Institut für
BW Betriebswirtschaft
Zeitschriftenbeiträge
Beiträge in referierten Zeitschriften von Stefan Erasmi
- 0
Wenzel A, Westphal C, Ballauff J, Berkelmann D, Brambach F, Buchori D, Camaretta N, Corre MD, Daniel R, Darras K, Erasmi S, Formaglio G, Hölscher D, Al-Amin Iddris N, Irawan B, Knohl A, Kotowska MM, Krashevska V, Kreft H, Mulyani Y, et al (2024) Balancing economic and ecological functions in smallholder and industrial oil palm plantations. Proc Nat Acad Sci USA 121(17):e2307220121, DOI:10.1073/pnas.2307220121
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Paterno GB, Brambach F, Guerrero-Ramírez N, Zemp DC, Cantillo AF, Camarretta N, Moura CCM, Gailing O, Ballauff J, Polle A, Schlund M, Erasmi S, Iddris NA, Khokthong W, Sundawati L, Irawan B, Hölscher D, Kreft H (2024) Diverse and larger tree islands promote native tree diversity in oil palm landscapes. Science 386(6723):795-802, DOI:10.1126/science.ado1629
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May PB, Schlund M, Armston J, Kotowska MM, Brambach F, Wenzel A, Erasmi S (2024) Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models. Remote Sens Environ 313:114384, DOI:10.1016/j.rse.2024.114384
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Pham V-D, Tetteh GO, Thiel F, Erasmi S, Schwieder M, Frantz D, van der Linden S (2024) Temporally transferable crop mapping with temporal encoding and deep learning augmentations. Int J Appl Earth Observ Geoinf 129:103867, DOI:10.1016/j.jag.2024.103867
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Brög T, Don A, Gocht A, Scholten T, Taghizadeh-Mehrjardi R, Erasmi S (2024) Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma 444:116850, DOI:10.1016/j.geoderma.2024.116850
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Lobert F, Löw J, Schwieder M, Gocht A, Schlund M, Hostert P, Erasmi S (2023) A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level. Remote Sens Environ 298:113800, DOI:10.1016/j.rse.2023.113800
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Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023) Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x
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Holtgrave A-K, Lobert F, Erasmi S, Röder N, Kleinschmit B (2023) Grassland mowing event detection using combined optical, SAR, and weather time series. Remote Sens Environ 295:113680, DOI:10.1016/j.rse.2023.113680
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Hauck M, Klinge M, Erasmi S, Dulamsuren C (2023) No signs of long-term greening trend in Western Mongolian Grasslands. Ecosystems 26(5):1125-1143, DOI:10.1007/s10021-023-00819-3
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Schlund M, Wenzel A, Camarretta N, Stiegler C, Erasmi S (2023) Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM-X supported by GEDI data. Methods Ecol Evol 14(7):1639-1656, DOI:10.1111/2041-210X.13933
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Schwieder M, Wesemeyer M, Frantz D, Pfoch K, Erasmi S, Pickert J, Nendel C, Hostert P (2022) Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sens Environ 269:112795, DOI:10.1016/j.rse.2021.112795
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Blickensdörfer L, Schwieder M, Pflugmacher D, Nendel C, Erasmi S, Hostert P (2022) Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831
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Nketia KA, Asabere SB, Ramcharan A, Herbold S, Erasmi S, Sauer D (2022) Spatio-temporal mapping of soil water storage in a semi-arid landscape of northern Ghana - A multi-tasked ensemble machine-learning approach. Geoderma 410:115691, DOI:10.1016/j.geoderma.2021.115691
- 13
Tetteh GO, Gocht A, Erasmi S, Schwieder M, Conrad C (2021) Evaluation of sentinel-1 and sentinel-2 feature sets for delineating agricultural fields in heterogeneous landscapes. IEEE Access 9:116702-116719, DOI:10.1109/ACCESS.2021.3105903
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Klinge M, Dulamsuren C, Schneider F, Erasmi S, Bayarsaikhan U, Sauer D, Hauck M (2021) Geoecological parameters indicate discrepancies between potential and actual forest area in the forest-steppe of Central Mongolia. For Ecosyst 8:55, DOI:10.1186/s40663-021-00333-9
- 15
Erasmi S, Klinge M, Dulamsuren C, Schneider F, Hauck M (2021) Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe. Environ Monit Assessm 193:200, DOI:10.1007/s10661-021-08996-1
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Lobert F, Holtgrave A-K, Schwieder M, Pause M, Vogt J, Gocht A, Erasmi S (2021) Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sens Environ 267:112751, DOI:10.1016/j.rse.2021.112751
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Schlund M, Kotowska MM, Brambach F, Hein J, Wessel B, Camarretta N, Silalahi M, Surati Jaya IN, Erasmi S, Leuschner C, Kreft H (2021) Spaceborne height models reveal above ground biomass changes in tropical landscapes. Forest Ecol Manag 497:119497, DOI:10.1016/j.foreco.2021.119497
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Camarretta N, Ehbrecht M, Seidel D, Wenzel A, Zuhdi M, Merk MS, Schlund M, Erasmi S, Knohl A (2021) Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics. Remote Sensing 13:4794, DOI:10.3390/rs13234794
- 19
Holtgrave A-K, Röder N, Ackermann A, Erasmi S, Kleinschmit B (2020) Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing 12:2919, DOI:10.3390/rs12182919
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Schlund M, Erasmi S (2020) Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sens Environ 246:111814, DOI:10.1016/j.rse.2020.111814
- 21
Tetteh GO, Gocht A, Schwieder M, Erasmi S, Conrad C (2020) Unsupervised parameterization for optimal segmentation of agricultural parcels from satellite images in different agricultural landscapes. Remote Sensing 12(18):3096, DOI:10.3390/rs12183096
- 22
Erasmi S, Semmler M, Schall P, Schlund M (2019) Sensitivity of bistatic TanDEM-X data to stand structural parameters in temperate forests. Remote Sensing 11(24):2966, DOI:10.3390/rs11242966
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Schneider J, Jungkunst HF, Wolf U, Schreiber P, Gazovic M, Miglovets M, Mikhaylov O, Grunwald D, Erasmi S, Wilmking M, Kutzbach L (2016) Russian boreal peatlands dominate the natural European methane budget. Environ Res Lett 11(1):14004, DOI:10.1088/1748-9326/11/1/014004
