Although gravitropism forces trees to grow vertically, stems have shown to prefer specific orientations. Apart from wind deforming the tree shape, lateral light can result in prevailing inclination directions. In recent years a species dependent interaction between gravitropism and phototropism, resulting in trunks leaning down-slope, has been confirmed, but a terrestrial investigation of such factors is limited to small scale surveys. ALS offers the opportunity to investigate trees remotely. This study shall clarify whether ALS detected tree trunks can be used to identify prevailing trunk inclinations. In particular, the effect of topography, wind, soil properties and scan direction are investigated empirically using linear regression models. 299.000 significantly inclined stems were investigated. Species-specific prevailing trunk orientations could be observed. About 58% of the inclination and 19% of the orientation could be explained by the linear models, while the tree species, tree height, aspect and slope could be identified as significant factors. The models indicate that deciduous trees tend to lean down-slope, while conifers tend to lean leeward. This study has shown that ALS is suitable to investigate the trunk orientation on larger scales. It provides empirical evidence for the effect of phototropism and wind on the trunk orientation.
Die organische Bodensubstanz (OBS) ist eine fundamentale Steuergröße aller biogeochemischen Prozesse und steht in engem Zusammenhang zu Kohlenstoffkreisläufen und globalem Klima. Die derzeitige Herausforderung der Ökosystemforschung ist die Identifizierung der für die Bodenqualität relevanten Bioindikatoren und deren Erfassung mit Methoden, die eine nachhaltige Nutzung der OBS in großem Maßstab überwachen und damit zu globalen Erderkundungsprogrammen beitragen können. Die fernerkundliche Technik der Vis-NIR Spektroskopie ist eine bewährte Methode für die Beurteilung und das Monitoring von Böden, wobei ihr Potential bezüglich der Erfassung biologischer und mikrobieller Bodenparameter bisher umstritten ist. Das Ziel der vorgestellten Arbeit war die quantitative und qualitative Untersuchung der OBS von Ackeroberböden mit unterschiedlichen Methoden und variierender raumzeitlicher Auflösung sowie die anschließende Bewertung des Potentials non-invasiver, spektroskopischer Methoden zur Erfassung ausgewählter Parameter dieser OBS. Dafür wurde zunächst eine umfassende lokale Datenbank aus chemischen, physikalischen und biologischen Bodenparametern und dazugehörigen Bodenspektren einer sehr heterogenen geologischen Region mit gemäßigten Klima im Südwesten Deutschlands erstellt. Auf dieser Grundlage wurde dann das Potential der Bodenspektroskopie zur Erfassung und Schätzung von Feld- und Geländedaten ausgewählter OBS Parameter untersucht. Zusätzlich wurde das Optimierungspotential der Vorhersagemodelle durch statistische Vorverarbeitung der spektralen Daten getestet. Die Güte der Vorhersagewahrscheinlichkeit gebräuchlicher fernerkundlicher Bodenparameter (OC, N) konnte für im Labor erhobene Hyperspektralmessungen durch statistische Optimierungstechniken wie Variablenselektion und Wavelet-Transformation verbessert werden. Ein zusätzliches Datenset mit mikrobiellen/labilen OBS Parametern und Felddaten wurde untersucht um zu beurteilen, ob Bodenspektren zur Vorhersage genutzt werden können. Hierzu wurden mikrobieller Kohlenstoff (MBC), gelöster organischer Kohlenstoff (DOC), heißwasserlöslicher Kohlenstoff (HWEC), Chlorophyll α (Chl α) und Phospholipid-Fettsäuren (PLFAs) herangezogen. Für MBC und DOC konnte abhängig von Tiefe und Jahreszeit eine mittlere Güte der Vorhersagewahrscheinlichkeit erreicht werden, wobei zwischen hohen und niedrigen Konzentration unterschieden werden konnte. Vorhersagen für OC und PLFAs (Gesamt-PLFA-Gehalt sowie die mikrobiellen Gruppen der Bakterien, Pilze und Algen) waren nicht möglich. Die beste Prognosewahrscheinlichkeit konnte für das Chlorophyll der Grünalgen an der Bodenoberfläche (0-1cm Bodentiefe) erzielt werden, welches durch Korrelation mit MBC vermutlich auch für dessen gute Vorhersagewahrscheinlichkeit verantwortlich war. Schätzungen des Gesamtgehaltes der OBS, abgeleitet durch OC, waren hingegen nicht möglich, was der hohen Dynamik der mikrobiellen OBS Parameter an der Bodenoberfläche zuzuschreiben ist. Das schränkt die Repräsentativität der spektralen Messung der Bodenoberfläche zeitlich ein. Die statistische Optimierungstechnik der Variablenselektion konnte für die Felddaten nur zu einer geringen