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Jan Bumberger     Post Doctoral Researcher 
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Jan Bumberger published an article in August 2018.
Top co-authors See all
Josef Settele

226 shared publications

Helmholtz Centre for Environmental Research–UFZ, Leipzig, Germany

Michael E. Schaepman

189 shared publications

Remote Sensing Laboratories, Department of Geography, University of Zurich, CH-8057 Zurich, Switzerland

Peter Dietrich

144 shared publications

Helmholtz Centre for Environmental Research - UFZ

Marco Heurich

126 shared publications

Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Grafenau, Germany

Martin Wegmann

58 shared publications

Department of Remote Sensing; University of Wuerzburg; Wuerzburg Germany

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Publication Record
Distribution of Articles published per year 
(2011 - 2018)
Total number of journals
published in
 
10
 
Publications See all
Article 0 Reads 0 Citations Spatial Retrieval of Broadband Dielectric Spectra Jan Bumberger, Juliane Mai, Felix Schmidt, Peter Lünenschloß... Published: 23 August 2018
Sensors, doi: 10.3390/s18092780
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
A broadband soil dielectric spectra retrieval approach (1 MHz–2 GHz) has been implemented for a layered half space. The inversion kernel consists of a two-port transmission line forward model in the frequency domain and a constitutive material equation based on a power law soil mixture rule (Complex Refractive Index Model—CRIM). The spatially-distributed retrieval of broadband dielectric spectra was achieved with a global optimization approach based on a Shuffled Complex Evolution (SCE) algorithm using the full set of the scattering parameters. For each layer, the broadband dielectric spectra were retrieved with the corresponding parameters thickness, porosity, water saturation and electrical conductivity of the aqueous pore solution. For the validation of the approach, a coaxial transmission line cell measured with a network analyzer was used. The possibilities and limitations of the inverse parameter estimation were numerically analyzed in four scenarios. Expected and retrieved layer thicknesses, soil properties and broadband dielectric spectra in each scenario were in reasonable agreement. Hence, the model is suitable for an estimation of in-homogeneous material parameter distributions. Moreover, the proposed frequency domain approach allows an automatic adaptation of layer number and thickness or regular grids in time and/or space.
Article 0 Reads 3 Citations Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monito... Angela Lausch, Erik Borg, Jan Bumberger, Peter Dietrich, Mar... Published: 15 July 2018
Remote Sensing, doi: 10.3390/rs10071120
DOI See at publisher website ABS Show/hide abstract
Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
PROCEEDINGS-ARTICLE 0 Reads 0 Citations Dielectric Spectra Reconstruction of Layered Multi-Phase Soil Felix Schmidt, Norman Wagner, Juliane Mai, Peter Lunenschlob... Published: 01 June 2018
2018 12th International Conference on Electromagnetic Wave Interaction with Water and Moist Substances (ISEMA), doi: 10.1109/isema.2018.8442324
DOI See at publisher website
Article 0 Reads 0 Citations Intercomparison of Cosmic-Ray Neutron Sensors andWater Balance Monitoring in an Urban Environment Martin Schrön, Steffen Zacharias, Gary Womack, Markus Köhli,... Published: 07 July 2017
Geoscientific Instrumentation, Methods and Data Systems Discussions, doi: 10.5194/gi-2017-34
DOI See at publisher website ABS Show/hide abstract
Sensor-to-sensor variability is a source of error common to all geoscientific instruments, which needs to be assessed before comparative and applied research can be performed with multiple sensors. Consistency among sensor systems is especially critical when the signal is an integral value that covers a large volume within complex, urban terrain. Cosmic-Ray Neutron Sensors (CRNS) are a recent technology that is used to monitor large-scale environmental water storages, for which a rigorous comparison study of numerous co-located sensors has never been performed. In this work, nine stationary CRNS probes of type CRS1000 were installed in relative proximity on a grass patch surrounded by complex urban terrain. While the dynamics of the neutron count rates were found to be similar, offsets of a few percent from the absolute average neutron count rates were found. Technical adjustments of the individual detection parameters brought all instruments into good agreement. Furthermore, the arrangement of multiple sensors allowed to find a critical integration time of 6 hours above which all sensors showed consistent dynamics in the data and their RMSE fell below 1 % of gravimetric water content. The residual differences between the nine signals indicated local effects of the complex urban terrain at the scale of several meters. Mobile CRNS measurements and spatial neutron transport simulations in the surrounding area (25 ha) have revealed that CRNS detectors are sensitive to sub-footprint heterogeneity despite their large averaging volume. The paved and sealed areas in the footprint furthermore damp the dynamics of the CRNS soil moisture product. We developed strategies to correct for the sealed-area effect based on theoretical insights about the spatial sensitivity of the sensor. This procedure not only led to reliable soil moisture estimation in drying periods, it further revealed a strong signal of interception and evaporation water that emerged over the sealed ground during and shortly after rain events. The presented arrangement offered a unique opportunity to demonstrate the CRNS performance in complex terrain, and the results indicate great potential for further applications in urban water sciences.
Conference 0 Reads 0 Citations Research in Progress on Integrating Health and Environmental Data in Epidemiological Studies Toralf Kirsten, Jan Bumberger, Galina Ivanova, Peter Dietric... Published: 24 January 2017
Lecture Notes in Business Information Processing, doi: 10.1007/978-3-319-52464-1_32
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BOOK-CHAPTER 0 Reads 0 Citations Research in Progress: Implementation of an Integrated Data Model for an Improved Monitoring of Environmental Processes Robert Schima, Tobias Goblirsch, Christoph Salbach, Bogdan F... Published: 24 January 2017
Lecture Notes in Business Information Processing, doi: 10.1007/978-3-319-52464-1_30
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