Determining the exact position of a forest inventory plot—and hence the position of the sampled trees—is often hampered by a poor Global Navigation Satellite System (GNSS) signal quality beneath the forest canopy. Inaccurate geo-references hamper the performance of models that aim to retrieve useful information from spatially high remote sensing data (e.g., species classification or timber volume estimation). This restriction is even more severe on the level of individual trees. The objective of this study was to develop a post-processing strategy to improve the positional accuracy of GNSS-measured sample-plot centers and to develop a method to automatically match trees within a terrestrial sample plot to aerial detected trees. We propose a new method which uses a random forest classifier to estimate the matching probability of each terrestrial-reference and aerial detected tree pair, which gives the opportunity to assess the reliability of the results. We investigated 133 sample plots of the Third German National Forest Inventory (BWI, 2011"2012) within the German federal state of Rhineland-Palatinate. For training and objective validation, synthetic forest stands have been modeled using the Waldplaner 2.0 software. Our method has achieved an overall accuracy of 82.7% for co-registration and 89.1% for tree matching. With our method, 60% of the investigated plots could be successfully relocated. The probabilities provided by the algorithm are an objective indicator of the reliability of a specific result which could be incorporated into quantitative models to increase the performance of forest attribute estimations.
Earth observation (EO) is a prerequisite for sustainable land use management, and the open-data Landsat mission is at the forefront of this development. However, increasing data volumes have led to a "digital-divide", and consequently, it is key to develop methods that account for the most data-intensive processing steps, then used for the generation and provision of analysis-ready, standardized, higher-level (Level 2 and Level 3) baseline products for enhanced uptake in environmental monitoring systems. Accordingly, the overarching research task of this dissertation was to develop such a framework with a special emphasis on the yet under-researched drylands of Southern Africa. A fully automatic and memory-resident radiometric preprocessing streamline (Level 2) was implemented. The method was applied to the complete Angolan, Zambian, Zimbabwean, Botswanan, and Namibian Landsat record, amounting 58,731 images with a total data volume of nearly 15 TB. Cloud/shadow detection capabilities were improved for drylands. An integrated correction of atmospheric, topographic and bidirectional effects was implemented, based on radiative theory with corrections for multiple scatterings, and adjacency effects, as well as including a multilayered toolset for estimating aerosol optical depth over persistent dark targets or by falling back on a spatio-temporal climatology. Topographic and bidirectional effects were reduced with a semi-empirical C-correction and a global set of correction parameters, respectively. Gridding and reprojection were already included to facilitate easy and efficient further processing. The selection of phenologically similar observations is a key monitoring requirement for multi-temporal analyses, and hence, the generation of Level 3 products that realize phenological normalization on the pixel-level was pursued. As a prerequisite, coarse resolution Land Surface Phenology (LSP) was derived in a first step, then spatially refined by fusing it with a small number of Level 2 images. For this purpose, a novel data fusion technique was developed, wherein a focal filter based approach employs multi-scale and source prediction proxies. Phenologically normalized composites (Level 3) were generated by coupling the target day (i.e. the main compositing criterion) to the input LSP. The approach was demonstrated by generating peak, end and minimum of season composites, and by comparing these with static composites (fixed target day). It was shown that the phenological normalization accounts for terrain- and land cover class-induced LSP differences, and the use of Level 2 inputs enables a wide range of monitoring options, among them the detection of within state processes like forest degradation. In summary, the developed preprocessing framework is capable of generating several analysis-ready baseline EO satellite products. These datasets can be used for regional case studies, but may also be directly integrated into more operational monitoring systems " e.g. in support of the Reducing Emissions from Deforestation and Forest Degradation (REDD) incentive. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Trier University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Dry tropical forests are facing massive conversion and degradation processes and they are the most endangered forest type worldwide. One of the largest dry forest types are Miombo forests that stretch across the Southern African subcontinent and the proportionally largest part of this type can be found in Angola. The study site of this thesis is located in south-central Angola. The country still suffers from the consequences of the 27 years of civil war (1975-2002) that provides a unique socio-economic setting. The natural characteristics are a representative cross section which proved ideal to study underlying drivers as well as current and retrospective land use change dynamics. The major land change dynamic of the study area is the conversion of Miombo forests to cultivation areas as well as modification of forest areas, i.e. degradation, due to the extraction of natural resources. With future predictions of population growth, climate change and large scale investments, land pressure is expected to further increase. To fully understand the impacts of these dynamics, both, conversion and modification of forest areas were assessed. By using the conceptual framework of ecosystem services, the predominant trade-off between food and timber in the study area was analyzed, including retrospective dynamics and impacts. This approach accounts for products that contribute directly or indirectly to human well-being. For this purpose, data from the Landsat archive since 1989 until 2013 was applied in different study area adapted approaches. The objectives of these approaches were (I) to detect underlying drivers and their temporal and spatial extent of impact, (II) to describe modification and conversion processes that reach from times of armed conflicts over the ceasefire and the post-war period and (III) to provide an assessment of drivers and impacts in a comparative setting. It could be shown that major underlying drivers for the conversion processes are resettlement dynamics as well as the location and quality of streets and settlements. Furthermore, forests that are selectively used for resource extraction have a higher chance of being converted to a field. Drivers of forest degradation are on one hand also strongly connected to settlement and infrastructural structures. But also to a large extent to fire dynamics that occur mostly in more remote and presumably undisturbed forest areas. The loss of woody biomass as well as its slow recovery after the abandonment of fields could be quantified and stands in large contrast to the amount of potentially cultivated food that is necessarily needed. The results of the thesis support the fundamental understanding of drivers and impacts in the study area and can thus contribute to a sustainable resource management.
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.