Verbesserung der Vorhersagemodelle führen. Die Untersuchung zur Herkunft der organischen Bestandteile und ihrer Auswirkungen auf die Quantität und Qualität der OBS konnte die mikrobielle Nekromasse und die Gruppe der Bodenalgen als zwei mögliche weitere signifikante Quellen für die Entstehung und Beständigkeit der OBS identifizieren. Insgesamt wird der mikrobielle Beitrag zur OBS höher als gemeinhin angenommen eingestuft. Der Einfluss mikrobieller Bestandteile konnte für die OBS Menge, speziell in der mineralassoziierten Fraktion der OBS in Ackeroberböden, sowie für die OBS Qualität hinsichtlich der Korrelation von mikrobiellen Kohlenhydraten und OBS Stabilität gezeigt werden. Die genaue Quantifizierung dieser OBS Parameter und ihre Bedeutung für die OBS Dynamik sowie ihre Prognostizierbarkeit mittels spektroskopischer Methoden ist noch nicht vollständig geklärt. Für eine abschließende Beurteilung sind deshalb weitere Studien notwendig.
Time series archives of remotely sensed data offer many possibilities to observe and analyse dynamic environmental processes at the Earth- surface. Based on these hypertemporal archives, which offer continuous observations of vegetation indices, typically at repetition rates from one to two weeks, sets of phenological parameters or metrics can be derived. Examples of such parameters are the beginning and end of the annual growing period, as well as its length. Even though these parameters do not correspond exactly to conventional observations of phenological events, they nevertheless provide indications of the dynamic processes occurring in the biosphere. The development of robust algorithms for the derivation of phenological metrics can be challenging. Currently, such algorithms are most commonly based on digital filters or the Fourier analysis of time series. Polynomial spline models offer a useful alternative to existing methods. The possibilities of using spline models in the analytical description of time series are numerous, and their specific mathematical properties may help to avoid known problems occurring with the more common methods for deriving phenological metrics. Based on a selection of different polynomial spline models suitable for the analysis of remotely sensed time series of vegetation indices, a method to derive various phenological parameters from such time series was developed and implemented in this work. Using an example data set from an intensively used agricultural area showing highly dynamic variations in vegetation phenology, the newly developed method was verified by a comparison of the results of the spline based approach to the results of two alternative, well established methods.
Soil and water conservation are cross-sectional assignments. The respective objectives of the individual interest groups cause conflicts of use and lead to different assessments of the soil's potential. Necessary decisions and the practical implementation of soil and water conservation measures require the use of data. These data, which are both spatial and temporal, characterise past, present and, in the case of predictions, also future environmental conditions. The multitude of relevant data necessitates the use of geographic information systems as an instrument for successful resource management. With the use of problem-oriented case studies, it was possible to show that an improved understanding of the system is necessary for both optimisation of the site-specific resource management within the framework of Precision Farming and for the assessment of local to regional conflicts of use with regard to land usage and soil and water conservation. By changing the method, sufficient respective measures regarding documentation, prevention and risk assessment were able to be introduced and implemented. With the objective of practical implementation of a sustainable resource management, the possibilities of short- to long-term initiation of self-organised systems through the networking of available (geo-)information as well as the respective interest groups involved in the conflict of use formed the focal point of this investigation. The creation of networks linking agriculture, water extractors and nature conservation promotes necessary synergies and emergences, due to increased communication. Not the conveyance of knowledge alone, but rather new forms of understanding cause the interest groups involved to change their behaviour, thus facilitating efficient resource management for the interests of soil and water conservation.
