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An Airborne Mobile Aerosol Lidar (AMALi) was constructed and built at Alfred-Wegener-Institute for Polar and Marine Research (AWI) in Potsdam, Germany for the lower tropospheric aerosol and cloud research under tough arctic conditions. The system was successfully used during two AWI airborne field campaigns, ASTAR 2004 and SVALEX 2005, performed in vicinity of Spitsbergen in the Arctic. The novel evaluation schemes, the Two-Stream Inversion and the Iterative Airborne Inversion, were applied to the obtained lidar data. Thereby, calculation of the particle extinction and backscatter coefficient profiles with corresponding lidar ratio profiles characteristic for the arctic air was possible. The comparison of these lidar results with the results of other in-situ and remote instrumentation (ground based Koldewey Aerosol Raman Lidar (KARL), sunphotometer, radiosounding, satellite imagery) allowed to provided clean contra polluted (Arctic Haze) characteristics of the arctic aerosols. Moreover, the data interpretation by means of the ECMWF Operational Analyses and small-scale dispersion model EULAG allowed studying the effects of the Spitsbergens orography on the aerosol load in the Planetary Boundary Layer. With respect to the cloud studies a new methodology of alternated remote AMALi measurements with the airborne in-situ cloud optical and microphysical parameters measurements was proved feasible for the low density mixed-phase cloud studies. An example of such approach during observation of the natural cloud seeding (feeder-seeder phenomenon) with ice crystals precipitating into the lower supercooled stratocumulus deck were discussed in terms of the lidar signal intensity profiles and corresponding depolarisation ratio profiles. For parts of the cloud system characterised by almost negligible multiple scattering the calculation of the particle backscatter coefficient profiles was possible using the lidar ratio information obtained from the in-situ measurements in ice-crystal cloud and water cloud. / Das Airborne Mobile Aerosol Lidar (AMALi) wurde am Alfred-Wegener-Institut für Polar- und Meeresforschung in Potsdam für die Untersuchung arktischer Aerosole und Wolken der unteren Troposphäre entwickelt und gebaut. Das AMALi wurde erfolgreich in zwei AWI Flugzeugmesskampagnen, der ASTAR 2004 und der SvalEx 2005, die in Spitzbergen in der Arktis durchgeführt wurden, eingesetzt. Zwei neue Lidar Datenauswertungsmethoden wurden implementiert: die Two-Stream Inversion und die Iterative Airborne Inversion. Damit erwies sich die Berechnung der Profile der Teilchen Rückstreu- und Extinktionskoeffizienten mit einem entsprechenden Lidar Verhältnis, das charakteristisch für arktische Luft ist, als möglich. Der Vergleich dieser Auswertungen mit den Resultaten, die mit verschiedenen Fernerkundungs- und In-situ Instrumenten gewonnen worden waren (stationäres Koldewey Aerosol Raman Lidar KARL, Sonnenphotometer, Radiosondierung und Satellitenbilder) ermöglichten die Interpretation der Lidar-Resultate und eine Charakterisierung sowohl der reinen als auch der verschmutzten Luft. Außerdem konnten die Lidardaten mit operationellen ECMWF Daten und dem kleinskaligen Dispersionsmodel EULAG verglichen werden. Dadurch konnte der Einfluss der Spitzbergener Orographie auf die Aerosolladung der Planetaren Grenzschicht untersucht werden. Für Wolkenmessungen wurde eine neue Methode der alternativen Fernerkundung mit dem AMALi und flugzeuggetragenen In-situ Messgeräten verwendet, um optische und mikrophysikalische Eigenschaften der Wolken zu bestimmen. Diese Methode wurde erfolgreich implementiert und auf Mixed-Phase Wolken geringer optischen Dicke angewendet. Ein Beispiel hier stellt das Besamen der Wolken (sogenannte Feeder-Seeder Effekt) dar, bei dem Eiskristalle in eine niedrige unterkühlte Stratokumulus fallen. Dabei konnten Lidarsignale, Intensitätsprofile und die Volumendepolarisation gemessen werden. Zusätzlich konnten in den weniger dichten Bereichen der Wolken, in denen Vielfachstreuung vernachlässigbar ist, auch Profile des Teilchen Rückstreukoeffizienten berechnet werden, wobei Lidarverhältnisse genommen wurden, die aus In-situ Messungen für Wasser- und Eiswolken ermittelt wurden.

