code LISFLOOD-FP and run for two flood events. Test models based on SRTM DEMs were run and output flood extents compared to the reference model by applying a measure of fit. This measure of fit, which was based on binary wet/dry maps of both model outputs, gave information on how well the test models simulated the flood inundation extents compared to the reference model by giving a percentage of the model performance from theoretically 0 to 100 %. SRTM-based models could not reproduce the promising results of previous studies. Flood extents were mostly underestimated and commonly flooded areas were almost exclusively made up out of the main channel surface. One of the reasons for this likely was the much steeper slope of the SRTM DEM as opposed to the LiDAR DEM where water probably was conducted much faster though the main channel. Too high bank cells as well as generally more pronounced elevation differences of the SRTM DEM throughout the whole floodplain were another problem of the SRTM DEM preventing accurate flood inundation simulations. Vegetation  signal  removal  was  successful  to  a  certain  degree  improving  the  fit  by  about  10 %. However, a realistic shape of flood extent could not be simulated due to too big pixel sizes of the used canopy  height  data  set. Also,  the  conditioned  models  overestimated  flooded  areas  with  increasing vegetation signal removal, rendering some of the models useless for comparison, as water leaving the model domain could not be accounted for in the measure of fit. This study showed the limitations of SRTM data for flood inundation modeling where an accurate approximation of the river slope as well as accurately captured bank cells and floodplain topography are crucial for the simulated outcome. Vegetation signal removal has been shown to be potentially useful but should rather be applied on more densely covered catchments. / Översvämningar skapar stora problem världen över och fler och fler människor lever i områden som är utsatta för risk för att svämmas över. Dessutom förväntas översvämningar förekomma mer frekvent i många delar av världen i framtiden på grund av klimatförändringar. Skada orsakad av översvämningar kan  överstiga  flera  miljarder  US$.  Men  översvämningar  orsakar  också  andra  problem,  förutom ekonomiska förluster. De senaste 10 åren har mer än 60 000 människor dött på grund av översvämningar. Ytterligare 900 000 000 människor har drabbats på något sätt. Därför är det viktigt att man vet vilka områden som är utsatta för hög risk. Ett av de verktyg för att avgöra  översvämningsrisker  är  hydrauliska  datormodeller  som  försöker  förutse  hur  en  bestämd översvämning breder ut sig. Modellerna är baserade på fysiska principer och topografisk information. Helst vill man ha topografisk information med hög kvalitet och upplösning. Ofta har man data från fjärranalyser, insamlade från flygplan. Ett exempel på det är LiDAR-data som är baserad på laser. Dock är det ofta dyrt eller inte tillgängligt med LiDAR i avlägsna områden och utvecklingsländer, där man behöver sådan data som mest. Därför har forskare försökt att använda globalt tillgängliga topografiska data av låg kvalitet för hydrauliska modeller. En sådan datauppsättning är det så kallade SRTM-datat från amerikanska NASA. SRTM samlas in med hjälp av radarstrålar från satelliter. I flera studier har man fått goda resultat inom översvämningsmodellering med SRTM. Dock måste man testa det vidare för fler avrinningsområden. I den här studien har man försökt att använda SRTM i en hydraulisk modell för den mexikanska floden  Papaloapan.  För  att  se  hur  bra  (eller  dålig)  SRTM-modellen  är  för  att  simulera  hur  en översvämning sprids har man jämfört den med en modell baserad på högkvalitativ LiDAR-data. Båda modellerna  simulerade  samma  översvämningar. Topografiska  information  från  SRTM-data  är  oftast inkorrekt där det finns väldigt tät och hög vegetation, eftersom radarsignalen då inte räcker till marken och den uppskattade höjden är därför för hög i sådana områden. Av denna anledning ville man därför i denna  studie  även  testa  hur  resultatet  av  SRTM-modellen  skulle  förbättras  om  man  tog  bort  viss vegetation. Dessvärre var den utformade SRTM-modellen inte så bra för det här fallstudieområdet och SRTM-modellen  förutspådde  mycket  mindre  översvämningar  än  den  förmodade  mer  korrekta  LiDAR-modellen. Då vegetation avlägsnandes kunde man förbättra SRTM-modellen till viss mån, men det var fortfarande  inte  tillräckligt  för  det  här  området.  Denna  studie  visar  att  det  är  viktigt  att  fortsätta undersöka hur passande och användbart SRTM är, eftersom det har visat sig att SRTM inte är lämpligt för att förutspå översvämningar i alla delar av världen.