A sustainable development of forests and their ecosystem services requires the monitoring of the forests" state and changes as well as the prediction of their future development. To achieve the latter, eco-physiological forest growth models are usually applied. These models require calibration and validation with forestry reference data. This data includes forest structural parameters such as tree height or stem diameter which are easy to measure and can be used to estimate the core model parameters, i.e. the tree- biomass pools. The methods traditionally applied to derive the structural parameters are mainly manual and time-consuming. Hence, the in situ data acquisition is inefficient and limited in its ability to capture the vertical and horizontal variability in stand structure. Ground-based remote sensing bears the potential to overcome the limitations of the traditional methods. As they can be automated, ground-based remote sensing methods allow a much more efficient data acquisition and a larger spatial coverage. They are also able to capture forest structure in its three dimensions. Nevertheless, at present further research is required, in particular with respect to the practical integration of ground-based remote sensing data into forest growth models as well as regarding factors influencing the structural parameter retrieval from this data. Therefore, the goal of this PhD thesis was to investigate the influencing factors of two ground-based remote sensing methods (terrestrial laser scanning and hemispherical photography), which have not or only scarcely been studied to date. In addition, the use of forest structural parameters derived from these methods for the calibration of a forest growth model was assessed. Both goals were achieved. The results of this thesis could contribute significantly to a comprehensive assessment of ground-based remote sensing and its potential to derive the forest structural parameters. However, the use of these methods to calibrate forest growth models proved to be limited. An optimized data sampling design is expected to eliminate the major limitations, though. Furthermore, the combination of ground-based, airborne, and satellite remote sensing sensors was suggested to provide an optimized framework for the general integration of remotely sensed data into forest growth models. This combination of remote sensing observations at different scales will contribute greatly to a modern forest management with the purpose of warranting a sustainable forest development even under growing economic and ecological pressures.
Die Arbeit untersucht das Potential kleiner unbemannter Luftfahrtsysteme (UAS) in Landwirtschaft und Archäologie. Der Begriff UAS beinhaltet dabei: Fluggerät, Antriebsmechanismus, Sensorik, Bodenstation, Kommunikationsmittel zwischen Bodenstation und Fluggerät und weiteres Equipment. Aufgrund ihrer Flexibilität, fanden UAS seit der Jahrtausendwende eine blühende Entwicklung. Um die wachsende Weltbevölkerung zu ernähren, muss die landwirtschaftliche Produktion sensibel und nachhaltig intensiviert werden, um Nahrungssicherheit für alle zu gewährleisten und weitere Boden- und Landdegradation zu vermeiden. Präzisionslandwirtschaft umfasst technologische Verbesserungen hin zur effizienteren und weniger schädlichen landwirtschaftlichen Praxis. Hierbei ist die Verfügung über zeitnahe, leicht zugängliche hoch aufgelöste räumliche Daten eine Voraussetzung für die Nahrungsmittelproduktion. UAS schließen hier die Lücke zwischen Bodendaten und teuren bemannten Luftfahrtsysteme und selteneren Satellitenbildern. Die Vorteile der UAS-Daten liegen in der ad-hoc Akquisition großmaßstäbiger Fernerkundungsdaten, den geringeren Kosten gegenüber der bemannten