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Christiane Schmullius

Researcher at University of Jena

Publications -  179
Citations -  5943

Christiane Schmullius is an academic researcher from University of Jena. The author has contributed to research in topics: Land cover & Vegetation. The author has an hindex of 37, co-authored 167 publications receiving 5091 citations. Previous affiliations of Christiane Schmullius include University of Leicester & Wageningen University and Research Centre.

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Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets

TL;DR: There is a strong relationship between class accuracy, spatial agreement among the datasets, and the heterogeneity of landscapes and suggestions for future mapping projects include careful definition of mixed unit classes, and improvement in mapping heterogeneous landscapes.
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Model–data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications

TL;DR: In this paper, a review of model-data synthesis tools for terrestrial carbon observation is presented, highlighting several basic commonalities in formalism and data requirements, including the importance of data uncertainties.
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Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces

TL;DR: The requirements for future geomorphology monitoring are focused on the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems.
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Carbon stock and density of northern boreal and temperate forests

TL;DR: In this article, a forest carbon density map at 0.010 resolution from a radar remote sensing product for the estimation of carbon stocks in Northern Hemisphere boreal and temperate forests is presented.
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Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda

TL;DR: In this article, a regression tree-based model (Random Forest) produces good results (cross-validated R² 0.81, RMSE 13 T/ha) when trained with a sufficient number of field plots representative of the vegetation variability at national scale.