No âmbito das mudanças climáticas globais, a quantificação do estoque de carbono em povoamentos florestais tem recebido mais atenção, principalmente pelo fato das florestas exercerem um papel fundamental no equilíbrio do estoque de carbono global. Com o objetivo de contribuir para esse processo, a parte investigativa deste trabalho foi desenvolvida em duas etapas. A primeira etapa objetivou o ajuste de modelos alométricos para a estimativa do estoque de carbono presente na biomassa total (Ctotal), no lenho comercial (Cleco) e parte residual (Crds) (casca, folhas e galhos) em plantações de Eucalyptus spp. em nível de árvores, através de uma amostragem destrutivas de árvores, análise elementar do carbono em laboratório e medidas convencionais de inventário. A medição do diâmetro à altura do peito (DAP) e altura total das árvores em parcelas amostradas instaladas nos talhões onde as árvores foram coletadas para determinação direta de carbono também foi realizada. A segunda parte, consistiu na avaliação do uso da tecnologia LiDAR (Light Detection and Ranging) aerotransportada (Airborne LASER scanning) como uma alternativa eficiente e versátil para a estimativa do estoque de carbono total (Ctotal), no lenho comercial de toras (Cleco) e no resíduo da árvore (Crds) em nível de parcelas em plantações de Eucalyptus spp usando como base o estoque de carbono estimado na primeira fase. Os resultados obtidos encontram-se resumidos em dois artigos científicos. O primeiro artigo mostra que os modelos baseados no logaritmo do diâmetro à altura do peito (DAP) e da altura total da árvore (Ht) oferecem boas precisão e exatidão para estimar o estoque de carbono em nível de árvore. O segundo artigo, permitiu a determinação das melhores métricas LiDAR para o cálculo do teor total de carbono, tanto no carbono total, lenho comercial e nas partes residuais da árvore em nível de plantação. Esses resultados, bem como os indicadores estatísticos utilizados para avaliar a qualidade dos ajustes, são o cerne desta dissertação. / In the context of global climate change, the quantification of the carbon content in forest plantations have received great attention. This is because vegetation play an important role in the global carbon budget. This master thesis was developed in two main parts. The first part was to adjust allometric equations for the estimation of the carbon content at a tree level. This was performed for the above ground section (Ctotal), in commercial logs (Cleco) and residuals ( Crds) (e.g. bark, leaves and branches/twigs). The experiment was based on the destructive model of individual trees harvested in commercial plantations of Eucaliptus spp. The experiment encompasses both forest inventory and laboratory analyses procedures. Additionally, in-situ measurements such as the diameter at the breast height (DBH) and the total tree height were also performed. These sample plots were located in homogeneous forest units and close to the areas were the trees have been harvested. The second part of this master thesis was the evaluation of the airborne LiDAR technology as a tool for the retrieval of the above ground biomass (Ctotal), the carbon present in the commercial logs (Cleco) and residuals. This procedure was performed at the sample plots level. This procedure was based on the information provided in the first part. The results are presented as two scientific manuscripts. The first manuscript shows that allometric equations based on the log of the variables diameter at the breast height (DBH) and total tree height (Ht) were good predictors for the retrieval of the total carbon content at a tree level. The second manuscript allow the selection of the best LiDAR derived metrics for the retrieval of the total carbon content, either at above ground level, commercial logs and residual parts of the tree at a sample plots level. These results, as well as the statistical indicators for the adjustment of several statistical models is the core of this master thesis.