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.

Energy consumption is on a permanent rise and it is becoming increasingly concentrated in cities. Hence, cities have to work on saving energy and being more efficient by finding sources with great potential to produce their own energy and implanting the correct policies. Photovoltaics is the renewable energy technology with the higher potential in the urban context and Sweden is highly committed on its investment since it is the less developed renewable source in the country. The aim of the thesis is to compare two methodologies and determine which one is better or gives more relevant information for this kind of studies in order to evaluate how good a solar map is. For doing this, the first step is to create a solar map to have a general idea about the solar potential and to know which roofs are more suitable to install PV systems. This is made with LiDAR data using ArcGIS and SEES software. After that, another study on the quantity of solar power that could be obtained from those roofs will be performed using PVsyst, where it is possible to develop an entire PV system installation and obtain more exhaust results on energy production and shadowing. Four buildings are going to be evaluated, two public ones located in Gävle city centre (Library and Concert House) and two residential ones located in Sätra. Factors such as the optimal tilt, the best azimuth angle and the distance between panel rows are dimensioned in order to reduce shading loss and improve the performance ratio of the system in PVsyst. The final system is defined with 10° tilt, south orientation (0° azimuth), 1.5meters distance between rows and modules in strings of 9 panels connected in series for the four buildings. The simulated production from the best alternative is compared with the solar map results. Since the solar map contains information about total yearly irradiation, the energy production is obtained by means of visual exploration of the results combined with simple calculations that include GCR and system efficiency. The results show that a solar map is a reliable tool to obtain a general estimation of the solar potential in buildings but it is necessary to first identify its limitations and be able to filter the results. On the other hand, PVsyst software allows making several simulations and eases to obtain a PV system in a building or structure with detailed results of the system components. It can be concluded that since the PVsyst only allows to work with specific buildings or structures, a solar map permits big amounts of data calculations. It can be said that a solar map takes part in the process of obtaining a pre-project and the PVsyst is used in the project when a real installation is sized. Nevertheless, both methods are found to be reliable and suitable for solar potential assessment works since the results obtained match.

Cette thèse de doctorat traite de la mesure du dioxyde de carbone atmosphérique par LIDAR DIAL et plus particulièrement de la potentialité d'une mesure spatiale. Le CO2 est le deuxième gaz à effet de serre dans l'atmosphère et le premier d'origine anthropique. Afin de pouvoir prédire l'évolution du changement climatique et du climat il est nécessaire de pouvoir prédire l'évolution de ce gaz dans l'atmosphère. Cependant, le cycle du carbone est encore mal compris et des inconnues subsistent notamment sur la localisation des sources et des puits de carbone à la surface de la Terre. Afin de déterminer avec précision ces puits et ces sources, il est nécessaire de pouvoir caractériser avec précision les flux de surface du CO2 atmosphérique. Les stations de mesure au sol étant trop éparses, il est nécessaire d'avoir accès à une mesure globale du CO2. Cependant, les premières missions spatiales passives souffrent de limitations et ne permettent pas d'accéder à la précision requise pour contraindre les flux. C'est pourquoi une mesure active utilisant la technique LIDAR à absorption différentielle DIAL est étudiée ici. Ce travail en amont vise à préparer une telle mission, afin d'atteindre les précisions requises. Dans un premier temps, la raie d'absorption la plus appropriée est sélectionnée et j'énonce les précisions devant être atteintes sur la mesure. Dans une deuxième partie, l'étude spectroscopique de cette raie d'absorption (raie R 30 de la bande (2001)III

