Welcome to the Geomatics Lab!
The Geomatics Lab focuses on a better understanding of coupled human-environment systems based on remote sensing data and geoinformation. Specific interest lies on land use / land cover change and related ecosystem services.
Our methodological foundation is based on advanced remote sensing data analyses. Themes of interest include landscape to sub-continental mapping and monitoring, deep time series analysis and imaging spectroscopy. Our research contributes to the Global Land Project, the EnMAP Core Science Team and the Landsat Science Team.
published 14th July 2014
New paper in Global Environmental Change by postdoctoral researcher Daniel Müller et.al.
The paper is a product from the I-REDD+ project. In essence, it challenges the widely held view that land-system changes can be reliably predicted in dynamic tropical landscapes.
We demonstrate how sudden events and gradual changes in underlying drivers caused rapid, surprising and widespread land-system changes in a range of case studies in Southeast Asia. We argue that many of these changes in land systems qualify as regime shifts that were often difficult to anticipate. Therefore, the presence of regime shifts compromises the validity of predictions of future land-system changes. We discuss the implications of regime shifts for long-term initiatives such as REDD that must ensure that carbon investments achieve additionality in emission reductions (the "R" in REDD...). REDD, just like many other schemes of payments for ecosystem services (PES), must therefore account for the substantial knightian uncertainties inherent in future predictions of land-system change.
published 8th July 2014
New paper in Remote Sensing by doctoral researcher Akpona Okujeni
We evaluate the use of five different advanced regression techniques for quantitative mapping of urban land cover from HyMap data at 3.6 and 9 m. We adopt the concept of synthetically mixed training data (Remote Sensing of Environment, Volume 137, Oct. 2013), which helps to overcome the lack of quantitative training information required for empirical modeling. This paper presents a substantial follow-up study that allows readers to derive valuable knowledge on how to use different machine learning regression techniques. Our paper includes a comprehensive analysis and a thorough discussion of the findings. We highlight the excellent suitability of kernel-based SVR and KRR in combination with synthetically mixed training for quantifying thematically meaningful and spectrally challenging urban categories at multiple spatial scales.
We carried out the regression analyses with the following Toolboxes:
EnMAP-Box - developed at the Geomatics Lab, HU zu Berlin
ARTMO-Toolbox - developed at the Image Processing Laboratory, Universitat de València
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published 14th July 2014We are looking forward to your participation in the Geomatics Colloquium during the winter term 2014/2015 starting in mid October. Until then - enjoy the break!