No âmbito das mudanças climáticas globais, a quantificação do estoque de carbono em povoamentos florestais tem recebido mais atenção, principalmente pelo fato das florestas exercerem um papel fundamental no equilíbrio do estoque de carbono global. Com o objetivo de contribuir para esse processo, a parte investigativa deste trabalho foi desenvolvida em duas etapas. A primeira etapa objetivou o ajuste de modelos alométricos para a estimativa do estoque de carbono presente na biomassa total (Ctotal), no lenho comercial (Cleco) e parte residual (Crds) (casca, folhas e galhos) em plantações de Eucalyptus spp. em nível de árvores, através de uma amostragem destrutivas de árvores, análise elementar do carbono em laboratório e medidas convencionais de inventário. A medição do diâmetro à altura do peito (DAP) e altura total das árvores em parcelas amostradas instaladas nos talhões onde as árvores foram coletadas para determinação direta de carbono também foi realizada. A segunda parte, consistiu na avaliação do uso da tecnologia LiDAR (Light Detection and Ranging) aerotransportada (Airborne LASER scanning) como uma alternativa eficiente e versátil para a estimativa do estoque de carbono total (Ctotal), no lenho comercial de toras (Cleco) e no resíduo da árvore (Crds) em nível de parcelas em plantações de Eucalyptus spp usando como base o estoque de carbono estimado na primeira fase. Os resultados obtidos encontram-se resumidos em dois artigos científicos. O primeiro artigo mostra que os modelos baseados no logaritmo do diâmetro à altura do peito (DAP) e da altura total da árvore (Ht) oferecem boas precisão e exatidão para estimar o estoque de carbono em nível de árvore. O segundo artigo, permitiu a determinação das melhores métricas LiDAR para o cálculo do teor total de carbono, tanto no carbono total, lenho comercial e nas partes residuais da árvore em nível de plantação. Esses resultados, bem como os indicadores estatísticos utilizados para avaliar a qualidade dos ajustes, são o cerne desta dissertação. / In the context of global climate change, the quantification of the carbon content in forest plantations have received great attention. This is because vegetation play an important role in the global carbon budget. This master thesis was developed in two main parts. The first part was to adjust allometric equations for the estimation of the carbon content at a tree level. This was performed for the above ground section (Ctotal), in commercial logs (Cleco) and residuals ( Crds) (e.g. bark, leaves and branches/twigs). The experiment was based on the destructive model of individual trees harvested in commercial plantations of Eucaliptus spp. The experiment encompasses both forest inventory and laboratory analyses procedures. Additionally, in-situ measurements such as the diameter at the breast height (DBH) and the total tree height were also performed. These sample plots were located in homogeneous forest units and close to the areas were the trees have been harvested. The second part of this master thesis was the evaluation of the airborne LiDAR technology as a tool for the retrieval of the above ground biomass (Ctotal), the carbon present in the commercial logs (Cleco) and residuals. This procedure was performed at the sample plots level. This procedure was based on the information provided in the first part. The results are presented as two scientific manuscripts. The first manuscript shows that allometric equations based on the log of the variables diameter at the breast height (DBH) and total tree height (Ht) were good predictors for the retrieval of the total carbon content at a tree level. The second manuscript allow the selection of the best LiDAR derived metrics for the retrieval of the total carbon content, either at above ground level, commercial logs and residual parts of the tree at a sample plots level. These results, as well as the statistical indicators for the adjustment of several statistical models is the core of this master thesis.