Quantifying above-ground biomass (AGB) and carbon sequestration has been a significant focus of attention within the UNFCCC and Kyoto Protocol for improvement of national carbon accounting systems (IPCC, 2007; UNFCCC, 2011). A multitude of research has been carried out in relatively flat and homogeneous forests (Ranson & Sun, 1994; Beaudoin et al.,1994; Kurvonen et al., 1999; Austin et al., 2003; Dimitris et al., 2005), yet forests in the highlands, which generally form heterogeneous forest cover and sparse woodlands with mountainous terrain have been largely neglected in AGB studies (Cloude et al., 2001; 2008; Lumsdon et al., 2005; 2008; Erxue et al., 2009, Tan et al., 2010; 2011a; 2011b; 2011c; 2011d). Since mountain forests constitute approximately 28% of the total global forest area (Price and Butt, 2000), a better understanding of the slope effects is of primary importance in AGB estimation. The main objective of this research is to estimate AGB in the aforementioned forest in Glen Affric, Scotland using both SAR and LiDAR data. Two types of Synthetic Aperture Radar (SAR) data were used in this research: TerraSAR-X, operating at X-band and ALOS PALSAR, operating at L-band, both are fully polarimetric. The former data was acquired on 13 April 2010 and of the latter, two scenes were acquired on 17 April 2007 and 08 June 2009. Airborne LiDAR data were acquired on 09 June 2007. Two field measurement campaigns were carried out, one of which was done from winter 2006 to spring 2007 where physical parameters of trees in 170 circular plots were measured by the Forestry Commission team. Another intensive fieldwork was organised by myself with the help of my fellow colleagues and it comprised of tree measurement in two transects of 200m x 50m at a relatively flat and dense plantation forest and 400m x 50m at hilly and sparse semi-natural forest. AGB is estimated for both the transects to investigate the effectiveness of the proposed method at plot-level. This thesis evaluates the capability of polarimetric Synthetic Aperture Radar data for AGB estimation by investigating the relationship between the SAR backscattering coefficient and AGB and also the relationship between the decomposed scattering mechanisms and AGB. Due to the terrain and heterogeneous nature of the forests, the result from the backscatter-AGB analysis show that these forests present a challenge for simple AGB estimation. As an alternative, polarimetric techniques were applied to the problem by decomposing the backscattering information into scattering mechanisms based on the approach by Yamaguchi (2005; 2006), which are then regressed to the field measured AGB. Of the two data sets, ALOS PALSAR demonstrates a better estimation capacity for AGB estimation than TerraSAR-X. The AGB estimated results from SAR data are compared with AGB derived from LiDAR data. Since tree height is often correlated with AGB (Onge et al., 2008; Gang et al., 2010), the effectiveness of the tree height retrieval from LiDAR is evaluated as an indicator of AGB. Tree delineation was performed before AGB of individual trees were calculated allometrically. Results were validated by comparison to the fieldwork data. The amount of overestimation varies across the different canopy conditions. These results give some indication of when to use LiDAR or SAR to retrieve forest AGB. LiDAR is able to estimate AGB with good accuracy and the R2 value obtained is 0.97 with RMSE of 14.81 ton/ha. The R2 and RMSE obtained for TerraSAR-X are 0.41 and 28.5 ton/ha, respectively while for ALOS PALSAR data are 0.70 and 23.6 ton/ha, respectively. While airborne LiDAR data with very accurate height measurement and consequent three-dimensional (3D) stand profiles which allows investigation into the relationship between height, number density and AGB, it's limited to small coverage area, or large areas but at large cost. ALOS PALSAR, on the other hand, can cover big coverage area but it provide a lower resolution, hence, lower estimation accuracy.

In farming of fruit trees it is of interest to know the light interception of the trees. Therefore, in this project, a geometric model of the trees was derived using LiDAR data and this was combined with a sky model to estimate the light interceptionof orchard trees. The light interception was estimated by first synthesising a discrete model of the hemispherical sky, which holds a measure of global lightradiation in each node. The light interception of the trees was then estimated by ray tracing from the sky, applying a radiation absorption model where rays passed the point cloud representation of the trees. Comparing the interception model to measurements of photosynthetically active radiation (PAR) underneath a tree, the qualitative agreement was high and the quantitative analysis showed a reasonable, albeit noisy, correspondence between the model output and the real world measurements. When comparing the estimations produced by the solar-geometry model and the tree volume (estimated also with LiDAR), a correspondence between the surface area of the tree and the interception was found. When comparing tree volume and light interception against actual yield numbers (total weight, average fruit weight and fruit count per tree), the observable trend was that light interception did better in predicting the average fruit size, while the volume did a better job of estimating the two others. The results were encouraging, however, because ground truth data were only available for 18 trees, future work will have to compare with a greater number of measurements over multiple growing seasons.

