The design of a texture display for three-dimensional Light Detection and Ranging (LiDAR) point clouds is investigated. The objective is to present a low fidelity display that is simple to compute in real-time, which utilizes the pattern processing capabilities of a human operator to afford an understanding of the environment. The efficacy of the display is experimentally evaluated by in comparison with a baseline point cloud rendering. Subjects were shown data based on virtual hills, and were asked to plan the least-steep traversal, and identify the hill from a set of distractors. The major conclusions are: comprehension of LiDAR point clouds from the sensor origin is difficult without further processing of the data, a separated vantage point improves understanding of the data, and a simple computation to present local point cloud derivative data significantly improves the understanding of the environment, even when observed from the sensor origin.

Global modeling of gross primary production (GPP) is a critical component of climate change research. On local scales, GPP can be assessed from measuring CO₂ exchange above the plant canopy using tower-based eddy covariance (EC) systems. The limited footprint inherent to this method however, restricts observations to relatively few discrete areas making continuous predictions of global CO₂ fluxes difficult. Recently, the advent of high resolution optical remote sensing devices has offered new possibilities to address some of the scaling issues related to GPP using remote sensing. One key component for inferring GPP spectrally is the efficiency (ε) with which plants can use absorbed photosynthetically active radiation to produce biomass. While recent years have seen progress in measuring ε using the photochemical reflectance index (PRI), little is known about the temporal and spatial requirements for up-scaling these findings continuously throughout the landscape. Satellite observations of canopy reflectance are subject to view and illumination effects induced by the bi-directional reflectance distribution function(BRDF) which can confound the desired PRI signal. Further uncertainties include dependencies of PRI on canopy structure, understorey, species composition and leaf pigment concentration. The objective of this research was to investigate the effects of these factors on PRI to facilitate the modeling of GPP in a continuous fashion. Canopy spectra were sampled over a one-year period using an automated tower-based, multi-angular spectroradiometer platform (AMSPEC), designed to sample high spectral resolution data. The wide range of illumination and viewing geometries seen by the instrument permitted comprehensive modeling of the BRDF. Isolation of physiologically induced changes in PRI yielded a high correlation (r²=0.82, p<0.05) to EC-measured ε, thereby demonstrating the capability of PRI to model ε throughout the year. The results were extrapolated to the landscape scale using airborne laser-scanning (light detection and ranging, LiDAR) and high correlations were found between remotely-sensed and EC-measured GPP (r²>0.79, p<0.05). Permanently established tower-based canopy reflectance measurements are helpful for ongoing research aimed at up-scaling ε to landscape and global scales and facilitate a better understanding of physiological cycles of vegetation and serve as a calibration tool for broader band satellite observations.

Au cours des dernières années, on a assisté à un virage vers une gestion plus durable de la forêt publique et des ressources forestières. Pour ce faire, il est nécessaire d'acquérir des informations précises sur le statut des forêts. Ceci permettra, d'une part, de mieux évaluer les ressources disponibles et, d'autre part, de faire un meilleur suivi de leur dynamique afin, par exemple, de mieux comprendre et de prévoir les impacts de l'activité sylvicole. Il a été démontré que les capteurs lidars pouvaient être utilisés afin d'obtenir des données pouvant être segmentées afin de décrire assez fidèlement la géométrie de la couronne d'arbres individuels. Parmi les attributs de la forêt pouvant être extraits de ces données, la hauteur de la base de la couronne vivante est probablement un de ceux pour lequel le potentiel du lidar a été le moins étudié et ce, malgré l'importance que la connaissance de ce paramètre peut avoir pour, par exemple, le calcul de la biomasse ou la prévention des feux de forêts. Une nouvelle méthode permettant d'estimer la hauteur de la couronne vivante à partir de données lidar est proposée. Les données utilisées pour la recherche ont été acquises au Québec, sur le territoire de la forêt d'enseignement et de recherche du Lac Duparquet. Les trois espèces à l'étude sont le peuplier faux-tremble, l'épinette blanche et le pin gris. L'appareil lidar utilisé pour acquérir les données est un capteur ALTM3100 de Optech permettant l'enregistrement des premiers et derniers retours, ainsi que jusqu'à deux retours intermédiaires. La méthode présentée a donné un biais de -1.37 mètres et une erreur type de 2.36 mètres. Ces résultats sont comparables à ceux obtenus par d'autres auteurs et semblent confirmer leur observation que le lidar à tendance à surestimer la hauteur de la base de la couronne. Il est noté que cette tendance à surestimer la mesure n'est pas systématique; elle semble se manifester différemment selon l'espèce. Des hypothèses concernant les différentes variables pouvant affecter la précision des mesures sont avancées. Des pistes pour mieux évaluer leur effet respectif ainsi que deux méthodes pouvant être utilisées pour améliorer l'exactitude et la précision des estimés sont proposées. ______________________________________________________________________________

