Investigating the use of LiDAR scanning as a method for the measurement of timber distortion features

Measuring the extent to which a piece of structural timber has distorted at a macroscopic scale is fundamental to assessing its viability as a structural component. From the sawmill to the construction site, as structural timber dries, distortion can render it unsuitable for its intended purposes. This rejection of unusable timber is a considerable source of waste to the timber industry and the wider construction sector. As such, ensuring accurate measurement of distortion is a key step in addressing ineffciencies within timber processing. Currently, the FRITS frame method is the established approach used to gain an understanding of timber surface profile. The method, while reliable, is dependent upon relatively few measurements taken across a limited area of the overall surface, with a great deal of interpolation required. Further, the process is unavoidably slow and cumbersome, the immobile scanning equipment limiting where and when measurements can be taken and constricting the process as a whole. This thesis seeks to introduce LiDAR scanning as a new, alternative approach to distortion feature measurement. In its infancy as a measurement technique within timber research, the practicalities of using LiDAR scanning as a measurement method are herein demonstrated, exploiting many of the advantages the technology has over current approaches. LiDAR scanning creates a much more comprehensive image of a timber surface, generating input data multiple magnitudes larger than that of the FRITS frame. Set-up and scanning time for LiDAR is also much quicker and more flexible than existing methods. With LiDAR scanning the measurement process is freed from many of the constraints of the FRITS frame and can be done in almost any environment. For this thesis, surface scans were carried out on seven Sitka spruce samples of dimensions 48.5x102x3000mm using both the FRITS frame and LiDAR scanner. The samples used presented marked levels of distortion and were relatively free from knots. A computational measurement model was created to extract feature measurements from the raw LiDAR data, enabling an assessment of each piece of timber to be carried out in accordance with existing standards. Assessment of distortion features focused primarily on the measurement of twist due to its strong prevalence in spruce and the considerable concern it generates within the construction industry. Additional measurements of surface inclination and bow were also made with each method to further establish LiDAR's credentials as a viable alternative. Overall, feature measurements as generated by the new LiDAR method compared well with those of the established FRITS method. From these investigations recommendations were made to address inadequacies within existing measurement standards, namely their reliance on generalised and interpretative descriptions of distortion. The potential for further uses of LiDAR scanning within timber researches was also discussed.

Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.

Aquisição e construção de modelos estáticos análogos a reservatórios petrolíferos com tecnologia lidar e georadar

The present work develops a methodology to establish a 3D digital static models petroleum reservoir analogue using LIDAR and GEORADAR technologies. Therefore, this work introduce The methodolgy as a new paradigm in the outcrop study, to purpose a consistent way to integrate plani-altimetric data, geophysics data, and remote sensing products, allowing 2D interpretation validation in contrast with 3D, complexes depositional geometry visualization, including in environmental immersive virtual reality. For that reason, it exposes the relevant questions of the theory of two technologies, and developed a case study using TerraSIRch SIR System-3000 made for Geophysical Survey Systems, and HDS3000 Leica Geosystems, using the two technologies, integrating them GOCAD software. The studied outcrop is plain to the view, and it s located at southeast Bacia do Parnaíba, in the Parque Nacional da Serra das Confusões. The methodology embraces every steps of the building process shows a 3D digital static models petroleum reservoir analogue, provide depositional geometry data, in several scales for Simulation petroleum reservoir

Modelos para estimativa de volume de árvores individuais pela morfometria da copa obtida com LiDAR.

A estimativa volumétrica, a partir do escaneamento digital de florestas por meio do uso do LIDAR, potencializa o emprego de técnicas de manejo de precisão no planejamento da exploração nas florestas tropicais. A utilização dessa tecnologia de sensoriamento remoto permite a incorporação de variáveis da morfometria de copa, ainda pouco empregadas e menos conhecidas em decorrência da dificuldade de coleta em campo. O objeto deste estudo foi construir equações capazes de estimar o volume do fuste de árvores individuais dominantes e codominantes, a partir da morfometria da copa obtida por meio do LIDAR aerotransportado, considerando duas situações de inventário florestal: a) com a coleta do DAP, conjuntamente com as variáveis morfométrica da copa obtidas pelo LIDAR e b) apenas com os dados de morfometria de copa. Para seleção dos modelos foram considerados: a matriz de correlação das variáveis preditoras e a combinação das variáveis que geraram os melhores resultados estatísticos pelos critérios Syx, Syx(%) e Pressp, e que foram homocedásticos e com disposição dos resíduos normais e independentes. Para as melhores equações foram realizadas análise de influência. Os resultados estatísticos do ajuste dos modelos para as duas situações permitiram selecionar equações com e sem DAP, com resultados R2 aj.(%) de a) 92,92 e b) 79,44; Syx (%) de a) 16,73 e b) 27,47; e, critério de Pressp de a) 201,15 m6 e b) 537,47 m6, respectivamente. Por meio das variáveis morfométricas, foi possível desenvolver equações capazes de estimar com precisão o volume do fuste de árvores dominantes e codominantes em florestas tropicais.

Lidar Elevation Data Download App

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Comparação da estimativa de parâmetros estruturais e de mapas de predição de biomassa produzidos por meio de simulações de métodos de amostragem e dados LIDAR na reserva florestal da Embrapa Acre.