This is a study concerning the use of starphotometry to retrieve night-time aerosol optical depths (AODs). In the summer of 2007 a SPSTAR03 starphotometer was installed at a rural site at Egbert, Ontario for the purpose of the nighttime AOD measurements. Two series of daytime / nighttime AODs were acquired using the CIMEL CE 318 sunphotometer and the SPSTAR03 from Aug. 31 to Sept. 19 2007 and from June 30 to July 5, 2008. The measurements were complemented by vertical backscatter coefficient profiles acquired using a pulsed lidar. We found that starphotometer AOD estimates, based on the application of a two star method (TSM) to low and high elevation stars, are susceptible to atmospheric inhomogeneity effects. Starphotometer AOD estimates based on the one star method (OSM) reduce this sensitivity, but require absolute calibration values. A level of continuity was obtained between the daytime sunphotometry and nighttime starphotometry data. A continuity parameter (defined as the average difference between the measured nighttime and interpolated daytime values) was calculated over four distinct periods. It yielded the differences of 0.160, 0.053, 0.139 (total, fine and coarse mode optical depths) for the low star and 0.195, 0.070, 0.149 for the high star. We argue that cloud screening would have reduced the continuity parameter differences for the coarse and total optical depths. For 5 out of , 8 nights of lidar operation, a combination of the Angstrom and Spectral Deconvolution Algorithm (SDA) analysis provided an indication of the nature of the atmospheric features seen in the lidar data. Fine and coarse-mode events were detected during the measurement periods using the SDA. Lidar data was used to better understand complex atmospheric phenomena and was found especially effective for cloud detection and general signal increase/decrease analysis.

Accurate estimates of tree and forest biomass are essential for a wide range of applications. Automated treetop detection and tree crown discrimination using LiDAR data can greatly facilitate forest biomass estimation. Previous work has focused on homogenous or single-species forests, while few studies have focused on mixed forests. In this study, a new method for treetop detection is proposed in which the treetop is the cluster center of selected points rather than the highest point. Based on treetop detection, tree crowns are discriminated through comparison of three-dimensional shape signatures. The methods are first tested using simulated LiDAR point clouds for trees, and then applied to real LiDAR data from the Soquel Demonstration State Forest, California, USA. Results from both simulated and real LiDAR data show that the proposed method has great potential for effective detection of treetops and discrimination of tree crowns.

A three part investigation into the effects of riparian reforestation on small streams demonstrated the timing, nature, and processes of morphologic change. First, measurements of two small streams in northeastern Vermont collected in 1966 and 2004 – 2005 documented considerable change in channel width following a period of passive reforestation. Channel widths of several tributaries to Sleepers River were measured in 1966 when the area had more non-forested riparian vegetation than today. A longitudinal survey in 2004 of two of these tributaries, followed by detailed measurements at specific reaches in 2005, provided information on channel size, large woody debris (LWD), and riparian vegetation. Reforested reaches have widened and incised markedly since 1966. Reaches with the oldest forest were widest for a given drainage area, and the non-forested reaches were substantially narrower. A conceptual model was developed that describes a multi-phase process of incision, widening, and recovery following riparian reforestation of non-forested areas. Second, a fixed-bed hydraulic model of one of the streams was developed to evaluate the impact of forested riparian vegetation on near-bank turbulence during overbank flows. Flume experiments with kinematic similitude and a 1:5 scale represented half a channel and its floodplain, mimicking the size of a non-forested reach. Two types of vegetation were simulated: non-forested, with synthetic grass carpet; and forested, where wooden dowels were added. Three-dimensional velocities were measured with an acoustic Doppler velocimeter. Velocities, turbulent kinetic energy (TKE), and Reynolds shear stress showed significant differences between forested and non-forested runs. Forested runs exhibited a narrow band of high TKE in the near-bank region that was roughly two times greater than in non-forested runs. Hydraulic characteristics of forested runs appear to create an environment with higher erosion potential, thereby indicating a possible driving mechanism for channel widening in reforesting stream reaches. Third, Light Detection and Ranging (LiDAR) data from Chittenden County were analyzed to develop a method capable of classifying riparian buffers into broad classes according to forest type and age. The geospatial characteristics of the LiDAR data in forested areas were explored using semivariogram analysis, and LiDAR-based metrics were derived in a geographic information system (GIS) to quantify vegetation height and variance. The LiDAR-based metrics were then used in two discriminant analysis procedures that distinguished: 1) forest type as deciduous or coniferous; and 2) forest age in four age classes. With the resulting linear discriminant functions, a GIS-based classification method was developed. The classification method was highly successful at determining forest type but only moderately successful at determining forest age.