El trabajo se divide en tres partes: 1) estudio sintético de las técnicas de calculo de los efectos de las fluctuaciones aleatorias del índice de refracción inducidas por la turbulencia atmosférica en la propagación de ondas electromagnéticas a longitudes de onda óptica, aportándose el desarrollo de herramientas numéricas que permiten el calculo de esos efectos para cualquier tipo de proyecto. Destaca el desarrollo de un método de simulación de propagación de haces a través de realizaciones de la atmósfera. 2) estudio experimental mediante el cual se han cotejado las teorías tratadas en la parte anterior. 3) aplicación de los resultados de los apartados 1) y 2) al estudio de las limitaciones impuestas por la propagación a través de un medio turbulento en un sistema de comunicaciones y de las mejoras que impone la utilización de sistemas de recepción multiapertura.

Den här rapporten har utrett möjligheten att automatiskt identifiera diken frånflygburet insamlat LiDAR-data. Den metod för identifiering som har valts harförst skapat en höjdbild från LiDAR-data. Därefter har den tagit fram kandidatertill diken genom att vektorisera resultatet från en linjedetektering. Egenskaper-na för dikeskandidaterna har sedan beräknats genom en analys av höjdprofilerför varje enskild kandidat, där höjdprofilerna skapats utifrån ursprungliga data.Genom att filtrera kandidaterna efter deras egenskaper kan dikeskartor med an-vändarspecificerade mått på diken presenteras i ett vektorformat som underlättarvidare användning. Rapporten beskriver hur algoritmen har implementerats ochpresenterar också exempel på resultat. Efter en analys av algoritmen samt förslagpå förbättringar presenteras den viktigaste behållningen av rapporten; Att det ärmöjligt med automatisk detektering av diken. / This Master’s thesis is investigating the possibility of automatically identifyingditches in airborne collected LiDAR data. The chosen approach to identificationcommences by creating an elevation picture from the LiDAR data. Then it usesthe result of a line detection to exhibit candidates for ditches. The properties forthe various candidates are calculated through an analysis of the elevation profile forthe candidates, where the elevation profiles are created from the original data. Byfiltering the candidates according to their calculated properties, maps with ditchesconforming to user-specified limits are created and presented in vector format.This thesis describes how the algorithm is implemented and gives examples ofresults. After an analysis of the algorithm and a proposal for improvements, itis suggested that automatic detection of ditches in LiDAR collected data is anachievable objective.

The use of airborne LiDAR (Light Detection and Ranging) as a direct method to evaluate forest canopy parameters is vital in addressing both forest management and ecological concerns. The overall goal of this study was to develop the use of airborne LiDAR in evaluating canopy parameters such as percent canopy cover (PCC) and leaf area index (LAI) for mixed pine and hardwood forests (primarily loblolly pine, Pinus taeda, forests) of the southeastern United States. More specific objectives were to: (1) Develop scanning LiDAR and multispectral imagery methods to estimate PCC and LAI over both hardwood and coniferous forests; (2) investigate whether a LiDAR and normalized difference vegetation index (NDVI) data fusion through linear regression improve estimates of these forest canopy characteristics; (3) generate maps of PCC and LAI for the study region, and (4) compare local scale LiDAR-derived PCC and regional scale MODIS-based PCC and investigate the relationship. Scanning LiDAR data was used to derive local scale PCC estimates, and TreeVaW, a LiDAR software application, was used to locate individual trees to derive an estimate of plot-level PCC. A canopy height model (CHM) was created from the LiDAR dataset and used to determine tree heights per plot. QuickBird multispectral imagery was used to calculate the NDVI for the study area. LiDAR- and NDVI-derived estimates of plot-level PCC and LAI were compared to field observations for 53 plots over 47 square kilometers. Linear regression analysis resulted in models explaining 84% and 78% of the variability associated with PCC and LAI, respectively. For these models to be of use in future studies, LiDAR point density must be 2.5 m. The relationship between regional scale PCC and local scale PCC was investigated by resizing the local scale LiDAR-derived PCC map to lower resolution levels, then determining a regression model relating MODIS data to the local values of PCC. The results from this comparison showed that MODIS PCC data is not very accurate at local scales. The methods discussed in this paper show great potential for improving the speed and accuracy of ecological studies and forest management.

We revisited an empirical relationship between the integrated volume depolar- ization ratio, oacc, and the effective multiple scattering factor, -n, on the basis of Monte Carlo simulations of spaceborne lidar backscatter associated with homogeneous wa- ter clouds. The relationship is found to be sensitive to the extinction coefficient and to the particle size. The layer integrated attenuated backscatter is also obtained. Comparisons made between the simulations and statistics derived relationships of the layer integrated depolarization ratio, oacc, and the layer integrated attenuated backscatter, -n, based on the measurement by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show that a cloud with a large effective size or a large extinction coefficient has a relatively large integrated backscatter and a cloud with a small effective size or a large extinction coefficient has a large integrated volume depolarization ratio. The present results also show that optically thin water clouds may not obey the empirical relationship derived by Y. X. Hu. and co-authors.