Multi-spectral Satellite imagery, among remotely sensed data from airborne and spaceborne platforms, contained the NIR band information is the major source for the land- cover classification. The main purpose of aerial imagery is for thematic land-use/land-cover mapping which is rarely used for land cover classification. Recently, the newly developed digital aerial cameras containing NIR band with up to 10cm ultra high resolution makes the land-cover classification using aerial imagery possible. However, because the urban ground objects are so complex, multi-spectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, aerial LiDAR (ULiUght UDUetection UAUnd URUanging) data have been integrated with remotely sensed data to obtain better classification results. The LiDAR-derived normalized digital surface models (nDSMs) calculated by subtracting digital elevation models (DEMs) from digital surface models (DSMs) becomes an important factor for urban classification. This study proposed an adaptive raw-data-based, surface-based LiDAR data-filtering algorithm to generate DEMs as the foundation of generating the nDSMs. According to the experiment results, the proposed adaptive LiDAR data-filtering algorithm not only successfully filters out ground objects in urban, forest, and mixed land cover areas but also derives DEMs within the LiDAR data measuring accuracy based on the absolute and relative accuracy evaluation experiments results. For the aerial imagery urban classification, this study first conducted maximum likelihood classification (MLC) experiments to identify features suitable for urban classification using LiDAR data and aerial imagery. The addition of LiDAR height data improved the overall accuracy by up to 28 and 18%, respectively, compared to cases with only red¡Vgreen¡Vblue (RGB) and multi-spectral imagery. It concludes that the urban classification is highly dependent on LiDAR height rather than on NIR imagery. To further improve classification, this study proposes a knowledge-based classification system (KBCS) that includes a three-level height, ¡§asphalt road, vegetation, and non-vegetation¡¨ (A¡VV¡VN) classification model, rule-based scheme and knowledge-based correction (KBC). The proposed KBCS improved overall accuracy by 12 and 7% compared to maximum likelihood and object-based classification, respectively. The classification results have superior visual interpretability compared to the MLC classified image. Moreover, the visual details in the KBCS are superior to those of the OBC without involving a selection procedure for optimal segmentation parameters.

Die letzten zwanzig Jahre haben gezeigt, dass die Integration luftgestützter Lasertechnologien (Light Detection and Ranging; LiDAR) in die Erfassung von Waldressourcen dazu beitragen kann, die Genauigkeit von Schätzungen zu erhöhen. Um diese zu ermöglichen, müssen Feldaten mit LiDAR-Daten kombiniert werden. Diverse Techniken der Modellierung bieten die Möglichkeit, diese Verbindung statistisch zu beschreiben. Während die Wahl der Methode in der Regel nur geringen Einfluss auf Punktschätzer hat, liefert sie unterschiedliche Schätzungen der Genauigkeit. In der vorliegenden Studie wurde der Einfluss verschiedener Modellierungstechniken und Variablenauswahl auf die Genauigkeit von Schätzungen untersucht. Der Schwerpunkt der Arbeit liegt hierbei auf LiDAR Anwendungen im Rahmen von Waldinventuren. Die Methoden der Variablenauswahl, welche in dieser Studie berücksichtigt wurden, waren das Akaike Informationskriterium (AIC), das korrigierte Akaike Informationskriterium (AICc), und das bayesianische (oder Schwarz) Informationskriterium. Zudem wurden Variablen anhand der Konditionsnummer und des Varianzinflationsfaktors ausgewählt. Weitere Methoden, die in dieser Studie Berücksichtigung fanden, umfassen Ridge Regression, der least absolute shrinkage and selection operator (Lasso), und der Random Forest Algorithmus. Die Methoden der schrittweisen Variablenauswahl wurden sowohl im Rahmen der Modell-assistierten als auch der Modell-basierten Inferenz untersucht. Die übrigen Methoden wurden nur im Rahmen der Modell-assistierten Inferenz untersucht. In einer umfangreichen Simulationsstudie wurden die Einflüsse der Art der Modellierungsmethode und Art der Variablenauswahl auf die Genauigkeit der Schätzung von Populationsparametern (oberirdische Biomasse in Megagramm pro Hektar) ermittelt. Hierzu wurden fünf unterschiedliche Populationen genutzt. Drei künstliche Populationen wurden simuliert, zwei weitere basierten auf in Kanada und Norwegen erhobenen Waldinveturdaten. Canonical vine copulas wurden genutzt um synthetische Populationen aus diesen Waldinventurdaten zu generieren. Aus den Populationen wurden wiederholt einfache Zufallsstichproben gezogen und für jede Stichprobe wurden der Mittelwert und die Genauigkeit der Mittelwertschätzung geschäzt. Während für das Modell-basierte Verfahren nur ein Varianzschätzer untersucht wurde, wurden für den Modell-assistierten Ansatz drei unterschiedliche Schätzer untersucht. Die Ergebnisse der Simulationsstudie zeigten, dass das einfache Anwenden von schrittweisen Methoden zur Variablenauswahl generell zur Überschätzung der Genauigkeiten in LiDAR unterstützten Waldinventuren führt. Die verzerrte Schätzung der Genauigkeiten war vor allem für kleine Stichproben (n = 40 und n = 50) von Bedeutung. Für Stichproben von größerem Umfang (n = 400), war die Überschätzung der Genauigkeit vernachlässigbar. Gute Ergebnisse, im Hinblick auf Deckungsraten und empirischem Standardfehler, zeigten Ridge Regression, Lasso und der Random Forest Algorithmus. Aus den Ergebnissen dieser Studie kann abgeleitet werden, dass die zuletzt genannten Methoden in zukünftige LiDAR unterstützten Waldinventuren Berücksichtigung finden sollten.