O uso de tecnologias no setor florestal tem permitido dentre outras possibilidades, conhecer a real condição da floresta desempenhando o menor trabalho possível, o que garante uma maior eficiência ao se tratar, por exemplo, em tipos de amostragem no inventário florestal. A pesquisa teve como objetivo testar a eficiência da amostragem aleatória e sistemática em quatro níveis de intensidade amostral para produzir estimativas de biomassa seca acima do solo e comparar mapas de predição de biomassa com dados gerados pelo LIDAR (Light Detection and Ranging). O trabalho foi realizado em uma reserva florestal de 800 ha do Campo Experimental da Embrapa Acre. Os dados foram fornecidos pela Embrapa Acre e gerados em duas fases, a primeira por meio de um inventário 100%, no qual foi utilizado para simular a amostragem na área de estudo, sendo utilizado todas as árvores vivas com DAP > 30 cm, a segunda fase através de dados LIDAR, ou seja, utilizando o perfilhamento à Laser aerotransportado. Para simular a amostragem foram utilizados três tamanhos de parcelas distintos 20mx20m, 50mx50m e 100mx100m em diferentes intensidades amostrais que foram 0,5%, 1%, 5% e 10%. O parâmetro utilizado para comparação foi o da biomassa seca acima do solo em Mg.ha-1 pelo teste Tukey, a 95% de probabilidade através do programa Minitab17 e as parcelas foram sorteadas e distribuídas por meio de simulações de instalação de parcelas utilizando o Arc GIS 10. Os dados LIDAR foram amostrados por uma empresa contratada, a partir deles foram realizados todos os modelos e a extrapolação das métricas para toda a área através do comando gridmetrics. Os mapas de predição foram confeccionados pela ferramenta de interpolação vizinhos próximos do Arc GIS 10 e as comparações entre os mapas foram feitas pela ferramenta do Arc GIS 10, Zonal statistic. A biomassa média obtida do inventário florestal foi de 155,2 Mg.ha-1, sendo que o tamanho de parcela ótimo encontrado foi de 50mx50m e os tratamentos que mais se aproximaram da média do inventário florestal foram o aleatório com intensidade amostral de 5% e o sistemático com intensidade amostral de 10%. Os tratamentos que atenderam o erro aceitável de 10% foram à amostragem aleatória com intensidades amostrais de 5% e 10% e a amostragem sistemática com intensidade amostral de 10%. Não houve diferença estatística significativa entre os tratamentos. Os mapas de vegetação baseados na biomassa que melhor representaram a biomassa seca acima do solo no tamanho de parcela 50mx50m foram na amostragem aleatória com intensidade amostral de 10%, e na amostragem sistemática com intensidades amostrais de 5% e 10%, comparando com os mapas gerados a partir do inventário 100% e dos dados LIDAR. Pode-se concluir que o tamanho ótimo de parcela foi de 50mx50m, com intensidades amostrais acima de 5% não havendo diferença entre os métodos de amostragem e que os mapas gerados pelo inventário 100% e pelos dados LIDAR foram equivalentes.

Analysis of spatial variation of tree heights in an urban area using LiDAR data.

Estimating with greater precision and accuracy the height of plants has been a challenge for the scientific community. The objective this study is to evaluate the spatial variation of tree heights at different spatial scales in areas of the city of Recife, Brazil, using LiDAR remote sensing data. The LiDAR data were processed in the QT Modeler (Quick Terrain Modeler v. 8.0.2) software from Applied Imagery. The TreeVaW software was utilized to estimate the heights and crown diameters of trees. The results obtained for tree height were consistent with field measurements.

Motivation, development and validation of a new spectral greenness index : a spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set. The reference landscape, a 2200 km2 area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

A synthesizable hardware evolutionary algorithm design for unmanned aerial system real-time path planning

The main objective of this paper is to detail the development of a feasible hardware design based on Evolutionary Algorithms (EAs) to determine flight path planning for Unmanned Aerial Vehicles (UAVs) navigating terrain with obstacle boundaries. The design architecture includes the hardware implementation of Light Detection And Ranging (LiDAR) terrain and EA population memories within the hardware, as well as the EA search and evaluation algorithms used in the optimizing stage of path planning. A synthesisable Very-high-speed integrated circuit Hardware Description Language (VHDL) implementation of the design was developed, for realisation on a Field Programmable Gate Array (FPGA) platform. Simulation results show significant speedup compared with an equivalent software implementation written in C++, suggesting that the present approach is well suited for UAV real-time path planning applications.

Compensation of unmodeled aircraft dynamics in airborne inspection of linear infrastructure assets

Fixed-wing aircraft equipped with downward pointing cameras and/or LiDAR can be used for inspecting approximately piecewise linear assets such as oil-gas pipelines, roads and power-lines. Automatic control of such aircraft is important from a productivity and safety point of view (long periods of precision manual flight at low-altitude is not considered reasonable from a safety perspective). This paper investigates the effect of any unwanted coupling between guidance and autopilot loops (typically caused by unmodeled delays in the aircraft’s response), and the specific impact of any unwanted dynamics on the performance of aircraft undertaking inspection of piecewise linear corridor assets (such as powerlines). Simulation studies and experimental flight tests are used to demonstrate the benefits of a simple compensator in mitigating the unwanted lateral oscillatory behaviour (or coupling) that is caused by unmodeled time constants in the aircraft dynamics.