Systeme und einer relativen Wetterunabhängigkeit, da auch unter Wolken geflogen werden kann. Den größten Anteil innerhalb der UAS stellen die Mini-UAS (Abfluggewicht von 5kg) und dabei vertikale Start- und Landesysteme. Diese können über Untersuchungsgebieten schweben, sind dadurch jedoch langsamer und eher geeignet für kleinere Flächen. Flugregularien und die Integration in den bemannten Luftraum werden derzeit europaweit harmonisiert und in den Mitgliedstaaten umgesetzt. Die Hauptziele dieser Arbeit lagen in der Evaluierung wie Schlüsselparametern landwirtschaftlicher Nutzpflanzen (Chlorophyll-, Stickstoffgehalt, Erntemenge, sonnendinduzierter Chlorophyll-Fluoreszenz) mittels UAS abgeleitet und wie UAS-Daten für archäologische Aufklärung genutzt werden können. Dazu wurde ein Quadrokopter (md4-1000, microdrones GmbH) mit einer digitalen Spiegelreflexkamera, einem Multispektralsensor (MiniMCA-6, Tetracam Inc.) und einer Thermalkamera (UCM, Zeiss) ausgestattet. Eine Sensitivitätsanalyse führte zur Ableitung geeigneter Wellenlängenbereiche und untersuchte bidirektionale und Flughöheneffekte auf das Multispektralsignal. Die Studie beschreibt außerdem die Vorgehensweise bei Bildaufnahme und Vorprozessierung mit besonderem Schwerpunkt auf die Multispektralkamera (530-900 nm). Die Vorprozessierung beinhaltet die Korrektur von Sensorfehlern (Linsenverzeichnung, Vignettierung, Kanalkalibrierung), die radiometrische Kalibrierung über eine empirische Korrektur mit Hilfe von Referenzspektren, Atmosphärenkorrektur und schließlich die geometrische Verarbeitung unter Verwendung von Structure from Motion Programme zur Generierung von Punktwolkenmodellen bis hin zum digitalen Orthophotomosaik und Höhenmodell in Zentimeterauflösung. In einer Weinbergsstudie (2011, 2012) wurden geeignete Beobachtungswinkel für die Untersuchung des Einflusses von Bodenbearbeitungsstrategien auf das Multispektralsignal evaluiert. Schrägichtaufnahmen von 45-° Beobachtungswinkel gegenüber Nadir waren am besten geeignet zur Ableitung pflanzenphysiolgischer Parameter und multispektraler Unterscheidung von Bodenbearbeitungstypen. So konnten Chlorophyll-Gehalte über Regressionsanalysen über mehrere saisonale Aufnahmen mit einem kreuzvalidierten R-² von 0.65, Stickstoffgehaltsindex von 0.76 (2012) und Ernte mit 0.84 (2011) und für verschiedene Zeitpunkte nach der Blüte (0.87) und während der Reifephase (0.73) ermittelt werden. Desweiteren wurde die (Fs) in einem Stickstoff-Düngung-Experiment bei Zuckerrüben von Multispektral-, Indizes und Thermaldaten untersucht (HyFlex-Kampagne 2012). Zuckerrübenvarietäten konnten spektral und thermal unterschieden werden, die Fluoreszenzindizes waren wetterbedingt, weniger erfolgreich. Außerdem konnte der Tagesgang der Fs trotz instabiler Einstrahlungsverhältnisse am Morgen abgeleitet werden. Die Werte waren jedoch gegenüber Bodenmessungen um ein Vielfaches erhöht. Archäologische Fernerkundung durch UAS wird bereits seit Jahren (z.B. mit Fesselballons) durchgeführt. Die Mustererkennung profitiert von der spektralen Ausdehnung vom menschlichen Auge hin zu multispektralen, neuerdings auch hyperspektralen Sensoren. Studien in Los Bañales, Spanien, zeigten die Möglichkeiten des Informationsgewinns durch Bildverarbeitung von UAS-Daten: vermutliche historische Siedlungsmuster konnten durch Landoberflächenklassifikation von Multispektraldaten mittels Support Vector Machines und Bestandsmusterdetektion beschrieben werden. Um qualitative hochwertige, hochaufgelöste UAS-Daten zu erhalten, sollten die Daten mit hoher Überlappung (80%) und auch Schrägsicht akquiriert und ggf. durch Referenzmessungen zur radiometrischen Kalibrierung und GPS-Messungen für geometrische Referenzierung ergänzt werden.