Light Detection and Ranging (LIDAR) är en teknik för att samla in data om terräng. Genom att använda dessa data kan man skapa olika terrängmodeller. Denna studie syftar till att undersöka hur olika procentuella reduceringar av ursprungsdata påverkar kvalitén hos genererade höjdmodeller i form av Triangular Irregular Network (TIN). Detta görs genom att med hjälp av statistiska metoder göra jämförelser mellan punkter i den genererade TIN modellen och motsvarande punkter i det ursprungliga LIDAR punktmolnet. Studien visar att, beroende på noggrannhetskrav och topografi, en så liten andel som 5 % av punkterna kan vara tillräckligt, samt att noggrannhetsförbättring vid användning av mer än 50 % av ursprungsdata inte kan motivera den ökade arbetsbelastningen för datahantering.

O trabalho se estruturou em torno de dois estudos. O primeiro avaliou o ajuste de um modelo não linear de efeito misto para descrever o afilamento do tronco de árvores clonais de eucalipto. O modelo utilizado para descrever as variações da altura em função do raio foi o logístico de quatro parâmetros que, por integração permitiu a estimação do volume das árvores. A incorporação de funções de variância no processo de ajuste resultou em redução significativa no valor do Critério de informação de Akaike, mas os resíduos não apresentaram melhorias notáveis. Com a finalidade de compatibilizar precisão e parcimônia, o modelo que considera as variações do afilamento como uma função da altura total e do raio à altura do peito mostrou-se como o mais indicado para a estimativa do volume de árvores por funções de afilamento. O segundo estudo analisou uma nova proposta para inventários florestais em plantios clonais de eucalipto que integra modelagem geoestatística, medições de circunferência das árvores em campo e a tecnologia LiDAR aeroembarcada. As estatísticas propostas mostraram que o modelo geoestatístico com função para média foi estatisticamente superior ao modelo com média constante, com erros reduzidos em até 40%. A altura das árvores que compuseram o grid de predição para aplicação do modelo geoestatístico foi obtida pelo processamento da nuvem de pontos dos dados LiDAR. Obtidos os pares de diâmetro e altura, aplicou-se o modelo de afilamento selecionado no primeiro artigo em que se observaram diferenças médias na predição do volume próximas a 0,7%, e 0,18% para contagem de árvores, ambas com tendências de subestimativas. Diante dos resultados obtidos, o método é considerado como promissor e trabalhos futuros visam gerar um banco de parcelas permanentes que propiciem estudos de crescimento e produção florestal. / This study investigates the use of mixed-effect model and the use of LiDAR based model to estimate volume from eucalyptus forest plantation. At the first part, this study evaluates nonlinear mixed-effects to model stem taper of monoclonal Eucalyptus trees. The relation between radius and height variation was described by the four-parameter logistic model that integration returns stem volume. Embedding variance functions to the estimation process decreased significantly the Akaike\'s Information Criterion but did not improve the residual analysis. The best model to estimate stem volume from taper equations explained the stem taper as a function of the commercial height and the radius at breast height. The second part investigated the volume estimation fusing geostatistic derived from field information and airborne laser scanning data. The model based on geostatistic assumptions was statistically superior to the traditional one, with errors 40% lower. Thus, the geostatistical model was applied over tree heights extracted from the laser cloud. To each combination of diameter and height, the taper equation form the first part of this study was used. The volume and the number of trees were underestimated in 0.7% and 0.18%, respectively. The results look promising, and more permanent plots are necessary to allow studies about growth and yield of forest.