With the ever increasing population and economic activities in coastal areas, coastal hazards have become a major concern for coastal management. The fundamental requirement of coastal planning and management is the scientific knowledge about coastal forms and processes. This research aims at developing algorithms for automatically extracting coastal morphological information from LiDAR data. The primary methods developed by this research include automated algorithms for beach profile feature extraction and change analysis, and an object-based approach for spatial pattern analysis of coastal morphologic and volumetric change. Automated algorithms are developed for cross-shore profile feature extraction and change analysis. Important features of the beach profile such as dune crest, dune toe, and beach berm crest are extracted automatically by using a scale-space approach and by incorporating contextual information. The attributes of important feature points and segments are derived to characterize the morphologic properties of each beach profile. Beach profiles from different time periods can be compared for morphologic and volumetric change analysis. An object-oriented approach for volumetric change analysis is developed to identify and delineate individual elevation change patches as discrete objects. A set of two-dimensional and three-dimensional attributes are derived to characterize the objects, which includes planimetric attributes, shape attributes, surface attributes, volumetric attributes, and summary attributes. Both algorithms are implemented as ArcGIS extension modules to perform the feature extraction and attribute derivation for coastal morphological change analysis. To demonstrate the utility and effectiveness of algorithms, the cross-shore profile change analysis method and software tool are applied to a case study area located at southern Monterey Bay, California, and the coastal morphology change analysis method and software tool are applied to a case study area located on Assateague Island, Maryland. The automated algorithms facilitate the efficient beach profile feature analysis over large geographical area and support the analysis of the spatial variations of beach profile changes along the shoreline. The explicit object representation of elevation change patches makes it easy to localize erosion hot spots, to classify the elevation changes caused by various mechanisms, and to analyze spatial pattern of morphologic and volumetric changes.

The overall goal of this study was to develop a framework for using airborne lidar to derive inputs for the SPB infestation growth model TAMBEETLE. The specific objectives were (1) to estimate individual tree characteristics of XY location, individual bole height (IBH), diameter at breast height (DBH), length of crown (CrHT), and age for use in TAMBEETLE; (2) to estimate individual tree age using lidar-estimated height and site index provided by the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO); and (3) to compare TAMBEETLE simulation results using field measurements and lidarderived measurements as inputs. Diameter at breast height, individual bole height, and crown length were estimated using lidar with an error for mean measurements at plot level of 0.16cm, 0.19m, and 1.07m, respectively. These errors were within root mean square error (RMSE) for other studies at the study site. Age was estimated using the site index provided by SSURGO and the site index curves created for the study area with an RMSE of 4.8 years for mean plot age. Underestimation of tree height by lidar and error in the site index curve explained 91% of the error in mean plot age. TAMBEETLE was used to compare spot growth between a lidar-derived forest map and a forest map generated by TAMBEETLE, based on sample plot characteristics. The lidar-derived forest performed comparably to the TAMBEETLE generated forest. Using lidar to map forests can provide the large spatial extents of the TAMBEETLE generated forest while maintaining the spatially explicit forest characteristics, which were previously only available through field measurements.

The majority of biofuels are produced from corn and grain. The drawback to these sources of biofuels is the vast amount of cultivated land needed to produce substantial amounts of biofuel, potentially increasing the price of food and livestock products. Mesquite trees, a type of woody plant, are a proven source of bioenergy feedstock found on semi-arid lands. The overall objectives of this study were to develop algorithms for determining woody plant biomass on rangelands in Texas at plot-level using terrestrial lidar and at the local scale by integrating reference biomass and multispectral imagery. Terrestrial lidar offers a more efficient method for estimating biomass than traditional field measurements. Variables from the terrestrial lidar point cloud were compared to ground measurements of biomass to find a best fitting regression model. Two processing methods were investigated for analyzing the lidar point cloud data, namely: 1) percentile height statistics and 2) a height bin approach. Regression models were developed for variables obtained through each processing technique for estimating woody plant, above-ground biomass. Regression models were able to explain 81 percent and 77 percent of the variance associated with the aboveground biomass using percentile height statistics and height bins, respectively. The aboveground biomass map was generated by using the cokriging interpolation method with NDVI and ground biomass data. According to cross-validation, ordinary cokriging estimated biomass accurately (R^2 = 0.99). The results of this study revealed that terrestrial lidar can be used to accurately and efficiently estimate the aboveground biomass of mesquite trees in a semi-arid environment at plot level. Moreover, spatial interpolation techniques proved useful in scaling up biomass estimates to local scale.