Energy transition strategies in Germany have led to an expansion of energy crop cultivation in landscape, with silage maize as most valuable feedstock. The changes in the traditional cropping systems, with increasing shares of maize, raised concerns about the sustainability of agricultural feedstock production regarding threats to soil health. However, spatially explicit data about silage maize cultivation are missing; thus, implications for soil cannot be estimated in a precise way. With this study, we firstly aimed to track the fields cultivated with maize based on remote sensing data. Secondly, available soil data were target-specifically processed to determine the site-specific vulnerability of the soils for erosion and compaction. The generated, spatially-explicit data served as basis for a differentiated analysis of the development of the agricultural biogas sector, associated maize cultivation and its implications for soil health. In the study area, located in a low mountain range region in Western Germany, the number and capacity of biogas producing units increased by 25 installations and 10,163 kW from 2009 to 2016. The remote sensing-based classification approach showed that the maize cultivation area was expanded by 16% from 7305 to 8447 hectares. Thus, maize cultivation accounted for about 20% of the arable land use; however, with distinct local differences. Significant shares of about 30% of the maize cultivation was done on fields that show at least high potentials for soil erosion exceeding 25 t soil ha−1 a−1. Furthermore, about 10% of the maize cultivation was done on fields that pedogenetically show an elevated risk for soil compaction. In order to reach more sustainable cultivation systems of feedstock for anaerobic digestion, changes in cultivated crops and management strategies are urgently required, particularly against first signs of climate change. The presented approach can regionally be modified in order to develop site-adapted, sustainable bioenergy cropping systems.
Two areas were selected to represent major process regimes of Mediterranean rangelands. In the County of Lagads (Greece), situated east of the city of Thessaloniki, livestock grazing with sheep and goats is a major factor of the rural economy. In suitable areas, it is complemented by agricultural use. The region of Ayora (Spain) is located west of the city of Valencia. It is one of regions most affected by fires in Spain. First of all, long time series of satellite data were compiled for both regions on the basis of Landsat sensors, which cover the time until 1976 (Ayora) and 1984 (Lagadas) with one image per year. Using a rigorous processing scheme, the data were geometrically and radiometrically corrected Specific attention was given to an exact sensor calibration, the radiometric intercalibration of Landsat-TM and "MSS. Proportional cover of photosynthetically active vegetation was identified as a suitable quantitative indicator for assessing the state of rangelands. Using Spectral Mixture Analysis (SMA) it was inferred for all data sets. The extensive data base procured this way enabled to map fire events in the Ayora area based on sequential diachronic sets and provide fire dates, perimeter as well as fire recurrence for each pixel. The increasing fire frequency in the past decades is in large parts attributed to the accelerated abandonment of the area that leads to an encroachment of shrublands and the accumulation of combustible biomass. On the basis of the fire mapping results, a spatial and temporal stratification of the data set allowed to asses plant recovery dynamics on the landscape level through linear trend analysis. The long history of fire events in the Mediterranean frequently leads to processes of auto-succession. Following an initial dominance of herbaceous vegetation this commonly leads to similar plant communities as the ones present before the fire. On a temporal axis, this results in typical exponential post-fire trajectories which could also be shown in this study. The analysis of driving factors for post-fire dynamics confirmed the importance of aspect and slope. Locations with lower amounts of solar irradiation and favourable water supply yielded faster recovery rates and higher post-fire vegetation cover levels. In most cases, the vegetation cover levels observed before the fire were not reached within the post-fire observation period. In the area of Lagadas, linear trend analysis and additional statistical parameters were used to infer a degradation index. This could be used to illustrate a complex pattern of stability, regeneration and degradation of vegetation cover. These different processes and states are found in close proximity and are clearly determined by topography and elevation. Following a sequence of analyses, it was found that in particular steep, narrow valleys show positive trends, while negative trends are more abundant on plain or gently undulating areas. Considering the local grazing regime, this spatial differentiation was related to the accessibility of specific locations. Subsequently, animal numbers on community level were used to calculate efficient stocking rates and assess the temporal development of their relation with vegetation cover. This calculation of temporal trajectories illustrated that only some communities show the expected negative relation. To the contrary, a positive relation or even changing relation patterns are observed. This signifies recent concentration and intensification processes in the grazing scheme, as a result of which animals are kept in sheds, where additional feedstuffs are provided. In these cases, free roaming of livestock animals is often confined to some hours every day, which explains the spatial preference of easily accessible areas by the shepherds. Beyond these temporal trends, it was analysed whether the grazing pattern is equally reflected in a spatial trend. Making use of available geospatial information layers, the efforts required to reach each location was expressed as a cost. Then, cost zones could be defined and woody vegetation cover as a grazing indicator could be inferred for the different zones. Animal sheds were employed as starting features for this piospheric analysis, which could be mapped from very high spatial resolution Quickbird image data. The result was a clearly structured gradient showing increasing woody vegetation cover with increasing cost distance. On the basis of these two pilot studies, the elements of a monitoring and interpretation framework identified at the beginning of the work were evaluated and a formal interpretation scheme was presented.
