| Arthropod decline in grasslands and forests is associated with landscape-level drivers S Seibold, MM Gossner, NK Simons, N Blüthgen, J Müller, D Ambarlı, ... Nature 574 (7780), 671-674, 2019 | 1139 | 2019 |
| Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions MK Peters, A Hemp, T Appelhans, JN Becker, C Behler, A Classen, ... Nature 568 (7750), 88-92, 2019 | 453 | 2019 |
| Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation H Meyer, C Reudenbach, T Hengl, M Katurji, T Nauss Environmental Modelling & Software 101, 1-9, 2018 | 367 | 2018 |
| Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level MK Peters, A Hemp, T Appelhans, C Behler, A Classen, F Detsch, ... Nature communications 7 (1), 13736, 2016 | 321 | 2016 |
| Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction H Meyer, C Reudenbach, S Wöllauer, T Nauss Ecological Modelling 411, 108815, 2019 | 320 | 2019 |
| Multiple forest attributes underpin the supply of multiple ecosystem services MR Felipe-Lucia, S Soliveres, C Penone, P Manning, F van der Plas, ... Nature communications 9 (1), 4839, 2018 | 267 | 2018 |
| Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania T Appelhans, E Mwangomo, DR Hardy, A Hemp, T Nauss Spatial Statistics 14, 91-113, 2015 | 208 | 2015 |
| Quantifying and mapping ecosystem services supplies and demands: a review of remote sensing applications YZ Ayanu, C Conrad, T Nauss, M Wegmann, T Koellner Environmental science & technology 46 (16), 8529-8541, 2012 | 204 | 2012 |
| Improving the accuracy of rainfall rates from optical satellite sensors with machine learning—A random forests-based approach applied to MSG SEVIRI M Kühnlein, T Appelhans, B Thies, T Nauss Remote Sensing of Environment 141, 129-143, 2014 | 196 | 2014 |
| Heterogeneity–diversity relationships differ between and within trophic levels in temperate forests L Heidrich, S Bae, S Levick, S Seibold, W Weisser, P Krzystek, P Magdon, ... Nature Ecology & Evolution 4 (9), 1204-1212, 2020 | 131 | 2020 |
| Ground fog detection from space based on MODIS daytime data—A feasibility study J Bendix, B Thies, J Cermak, T Nauß Weather and Forecasting 20 (6), 989-1005, 2005 | 130 | 2005 |
| Middle Stone Age foragers resided in high elevations of the glaciated Bale Mountains, Ethiopia G Ossendorf, AR Groos, T Bromm, MG Tekelemariam, B Glaser, J Lesur, ... Science 365 (6453), 583-587, 2019 | 123 | 2019 |
| Mapping daily air temperature for Antarctica based on MODIS LST H Meyer, M Katurji, T Appelhans, MU Müller, T Nauss, P Roudier, ... Remote Sensing 8 (9), 732, 2016 | 121 | 2016 |
| Near surface air humidity in a megadiverse Andean mountain ecosystem of southern Ecuador and its regionalization A Fries, R Rollenbeck, T Nauß, T Peters, J Bendix Agricultural and forest meteorology 152, 17-30, 2012 | 120 | 2012 |
| Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania F Detsch, I Otte, T Appelhans, A Hemp, T Nauss Remote Sensing of Environment 178, 70-83, 2016 | 110 | 2016 |
| Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals H Meyer, M Kühnlein, T Appelhans, T Nauss Atmospheric research 169, 424-433, 2016 | 108 | 2016 |
| Climatic control of radial growth of Cedrela montana in a humid mountain rainforest in southern Ecuador A Bräuning, F Volland-Voigt, I Burchardt, O Ganzhi, T Nauss, T Peters Erdkunde, 337-345, 2009 | 106 | 2009 |
| Temperature versus resource constraints: which factors determine bee diversity on M ount K ilimanjaro, T anzania? A Classen, MK Peters, WJ Kindeketa, T Appelhans, CD Eardley, ... Global Ecology and Biogeography 24 (6), 642-652, 2015 | 104 | 2015 |
| Radar vision in the mapping of forest biodiversity from space S Bae, SR Levick, L Heidrich, P Magdon, BF Leutner, S Wöllauer, ... Nature Communications 10 (1), 4757, 2019 | 94 | 2019 |
| Precipitation estimates from MSG SEVIRI daytime, nighttime, and twilight data with random forests M Kühnlein, T Appelhans, B Thies, T Nauß Journal of Applied Meteorology and Climatology 53 (11), 2457-2480, 2014 | 93 | 2014 |
Christoph ReudenbachMarburg UniversityVerified email at staff.uni-marburg.de
