Transport von Dieselruß durch Wasser in gestörten und ungestörten Böden - Resultierende, kleinskalige Verteilung und Nachweismethoden

Durch den Diesel-Kfz-Verkehr gelangen jährlich ca. 1,8 g Ruß/km in straßennahe Böden. Ruß enthält hohe Gehalte an PAK und ist aufgrund seiner Sorptionseigenschaften gleichzeitig Senke für weitere hydrophobe Schadstoffe. Es ist daher zu vermuten, dass derartige Schadstoffe prioritär mit dem Ruß transportiert werden. Zur Verlagerung bzw. Retention von Ruß in Böden liegen jedoch kaum Informationen vor. Ziel dieser Arbeit war es daher, den Transport von Ruß in aggregierten bzw. homogenisierten Böden unterschiedlichen Stoffbestandes mit Hilfe von Säulenperkolationsexperimenten zu bestimmen. Zur Ermittlung des Durchbruchs bzw. der Retention des Rußes wurden DOC und DON im Perkolat sowie PAK-Gehalte in unterschiedlichen Bodentiefen bestimmt. Die resultierende räumliche Verteilung des Rußes in den Bodensäulen und Porenräumen wurde mittels bildgebender Verfahren untersucht. Dies erfolgte zum einem unter Verwendung einer Hyperspektralkamera (Neo HySpex VNIR-1600) und Maximum Likelihood Classification (MLC) sowie autoPartial Least Squares Regression (aPLSR) als Auswertungsmethoden. Die Identifizierung des Rußes erfolgte durch konfokale Raman-Imaging Mikroskopie. Die Ergebnisse zeigen, dass Ruß vorrangig an der Bodenoberfläche ausgefiltert wird, wo er potentiell einem Abtrag durch runoff, Auswehung, etc. unterliegt. Eine Tiefenverlagerung erfolgt über Makroporenräume als präferentielle Fließwege

Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. 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A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS

A BiomeBGC-based Evaluation of Dryness Stress of Central European Forests

Dryness stress is expected to become a more common problem in central European forests due to the predicted regional climate change. Forest management has to adapt to climate change in time and think ahead several decades in decisions on which tree species to plant at which locations. The summer of 2003 was the most severe dryness event in recent time, but more periods like this are expected. Since forests on different sites react quite differently to drought conditions, we used the process-based growth model BiomeBGC and climate time series from sites all over Germany to simulate the reaction of deciduous and coniferous tree stands in different characteristics of drought stress. Times with exceptionally high values of water vapour pressure deficit coincided with negative modelled values of net primary production (NPP). In addition, in these warmest periods the usually positive relationship between temperature and NPP was inversed, i.e., under stress conditions, more sunlight does not lead to more photosynthesis but to stomatal closure and reduced productivity. Thus we took negative NPP as an indicator for drought stress. In most regions, 2003 was the year with the most intense stress, but the results were quite variable regionally. We used the Modis MOD17 gross and net primary production product time series and MOD12 land cover classification to validate the spatial patterns observed in the model runs and found good agreement between modelled and observed behaviour. Thus, BiomeBGC simulations with realistic site parameterization and climate data in combination with species- and variety-specific ecophysiological constants can be used to assist in decisions on which trees to plant on a given site