In past years, desertification and land degradation have been acknowledged as a major threat to human welfare world-wide, and their environmental and societal implications have sparked the formulation of the UN Convention to Combat Desertification (UNCCD). Any measure taken against desertification, or the design of dedicated early warning systems, must take into account both the spatial and temporal dimensions of process driving factors. Equally important, past and present reactions of ecosystems to physical and socio-economical disturbances or management interventions need to be understood. In this context, remote sensing and geoinformation processing support the required assessment, monitoring and modelling approaches, and hence provide an essential contribution to the scientific component of the struggle against desertification. Supported by DG Research of the European Commission, the Remote Sensing Department of the University of Trier convened RGLDD to promote scientific exchange between specialists working on the interface of remote sensing, geoinformation processing, desertification/land degradation research and its socio-economic implications. Although targeted at the scientific community, contributions with application perspectives were of crucial importance and both an overview of the current state of the art as well as operational opportunities were presented. Hosted at the Robert-Schuman Haus in Trier, the conference gained widespread attention and attracted an international audience from all parts of the world, which underlines the global dimension of land degradation and desertification processes. Based on a rigorous review of submitted abstracts, more than 100 contributions were accepted for oral and poster presentation, which are found in these proceedings edition in full paper form. Please note: This document is optimised for screen resolution, to receive a high-resolution version please contact the editors.
It has been the overall aim of this research work to assess the potential of hyperspectral remote sensing data for the determination of forest attributes relevant to forest ecosystem simulation modeling and forest inventory purposes. A number of approaches for the determination of structural and chemical attributes from hyperspectral remote sensing have been applied to the collected data sets. Many of the methods to be found in the literature were up to now just applied to broadband multispectral data, applied to vegetation canopies other than forests, reported to work on the leaf level or with modelled data, not validated with ground truth data, or not systematically compared to other methods. Attributes that describe the properties of the forest canopy and that are potentially open to remote sensing were identified, appropriate methods for their retrieval were implemented and field, laboratory and image data (HyMap sensor) were acquired over a number of forest plots. The study on structural attributes compared statistical and physical approaches. In the statistical section, linear predictive models between vegetation indices derived from HyMap data and field measurements of structural forest stand attributes were systematically evaluated. The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify leaf area index and crown volume with good accuracy. For broadband multispectral data, the accuracy was generally lower. The physically-based approach used the invertible forest reflectance model (INFORM), a combination of well established sub-models FLIM, SAIL and LIBERTY. The model was inverted with HyMap data using a neural network approach. In comparison to the statistical approach, it could be shown that the reflectance model inversion works equally well. In opposition to empirically derived prediction functions that are generally limited to the local conditions at a certain point in time and to a specified sensor type, the calibrated reflectance model can be applied more easily to different optical remote sensing data acquired over central European forests. The study on chemical forest attributes evaluated the information content of HyMap data for the estimation of nitrogen, chlorophyll and water concentration. A number of needle samples of Norway spruce were analysed for their total chlorophyll, nitrogen and water concentrations. The chemical data was linked to needle spectra measured in the laboratory and canopy spectra measured by the HyMap sensor. Wavebands selected in statistical models were often located in spectral regions that are known to be important for chlorophyll detection (red edge, green peak). Predictive models were applied on the HyMap image to compute maps of chlorophyll concentration and nitrogen concentration. Results of map overlay operations revealed coherence between total chlorophyll and zones of stand development stage and between total chlorophyll and zones of soil type. Finally, it can be stated that the hyperspectral remote sensing data generally contains more information relevant to the estimation of the forest attributes compared to multispectral data. Structural forest attributes, except biomass, can be determined with good accuracy from a hyperspectral sensor type like HyMap. Among the chemical attributes, chlorophyll concentration can be determined with good accuracy and nitrogen concentration with moderate accuracy. For future research, additional dimensions have to be taken into account, for instance through exploitation of multi-view angle data. Additionally, existing forest canopy reflectance models should be further improved.